Subjects -> INSTRUMENTS (Total: 62 journals)
Showing 1 - 16 of 16 Journals sorted alphabetically
Annali dell'Istituto e Museo di storia della scienza di Firenze     Hybrid Journal  
Applied Mechanics Reviews     Full-text available via subscription   (Followers: 27)
Bulletin of Social Informatics Theory and Application     Open Access   (Followers: 1)
Computational Visual Media     Open Access   (Followers: 4)
Devices and Methods of Measurements     Open Access  
Documenta & Instrumenta - Documenta et Instrumenta     Open Access  
EPJ Techniques and Instrumentation     Open Access  
European Journal of Remote Sensing     Open Access   (Followers: 9)
Experimental Astronomy     Hybrid Journal   (Followers: 39)
Flow Measurement and Instrumentation     Hybrid Journal   (Followers: 18)
Geoscientific Instrumentation, Methods and Data Systems     Open Access   (Followers: 4)
Geoscientific Instrumentation, Methods and Data Systems Discussions     Open Access   (Followers: 1)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 2)
IEEE Sensors Journal     Hybrid Journal   (Followers: 103)
IEEE Sensors Letters     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Imaging & Microscopy     Hybrid Journal   (Followers: 9)
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
Instrumentation Science & Technology     Hybrid Journal   (Followers: 7)
Instruments and Experimental Techniques     Hybrid Journal   (Followers: 1)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 7)
International Journal of Instrumentation Science     Open Access   (Followers: 40)
International Journal of Measurement Technologies and Instrumentation Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Metrology and Quality Engineering     Full-text available via subscription   (Followers: 4)
International Journal of Remote Sensing     Hybrid Journal   (Followers: 274)
International Journal of Remote Sensing Applications     Open Access   (Followers: 43)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 4)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Journal of Applied Remote Sensing     Hybrid Journal   (Followers: 83)
Journal of Astronomical Instrumentation     Open Access   (Followers: 3)
Journal of Instrumentation     Hybrid Journal   (Followers: 32)
Journal of Instrumentation Technology & Innovations     Full-text available via subscription   (Followers: 1)
Journal of Medical Devices     Full-text available via subscription   (Followers: 5)
Journal of Medical Signals and Sensors     Open Access   (Followers: 3)
Journal of Optical Technology     Full-text available via subscription   (Followers: 5)
Journal of Sensors and Sensor Systems     Open Access   (Followers: 11)
Journal of Vacuum Science & Technology B     Hybrid Journal   (Followers: 2)
Jurnal Informatika Upgris     Open Access  
Measurement : Sensors     Open Access   (Followers: 3)
Measurement and Control     Open Access   (Followers: 36)
Measurement Instruments for the Social Sciences     Open Access  
Measurement Science and Technology     Hybrid Journal   (Followers: 7)
Measurement Techniques     Hybrid Journal   (Followers: 3)
Medical Devices & Sensors     Hybrid Journal  
Medical Instrumentation     Open Access  
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microscopy     Hybrid Journal   (Followers: 8)
Modern Instrumentation     Open Access   (Followers: 50)
Optoelectronics, Instrumentation and Data Processing     Hybrid Journal   (Followers: 4)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal  
Photogrammetric Engineering & Remote Sensing     Full-text available via subscription   (Followers: 29)
Remote Sensing     Open Access   (Followers: 54)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 8)
Remote Sensing of Environment     Hybrid Journal   (Followers: 93)
Remote Sensing Science     Open Access   (Followers: 24)
Review of Scientific Instruments     Hybrid Journal   (Followers: 22)
Sensors and Materials     Open Access   (Followers: 2)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Standards     Open Access  
Transactions of the Institute of Measurement and Control     Hybrid Journal   (Followers: 13)
Труды СПИИРАН     Open Access  
Similar Journals
Journal Cover
IEEE Sensors Journal
Journal Prestige (SJR): 0.619
Citation Impact (citeScore): 3
Number of Followers: 103  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1530-437X
Published by IEEE Homepage  [229 journals]
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Feb.1, 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • IEEE Sensors Journal publication information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Feb.1, 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • IEEE Sensors Council Information
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: Feb.1, 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Online Thermal Effect Modeling and Prediction of Implantable Devices
    • Pages: 2443 - 2453
      Abstract: The overheating caused by the operation of implantable device can cause damage to the surrounding tissue. In applications like neural prosthesis, 1 °C of temperature increase could lead to irreversible damage to the subject. Predicting the overheating effect is therefore critical to maintain safe operation. This work proposes a Bayesian recursive multi-step prediction method for implantable device to predict the overheating effect. The method proposed in this article achieves accurate prediction within a horizon with low complexity by model updating that iteratively minimizes a function of the ${j}$ -step-ahead prediction error. At each time instant, the new available input output data are stored in a First In First Out (FIFO) queue of fixed length, and the model parameters are updated by iteratively minimizing the ${j}$ -step-ahead prediction error of the new data. Moreover, the regularization methods are introduced to improve the prediction performance by taking the Bayesian interpretation of the parameters into consideration. Monte Carlo simulation studies indicate that the developed method is able to estimate the fundamental dynamics of the system when the prediction model is underparametered, and is robust to measurement noise. For time varying systems, the developed method can capture the system dynamics during the system variation. The proposed method is demonstrated via an in-vitro test vehicle, which shows that the temperature increase can be predicted with high accuracy and low complexity.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Vision and Inertial Sensing Fusion for Human Action Recognition: A Review
    • Pages: 2454 - 2467
      Abstract: Human action recognition is used in many applications such as video surveillance, human–computer interaction, assistive living, and gaming. Many papers have appeared in the literature showing that the fusion of vision and inertial sensing improves recognition accuracies compared to the situations when each sensing modality is used individually. This article provides a survey of the papers in which vision and inertial sensing are used simultaneously within a fusion framework in order to perform human action recognition. The surveyed papers are categorized in terms of fusion approaches, features, classifiers, as well as multimodality datasets considered. Challenges as well as possible future directions are also stated for deploying the fusion of these two sensing modalities under realistic conditions.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Dual-Functional Graphene-Based Flexible Material for Membrane Filtration
           and Electrochemical Sensing of Heavy Metal Ions
    • Pages: 2468 - 2475
      Abstract: Simultaneous removal and quantification of heavy metal ions are very important since they threaten human health. In this work, a dual-functional, free-standing and flexible reduced graphene oxide (rGO) based paper-like material including zinc oxide nanoparticles (ZnO-NPs) and ethylenediaminetetraaceticacid (EDTA) was produced using successive vacuum-filtration and hydrothermal reduction processes. First, graphene oxide (GO) layers were coated with ZnO-NPs and then EDTA was immobilized by ultrasonic treatment. Prepared GO/ZnO-NPs-EDTA suspension was vacuum-filtrated on a membrane and peeled to obtain GO/ZnO-NPs-EDTA paper. Hydrothermal reduction resulted in rGO/ZnO-NPs-EDTA paper and this material is first-time performed for two applications: Simultaneous removal of heavy metal ions of Cd2+, Pb2+, Cu2+, and Hg2+ and their further simultaneous electrochemical detection. Structural, chemical, crystallographic, optical, morphological, and electrochemical characterization of this paper-like material was executed using numerous techniques. Maximum adsorption performance of the rGO/ZnO-NPs-EDTA paper was determined as 2963, 8056, 600, and 1753 mg/g, while electrochemical simultaneous determination revealed linear ranges of 18.5-500, 22.4-700, 8.3-200, 3.3- $300~mu text{M}$ and detection limit values of 5.6, 6.8, 2.5, $1.0~mu text{M}$ for Cd2+, Pb2+, Cu2+, and Hg2+, respectively.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Selective Detection of Acyclovir on Poly(L–Methionine) Membrane Coated
           Reduced Graphene Oxide-Based Graphite Electrode Optimized by Central
           Composite Design
    • Pages: 2476 - 2484
      Abstract: In this study, a sensitive and facile electrochemical sensor is described to quantify the Acyclovir (ACV) in human biofluids. Firstly, a reduced graphene oxide (rGO) thin film was electrodeposited on a pencil graphite electrode (PGE). Then, a thin polymer film of L–methionine (L–Met) was electropolymerized on the rGO–coated PGE to prepare P–L–Met/rGO/PGE. Various techniques characterized the morphology and electrochemical response of the modified electrode. Furthermore, the central composite design (CCD) methodology was used as an experimental design strategy to optimize the sensor response’s useful variables. The P–L–Met/rGO/PGE presented better quantity and stability for the ACV compared to that of bare PGE. The effective surface area of the P–L–Met/rGO/PGE (0.217 cm2, which is 3.2 times more than the unmodified PGE), electron transfer coefficient ( $alpha =0.3$ ) and the transferred electron number in the rate-determining step ( $text{n}_{ {alpha }} =2$ ) for the ACV catalysis were also evaluated. Using P–L–Met amplified catalytic activity and stability of rGO on the PGE surface and improved the separation of analyte signal from other spices. Furthermore, the ACV was determined in the presence of the guanine (GU), adenine (AD), ascorbic acid (AA), epinephrine (EP) and acetaminophen (ACT) without interference. The sensor response to the ACV was linear in two ranges of 0.044– $2.98~mu text{M}$ and 2.98– $29.1~mu text{M}$ with a limit of detection of 30 nM. Besides, the sensor presented an excellent analytical performance in the quantification of the ACV in the real samples.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Facile Trace Mercury (II) Sensor Using Statistically Optimized
           Electrochemical Co-Deposited Gold Nanofilm Modified Screen-Printed Carbon
           Electrodes
    • Pages: 2485 - 2494
      Abstract: Rapid detection of trace mercury (II) in water is very challenging. A novel sensor for the facile detection of mercury (II) was developed using electrochemically co-deposited gold nanofilm modified screen-printed carbon electrodes (EcoD-AuNF/SPCE). Because a monolayer of gold nanofilm is freshly co-deposited during the mercury (II) preconcentration period, no additional modification on an electrode is required prior to a test. Central composite design (CCD) and response surface methodology (RSM) were used to evaluate the effects of the critical testing parameters simultaneously (i.e., the co-deposited gold concentration of $350,,mu text{g},,text{L}^{-1}$ , deposition potential of −0.75 V, and testing pH of 3.50). The response of square-wave anodic stripping voltammetry (SWASV) showed a linear relationship with the mercury (II) concentration over a range of 1 to $40~mu text{g}$ /L (ppb), with an LoD of $0.29~mu text{g}$ /L and sensitivity of $0.37~mu text{A}$ /ppb. Significant influences on the mercury (II) stripping peak potential, peak height, and peak shape were observed in the presence of chloride. Therefore, an optimized amount of chloride (i.e., 600 mg/L) was preemptively added to minimize the potential effect of naturally occurring chloride and to improve further the detection sensitivity (i.e., LoD of $0.16~mu text{g}$ /L and sensitivity of $0.55~mu text{A}$ /ppb). Validation testing using real-world water samples indicated reliable prediction of mercury (II) concentration could be obtained in compl-x media. In summary, this newly developed voltammetric approach has excellent detection performance and practical significance for potential on-site voltammetric determination of trace mercury (II) in water.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • High-Dimensional Time Series Feature Extraction for Low-Cost Machine
           Olfaction
    • Pages: 2495 - 2504
      Abstract: The complexity of airborne odors offers interesting challenges and opportunities for chemical detection and identification. Biological olfactory systems have evolved to extract information from spatiotemporally complex odor plumes, but many engineered electronic noses use only coarse time features while neglecting valuable transient fluctuations. In this paper, we use the TruffleBot, our low-cost e-nose platform, to dynamically ‘sniff’ odors while collecting multidimensional chemical, pressure and temperature time series. By extracting high-dimensional time series features (TSF) from a diverse set of relatively low-bandwidth sensor signals, we can identify subtle differences in odor concentration and composition. We use this approach to perform a variety of classification experiments, including the discrimination of three similar beers at >98% accuracy. Additionally, we demonstrate that time series features can be aggregated and applied to improve concentration estimation of ethanol by a factor of three, to the limit of our experimental calibration error.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Extraction Method of Cell’s Complex Permittivity in Cell Solutions From
           Measured Impedance by GHz Electrical Impedance Spectroscopy
    • Pages: 2505 - 2516
      Abstract: This paper has proposed a new dielectric properties extraction method of cell’s complex permittivity $varepsilon ^{ast }_{textit {cell}}$ even in cell solutions from the measured impedance ${Z}^{ast }_{textit {sol, exp}}$ by GHz electrical impedance spectroscopy (EIS) in the frequency range from 10 MHz to 3 GHz. This new extraction method is composed of the electrical impedance measurement of cell solutions and dispersed medium and the dielectric properties calculation based on a combination of plane wave and transmission line theories. By comparing with $varepsilon ^{ast }_{textit {cell, BH}}$ obtained by Bruggeman-Hanai equation, this new method is proved to extract the complex permittivity of cells $varepsilon ^{ast }_{textit {cell}}$ in cell solutions successfully from the experimental impedances of yeast cell solutions ${Z}^{ast }_{textit {sol, exp}}$ and dispersed medium ${Z}^{ast }_{textit {dis, exp}}$ by GHz EIS with the relative error less than 10% in the frequency range from 10 MHz to 3 GHz. Moreover, the conductivity of cell $sigma _{textit {cell}}$ among different concentrations obtained by this method shows the smaller discrepancy than that obtained by Bruggeman-Hanai equation in the frequency range from 1 GHz to 3 GHz. The dielectric properties $varepsilon ^{ast }_{textit {cell}}$ and $varepsilon ^{ast }_{textit {cell, BH}}$ are brought into a simulation model, respectively. The simulated impedance ${Z}^{ast }_{textit {sol, sim}}$ shows a better agreement with the experimental impedance ${Z}^{ast }_{textit {sol, exp}}$ compared with the simulation impedance ${Z}^{ast }_{textit {sol, sim, BH}}$ , which means the dielectric properties $varepsilon ^{ast }_{textit {cell}}$ obtained by this new method is accurate for the future research of cell’s dielectric properties in the GHz frequency range.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Aptamer-Modified Ultrastable Gold Nanoparticles for Dopamine Detection
    • Pages: 2517 - 2525
      Abstract: Metallic nanoparticles, in particular silver and gold nanoparticles, have been used in various fields for centuries. In the last few decades, their plasmon resonance has made them particularly attractive to biochemists because of their unparalleled optical properties, making them ideal probes for molecular detection. In this article, a new approach to dopamine detection based on ultrastable gold nanoparticles is presented. A dopamine-binding aptamer was used to modify ultrastable gold nanoparticles for the sensitive and selective detection of different concentrations of dopamine without inducing gold nanoparticles aggregation. Indeed, when dopamine binds to the aptamer present at the surface of the gold nanoparticle, the latter exhibits a plasmon shift relative to the dopamine concentration, allowing measurement of its dosage. Besides that, the target molecules can be filtered out to permit nanoparticles reuse. The detection assay showed good linearity between the dopamine concentration and gold nanoparticles’ plasmon shift while common interfering molecules, such as ascorbic acid and tyramine, induced no or little plasmon shift.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Performance Assessment of a Cavity on Source ChargePlasmaTFET-Based
           Biosensor
    • Pages: 2526 - 2532
      Abstract: The promising candidate for designing a highly sensitive biosensor is the tunneling field effect transistor (TFET). In this work, for the first time the performance analysis of a cavity on source charge plasma tunneling field effect transistor (CS-CPTFET) based lable-free biosensor has been proposed. To design the device,charge plasma concept is adapted, where suitable metal workfunctions have been used to create the source and drain regions. The charge plasma technique reduces random dopant fluctuation, thermal budget and steps required for the fabrication. The significant advantage of the proposed device includes the creation of abrupt doping profile at the tunneling junction (source-channel).The achievement of abrupt doping profile at the tunneling junction is responsible for the enhanced sensitivity and suppression of negative conduction (ambipolar) since the cavity is created in the source oxide region of CS-CPTFET biosensor. The response of the proposed biosensor for various biomolecules has been analyzed in terms of band energy variation, electric field, surface potential and transfer characteristics by using Silvaco ATLAS device simulator. Various biomolecules such as uricase (k = 1.54), Glucose oxidase (k = 3.46), APTES (k = 3.57), bacteriophage T7 (k = 6.3), keratin (k = 8) and gelatin (k = 12) have been considered for examining the performance of proposed biosensor.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Refractometric Uric Acid Biosensor Based on Immobilized Uricase and
           PVA+PEG Composite Hydrogels
    • Pages: 2533 - 2543
      Abstract: A mechanically stable, high-sensitivity, high-selectivity, optical uric acid sensitive membrane (UASM) was prepared by immobilizing uricase in a mixture of SiO2 mesocellular foams (SiMCFs) and nanoparticles (SiNPs), then embedding it in a composite gel of polyvinyl alcohol (PVA) and polyethylene glycol (PEG). The UASM was spin-coated onto a gold-glass sheet to create a surface plasmon resonance (SPR) biosensor without electromagnetic and light-intensity disturbances. A series of prism-based SPR sensing experiments showed that, for a UASM consisting of SiMCFs and SiNPs at a mass ratio of 6:4, PVA and PEG at a mass ratio of 4:1, the resonance angle of the sensor decreased by about 2.610° and the average sensitivity was 2.175°/mM within a UA concentration range of 0 – 1.2 mM. Young’s moduli of four dry UASM strips (670 – 1050 MPa) were obtained, demonstrating that UASMs have good biomechanical stability. Langmuir adsorption isotherm could explain the adsorption law of the UASMs for UA molecules in the concentration range of 0.2 – 1.2 mM.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • First Principles Study of Noble Metal (Single Atom and Cluster) Decorated
           Reduced Graphene Oxide for Efficient Formaldehyde Adsorption
    • Pages: 2544 - 2551
      Abstract: This article presents a computational study on formaldehyde (HCHO) adsorption performances of noble metal (Pt, Au, and Ag) nanoparticles functionalized reduced graphene oxide (RGO). The molecular adsorption over all the RGO-metal hybrids with varying sizes of metal clusters (n = 2, 4, and 13) has been characterized in terms of changes in electronic structures, ground state energies and electron distributions of the respective materials upon HCHO adsorption. The density functional theory (DFT) based quantum simulation results reveal that the HCHO adsorption strength of the RGO-metal nanoparticle composite differs as per the sequence: ${text{RGO-Pt}}_{n}>$ ${text{RGO-Au}}_{n}>$ RGO-Agn. The strongest adsorption affinity of platinum is primarily attributed to incomplete electron occupancy of valance ${d}$ orbital (Pt- $4d^{9}$ ). While, relatively larger degree of closeness in ${s}$ and ${d}$ band energies makes Au a superior HCHO adsorbent over Ag. Single metal atom (n = 1) owing to have high energy and chemical reactivity produces enhanced values of adsorption energy and charge transfer which eventually saturate as the cluster size grows (n = 13). The influence of carbon vacancies (both single and double) of the underlying RGO sheet on HCHO adsorption process has also been examined. Observed results indicate that the vacancy defect has some unfavorable effects in binding molecule to all these metal decorated RGO structure- irrespective of metal types and cluster sizes. Thus, the present computational work covering important practical aspects is expected to have great contribution to the exploration of HCHO adsorbing materials suitable for the development of RGO based resistive HCHO sensors.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Ethanolamine Modified ZnO Nanorods-Based Disposable Gate-AlGaN/GaN High
           Electron Mobility Transistor for pH Sensing
    • Pages: 2552 - 2558
      Abstract: In this paper, a pH sensor based on disposable gate-AlGaN/GaN high electron mobility transistor was proposed, where ethanolamine modified ZnO nanorods was functionalized as sensing membrane. Different from those reported before that molecular modifications were utilized as sensing probes, disposable gate with functionalized ZnO nanorods possesses higher sensitivity and better stability. Moreover, ethanolamine modified on ZnO nanorods could provide highly reproducible homogeneous and dense monolayer-coating with amines, which not only protect ZnO from being dissolved in acidic and alkaline solutions and enables the sensor conduct pH detection in a wider range, but also promotes the sensitivity of the pH sensor. The physical and pH sensing characteristics has been investigated. These results demonstrate the new proposed disposable gate-AlGaN/GaN HEMT pH sensor a promising pH sensor for practical application.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Neodymium Modified Chitosan/PMMA Integrated Thin-Core Fiber for Trace
           Fluoride Ion Detection
    • Pages: 2559 - 2564
      Abstract: Using a segment of thin-core fiber (TCF), multimode fiber (MMF) and single-mode fiber (SMF), a fiber-optic sensor based on the SMF-MMF-TCF-MMF-SMF structure is fabricated and experimentally demonstrated. Based on the layer-by-layer self-assembly method, neodymium modified chitosan (Nd/CTS)/polymethyl methacrylate (PMMA) film is coated on the TCF surface, a Mach-Zehnder interferometer (MZI) sensor for fluoride-ion detection is obtained. With the increase of the fluoride-ion concentration, the monitoring trough shows redshift. Also, the high sensitivity of 14.145 nm/ppm, the rapid response time (about 60 s), the good logarithmic linear relationship ( ${R}^{2}=0.97175$ ) at the concentration range of 0.002–0.04 ppm with high selectivity for fluoride ion are achieved. The sensor has the advantages of easy fabrication, good selectivity, low-temperature cross-sensitivity, and good stability, which is suitable for sensitive detection of trace fluoride ion.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Microfluidic Viscometer With Capacitive Readout Using Screen-Printed
           Electrodes
    • Pages: 2565 - 2572
      Abstract: Microfluidic co-flow viscometers require the measurement of the interface position between two fluids (one sample and one reference fluid) flowing in parallel inside a microfluidic channel. From the interface position, the flow rates of both fluids and the known viscosity of the reference fluid, the viscosity of the sample fluid can be calculated. The detection of the interface position is usually done by optical means, therefore requiring optical access, and often coloring of the liquids or suspension of tracer particles to achieve optical contrast. In this contribution we present a device that is capable of determining the interface position in an alternative manner by capacitive measurements. The fully polymeric microfluidic chip is fabricated by simple processes requiring no specialized equipment. The capacitive sensor is fabricated by screen-printing. In order to enhance sensitivity, an insulation coating made from PMMA and barium titanate is applied. To assess the interface detection by capacitive means, measurements using different water/glycerol mixtures are performed and compared to optical images of the interface position, establishing a relationship between capacitance and interface position. The capacitive measurement does not require coloring of the liquids or suspension of tracer particles, if there is a dielectric contrast at the measurement frequency.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Fabrication and Characterization of ZnO:CuO Electronic Composites for
           Their Application in Sensing Processes
    • Pages: 2573 - 2580
      Abstract: Sensing behaviour of samples belonging to the system ZnO:CuO has been investigated. Samples with different proportions of ZnO and CuO have been sintered and exposed to a reducing agent as ethanol (C2H5OH). All the samples have been characterized not only from the point of view of the sensing properties but also regarding morphology, composition, crystallinity and luminescent properties in order to correlate these properties to the sensing behaviour. Sensitivity, response times, recovery and stability have been measured under exposure to ethanol vapour in concentrations 800, 4700 and 16000 ppm. Sensing cycles have been performed at three different temperatures: 25, 50 and 100°C.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Dandelion Flower Like GaN Humidity Sensor: Fabrication and Its Excellent
           Linearity Towards Entire Relative Humidity Range
    • Pages: 2581 - 2588
      Abstract: This article reports humidity sensors based on hierarchical gallium nitride (GaN) with flower-like microstructures based on a scalable hybrid approach. We have fabricated 3D dandelion flower-like GaN hierarchical microspheres with an ammonothermal- conjugated ammoni- fication method (750-1150 °C) for the humidity sensors. The GaN microspheres are analyzed by scanning electron microscopy, X-ray diffraction, Raman and Fourier-transform spectroscopic methods. It is found that the use of oleylamine in the ammonothermal-ammonification hybrid approach, plays an essential role in the GaN morphology. The GaN sensors are prepared by dip-coating the sensor materials on the interdigitated electrode and their humidity sensing performance have been characterized. The humidity sensing performance is investigated at an applied 1V AC bias, by measuring the impedance and charge transfer resistance as a function of the broad range (10 to 95%) of RH. The GaN sensor exhibits a high response of above four orders of magnitude with excellent linearity and stability within the whole measurement range, while the bare GaN humidity sensors exhibited a low response of less than two orders of magnitude with poor linearity and stability. We have also proposed a possible charge transfer mechanism and studied the influence of the defect degree on the sensing performance of the GaN humidity sensors.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Development of Cost-Effective, Selective and Stable Room Temperature
           Methanol Sensor
    • Pages: 2589 - 2596
      Abstract: In the present work, the structural and morphological of 5, 10, 15, 20 tetratoloylphenylporphyrinato Zinc (II) (ZnTTP) thin films prepared by spin coating on low cost glass substrate have been investigated using X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM). At room temperature, the electrical responses of ZnTTP thin films were measured under five reducing volatile organic compounds, including toluene, acetone, methanol, ethanol, and isopropanol. The obtained results indicate that the ZnTTP behaves as a p-type semiconductor. The developed sensor exhibits the highest linear response with methanol vapor. The selective methanol sensor based on ZnTTP thin film reveals a good reproducibility, great stability for more than 28 days, and a limit of detection as low as 2 ppm. At 260 ppm, it has fast response and recovery times of 51 s and 92 s, respectively. The principal component analysis (PCA) treatment was successfully used to discriminate methanol vapor.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Structural and Sensing Characteristics of NiOx Sensing Films for
           Extended-Gate Field-Effect Transistor pH Sensors
    • Pages: 2597 - 2603
      Abstract: In this article, the impact of post-deposition annealing (PDA) on the structural features and sensing properties of the NiOx sensing films deposited on a n+-type Si substrate was studied for an extended-gate field-effect transistor (EGFET) pH sensor. X-ray diffraction, X-ray photoelectron spectroscopy, atomic force microscope, and transmission electron microscopy were applied to explore the crystal structure, elemental composition, film morphology, and film microstructure of the NiOx sensing films after PDA at five different temperatures, respectively. The NiOx sensing film after PDA at 500 °C showed a higher pH sensitivity of 60.65 mV/pH, a smaller hysteresis width of 1.8 mV and a lower drift coefficient of 0.28 mV/h than those at different PDA temperatures. This result may be attributable to the promoted proton-exchange process and increased the number of surface bind-sites due to the NiOx film featuring the column-like polycrystalline structure, possessing a high Ni2+ content and forming of a thinner silicide layer at the NiOx/Si interface. In addition, this PDA temperature can minimize the oxygen vacancies and passivate the trap sites, thus reducing the formation of a hydrated layer on the NiOx film surface.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Self-Powered Humidity Sensor Based on Polypyrrole/Melamine Aerogel for
           Real-Time Humidity Monitoring
    • Pages: 2604 - 2609
      Abstract: Measuring or monitoring air humidity is an increasing demands in the various fields. In this work, a simple and convenient method was utilized to prepare a melamine aerogel (MA) based sensor for humidity measurement. The sensing material was made from polymerization of polypyrrole (PPy) in the matrix of MA. The surface morphology was characterized by scanning electron microscopy (SEM) to examine the inner structure of the sensing material. MA was chosen as the substrate owing to the porous structure, which facilities the adsorption of water molecules from the air. The self-powered humidity sensor was manufactured by adhering conductive tapes on both side of PPy modified MA (PPy@MA). The sensor has a flexible, ultralight and ultraporous structure, and could generate humidity-induced open-circuit voltage from the concentration gradient of the H+ ions. The PPy@MA sensor exhibited the response and recovery time of 1.1 s and 4.5 s, respectively, when it is used for sensing the flowing wet air (RH 75 %). Furthermore, the sensitivity of the self-powered humidity sensor was evaluated by assessing the moisture changes of the human skin, and the moisture from the human breath.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Mesoporous Tyrosine Functionalized BTC-ZnO Composite for Highly Selective
           Capacitive CO Sensor
    • Pages: 2610 - 2617
      Abstract: Drop-casting is a simple deposition technique where a slurry-like solution of nanomaterial is coated on small substrates, which are then allowed to dry under controlled temperature and pressure ambience. The present work focuses on the tyrosine functionalized benzene-tri-carboxyamide (Tyr-BTC) and ZnO composite based sensor deposited using drop-cast method for sensing carbon monoxide (CO) at room temperature. The composition of the composite surface was varied by altering the molar concentration of the constituents. The performance of the mesoporous structures in room temperature gas sensing was studied for the CO concentration range of 5–300 ppm in synthetic air. The highly amplified gas sensing response of approximately 94% at 300 ppm of CO by Tyr-BTC-ZnO in molar concentration 2:3 of Tyr-BTC-ZnO was observed. The improvement in the capacitance change, quicker response time, good linearity and reversibility was attributed to the synergistic effect between ZnO and tyrosine functionalized BTC.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An Ultra-High Accuracy Temperature Measurement Method Using Acoustic
           Waveguide
    • Pages: 2618 - 2626
      Abstract: This article presents a very precise approach to measuring temperature in a wide temperature range using ultrasonic waves. A lead zirconate titanate (PZT) piezoelectric transducer is used to excite ultrasonic shear waves and a solid stainless steel waveguide is selected to confine the ultrasonic wave propagation path. The shape and dimensions of the waveguide were theoretically optimized and numerically simulated to propagate robust, non-dispersive wave, and protect the fragile PZT from high temperature. Ultrasonic wave velocity is highly temperature dependent. The travelling time of wavepacket along the waveguide exhibits a corresponding relationship with the average temperature at measurement zone of the waveguide. Detailed experimental verification and validation processes, together with a calibration stage, were conducted up to 200°C, a temperature that is on par with the operating range of the resistance temperature detector (RTD) used for calibration. Stability test demonstrated that our technique attains a high accuracy (i.e. ±0.1%) which is comparable with the highest precision standard of commercial RTDs along the calibrated temperature range. Temperature tracking test was operated to unfold the temperature measuring and tracking capability of the ultrasonic wave technique in different liquids. This ultrasonic technique is robust and customizable, hence providing a promising alternative for accurate and stable contact thermometry.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Amorphous Silicon and Carbon Nanotubes Layered Thin-Film Based Device for
           Temperature Sensing Application
    • Pages: 2627 - 2633
      Abstract: This paper proposes an integrated layered doped and undoped amorphous silicon thin-film based temperature sensing device. Temperature sensing performance has been measured for thin film p-i-n (p-type- intrinsic-n-type) configuration-based diode. Linear dependency of voltage on the temperature for forward-biased diode at a constant bias current is demonstrated in the temperature range of 30 – 200 °C. Further, the same device has been introduced with double-walled carbon nanotubes (DWCNTs) to improve the linearity of the sensor. Comparative performance of two configurations p-i-n and p-i-n/DWCNTs for temperature sensing application has been studied. Moreover, this paper discussed the effect of the DWCNTs on the sensor parameters such as sensitivity, S and coefficient of determination, R2. The maximum sensitivity of the sensor, 22.34 mV/ °C for p-i-n configured device and 21.06 mV/°C for p-i-n/DWCNTs configuration in a biasing current range of 10– 60 mA have been found. We achieved a maximum value of the coefficient of determination equal to 0.99889 for a p-i-n configuration and 0.99922 for a p-i-n/DWCNTs configured device.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Whispering-Gallery-Mode Optical Microshell Resonator Infrared Detector
    • Pages: 2634 - 2641
      Abstract: We demonstrate a thermal infrared (IR) detector based on a high quality-factor (Q) whispering gallery mode (WGM) borosilicate glass microspherical shell resonator and investigate its performance to detect IR radiation in 1 – 20 ${mu }text{m}$ wavelength range. The resonator exhibits a temperature sensitivity of 1.17 GHz/K with a Q-factor of 3 million and can be configured as a high sensitivity infrared sensor. The microspherical shell IR sensor exhibited a responsivity of 7.88 kHz/nW and achieved a noise-equivalent-power (NEP) of 19 nW/ $sqrt {Hz}$ experimentally. A laser Doppler vibrometer (LDV) is used to measure the physical expansion of the microspherical glass resonator when IR radiation is absorbed. Based on the experimentally measured diametric expansion of the shell per unit IR power absorbed, the NEP of 19 nW/ $sqrt {Hz}$ corresponds to a dimensional change of 2 pm which can be resolved using the resonator. Using COMSOL modeling, thermal expansion analysis was performed for the absorbed IR Power. Using these values of dimension change of the microspherical shell, the dependence of resonance frequency shift on absorbed IR power was simulated. These models show that a NEP of 690 pW/ $sqrt {Hz}$ can be achieved for the microspherical shell with a diameter of 2 mm and a thickness of 2 ${mu }text{m}$ .
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Noninvasive and Nonocclusive Blood Pressure Monitoring via a Flexible
           Piezo-Composite Ultrasonic Sensor
    • Pages: 2642 - 2650
      Abstract: Continuous blood pressure monitoring in everyday life is important and necessary to detect and control high blood pressure in advance. While the existing blood pressure monitoring techniques are well suited for applications in current clinical settings, they are inadequate for next-generation wearable long-term monitoring of blood pressure on a daily basis. In this study, a flexible piezo-composite ultrasonic sensor was reported, for the first time, for continuous blood pressure measurement through ultrasonic motion tracking of blood vessel wall. A flexible piezo-composite ultrasonic sensor was designed and fabricated with a layer of PZT-5A/ polydimethylsiloxane (PDMS) anisotropic 1–3 composite and silver nanowire based stretchable electrodes. The material properties and dimensions of the sensor were determined according to the volume fraction of PZT-5A and the material properties of PZT-5A and PDMS. The experimental results illustrated that the flexible sensor possessed adequate bandwidth and sensitivity for blood pressure monitoring. Continuous blood pressure measurement was successfully conducted with the ulnar artery on a volunteer’s right arm. Compared with the measurement results using a clinical ultrasound probe and a commercial upper arm blood monitor, the results obtained in this study demonstrated the capability of the proposed flexible sensor to continuously monitor blood pressure waveforms during cardiac cycles. The flexible sensor provides a promising solution for noninvasive, nonocclusive and calibration-free blood pressure monitoring. It has great potential to be integrated into a wearable ultrasonic healthcare sensing system for blood pressure and flow monitoring.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Cantilever Supported Fiber Bragg Grating Flow Sensor for Space
           Applications
    • Pages: 2651 - 2657
      Abstract: In this article we report design, mathematical formulation, simulation and fabrication of fiber optic flow sensor using fiber Bragg gratings (FBG) mounted on a cantilever-based transducer support. Design, fabrication and material parameters are optimized in a way that the sensor is operational even at higher flow velocities and finds potentially capable of determining flow rate in rocket fuel tanks for space applications. The reported cantilever supported FBG flow sensor based on simulation results has shown sensitivity to flow rates ranging from 0.1 m/s to as high as 10 m/s flow velocities with varied fuels ranging from liquid hydrogen with density 70.99 kg/m3 to Dinitrogen tetroxide with density 1110 kg/m3. Effect of flow rate is simulated and experimented by placing different weights on the disc and the wavelength shift corresponding to the weights from the designed sensor are recorded. It is observed that the experimental results matched with simulation results to around 50-60%. Flow sensor fabricated is thermally, electrically stable and with its minimal weight finds its extensive use in space applications.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Performance Evaluation of Different Grade IMUs for Diagnosis Applications
           in Land Vehicular Multi-Sensor Architectures
    • Pages: 2658 - 2668
      Abstract: Vehicular positioning systems are necessary for the development of autonomous vehicles and advanced driver assistance systems (ADAS). In recent years, Inertial Measurement Units (IMU) based on micro-electromechanical systems (MEMS) have been included in proposals for multi-sensor positioning system architectures in order to take advantage of their cost and size. The measurement errors propagation to the positioning solution have limited its application for long-term positioning solution. However, it can play a key role in those applications where tri-axial attitude and accelerations can be indicators of curves, slopes, cants, etc. and it can be used as diagnostic of other sensors measurements. This work is focused on the use of IMUs for diagnostic applications and it compares medium-end (xSens MTi-100) and low-end (Bosch BMI160) grade MEMS-based IMUs in an experimental road test using a fusion of a high-end IMU (KVH GEO-FOG), GNSS and wheel speed sensor as reference. In addition, a research question about whether the calibration is the main reason between different grade IMUs has been formulated. Thus, a simple, manufacturable and cost-efficient calibration technique is applied to the low-end IMU in order to compare its performance improvement with the medium-end one. The raw measurements (angular rates and specific forces) and navigation states (tri-axial attitude and accelerations) are considered for diagnosis and they are statistically compared to evaluate the performance of each IMU. It is concluded that the calibration technique used makes the low-end IMU performance similar to that of the medium-end one. Consequently, this work contributes to optimizing the cost of land vehicular positioning systems when choosing the most appropriate sensor based on the accuracy and precision required for the application.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Fixed-Frequency Angular Displacement Sensor Based on Dielectric-Loaded
           Metal Strip Resonator
    • Pages: 2669 - 2675
      Abstract: A fixed-frequency angular displacement sensor using a dielectric-loaded metal strip resonator is proposed. The dielectric body on which the strip resonator is attached is a cylinder having a high dielectric constant ( $varepsilon _{mathbf {r}} gg ~1$ ). The resulting resonator operates at the half-wave resonant frequency of the quasi-TEM mode of the loaded strip, which is much lower than the nominal resonant frequency of the strip. To enable angle sensing, the resonator is placed symmetrically between two $50~Omega $ , open-ended microstrip lines, so that the magnitude of the transmission coefficient ( $vert text{S}_{mathbf {21}} vert$ ) between the lines varies with the strip angle, i.e., $0^{mathbf {0}} le theta le 90^{mathbf {0}}$ . Also, this configuration ensures constant resonant frequency operation for all angles. Simulations show that although the $vert text{S}_{mathbf {21}} vert $ versus angle curve varies exponentially in the range of $0^{mathbf {0}} le theta le 90^{mathbf {0}}$ , the corresponding coupling coefficient varies quasi-linearly in $10^{mathbf {0}} le theta le 80^{mathbf {0}}$ . A laboratory prototype for ~ 2.4 GHz resonant peak is fabricated and the simulation results are verified through prototype measurement. The advantages of the proposed sensor are that it is compact, has a simple design, and operates at a fixed frequency, enabling a low-cost, robust angular sensor.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Fault Diagnosis Framework Insensitive to Noisy Labels Based on Recurrent
           Neural Network
    • Pages: 2676 - 2686
      Abstract: Deep neural network (DNN)-based fault diagnosis is one of the effective means to ensure the safe and reliable operation of wind turbines (WTs). However, in practice, the complexity of labeling health condition samples leads to the possibility of health condition dataset corruption, resulting in a reduction of diagnosis effect. Considering the different distributions of noisy labels, this paper proposes a recurrent and convolutional neural network with clean revision (CRRCNN) framework, which consists of the recurrent and convolutional neural network (RCNN) and the clean revision. First, RCNN containing multi-axis input is constructed as the baseline network. Second, the clean revision containing three variations, namely backward cross-entropy loss, clean estimation of noisy transition matrix, and clean noisy joint training strategy, is embedded in the framework smoothly for better training. Finally, the proposed framework is verified by two type distribution noisy label datasets and the experiment results show the superiority of the proposed framework. Furthermore, the inner operation of CRRCNN is explored by sensitivity analysis (SA).
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An Angular Accelerometer With High Sensitivity and Low Crosstalk Utilizing
           a Piezoresistive Cantilever and Spiral Liquid Channels
    • Pages: 2687 - 2692
      Abstract: This article describes an angular accelerometer with high sensitivity and low crosstalk that uses a MEMS (Micro Electro Mechanical Systems) piezoresistive cantilever and spiral liquid channels. The fabricated device is composed of two spiral channels aligned in parallel with the piezoresistive cantilever placed in between the channels. When angular acceleration is applied, the liquid inside the spiral channels produces an inertial force. This inertial force generates a pressure difference between the spiral channels, and that difference is measured by a piezoresistive cantilever with a high resolution of 0.01 Pa. The pressure difference causes deformation of the piezoresistive cantilever which results in resistance change. Finally, the applied angular acceleration can be calculated from the resistance change. By increasing the number of turns of the spiral channel, both high sensitivity and low crosstalk from the nontarget axis can be achieved. Our proposed device with 12.5 turns achieved a sensitivity of 0.72 mV/(rad/s2) and crosstalk of less than 2%.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Bolt-Shaped Triboelectric Nanogenerator for Rock-Climbing Training
           Trajectory Detection
    • Pages: 2693 - 2701
      Abstract: Rock-climbing trajectories are fundamental data for coaches in selecting athletes and formulating subsequent training plans. This study proposes a bolt-shaped triboelectric nanogenerator (BS-TENG) that can be used for trajectory detection during rock-climbing training. BS-TENG, which is made of polytetrafluoroethylene, expanded polyethylene, copper, and so on, outputs a voltage pulse as the corresponding signal when it is pressed by the athletes, and the envelope of the area formed by all the touched BS-TENG then creates the athlete trajectory when combined with self-written software. The peak value of the output-voltage pulse is between 4 and 7 V, which shows a strong signal-to-noise ratio and an anti-interference ability. The maximum working frequency is 5 Hz, which indicates that the time interval between two consecutive touches of the same BS-TENG must be more than 0.2 s. In addition, the power-generation parameter test results show that the output current sharply drops from ${sf 9.27} times {sf E}^{- {sf 7}}$ A when the resistance exceeds 47 k $Omega $ , and maximum output power of ${sf 255} times {sf E}^{- {sf 10}}$ W is obtained when a 510-k $Omega $ resistance is connected in series. All these results prove that the BS-TENG can be used to determine the rock-climbing trajectory in real time, and the self-powered function offers more advantages in application promotion.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Direct Measurement of Contraction Force in Cardiac Tissue Construct in
           2D-Plane Using Dual Axis Cantilever Sensor
    • Pages: 2702 - 2711
      Abstract: In this work, we present a technique for a dual axis contraction force measurement of human cell based cardiac tissue constructs. The cardiac tissue constructs consist of a vascular-like network and induced pluripotent stem cell derived cardiomyocytes. Before the force measurements, the cardiac tissue constructs were detached from the culture substrate to allow less restricted contraction. The in-house prepared force sensors are composed of piezoelectric sensing elements and a metallic cantilever for contacting the cardiac tissue constructs. A dedicated measurement platform with embedded signal processing software is used for data acquisition from the sensors. Dual axis force sensor results are compared with our previously developed single axis force sensor technique. Additionally, the proposed dual axis force measurement system can measure two-dimensional displacement trajectories of the cantilever probe tip. We propose a pattern matching method for classification of the captured cardiac contraction cycle patterns and for extracting anomalies in the measured cycles. We demonstrate both single and dual axis peak cardiac construct contraction force measurement results in the ranges of 3.4 - $6.7~mu text{N}$ and 9.4 - $10.6~mu text{N}$ , respectively. The relative standard deviation of the peak contraction force results varied between 1.0 and 4.1% in eight captured 60 second measurement sequences.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An ARMA-Based Digital Twin for MEMS Gyroscope Drift Dynamics Modeling and
           Real-Time Compensation
    • Pages: 2712 - 2724
      Abstract: The applications of Inertial Measurement Unit (IMU) based on Micro Electro Mechanical System (MEMS) are widely used because of its low-cost, small volume and low energy consumption. Unlike the accelerometers and magnetometers, which are respectively sensitive to vibrations and ferrous materials interference, MEMS type gyroscope is able to provide stable orientation information for a short period of time. Compared with these two sensors, gyroscopes are neither disturbed by vibrations nor affected by metallic materials, and therefore it would be one of the primary sensors for industrial applications. Reviewing the most recently related works, accurate attitude estimations are usually obtained by fusing gyro integration. However, it is well known that series drifts will be induced after a long-term integration. Furthermore, according to the literature and the associated experiments, they all demonstrated that MEMS type gyroscopes have strong temperature correlation because of the silicon structure. Put it simply, the temperature variations will give rise to gyro drifts severely and thereby degrade the orientation estimation precision. To address this problem, this article proposed a temperature modeling technique and a real-time drift pre-compensation procedure by applying autoregressive moving average (ARMA) technology. Different from the traditional modeling methods, the proposed method is able to describe transient drifting behaviors as well as steady state ones. Moreover, comparison studies using artificial neural networks (ANNs) are also presented. Experiments show that the proposed method provides a stable and reliable temperature-caused gyroscope drift pre-compensation. The developed method is also very suitable for the realization in a low-cost embedded system.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Towards Acoustic Radiation Free Lamb Wave Resonators for High-Resolution
           Gravimetric Biosensing
    • Pages: 2725 - 2733
      Abstract: A novel Lamb wave resonator with fully suppressed acoustic radiation in water is proposed for high-resolution mass-sensitive detection of biomolecules. The elimination of acoustic radiation is achieved by slowing down the Lamb wave to a velocity lower than the sound speed in water. This enables high-quality-factor resonance in water and reduces the sensing frequency noise. High aspect ratio electrodes (HAREs) are used to slow down the Lamb wave. The elastic resonance and large surface area of the HAREs can also enhance the mass sensitivity of the device. The improved mass sensitivity together with the low frequency noise substantially improves the overall sensing resolution. Although reducing the plate thickness can also slow down the Lamb wave, it makes the device very fragile and not practical to use. In contrast, slowing down the Lamb wave by increasing electrode height allows the use of thick plates which is robust. In this article, the behavior and performance of the proposed high aspect ratio electrode Lamb wave resonator (HARE-LWR) are theoretically analyzed using finite element method simulations. Optimum design parameters were found through the simulations. Reported results show that a significant figure of merit improvement was achieved by the proposed HARE-LWR design.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Investigation on Electromagnetic Vibration Energy Harvesting in Water
           Distribution Control Valves
    • Pages: 2734 - 2741
      Abstract: Control stations of a water distribution system monitor several variables such as the pressure, the flow, and the quality of water. For these monitoring tasks, wireless sensor networks with ultra-low power consumption powered by vibration-based energy harvesters as an alternative to the usage of batteries or wired connections might be a suitable option in these facilities. This article investigates the potential applicability of an electromagnetic vibration energy harvester prototype in different control valves of a water distribution system in the province of Barcelona by means of experimental measurements and numerical simulations. The low-amplitude vibration with random excitation is measured with piezoelectric accelerometers in three control valves under normal operating conditions to process each signal and determine the dominant frequency in the complete spectrum, which is found to be in the order of magnitude of kHz, and the dominant frequency in the range of 10 to 100 Hz, where commercial harvesters normally operate. Numerical simulations of the harvester prototype are conducted in all cases with the same materials, geometries, and coil parameters, generating a maximum RMS load voltage and output power when the harvester’s natural frequency matches the dominant frequencies of each vibration signal. The maximum output power estimated in these simulations is 1573.04 nW with a corresponding RMS load voltage of 53.6 mV and optimal load resistance of $1830~Omega $ .
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Optical Magnetic Sensing Using Whispering- Gallery Mode Optomagnonical
           Cavity
    • Pages: 2742 - 2748
      Abstract: Optical sensors is a hot topic of modern sensing technology with anti-electromagnetic interference, small size and controllable characteristics. In this study, we propose a wide range and $mu text{T}$ resolution magnetometer with the optomagnonics. In the optomagnonical cavity, the existence of opto-magnetic interaction can converse the magnon mode whose frequency is determined by the external magnetic field into an optical mode. We can get this frequency of the magnetic oscillator from the optical mode by optical-microwave synchronous scanning, and then the variation of the external field can be detected. Compared with the previous works that requires additional degrees of freedom, our scheme directly reads out the magnetic field through the optomagnonics which is more stable. Moreover, we present the numerical simulations based on experimental parameters that have been implemented, so our scheme is feasible with current experimental technologies.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Optimization of the Temperature Stability of Fluxgate Sensors for Space
           Applications
    • Pages: 2749 - 2756
      Abstract: Fluxgate magnetometers are widely used in many places for the measurement of weak magnetic field, but are sensitive to variations of sensor temperature. Therefore, their stabilization against temperature variation is required especially for outdoor applications. In this work, temperature dependencies of fluxgate sensors having the cores of one homemade, one commercially obtained finemet alloy, and a commercially obtained supermalloy alloy in ribbon form were examined. It was observed that the heat treatments of the studied cores affect the sensitivity of the sensors in different ways. For instance, while the scale factor of the sensor with the core from in-house prepared finemet ribbon increased from 1.19 MV/T to 3.12 MV/T, the scale factor of the sensor with the supermalloy core increases by 20 times in magnitude (41.6 kV/T to 0.92 MV/T) after heat treatment. Our analysis performed in the temperature interval from −50 °C to +85 °C reveals that temperature dependencies of the sensors also show differences depending on the core material. In addition to differences seen on the temperature dependence of the studied cores, the most stable one was the sensor with the heat treated in-house prepared finemet core. The variation of the scale factor in the 135 °C temperature range was 9.8 %. However, by analyzing the temperature dependences of the scale factors we could decrease the error coming from temperature variations to less than 1.0 % in magnitude of the output signal due to the observed linear dependence between the scale factor and the temperature of this sensor.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • High Sensitivity and Wide Range Soft Magnetic Tactile Sensor Based on
           Electromagnetic Induction
    • Pages: 2757 - 2766
      Abstract: Tactile sensors play a significant role in robotic systems to interact with the external world. In this article, a novel soft magnetic tactile sensor (SMTS) based on the law of electromagnetic induction is proposed. This new sensor uses the configuration of a transformer with planar coils and a Polydimethylsiloxane silicone elastomer with an inverted cone structure embedded in a copper sheet. The SMTS measures the induced voltage amplitude variation of the secondary coil that is caused by the changes in alternating current magnetic field between the coil and the conductive or ferromagnetic sheets. The principle of the new sensor is explained first and then the design methods are outlined. Prototypes are manufactured and a three-dimensional finite element model is built to analyze the mechanical property of the elastomer. Besides, a performance analysis platform and an electronic interface system with the excitation source, the signal processing and the data acquisition, are developed, in order to calibrate the sensor using the polynomial fitting method. The calibrated prototype shows competitive performances in comparison with other existing counterparts, which has a high sensitivity of 56.3 mV/N, a wide measurement range up to 15 N, and a low hysteresis of 2.02%. The new sensor is also of low cost, durable, and has potential applications in dexterous robotic manipulation.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Magnetoresistance Behavior of Cryogenic Temperature Sensors Based on
           Single-Walled Carbon Nanotubes
    • Pages: 2767 - 2774
      Abstract: We report on the experimental investigation of the magnetoresistance behavior of cryogenic temperature sensors based on single-walled carbon nanotubes (SWCNTs) as a function of temperature and magnetic flux density; one sensor was based on layers/networks of unsorted SWCNT of various chiralities, and one on SWCNTs layers of (7,6) chirality. SWCNT-based sensors were fabricated according to a straightforward method, and the electrical properties were measured from room temperature down to 2 K using a Quantum Design Physical Property Measurement System (PPMS). The two sensors present different magneto-transport behavior, with the magnetoresistance dependence on the magnetic field being a sum of two terms with positive and negative contributions respectively. The expression for magnetoresistance is derived together with its experimentally determined coefficients. For both sensors, the electrical resistance dependence on temperature below the 80 K mark is explained by Mott’s law of variable-range hopping. The experimentally obtained hopping dimension ${d}$ values of 2.67 for the system comprised of purified (7,6) chirality SWCNTs and 2.97 for the system comprised of unsorted SWCNTs are in good agreement with the theory. As a second step, the magnetic field was kept constant and at different values, while the temperature was scanned over the investigated domain.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Novel Method for Eliminating Residual Voltage in a Resolver With Signal
           Fitting Implementation
    • Pages: 2775 - 2782
      Abstract: This paper presents a compensation algorithm with software implementation for eliminating the residual voltage in a resolver. The algorithm provides lower-cost implementation and more significant effects than the existing adopted compensation coils. The paper studies the characteristics of the resolver output signals, which mainly consist of the ideal induced signal, the odd-order harmonic components and the residual voltage. First, the resolver output parameters—amplitude, phase and DC component—are estimated by signal fitting methods when the shaft stops at a certain position. Then, the three parameters are again obtained after the driving shaft rotates 180 degrees with respect to the initial position. Because the induced voltage varies with the shaft rotational angle but the residual voltage does not, the amplitude of the residual voltage is obtained by employing the trigonometric identity of the rotational angles to eliminate the amplitude of the induced signal. The experimental results indicate that the proposed compensation method can effectively eliminate the residual voltage in the induced signal and improve the measurement accuracy of the resolver sensors.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Gray System-Based Identification and Pre-Culling of Outliers Applied to
           Magnetic Sensor in Aeromagnetic Compensation
    • Pages: 2783 - 2790
      Abstract: In aeromagnetic surveys, poor aircraft heading and weather may frequently cause a classical optically-pumped sensor to enter into or close to its dead zone, which results in unavoidable outliers that seriously reduce aeromagnetic compensation. To address these problems, a method to identify rapidly and pre-cull magnetic outliers based on the gray system theory is proposed to reduce their negative influence during the estimation of coefficients and target detection robustness. By constructing a gray region of aeromagnetic data and then checking whether the data at the end points of the region are normal, aeromagnetic outliers can be culled. The simulation results show that even if the outlier rate is increased to 20%, the average of the correct culling rate of the proposed method can still reach 99.67%, at which the culling effect is highly robust. We constructed an experimental survey platform and conducted a flight test. The results show that the improvement ratio of the proposed method can reach 4.36, which is 10.38 times higher than the conventional method.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • High-Performance Sensitive TE/TM Mode Switch With Graphene-Based
           Metal-Dielectric Resonances
    • Pages: 2791 - 2797
      Abstract: We explore the performance of the monolayer graphene with a metal-dielectric compound grating via numerical simulation. The results show that the absorption peaks can be near 100% at 68.36 THz for both TE and TM polarizations. Different spectral line shapes are displayed under different polarization states, we attribute the excellent absorption performance to the excitation of magnetic resonance (MR) and cavity mode resonance (CMR), and then an equivalent capacitance-inductance (LC) circuit model and the waveguide theory are used to explain it theoretically. We have also studied the influence of the polarization angle on the absorption performance, and found that as the polarization angle increases, the resonance wavelength remains unchanged but the FWHM of absorption spectrum increases gradually. Based on this feature, we apply it in mode switch which can easily switch its function as absorption or sensing by adjusting the polarization states of incident radiations.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Novel Hydrogen Sensor Based on a Guided-Mode Resonance Filter
    • Pages: 2798 - 2804
      Abstract: In this study, we demonstrated a hydrogen (H2) sensor based on a guided-mode resonance (GMR) filter coated with a layer of palladium (Pd). The sensor consists of three key layers, namely the substrate (replicated grating structure on an optical adhesive), waveguiding layer of TiO2, and active-sensing layer of Pd. The sensor functions as an optical resonator. On absorption of H2, the change of the refractive index in Pd changes the optical response of the sensor. To monitor the output optical response, a transmission setup was used, and two detection modalities—the change in transmission efficiency at a specific wavelength and the shift in the resonant wavelength—were demonstrated. Upon injection of a mixture of 4% H2 and 96% argon, the resonant wavelength shifted 6.38 nm, and transmission efficiency increased by 45% in 24 s.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Nanophotonic Device Based on Fano Resonance in Engineered Slot Waveguide
           for Optical Detection of Viral Infections
    • Pages: 2805 - 2812
      Abstract: A nanophotonic biosensing platform based on an engineered slot waveguide is proposed. The proposed device is based on Fano resonance which consists of a periodic arrangement of two rows of nano-disks and rectangular grating. The periodically arranged nano-disk structure in the form of engineered slot waveguide provides enhanced light-matter interaction and easy accommodation of bio-samples. The surface morphology of (3-Aminopropyl) trimethoxysilane (APTMS) as a functionalized layer on the silicon surface is experimentally analyzed. The proposed sensor device can detect very small refractive index change of 0.0008. Based on refractive index sensing, the device numerically exhibits a high sensitivity of 1463 nm/RIU for some viral infections including hepatitis B the with a Figure of Merit (FOM) of 471 for refractive index change of 0.02. The limit of detection for the proposed device is 1.02 $times10^{-3}$ RIU (Refractive Index Unit). The proposed scheme with Fano like resonance spectrum carries a great potential for on-chip and off-chip nanoscale optical devices and sensors.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Novel Single Hole Exposed-Suspended Core Localized Surface Plasmon
           Resonance Sensor
    • Pages: 2813 - 2820
      Abstract: Flexibility in design and controlling the wave- guide properties in photonic crystal fiber (PCF) has enabled diverse plasmonic sensing devices with attractive features. Here, we propose a novel single air hole exposed core PCF sensor based on localized surface plasmon resonance (LSPR) for bio-analyte sensing. To supports the LSPR, a gold (Au) nano strip is considered instead of continuous metal film making the proposed sensor cost-effective while experiencing low loss. A thin layer of titanium oxide (TiO2) is also used between the Au and silica glass to assist adhesion, which also contributes to enhancing the sensing performance. Considering the refractive index (RI) variation in the dielectric layer, the sensor performance analysis is carried out via finite element method (FEM) based commercially available software COMSOL Multiphysics in the visible to mid-infrared spectrum. According to numerical results, the proposed sensor shows maximum amplitude sensitivity (AS) of 1449 RIU−1 and a maximum wavelength sensitivity (WS) of 50,000 nm/RIU with a corresponding resolution of $2times 10^{-6}$ in a wide RI range. Besides, being highly linear in sensing performance, the sensor attains low loss, with an improved figure of merit (FOM). Considering the simple architecture and competitive sensing performance, the proposed sensor could be used effectively in RI sensing applications, especially in bio-sensing.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Ultra-Compact Organic Vertical-Cavity Laser With High-Contrast Grating
           Feedback for Gas Detection
    • Pages: 2821 - 2827
      Abstract: The miniaturization of optical sensors is a key step towards the development of highly integrated optical sensing techniques and devices. The present work proposes an ultra-compact organic vertical-cavity microlaser based on an active micro-pillar design, and explores its potential applications as a highly integrated gas sensor. The vertical-cavity is realized by embedding an organic Rhodamine 6G doped SiO2 gain layer inside one small cavity which is formed by distributed bragg reflectors and a high contrast grating thin layer. The hybrid structure of top reflector of cavity enables part of the field to interact with the medium surrounding the cavity, while maintaining a high confinement factor, low lasing threshold, and single-mode operation around 570 nm. In particular, the vertical microlaser can be used as a convenient and versatile wavelength-interrogation sensing architecture, whereby variations of the laser spectral line can be correlated to small changes in the gas concentration. Through using full-wave electromagnetic simulations, we obtain a significantly improved figure of merit (441 RIU−1) for the bulk sensitivity. The vertical architecture and small design of the proposed micro-laser-based sensor render it attractive for highly integrated remote sensing of gases.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Micro-Polarizer Array Configuration Design Method for Division of Focal
           Plane Imaging Polarimeter
    • Pages: 2828 - 2838
      Abstract: The division of focal plane (DoFP) imaging polarimeter operates by integrating a focal plane array (FPA) with a micro-polarizer array (MPA). While the instantaneous field of view (IFoV) error is an inherent problem in such imaging sensor, which negatively affects the imaging accuracy. The influence of IFoV error can be effectively mitigated by optimizing the MPA configuration. In this paper, we propose an MPA configuration design scheme with four basic polarization angles 0°, 45°, 90° and 135° for DoFP imaging polarimeter. First, we analyze the frequency spectrum characteristics of MPA-filtered images and polarization frequency structure is proposed to record the frequency components and their positions. Then we analyze the frequency components and the crosstalk between them. Finally, we propose our design method that formulates the MPA design as a multi-objective optimization problem. And we design MPAs with better reconstruction performance of various sizes by optimizing their spectrum distribution. Besides, the reconstruction performance of many existing MPA designs were tested on statistically estimated scenes or a single image. This paper builds a database of 500 groups of infrared images of four polarization angles to test the performance of various MPA designs. Experiment results demonstrate that the proposed MPA designs outperform the state-of-the-art MPA designs in both objective and visual evaluations. Thus, the effectiveness of our proposed design method is further confirmed.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Smartphone-Based Spectrometric Analyzer for Accurate Estimation of pH
           Value in Soil
    • Pages: 2839 - 2845
      Abstract: This article demonstrates a cost-effective, compact, and handheld smartphone-based sensing tool for accurate estimation of pH values of agricultural farmlands. We develop a spectrometric tool with a resolution ability of 0.22 nm/pixel by utilizing 3D printing technology, regular optical components, a Digital Versatile Disc (DVD) as a grating element, and the rear camera of the phone. The sensor responses for standard pH samples within the pH range 4 to 10 are observed to be linear yet yield a sensitivity of 0.129 per pH unit. The resolution of the proposed sensor for the considered samples is observed to be 0.09 pH units. The results obtained from the designed tool while measuring the pH values of six field-collected soil samples are found to be accurate. The designed sensor’s performance has been evaluated by comparing the experimental data with the commercial-grade pH sensing tool. With the advantages of being a low-weight and data-sharing ability, we envision that the proposed sensing scheme could emerge as a promising alternative platform for in-field estimation of pH values of soil and water resources of our environment.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Theoretical Investigation of a Sensor Based on One-Dimensional Photonic
           Crystals to Measure Four Physical Quantities
    • Pages: 2846 - 2853
      Abstract: In this study, a sensor based on the located defect mode resonance is proposed, which can be used to simultaneously measure changes in magnetic induction intensity, plasma density, refractive index, and incident light angle. Plasma is introduced as the defect into a one-dimensional periodic structure, exciting the located defect mode resonance. The sensitivity, linear range, and figure of merit of the sensor are investigated using the transfer matrix method. The increase in the number of cycles can be used to improve the quality factor and FOM. We also consider the influence of the loss tangent on the sensor to a certain extent. The one-dimensional layered structure is utilized, which has the merits of small volume and simple manufacture. In addition, compared with the traditional sensors design, which focuses on the improvement of performance parameters, our proposed sensor concentrates on the study of multiple physical quantities. Therefore, we hope that our work can have some application potential in the field of measurement.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Printed Microwave Frequency Humidity Sensor Operating With Phase Shifting
           Scheme
    • Pages: 2854 - 2863
      Abstract: This paper investigates a shifting sensing scheme combining slots, transmission lines, and printing technologies. This sensing scheme translates the electrical sensitivity of a transmission line conductor to the insertion phase as a measurement variable. A coplanar waveguide (CPW) based structure was designed, screen-printed, and tested on relative humidity (RH) conditions ranging from 22.8 – 75.3 %RH. For the first time, a composite material made of poly-pyrrole and TEMPO Oxidized Cellulose Nanofibers (TOCN/PPy) was integrated to the structure and studied as a humidity sensitive conductor in microwave frequencies. The measured sensitivity was 0.154°/%RH at 5.870 GHz, while insertion losses decreased by 1.26 dB. The effects of sensing layers thickness as well as trade-off considerations between phase sensitivity and signal attenuation were analyzed by simulation.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Highly Sensitive Phase Variation Sensors Based on Step-Impedance Coplanar
           Waveguide (CPW) Transmission Lines
    • Pages: 2864 - 2872
      Abstract: Reflective-mode step-impedance transmission line based sensors for dielectric characterization of solids or liquids have been recently proposed. In this article, in order to further increase the sensitivity, the sensor is implemented in coplanar waveguide (CPW technology), and this constitutes the main novelty of this work. The sensor thus consists of a high-impedance 90° (or low-impedance 180°) open-ended sensing line cascaded to a low-impedance 90° (or high-impedance 90°) line. The output variable is the phase of the reflection coefficient, which depends on the dielectric constant of the material under test (MUT), the input variable. Placing a MUT on top of the sensing line causes a variation in the effective dielectric constant of the line, thereby modifying the phase of such line. This in turn produces a multiplicative effect on the phase of the reflection coefficient, by virtue of the step-impedance discontinuity. The main advantage of the CPW-based sensor, over other similar sensors based on microstrip technology, is the stronger dependence of the phase velocity of the sensing line with the dielectric constant of the MUT, resulting in sensitivities as high as −45.48° in one of the designed sensors. The sensor is useful for dielectric characterization of solids and liquids, and for the measurement of variables related to changes in the dielectric constant of the MUT (defect detection, material composition, etc.).
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Synchronism and Direction Detection in High-Resolution/High-Density
           Electromagnetic Encoders
    • Pages: 2873 - 2882
      Abstract: Recently, electromagnetic encoders with synchronous reading and direction detection capability have been reported. Such structures are useful for the implementation of (i) displacement/velocity sensors and (ii) chipless-RFID systems based on near-field coupling and sequential bit reading. In the latter, synchronous reading and motion direction detection are a need in order to avoid false readings of the identification (ID) code, if the relative velocity between the reader and the encoder is not constant, and to read the correct ID code (rather than the inverse one), respectively. On the other hand, synchronous reading and motion direction detection, are essential to determine the encoder direction in displacement/velocity sensors, as well as to provide the absolute position of the encoder, provided that the whole encoder is encoded with the Bruijn sequence. In this paper, synchronous reading and direction detection in high-resolution/high-density electromagnetic encoders based on chains of linearly-shaped metallic inclusions are reported. To this end, it is necessary to add two chains of metallic inclusions to the one containing the ID code. In the reader side, three harmonic signals are necessary in order to generate the clock signals and to obtain the ID code. The reader consists of a microstrip line loaded with three pairs of open-ended folded stubs positioned face-to-face by their extremes. By displacing the encoder chains over the extreme of the stubs, at short distance, stub coupling is enhanced when a metallic inclusion lies on top of the stubs, and the frequency response of the reader is shifted towards lower frequencies. Thus, by injecting three (properly tuned) harmonic signals at the input port of the microstrip transmission line, three amplitude modulated (AM) signals are generated by tag motion at the output port of such transmission line, and the envelope functions contain the velocity, the ID code and the absolute position. The reported reader/encode- system exhibits superior space resolution and information density as compared to other similar systems based on synchronous reading.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Detection of Defects in Non-Metallic Composite Material Based on
           Electronically Controlled Spoof Surface Plasmon Polaritons
    • Pages: 2883 - 2890
      Abstract: A novel sensor based on electronically controlled spoof surface plasmon polaritons (SSPPs) is proposed to detect internal defects in non-metallic composites. The designed sensor is flexible and can be wrapped around the materials under test (MUT), therefore, compared with non-flexible sensors, it has a great advantage in detecting curved MUT. Moreover, traditional microwave sensors have to scan the MUT by the way of mechanical moving which consumes much time. For improving the detection speed, the electronic control scheme is adopted to control the sensing area of designed sensor, so as to replace mechanical moving. The detection is based on monitoring change in resonance frequency of the designed sensor which is caused by the defects. The sensor has the advantages of high sensitivity, fast detection speed and it is very convenient to detect curved MUT.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Reflection Measurement of Fire Over Microwave Band: A Promising Active
           Method for Forest Fire Detection
    • Pages: 2891 - 2898
      Abstract: This work presents a technique to detect forest fire using incoherent scattering of radio waves from the ionized electron cloud of combusting plant material. Superheated forest matter ionizes a certain amount of the Alkali salt in plants creating a natural plasma frequency. This work investigates this plasma using a calibrated transmission line to identify the electromagnetic plasma frequency of forest fire. A plasma frequency of approximately 500 MHz has been identified by burning 4 grams and 6 grams of dry Eucalypt leaves. The plasma frequency was found to be stable as long as the biomass density is constant.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An Angular Displacement Microwave Sensor With 360° Dynamic Range
           Using Multi-Mode Resonator
    • Pages: 2899 - 2907
      Abstract: This article presents the application of a novel multi-mode resonator (MMR) named rotational cross-shaped resonator (RCSR) to the design of a planar angular displacement microwave sensor. This MMR developed by four connected stubs with short and open terminations. Since the generated transmission zeros of the RCSR vary linearly with changes in stub lengths, the RCSR structure that makes use of this principle is proposed for implementing angular displacement sensing. The sensor consists of an open-ended stub of RCSR housed by a circular rotor and the rest of the RCSR on the stator. As the position of the stub of RCSR is modified by rotating the rotor, the generated transmission zeros will drift accordingly. Based on the multiple zeros, the proposed angular displacement sensor can provide distinctive direction of rotation detection in addition to dual-sensing measures output. A sensor prototype at 1.95 GHz is designed and experimentally characterized, which reports that both the angle and direction of rotation detections can be realized. The average frequency sensitivity in terms of the variation of a single transmission zero is 1.22 MHz/degree for the dynamic range from −180° to 180°, as well as the average bandwidth sensitivity in terms of the frequency difference between two zeros is 2.61 MHz/degree for the dynamic range from 0° to 180°.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Novel Chipless RFID High Resolution Crack Sensor Based on SWB Technology
    • Pages: 2908 - 2920
      Abstract: A novel chipless RFID based structural health monitoring sensor is proposed in this paper. By using the time domain reflectometry (TDR) based technique, this device enables a wireless, low-cost and pervasive sensing system for detecting the presence of crack in a structure. The proposed sensor is designed on the Taconic TLX-0 substrate and it integrates a super wide band (SWB) antenna with a long transmission line. The SWB antenna covers a bandwidth of 2–20 GHz which offers extremely short time domain pulses. This eventually enables a high-resolution crack sensing scheme that results in higher accuracy and greater precision. A thorough analysis on the designed sensors with a straight and a meandered transmission line is carried out and a comparative study between the SWB and UWB technologies is performed to imply the superiority of the proposed technique. The experimental results from a fabricated prototype of the sensor validate the theoretical analysis.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Movement Detection System Using Continuous-Wave Doppler Radar Sensor and
           Convolutional Neural Network to Detect Cough and Other Gestures
    • Pages: 2921 - 2928
      Abstract: The 2019 coronavirus disease (COVID-19) pandemic has contaminated millions of people, resulting in high fatality rates. Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. CNN combined with other sensors creates a new solution to fight COVID-19 transmission. This paper presents a novel method to detect coughs (an important symptom of COVID-19) using a K-band continuous-wave Doppler radar with most popular CNNs architectures: AlexNet, VGG-19, and GoogLeNet. The proposed method has cough detection test accuracy of 88.0% using AlexNet CNN with people 1 m away from the microwave radar sensor, test accuracy of 80.0% with people 3 m away from the radar sensor, and test accuracy of 86.5% with a single mixed dataset with people 1 m and 3 m away from the radar sensor. The K-band radar sensor is inexpensive, completely camera-free and collects no personally-identifying information, allaying privacy concerns while still providing in-depth public health data on individual, local, and national levels. Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. The proposed cough detection system using microwave radar sensor has environmental robustness and dark/light-independence, unlike traditional cameras. The proposed microwave radar sensor can be used alone or in group with other sensors in a fusion sensor system to create a robust system to detect cough and other movements, mainly if using CNNs.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Optomechanical Modeling and Validation of a Distributed Bragg Reflector
           Force Sensor With Drift and Temperature Compensation
    • Pages: 2929 - 2941
      Abstract: Distributed Bragg reflector fiber laser with the dual-polarization mode is commonly used as a sensing element in optical sensors. Lateral force on such a fiber induces birefringence and results in beating frequency generation in it. Also, the change in the magnitude of the lateral force is correlated to the shift in the beating frequency. This article presents a multi-physics optomechanical model for a fiber laser force sensor based on the birefringence phenomenon. To this end, a theoretical model for the sensing principle was developed through employing the elastic beam theory, Hertzian contact mechanics, and optical birefringence principle. Based on the linearity of the developed optomechanical model to the lateral force and ambient temperature variation, a multi-linear regression calibration for the force sensor was proposed and experimentally validated. Also, the temperature-induced drift in the beating frequency was compensated through exponential adjustment. Moreover, the calibration results showed a relatively small sensitivity for temperature sensing with respect to the external force (cross-talk). This phenomenon was predicted with the theoretical model. Verification of the calibration revealed a root-mean-square error of 0.12°C and 0.04 N for temperature and force sensing, respectively. Furthermore, the validation study showed a root-mean-square error of 0.04± 0.03 N and zero hysteresis for the developed sensor. Moreover, the theoretical sensitivity to external force was similar to the experimental results. For future applications, the compliance of the sensor specifications with the requirements of three surgical procedures was confirmed through comparison with the available literature.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • High Spatial Resolution Optical Fiber Two Color Pyrometer With Fast
           Response
    • Pages: 2942 - 2950
      Abstract: Among the different temperature measurement techniques providing micrometer resolution none of them provide fast response and easy access to close distances to the target surface in difficult to access areas. Optical fiber pyrometers provide that access but previous works used large optical fibers with numerical apertures limiting the minimum spot size to be measured. In this study, we propose a novel two colour optical fiber pyrometer based on a low diameter and numerical aperture optical fiber, low-noise photodetectors and high-gain transimpedance amplifiers with a high spatial resolution in the micrometre range and fast response. Using standard optical fibers and related devices provides also a low-cost system. The developed pyrometer presents a high spatial resolution of $16~mu text{m}$ for a target surface at $25~mu text{m}$ with a wide temperature range of 300 to 1200°C it being the highest spatial resolution for this kind of temperature systems. Theoretical analysis and measurements for different pyrometer configurations are reported. This study will help further the microthermography applications in machining processes.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Position Prediction of Soft Finger Driven by SMA Based on Fiber Bragg
           Grating Sensor
    • Pages: 2951 - 2962
      Abstract: The purpose of this study is to analyze the deformation of soft finger driven by shape memory alloy (SMA) under fiber Bragg grating (FBG) sensor. Firstly, the structure and material properties of the finger, as well as the theoretical calculation of the shape of the finger by using the Cosserat elastic rod theory are introduced. Next, FBG sensors are arranged on the soft finger skeleton to analyze the theoretical relations such as FBG central wavelength and curvature, and the mathematical algorithm is used to realize the actual bending shape fitting of the soft finger. Finally, the flexural deformation experiment of the soft finger is carried out to validate the theoretical results based on Cosserat theory and ABAQUS simulation. The experimental results show the FBG sensor can effectively predict the deformation of the soft finger in real-time.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An FBG Displacement Sensor in Deformation Monitoring of Subway Floating
           Slab
    • Pages: 2963 - 2971
      Abstract: In order to monitoring vibration-reduction and vibration-isolation effects of subway floating slab, an FBG displacement sensor was developed to monitor the deformation of the subway floating slab. The proposed sensor mainly consists of an FBG, a thin-walled ring, a steel-spring, connecting rod probe, linear bearing, sleeve, and other auxiliary unit. The connecting rod probe transmits the external displacement to the steel-spring and elastic thin-walled ring, and then leads to the wavelength shift of the FBG pasted in the reserved groove of the thin-walled ring. Meanwhile, the linear bearing can reduce the friction loss and increase the sensor stability. Lots of sensing tests showed that, the sensor has a sensitivity of 36.36 pm/mm and a precision of $8.25times 10$ -2 mm in the displacement range of 0 - 20 mm, and also has good micro-displacement measurement capabilities. The theoretically calculated data for the vertical displacement of the floating slab are consistent with the actual measured data, which verifies the feasibility of the proposed displacement sensor in the monitoring of the subway floating slab.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • BP Method With Rectified Linear Unit-Based Nonlinear Decoupling for 3-Axis
           FBG Force Sensor
    • Pages: 2972 - 2979
      Abstract: A fiber Bragg grating (FBG) force sensor with a compact size has been designed which can achieve the detection of three force components (Fx, Fy, Fz). The sensor is mainly comprised of a deformable body, a support body, and 5 FBGs. Although the FBGs show advantageous sensitivity on forces, the coupled effect among the measured components along different directions brings difficulties in the precision improvement of the sensor. In this article, a Rectified Linear Unit (ReLU) -based Back Propagation (BP) method has been adopted to decouple the designed sensor and improve the precision. Its theoretical sensing model and nonlinear decoupling algorithm have been derived and introduced, respectively. Experiments have been implemented to investigate the feasibility of the adopted method. Compared with the linear method (LM) and sigmoid function-based BP method, the ReLU-based BP method has better measurement accuracy that the average relative errors are less than 2% of full scale (F.S.) for the designed 3-axis force sensor. Such a result validates the feasibility and effectiveness of the adopted method to decouple the nonlinear response of the designed sensor.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Predicting Onset of Combustion Instability in Gas Turbines Using Fiber
           Optic Sensors
    • Pages: 2980 - 2988
      Abstract: A data driven method based on cross-entropy derived from symbolic time series analysis of the chemiluminescence and pressure variations has recently been found to be robust and computationally efficient among existing methods for predicting instability. In this article, we report the development and testing of compact and field-deployable optical fiber-based modules for sensing chemiluminescence and pressure variations in the combustor. The time-series data obtained from the above sensor modules are used to deduce the D-Markov cross-entropy for different protocols corresponding to typical combustor operation conditions. Such cross-entropy data is compared with similar data obtained from standard high speed camera and PZT-based pressure sensors. It is found that the fiber optic sensor modules compare favorably with the conventional sensors thereby providing an exciting new pathway for field-deployable solutions to monitor the onset of thermo-acoustic instability in combustors.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Sensors in Tilted Fiber Bragg Gratings via Spherical Metal-Reflective Ends
    • Pages: 2989 - 2994
      Abstract: This study proposes a refractive index sensor based on tilted fiber Bragg gratings (TFBGs) that connect metal-reflective spherical ends. The proposed fabrication method enables it to reflect light to increase the overall energy spectrum. We used the end of the fiber with a spherical shape and the coating reflective metal to compare the reflective energy in TFBGs. The reflective technique was employed to deposit nickel (Ni) and silver (Ag) onto the surface of the spherical end of TFBGs to precisely form the spherical-shaped tilted fiber Bragg gratings (STFBGs). The Ni-covered NiSTFBG sensor had an average sensitivity of 8.028 nm/RIU wavelength, which is a 1.64-fold improveme nt. The result shows the backward reflected wave is reflected by the metal layer has improved performance for the wavelength and transmission loss via the reflected metal spherical-shaped end of tilted fiber Bragg gratings.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Wearable and Fully-Portable Smart Garment for Mechanical Perturbation
           Detection With Nanoparticles Optical Fibers
    • Pages: 2995 - 3003
      Abstract: This paper presents the development of a wearable smart textile for mechanical perturbation assessment during gait based on distributed optical fiber sensor system using the transmission-reflection analysis (TRA). In this case, magnesium and erbium co-doped optical fiber is employed as a high scattering medium, which results in high spatial resolution for the technique (in the order of a few millimeters). The optical fiber was embedded in a garment and controlled displacements at predefined locations on the garment were applied, where the simultaneous assessment of displacement amplitude and location was obtained using a slope-assisted technique. In the proposed technique, the slope inverses of the transmission-reflection curves are correlated with the displacement applied on the fiber, where a determination coefficient (R2) of 0.996 was obtained. Then, the sloped-normalized reflection (or backscattered) response is used on the disturbance location estimation with relative errors as low as 3.7%. The on-body application tests show an inherent insensitivity of the proposed smart garment to body movement due to natural gait movements, indicating the feasibility of the proposed approach on detecting only the transverse induced mechanical perturbations. In the wearable tests, the sensor system shows high correlation (R2 higher than 0.98) in the tests with different volunteers (subjected to similar displacements at predefined locations) and millimeter-accuracy on the disturbance location estimation.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Measuring the Thermo-Optic Coefficient of Liquids With Athermal Multimode
           Interference Devices
    • Pages: 3004 - 3012
      Abstract: We demonstrate the use of thermally insensitive fiber optic multimode interference (MMI) devices to measure the thermo-optic coefficient (TOC) of liquids. Such athermal MMI devices are all-fiber, easy to fabricate and, in general, consist of a cascade of several multimode sections. Removing the inherent thermo-optical response of the MMI device is achieved passively in a material-based fashion. Starting from the general theory, we derive simple analytical expressions for the design of two-element MMI cascaded devices with arbitrary materials. Moreover, we verify that those expressions are valid irrespectively of the self-imaging operation. We designed and fabricated an athermal MMI device with a simple architecture consisting of a so-called no-core optical fiber (NCF), which is partly coated by PDMS, and we used it to measure the TOC of several standard liquids. Our results are in good agreement with the values of the TOC reported in the literature. By removing the thermal cross-sensitivity in our device, only one calibration measurement is necessary, which allows for straightforward information retrieval.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Real-Time Optical Fiber-Based Distributed Temperature Monitoring of
           Insulation Oil-Immersed Commercial Distribution Power Transformer
    • Pages: 3013 - 3019
      Abstract: Timely detection of the incipient fault of a power transformer is of utmost importance to prevent potential malfunctioning. Monitoring the condition of the transformer insulation oil is the essential step as it contains most of the transformer’s health diagnostic information. Thus, the temperature of the oil is considered the most crucial parameter that has to be monitored continuously in real-time. Distributed optical fiber sensors for temperature monitoring has various advantages over traditional methods. In this study, an optical fiber-based distributed temperature measurement of insulation oil of a fully energized 100 kVA commercial distribution transformer is demonstrated. Two types of techniques were deployed to monitor the temperature of the transformer inside and outside of the tank using a distributed Optical Frequency Domain Reflectometry (OFDR) and a quasi-distributed Fiber Bragg Grating (FBG) sensor array respectively. The insulation oil temperature was monitored with different load conditions in a distributed fashion and compared with the conventional method of single point thermocouple and infrared thermal imaging. The test results show very good agreement between the conventional methods and proposed distributed fiber temperature sensors.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Dynamic Effects of Temperature on FBG Pressure Sensors Used in Combustion
           Engines
    • Pages: 3020 - 3027
      Abstract: In this work, we described a study of the influence of dynamical temperatures on pressure measurements in two different packaging types used in FBG pressure sensors. We developed these sensors to measure internal combustion pressure in closed-loop control of thermoelectric engines. The packaging techniques development considered the available monitoring points in the thermoelectric engine and the harsh environment of the monitoring points. In a monitoring point, the temperature can exceed 300 °C and the pressure can reach 250 bar. We detailed the use of single-ended and double-ended FBG fixation methods as packaging techniques. The FBG pressure sensors using these techniques were tested in the laboratory and a thermoelectric engine. The results demonstrated that the dynamical behavior of the high-temperature gas originated from the combustion chamber affects the dynamical pressure measurements performed by the single-ended FBG pressure sensors when they were exposed to the hot gas. This effect can be suppressed by using a double-ended FBG pressure sensor mainly due to its packaging method. Its signal shape is comparable to a reference sensor used in the same condition became this FBG packaging solution the most suitable to use in the closed-loop control of thermoelectric engines.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Cortisol In-Fiber Ultrasensitive Plasmonic Immunosensing
    • Pages: 3028 - 3034
      Abstract: Cortisol is a stress biomarker whose chronic elevated levels are associated with higher risk of metabolic syndromes, anxiety, and cardiovascular diseases, among other medical conditions. A new immunosensor based on plasmonic tilted fiber Bragg grating (TFBG) has been developed and tested for rapid and ultrasensitive cortisol detection. The gold coated TFBG was characterized to surrounding refractive index (SRI) changes and functionalized with anti-cortisol antibodies via cysteamine. The functionalization was monitored, allowing to verify the SRI alteration at the fiber surface by the respective molecular adhesion. In this work, an alternative method to the monitoring of the most sensitive surface plasmon resonance mode was explored, based on tracking the local maximum of the plasmonic signature of the lower envelope of the spectra. With this interrogation method, the sensor achieved a sensitivity to cortisol detection of 0.275 ± 0.028 nm/ng.mL−1, for the detection range of 0.1-10 ng/mL, with a total wavelength shift of around 3 nm, which is higher several orders of magnitude than the usually reported TFBG plasmonic immunosensors. The proposed biosensor provides a rapid, highly sensitive, label-free, low-volume consumption method for cortisol detection, with a working range suitable to monitor different biological samples.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Ultrasensitive Fabry–Perot Strain Sensor Based on Vernier Effect and
           Tapered FBG-in-Hollow Silica Tube
    • Pages: 3035 - 3041
      Abstract: A dual Fabry–Perot interferometers (FPIs) sensor is demonstrated theoretically and experimentally for ultrasensitive strain sensing by using the Vernier effect and the tapered FBG-in-hollow silica tube (HST) structure. The proposed sensor consists of two parallel structured FPIs connected by a 3dB coupler to generate the Vernier effect, and the tapered FBG-in-HST structure has long active length to achieve strain sensitivity enhancement and plays the role as the sensing interferometer. Moreover, utilizing the characteristic of FBG not affected by strain, the temperature compensation has been achieved by means of a demodulating matrix. The fabricated strain sensor achieves an ultra-high strain sensitivity of 1.307 nm/ $mu varepsilon $ , which is the highest strain sensitivity of the FPI-based sensors reported so far. It is low-cost, robust, easy-fabrication, and expected to have great application prospects in several fields where large strain sensitivity is really required.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Fiber Bragg Grating Force Sensor With Sensitization Structure
    • Pages: 3042 - 3048
      Abstract: This article presents a fiber Bragg grating (FBG) force sensor with enhanced sensitivity. a strain-reinforcing mechanism comprised of a linear structure and loop-shaped structure is proposed. When external tension is applied, the strain directions are opposite in the two structures. Two FBGs were adhered to the surfaces of positive and negative strain bodies and sensor data were compared. Then, the sensing principle of the FBG force sensor was deduced based on the theory of material mechanics. Results of theoretical calculations and finite element analysis (FEA) show that sensitivity of the proposed sensor is about 5.07 times higher than an FBG directly adhered to the surface of the substrate. Comprehensive testing of the sensor prototype was conducted. Experimental results show that the sensor can achieve a sensitivity of 38.25 pm/kN with a repeatability error and hysteresis error of 2.11% and 1.76%, respectively. The enhanced coefficient is 4.84, which is basically consistent with theoretical design value of 5.07. In addition, favorable temperature compensation and creep resistance were obtained during performance tests. Good capability for alternating strain measurement has also been demonstrated. Our results demonstrate the potential for superior structural health monitoring in civil engineering applications using the proposed sensor structure.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Continuous and Accurate Blood Pressure Monitoring Based on Wearable
           Optical Fiber Wristband
    • Pages: 3049 - 3057
      Abstract: Continuous monitoring of blood pressure has significant clinical value for the cardiovascular disease prevention and diagnosis in daily life. However, conventional blood pressure measurement is unsuitable for long-term monitoring due to its discomfort and poor portability. Wearable device is an ideal solution to overcome this problem, which has attracted increasing interests of many researchers. While, high-performance wearable blood pressure sensor still remains a challenge. Here, we present a wearable blood pressure wristband system based on optical fiber. The end face of optical fiber was coated with a Polydimethylsiloxane (PDMS) + Ag composite diaphragm. The continuous and accurate measurement of the pulse waveform was achieved through the phase variation of the light reflected from the diaphragm. Then, the blood pressure can be estimated from the pulse transit time (PTT) which is extracted from the high-fidelity pulse waveform. The estimation model was established using data from 45 subjects. The clinical experiment was also carried out on 17 subjects to test the performance of the proposed wearable blood pressure wristband. The results show that the errors of systolic pressure (SBP) and diastolic pressure (DBP) are 0.24 ± 2.39 mmHg and 0.12 ± 2.62 mmHg compared with commercial sphygmomanometer, respectively. The measurement errors were within the required range of the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) Grade A, which indicates that the blood pressure wristband has a good accuracy and practicability. This wearable and user-friendly optical fiber blood pressure monitor is a competitive alternative to current commercial products.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Horse Gait Identification Using Distributed Acoustic Sensing
    • Pages: 3058 - 3065
      Abstract: In this paper field measurements are carried out using a distributed acoustic sensing system (DAS) for the identification of horse gait. The DAS system has a spatial resolution of 1 m, frequency range of 1–5 kHz and the fiber length is of about 70 m. The optical fiber cable lays down into trench of 15 cm of depth. Several measurements are also carried out in order to fully understand the measuring capacity of the system, including a 2 kg calibrated impact hammer and a small controlled blast of 12.6 Pa of air pressure. Results demonstrate the capacity to measure soil mechanical waves speed of 171 m/s for the impact hammer tests and sound speeds in air of about 366 m/s for the controlled blast tests. The experiments involving horses allowed the identification of three distinct horse gait patterns: step, trot and gallop, as well the horse speed in each gait with SNR about 30 dB.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Data-Driven Shape Sensing of a Surgical Continuum Manipulator Using an
           Uncalibrated Fiber Bragg Grating Sensor
    • Pages: 3066 - 3076
      Abstract: This article proposes a data-driven learning-based approach for shape sensing and Distal-end Position Estimation (DPE) of a surgical Continuum Manipulator (CM) in constrained environments using Fiber Bragg Grating (FBG) sensors. The proposed approach uses only the sensory data from an unmodeled uncalibrated sensor embedded in the CM to estimate the shape and DPE. It serves as an alternate to the conventional mechanics-based sensor-model-dependent approach which relies on several sensor and CM geometrical assumptions. Unlike the conventional approach where the shape is reconstructed from proximal to distal end of the device, we propose a reversed approach where the distal-end position is estimated first and given this information, shape is then reconstructed from distal to proximal end. The proposed methodology yields more accurate DPE by avoiding accumulation of integration errors in conventional approaches. We study three data-driven models, namely a linear regression model, a Deep Neural Network (DNN), and a Temporal Neural Network (TNN) and compare DPE and shape reconstruction results. Additionally, we test both approaches (data-driven and model-dependent) against internal and external disturbances to the CM and its environment such as incorporation of flexible medical instruments into the CM and contacts with obstacles in taskspace. Using the data-driven (DNN) and model-dependent approaches, the following max absolute errors are observed for DPE: 0.78 mm and 2.45 mm in free bending motion, 0.11 mm and 3.20 mm with flexible instruments, and 1.22 mm and 3.19 mm with taskspace obstacles, indicating superior performance of the proposed data-driven approach compared to the conventional approaches.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Constrained Robust Unscented Kalman Filter for BDS Navigation in Dense
           Urban Areas
    • Pages: 3077 - 3086
      Abstract: In dense urban environment, Multipath (MP) and Non-line-of-sight (NLOS) signals will degrade the performance of BeiDou Navigation Satellite System (BDS) Position, Velocity and Timing (PVT). In order to mitigate this negative impact, a Constrained Robust Unscented Kalman Filter (CRUKF) is implemented based on pseudorange/Doppler shift measurements. An equivalent weight function based on the innovation vector is constructed, which can overcome the problem of performance degrading of traditional robust methods caused by the inaccurate initial state vector. Then, the motion information, navigation direction and elevation, is included to further constrain the Robust UKF (RUKF) solution. The performance of CRUKF is analyzed using two real car tests in a dense building area, Tokyo. It is shown that there is a clear correlation between MP/NLOS errors and Position Dilution of Precision (PDOP), which seriously lows the positioning accuracy. Regarding the horizontal position, the Root Mean Square Error (RMSE) of CRUKF is 5.6 m, while those of Robust Iterative Least Square (RILS) and RUKF are 15.3 m and 6.4 m, respectively. Similar improvements are presented in vertical position and velocity and hence show the superior positioning performance of CRUKF. In addition, without sensor aiding or coupling, CRUKF can be suitable for real-time application of low-cost receiver.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Phase Compensation Method for MEMS Quadruple Mass Gyroscope in Zero Bias
           Drift
    • Pages: 3087 - 3096
      Abstract: This paper proposes a system phase analysis for MEMS quadruple mass gyroscope (QMG) and improves the stability by 20 times. The improvement factors come from the phase noise model analysis and front-end amplifier design. To make sure which factor is dominant in zero bias and improve the stability, a series of experiments are carried out here to discuss their drift contribution, including environmental change. Meanwhile, a new systematic phase noise method is applied for stability analysis, which helps find the dominant physics sources to improve the zero bias. Combing the experimental results and the theoretical noise model, we establish the actual noise model. By analysis, the dominant noise sources mainly come from the demodulation reference signal and the sense signal, both influenced by the front-end amplifier. In this paper, we design a two-stage capacitive amplifier instead of the trans-impedance amplifier, in which, the noise of input current introduced by cross-resistance (signal noise) and the overall phase shift of the loop (demodulation noise) are greatly reduced. Finally, the stability is improved by an order of magnitude after relative compensation circuit design.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Large-Stroke Capacitive MEMS Accelerometer Without Pull-In
    • Pages: 3097 - 3106
      Abstract: In this study, the feasibility of obtaining electrical read-out data from a capacitive MEMS accelerometer that employs repulsive electrode configuration is demonstrated. This configuration allows for large-stroke vibrations of microstructures without suffering from pull-in failure that exists in conventional accelerometers based on the parallel-plate configuration. With initial fabrication gap of $2.75 mu {m}$ , the accelerometer can reach a $4.2 mu {m}$ dynamical displacement amplitude. The accelerometer is tested up to $95 ({V})$ without exhibiting pull-in failure. For comparison, the pull-in voltage of an accelerometer with same dimensions but with conventional parallel-plate electrode configuration is $textit {0.8} ({V})$ . The MEMS device is fabricated using the POLYMUMPs fabrication standard. An electrical circuit is built to measure the capacitance change due to motion of the accelerometer proof-mass. The accelerometer has a mechanical sensitivity of $35 frac {textit {nm}}{g}$ and electrical sensitivity of $5.3 frac {textit {mV}}{g}$ . The ability to use large bias voltages without the typical adverse effects on the stability of the moving electrode will enable the design of capacitive MEMS accelerometers with enhanced resolution and tunable frequency range.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Development and Verification of a Novel Measurement and Position System
           for Confined Cabin
    • Pages: 3107 - 3120
      Abstract: The high-accuracy key points measurement is necessary for shipbuilding, and numerous measuring systems have been invented. However, there are still limitations and challenges in measurement frequency, calibration method and environmental disturbances. This paper presents a novel and automatic measurement and position system based on laser scan technology. The system components and mathematical model are introduced. Subsequently, a fast calibration method is established, also different calibration schemes and calibration algorithms are discussed. To avoid the environmental disturbances to this system, a robust positioning algorithm based on maximum likelihood estimation is presented. A series of simulation and verification experiments in normal and interferential environment are designed to evaluate the precision of the proposed system. The results shows that the system’s position accuracy can reach 1.00mm with 50Hz output whether there are disturbances or not, which can meet the need of position in the confined cabin.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Crosstalk Analysis and Current Measurement Correction in Circular 3D
           Magnetic Sensors Arrays
    • Pages: 3121 - 3133
      Abstract: The circular array of magnetic sensors is a research hotspot in the field of power electronics, especially for the measurement of direct current(DC). The high linearity, large dynamic range, small size and low power consumption of circular magnetic sensors arrays for current measurement are an improvement over the current sensors utilizing a ferromagnetic core. However, due to the absence of core magnetization, the external magnetic field can easily enter the magnetic sensors, which will affect the measurement accuracy. Therefore, this paper focuses on the influence of the interference field from the conductor positioned outside the circular array and proposes a model of the target conductor penetrating the circular array with the interference conductor outside the circular array. This model is used to analyze the influence of relevant parameters of the interference conductor and the target conductor on the crosstalk error. An interference-rejecting method of circular 3D magnetic sensor arrays is proposed, which establishes the model of magnetic flux density measurement to calculate the target conductor current. The simulation results show theoretically that the method can greatly reduce the crosstalk error and correct current measurement. 3-D tunnel magnetoresistance (TMR) chip is selected as the magnetic sensors of the circular array in the laboratory experiment, and the experiment errors can be reduced to around 1%. The simulation and experiment results verify that the interference-rejecting method can correct measurement results and improve the accuracy of the target current.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Wire Position Sensor for Controlling the Process of Electron Beam
           Layer-by-Layer Deposition: Modeling and Verification
    • Pages: 3134 - 3142
      Abstract: One of the difficulties of performing electron beam deposition with wire material is positioning the filler wire relative to the electron beam. The accuracy of the positioning of the beam relative to the wire significantly affects heat input characteristics, the transfer of molten metal from the wire to the substrate, and, as a consequence, the nature of the formation of the layers. In this paper, a model of the formation of a bremsstrahlung X-ray radiation during an electron beam’s interaction with a filler wire is developed and verified. We also present a model of a sensor for detecting the position of the filler wire relative to the axis of the electron beam. The sensor model establishes how the bremsstrahlung X-ray radiation changes depending on process parameters, such as: beam oscillation parameters, accelerating voltage, beam current, and beam diameter in the interaction spot, the diameter of the wire, the substrate materials and filler wire, and the wire’s displacement from the center of beam oscillation.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Multiple Model Poisson Multi-Bernoulli Mixture Filter for Maneuvering
           Targets
    • Pages: 3143 - 3154
      Abstract: The Poisson multi-Bernoulli mixture (PMBM) filter is conjugate prior composed of the union of a Poisson point process (PPP) and a multi-Bernoulli mixture (MBM). Considering that the single model is not enough to guarantee stable tracking performance for maneuvering targets, in this article, a multiple model PMBM (MM-PMBM) filter is proposed to cope with this problem. The proposed MM-PMBM filter extends the single-model PMBM filter recursion to multiple motion models by exploiting the jump Markov system (JMS). The performance of the proposed algorithm is examined from two scenarios with different detection probabilities. Moreover, the robustness of Markovian model transition probability matrices (TPMs) for the proposed MM-PMBM filter is also explored. The simulation results demonstrate that the proposed MM-PMBM filter performs well in terms of the tracking accuracy, including the target states and cardinality estimates, and also has good tolerance with respect to different TPMs.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Modeling of Active Fiber Loop Ring-Down Spectroscopy Considering
           Non-Linear Effects of EDFA Gain
    • Pages: 3155 - 3162
      Abstract: Non-linear effects of erbium-doped fiber amplifier (EDFA) gain have been significant factors in deteriorating the performance of active fiber loop ring-down spectroscopy (FLRDS). The non-linear effects involve two aspects: the gain saturation and the non-uniformity of the gain spectrum. The gain saturation of EDFA induces pulse-to-pulse gain fluctuations, which causes ring-down decays to deviate from the exponential law. The non-uniform gain spectrum introduces measurement errors in the active FLRDS systems operating in wavelength-tuning or wavelength-switching modes. A theoretical model of active FLRDS that integrates both non-linear gain effects is presented in this paper. It can be used for qualitative and quantitative analysis of the measurement errors introduced by the non-linear gain effects on the active FLRDS. The model provides theoretical guidance to the performance prediction and optimization of the active FLRDS systems.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Measurement of Equivalent Conductivity and Permeability of Liquid-Solid
           Mixture Based on Single-Layer Coil
    • Pages: 3163 - 3171
      Abstract: Coil-based non-contact testing is widely used for the measurement of conductivity and permeability of bulk metal. However, there is a lack of researches on the application of this method to liquid-solid mixture with lower conductivity and permeability. In this article, a model for simultaneously predicting the conductivity and permeability of liquid-solid mixture based on coil impedance and equivalent circuit analysis is proposed and verified by numerical simulation. The parameters of the model are determined by the experiments of the solutions with and without magnetic granule. With the proposed model, the prediction errors of the conductivity and permeability of the liquid-solid mixture used in this article are less than 5.0% and 3.4%, respectively.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Embedding Soft Material Channels for Tactile Sensing of Complex
           Surfaces—Mathematical Modeling
    • Pages: 3172 - 3183
      Abstract: Currently most of tactile array technologies is difficult to cope with three dimensional complex surfaces and there is an urgent need to develop tactile sensors that can provide a high-density integrated array on a surface of arbitrary shape. Our laboratory has been working on the development of a novel tactile sensing technique to solve this challenge, by “Embedding Soft material into Structure ENabling Tactile sensing” of complex surfaces (ESSENT). This method creates sub-millimeter multiple channels filled with low compressibility elastic material. Tactile information is obtained by measuring the micro deformation of the soft material via projecting light to the material channel and measuring light reflection. However, the relationship between light reflection and soft material deformation is a complex function related to the geometry of reflective surface, elasticity of the soft channel and lighting conditions. To gain fundamental understanding of this new tactile sensing principle, we developed a mathematical model which is able to predict the precise deformation of the reflective surface of a soft material and provide the theoretical prediction of sensing sensitivity with respect to the lighting condition and the magnitude of the applied force. We demonstrated that the shape of soft material channel, the shape of reflective surface, as well as the location of light resource have significant influence on the sensor behavior.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Scalable and Physical Radar Sensor Simulation for Interacting Digital
           Twins
    • Pages: 3184 - 3192
      Abstract: Automation, artificial intelligence and sensor-enabled applications are part of everyday life comprising technical systems that utilize environment-perceiving sensors. Here, rough environments often require robust sensor technologies such as radar, coupled with application-specific software evaluating the measured data. The rising complexity of underlying sensor systems (hard- and software) and complex physical interactions with dynamic environments require development-related validations that use virtual methods primarily focusing on close-to-reality sensor simulation. To allow for a wide range of applications, we propose the use of reusable building blocks that generically model and mimic the physical structure from the real world. This includes the whole path between sensor and 3D environment with respect to sensor-intrinsic characteristics as well as environmental influences such as rain. At the same time, computational scalability of each building block allows physical simulations generating reliable sensor data even in real-time applications. As a result, Digital Twins of relevant components, systems and environments are no longer acting on their own but interacting within Virtual Testbeds allowing for virtual validation of radar-based applications with respect to the operational environment.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • DFT Study on the Selective Adsorption Properties of Modified Graphene for
           SF6 Decompositions
    • Pages: 3193 - 3200
      Abstract: In the paper, the adsorption of H2S on three modified graphene by doping Pd atom and introducing oxygen–containing functional groups (Pd–G, G–O and G–OH) was studied based on first principles. All the three modification methods improve the adsorption performance of graphene for H2S, among which the introduction of hydroxyl shows the best with the adsorption energy of −1.255eV. Nevertheless, Pd–G, G–O and G–OH exhibited similar adsorption properties for SO2, SO2F2, SOF2 and H2S, with the small difference of adsorption energy values. So the three modified graphene structures show poor selective adsorption performance for H2S. Then, the influence of the combine interaction of Pd atom and oxygen–containing functional group on the performance of graphene (Pd–G–O, Pd–G–OH) adsorbing H2S is further studied. Among them, the structure of Pd–G–O is stable, and the adsorption energy of Pd–G–O adsorbing H2S is −1.623eV. However, the adsorption energies of SO2, SO2F2 and SOF2 adsorbed on Pd–G–OH are −0.751eV, −0.799eV and −0.552eV, respectively. The excellent adsorption capacity of H2S on Pd–G–O is verified from the practical feasibility and selectivity. Therefore, Pd–G–O is a potential gas sensor material for selective adsorption of H2S.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A New Simulation Approach of Transient Response to Enhance the Selectivity
           and Sensitivity in Tunneling Field Effect Transistor-Based Biosensor
    • Pages: 3201 - 3209
      Abstract: In this work, a new simulation approach of transient analysis on single cavity dielectric-modulated (DM) ${p}$ -type of tunnel field-effect transistor (TFET) is examined for biosensing applications. The device operation and performance are investigated using the 2D device simulator and results are well-calibrated with experimental data. In this work, we have examined DC transfer characteristics, the transient response of drain current, drain current sensitivity ( ${S}$ ), and selectivity ( $Delta {S}$ ). Focussing more on the transient results, we have obtained maximum sensitivity of orders greater than 108 for APTES biomolecule with respect to air and a significant selectivity value in orders of 103 for APTES with respect to Biotin biomolecule. The performance of the device in terms of selectivity can be further improved (~104) by optimizing the back-gate bias, and therefore, the impact of back-gate bias has been analysed. The results for charged biomolecules and partially filled cavity are further investigated & highlighted. The DM ${p}$ -TFET biosensor shows a significant improvement in the results with the transient response for biosensing applications with the feasibility of operating at low voltages (gate voltage of −2.0 V, drain voltage of −0.5 V and back gate voltage 0 to 0.5 V).
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Portable Data Acquisition System for Nano and Ultra-Micro Scale
           Electrochemical Sensors
    • Pages: 3210 - 3215
      Abstract: This work describes a flexible and portable data acquisition system that has been developed to interface to nano and ultra-micro scale electrochemical sensors at the point of use. It can perform a range of voltammetric tests, including Cyclic Voltammetry, Square Wave Voltammetry and Generator Collector Voltammetery. The data acquisition system interfaces to a smartphone, operates from a rechargeable battery and is of suitable form factor to ensure that it’s fully portable. By utilising commercially available components, this system has been developed to lower the barrier for entry for the development of emerging portable electrochemical sensing technologies at micro and nano scale. To show the full range of functionality of the system, a use case involving river water quality monitoring is presented through generation of a calibration curve, using a recently developed Tyndall National Institute ultra-microband electrochemical sensor, for the detection of dissolved oxygen in river water.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Novel Linearizing Signal Conditioner for Half-Bridge-Based TMR Angle
           Sensor
    • Pages: 3216 - 3224
      Abstract: This paper proposes a novel signal conditioning circuit for half-bridge topology based Tunneling Magneto-Resistance (TMR) angle sensor. The resistances of TMR angle sensor vary as a sine/cosine function of the shaft angle. The proposed circuit employs an enhanced dual-slope technique to process these non-linear resistance variations and render a linear digital output for full-circle range. The circuit uses easily available and low-cost circuit components, and a novel linearization logic in its architecture. It possesses low dependence on many circuit and sensor non-idealities. Further, a novel algorithm is proposed to reduce the measurement error due the mismatch in nominal resistances of the TMR sensor. The working of the circuit is verified using simulation as well as experimental studies. The maximum non-linearity in the circuit output, observed with expected sine/cosine characteristic, is 0.038 %. Later, a shaft angle sensing unit, employing AAT003-10E half-bridge TMR angle sensor IC, is designed, built, and characterized. The interfacing studies showcase the capability of the developed signal conditioning circuit for linearizing practical TMR angle sensors. The efficacy of the compensation algorithm is also proved in the experimentation.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Optical Wake-Up From Power-Off State for Autonomous Sensor Nodes
    • Pages: 3225 - 3232
      Abstract: Wireless sensor nodes spend most of their time in standby mode and wake up periodically to send measurement data. Typically, a radio-frequency antenna is coupled with an amplifier to provide this wake-up function; together, these two elements recognize instances of radio signal activation. Even during standby operation, the sensor node utilizes a certain base amount of energy, which can be critical when using an energy-harvesting source. In this study, we propose a novel optical approach to the wake-up function for autonomous sensor nodes, which employs a solar cell as the wake-up signal detector. A bright light flash coming from another node or a smartphone hits a solar cell and activates the sensor node. Unlike photodiodes or RF-antennas, solar cells do not require any additional energy to detect such a signal. Therefore, the proposed electrical circuit allows the sensor node to wake-up from a complete power-off state. The solar cell of the novel wake-up receiver has an area of sensitivity of 8 mm $times10$ mm. The wake-up signal can be recognized from a maximum distance of 25 cm at a range of ambient illumination from 0 – 1600 lx with a transmitter optical power of 20 mW. In the power-off state, the power consumption of this novel design is the lowest of all existing off-the-shelf wake-up receivers: 248 pW at 0 lx and 627 nW at 1600 lx.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Crossing the Nernst Limit (59 mV/pH) of Sensitivity Through Tunneling
           Transistor-Based Biosensor
    • Pages: 3233 - 3240
      Abstract: In this work, an underlap structure of tunneling field-effect transistor (TFET) containing electrolyte/watery solution is examined to enhance the Nernst limit (59 mV/pH) of sensitivity. After incorporating the electrolyte medium in TFET, effect of pH variation on device characteristics such as drain current vs front gate voltage, voltage sensitivity, and current sensitivity are investigated. The interface charge density at the oxide-silicon interface of TFET is obtained as a function of electrolyte pH from physics-based modelling. Voltage sensitivity value ~180 mV/pH that is greater than three times of Nernst limit of 59 mV/pH and current sensitivity value that is more than one decade per pH are observed for TFET based sensor. In order to validate the results, models used in TFET are well-calibrated with experimental data and the result of TFET are compared with inversion mode (IM) device. Results show that TFET gives superior performance than IM device; hence an underlap TFET can be a promising alternative for the next generation biosensor.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Rate-Integration Gyroscope (RIG) With Gain Self Calibration
    • Pages: 3241 - 3249
      Abstract: For the purpose of direct angle measurement with high accuracy, this paper focuses on the gain self-calibration of operating modes of MEMS disk gyroscope, when it works in the rate-integration mode. The difference of C/V conversion gain, quality factor and front-end amplifier’s gain of gyroscope operating modes will lead to the total gain difference, which causes angle error. Although one-time manual calibration can temporarily reduce the influence of gain difference, environmental factors (e.g. temperature) will interfere with the calibration results. In this paper, a novel self-calibration method is proposed, which detects the gain difference of gyroscope operating modes in real time and compensates it without adding additional circuits. This method is verified using a digital signal processing platform, and the angle dependent bias (ADB) error is reduced from 18 degrees to 2 degrees.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • FS-LSTM-Based Sensor Fault and Structural Damage Isolation in SHM
    • Pages: 3250 - 3259
      Abstract: A few sensing techniques have been developed for detecting defects in civil infrastructures in structural health monitoring (SHM) instead of onsite human inspection. Many false alarms observed are due to faults in sensors, and some of them may lead to incorrect interpretation of the measured data. In this paper, the differentiation of different sources of variability, i.e. the sensor fault and structural damage, is investigated for a vibration-based SHM system. A deep learning-based method with a new architecture of Fully connected Stateful Long Short Time Memory (FS-LSTM) Neural Networks for differentiating the sensor fault and structural damage without information on details of the fault is proposed. Information from the sensor fault is assumed local, and that from structural damage is relatively global affecting several sensors. Since the LSTM are capable to learn data features automatically, the proposed method performs well without an accurate mathematical model. Stationary random acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured data with estimation on four types of sensor fault. The response residues between the true and predicted values of the deep FS-LSTM NN is statistically analyzed to determine the threshold of sensor fault. An experimental study with a steel frame in the laboratory is also performed to validate the proposed method.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Gait Assessment Framework for Depression Detection Using Kinect Sensors
    • Pages: 3260 - 3270
      Abstract: As depression becomes more commonplace in society, the timely and effective detection of the signs of depression for its prevention and early treatment becomes more important. Gait analysis can provide a contactless and low-cost method for depression diagnosis. In this study, we propose a novel gait assessment framework to implement non-intrusive, real-time and automatic depression detection using Kinect, an inexpensive and portable depth sensor. We focus on extracting a novel time-domain and frequency-domain feature (TF-feature) and a spatial geometric feature (SG-feature), and investigating the effectiveness of fused features in detecting depression for the non-contact gait data. A pseudo-velocity model is firstly built to analyze the gait abnormalities of individuals with depression in the time domain. Subsequently, we perform the power spectral density (PSD) analysis on the model to extract the TF-feature. Then, the covariance matrices and the symmetric Stein divergence (S-divergence) are leveraged to obtain the SG-feature, which is fused with TF-feature to form new features for classification. The experimental results on 95 subjects (43 scored-depressed and 52 non-depressed individuals) show that the proposed method achieves a good classification accuracy of 93.75%, has superior performance compared to several other methods, and significantly alleviates the impact of individual differences. These results indicate the efficacy and robustness of the proposed framework for depression detection.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Leak Localization Method for Pipeline Based on Fusion Signal
    • Pages: 3271 - 3277
      Abstract: When locating the position of pipeline leaks with the traditional negative pressure wave method (NPW), the inflection point of the sudden change of the pressure signal must be accurately obtained. If the inflection point cannot be obtained, it is impossible to locate leaks with time difference. To overcome the shortcomings of NPW, a method of leak localization for pipeline based on fusion signal is proposed. By deriving the motion equation and the continuity equation, the leak localization method for the fusion of pressure and flow signals is established. The proposed method depend on the pressure difference and flow difference at the end of pipeline rather than the time difference of a negative pressure wave arriving upstream and downstream. By analyzing the data obtained from leaks in an experimental pipeline, the maximum localization error is 10 m and the minimum localization error is 5 m. The experimental results show that the proposed method can be used to locate the leak of short distance pipeline.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Adaptive GNSS Velocimetry Combining Doppler and Carrier Phase Measurements
           Based on Online Variance Component Estimation
    • Pages: 3278 - 3288
      Abstract: Instantaneous velocity is an important variable for navigation and measurement tasks, and its estimation using a stand-alone global navigation satellite system (GNSS) receiver has important theoretical and practical significance for many applications. Adopting a stand-alone receiver to determine the instantaneous velocity in real-time has the advantages of independence of reference station, simple equipment, superior real-time performance and low cost. With these advantages, a new method is proposed to determine the instantaneous velocity by combining Doppler and carrier phase measurements based on online variance component estimation. This is a data fusion problem described by a state space model, in which the uncertainty of the state space model is tuned online through adaptive filtering. The adaptive variables are optimized using the locally best invariant quadrature unbiased estimate (BIQUE), which are directly estimated without iteration. The GNSS velocimetry principle and Doppler and carrier phase combined velocimetry process are provided in the abstract figure. To verify the proposed approach, simulation and real GNSS data with different sampling intervals are used in experiments. All results demonstrate the efficacy and stability of the proposed approach compared to the conventional approaches.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • The Application of 3D Morphable Model (3DMM) for Real-Time Visualization
           of Acupoints on a Smartphone
    • Pages: 3289 - 3300
      Abstract: Acupuncture therapy is one of the main modalities of treatment in Traditional Chinese Medicine (TCM). Based on different symptoms of the patient, needling or massaging is applied to the corresponding acupuncture points to relieve the symptoms. However, given the large number of acupuncture points and the complexity of their specifities, it is difficult for one to remember the location and function of each acupuncture point without extensive training. In this work, through the use of augmented reality (AR), the acupuncture points can be displayed directly on the image of human body. Compared to existing acupoint probe devices that work by measuring the skin conductivity, our solution does not require any additional hardware and is purely software-based. In this paper, we propose a novel approach for acupoint localization by leveraging the landmark points and 3D morphable model (3DMM). The localization error of our system is about 2.4mm which outperforms the existing work on acupoint localization by 170 A prototype system is implemented on the Android phone. In the case of mild symptoms (e.g. headache, sleep disorder), with the aid of our proposed system, the patient can quickly locate the corresponding acupuncture points for the application of massage to relieve his/her symptoms without the help from TCM physicians.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Transition-Aware Detection of Modes of Locomotion and Transportation
           Through Hierarchical Segmentation
    • Pages: 3301 - 3313
      Abstract: Recognizing human daily activities with motion sensors data, specifically, modes of locomotion and transportation provides important contextual information that enhances the effectiveness of mobile applications. For instance, in assisted living or sports monitoring it is essential to log driving or running episodes. Previous studies in this field have utilized a fixed-size windowing technique for segmenting the sequential data of sensors. While segmenting signals into larger windows provides more information about the signal for classifiers, it increases misclassification rate when a transition occurs between the activities (i.e., multi-class windows). This will lead to inaccurate detection and logging of the activities of interest. To identify the exact time of transition from one to another activity as precisely as possible, this article proposes a fast and efficient hierarchical search algorithm that finds the exact sample at which transition occurs. This search algorithm can be applied to any activity recognition model with various lengths of segmentation window. To further improve the performance, we propose a new structure of 2D signal inputs to be used with 2D convolutional neural networks (CNN), which improves the ability of the CNN in capturing patterns underlying in inter-axes correlations. Experimental results show that the proposed transition detection method improves the F1-score by 28% compared to using fixed-size windowing approach for multi-class windows. In addition, the proposed method is 20 times faster than the naïve search.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Milli-RIO: Ego-Motion Estimation With Low-Cost Millimetre-Wave Radar
    • Pages: 3314 - 3323
      Abstract: Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fast-growing demand for location-based services in indoor environments. Among various solutions, frequency-modulated continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are gaining more prominence due to their intrinsic advantages such as penetration capability and high accuracy. Single-chip low-cost MMWave radar as an emerging technology provides an alternative and complementary solution for robust ego-motion estimation, making it feasible in resource-constrained platforms thanks to low-power consumption and easy system integration. In this paper, we introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and qualitative evaluations prove that the proposed method achieves precisions on the order of few centimeters for indoor localization tasks.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Wearable Microwave Imaging Sensor for Deep Tissue Real-Time Monitoring
           Using a New Loss-Compensated Backpropagation Technique
    • Pages: 3324 - 3334
      Abstract: We present a new imaging method for continuous real-time monitoring of deep human tissues using a body-worn radio-frequency sensor. The sensor consists of a set of transmitting and receiving dipole-like probes placed around the human body. The Green’s function of the medium is derived by modeling the scattering of the cross-sectional pixels using metallic cylinders of equivalent sections. A new method of overcoming the signal path loss in the biological medium is then introduced, which increases the image resolution. For the first time, the proposed wearable sensor is shown to overcome a number of challenges with body-worn imaging including the shape uncertainty of the torso. To analyze this, a new boundary mismatch parameter (BMP) is introduced and its effect on image recovery is examined quantitatively. Demonstration of the concept is carried out by retrieving differential permittivity image of the underlying cross-section, employing data obtained from several imaging scenarios using both full-wave simulations involving deep human tissues with multiple outer layers (i.e. skin, fat, muscle, bone) and experiments. Overall, the proposed sensor can be the basis for a portable and low-cost complement to conventional imaging techniques (such as, X-Ray CT, MRI etc.) suitable for a wide range of applications, such as, prevention of pulmonary diseases, brain functionality imaging and monitoring of gastric emptying etc.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Automated Nociceptive Pain Assessment Using Physiological Signals and a
           Hybrid Deep Learning Network
    • Pages: 3335 - 3343
      Abstract: Postoperative pain management has become a major medical and nursing challenge. Nowadays, hospitals have taken initiatives to measure acute pain using self-report measures like the Visual Analogue Scale and Numeric Pain Intensity Scale. But these methods are inaccurate as it depends on patient’s input. Therefore, there is a requirement for an objective, quantitative method to monitor pain continuously. In this work, an automated acute nociceptive pain recognition system was proposed to objectively measure nociceptive pain using physiological signals and a hybrid Deep Learning network. The hybrid deep learning network constitutes a shallow CNN network that extracts the essential information of the pain from the physiological signals and the extracted feature matrix is fed to the LSTM network for feature concatenation. This process realizes the mapping of nociceptive pain from input data to detection. This work utilizes the BioVid Heat Pain database. A Unimodal hybrid CNN_LSTM network (using ECG signals) has achieved 68.70 percent, 62.61 percent, 67.86 percent, and 75.21 percent of classification accuracy for classification events (BL1 Vs PA1, BL1 Vs PA2, BL1 Vs PA3, and BL1 Vs PA4). Similarly, for classification events (BL1 Vs PA1, BL1 Vs PA2, BL1 Vs PA3, and BL1 Vs PA4), the unimodal hybrid CNN_LSTM network (using EDA signals) achieved 85.65 percent, 74.47 percent, 80.80 percent, and 80.17 percent of classification accuracy. Finally, for classification events (BL1 Vs PA1, BL1 Vs PA2, BL1 Vs PA3, and BL1 Vs PA4), the CNN_LSTM multimodal hybrid network (using both ECG and EDA signals) achieved 93.91 percent, 86.97 percent, 90.75 percent, and 94.12 percent of the classification accuracy, respectively.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • UWB/Lidar Coordinate Matching Method With Anti-Degeneration Capability
    • Pages: 3344 - 3352
      Abstract: This article presents a ultra-wideband (UWB)/Lidar coordinate matching method to calculate the transformation relationship between the UWB global coordinate system and the Lidar global coordinate system in real-time without any prior information of the sensor locations. In the proposed method, point pairs sampled from the trajectories estimated by the UWB Simultaneous Localization and Mapping (SLAM) and the Lidar SLAM are used to calculate the transformation relationship between the two coordinate systems through a trajectory matching process. Additionally, an anti-degeneration algorithm which can estimate the degenerate directions of the UWB SLAM and mitigate the effects of the degeneration to improve the accuracy of the proposed method is also provided. In order to evaluate the performance of the proposed method, two experiments were carried out. The results of experiments show that the proposed anti-degeneration algorithm can improve the estimation accuracy of the UWB/Lidar coordinate matching method with the average errors being about 0.1m in well-conditioned directions and 0.5m in degenerate directions.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • HierHAR: Sensor-Based Data-Driven Hierarchical Human Activity Recognition
    • Pages: 3353 - 3365
      Abstract: Pervasive computing greatly advances the automatic recognition and understanding of human activities and effectively bridges the gap between the low-level sensor signals and high-level human-centric applications. The inherent complexity of human behavior, however, inevitably poses a huge challenge to the design of a robust activity recognizer, especially in classifying similar activities. In this study, we present a hierarchical framework, named HierHAR, that infers on-going activities in a multi-stage process for better distinguishing similar activities and improving the overall performance. Specifically, we propose a data-driven approach, rather than heavily rely on prior domain knowledge, to automatically determining the relationships among activities. Afterwards, we use the relationships to organize the activities into a tree structure and accordingly construct and train a tree-based activity recognition model. Furthermore, we train a graph-based model that aims to reduce the compounding errors induced by the prediction process of the tree-based model. Finally, extensive comparative experiments are conducted on public datasets and results demonstrate the power of HierHAR in facilitating the automatic organization of activities and the design of hierarchical recognizers without prior knowledge about activities. Besides, the graph-based activity recognizer generally generalizes better across different scenarios and outperforms the tree-based model.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Using Wearable Physiological Sensors for Affect-Aware Intelligent Tutoring
           Systems
    • Pages: 3366 - 3378
      Abstract: Intelligent Tutoring Systems (ITS) have shown great potential in enhancing the learning process by being able to adapt to the learner’s knowledge level, abilities, and difficulties. An aspect that can affect the learning process but is not taken into consideration by traditional ITS is the affective state of the learner. In this work, we propose the use of physiological signals and machine learning for the task of detecting a learner’s affective state during test taking. To this end, wearable physiological sensors were used to record electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals from 27 individuals while participating in a computerised English language test. Features extracted from the acquired signals were used in order to train machine learning models for the prediction of the self-reported difficulty level of the test’s questions, as well as for the prediction of whether the questions would be answered correctly. Supervised classification experiments showed that there is a relation between the acquired signals and the examined tasks, reaching a classification F1-score of 74.21% for the prediction of the self-reported question difficulty level, and a classification F1-score of 59.14% for predicting whether a question was answered correctly. The acquired results demonstrate the potential of the examined approach for enhancing ITS with information relating to the affective state of the learners.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Towards a Virtual Keyboard Scheme Based on Wearing One Motion Sensor Ring
           on Each Hand
    • Pages: 3379 - 3387
      Abstract: In this paper, we present an improved ring-type virtual keyboard scheme that can achieve impressive performance with only one smart ring on a finger of each hand. The smart ring integrates a 6-DoF Inertial Measurement Unit (IMU) and a 3-DoF magnetometer sensor for collecting motion data during typing. First, a new keyboard layout is designed, by changing the previous rectangular layout to an arc structure, this method increases the difference in attitude angle between adjacent keys, which greatly improved the keystroke recognition accuracy. Secondly, other than the attitude angle feature, we also adopt acceleration data, gyroscope data and magnetometer data to describe the subtle differences between different keystrokes motion. Then, feature importance evaluation and feature correlation analysis were used to select features with high contribution rate and low similarity to describe keystrokes. Finally, nine effective features were selected from the attitude angle and magnetometer data for the final keystroke recognition. By weighing the number of selected features, recognition speed and recognition accuracy of training models, the keystroke recognition speed can increase by nearly 4 times while ensuring 98.53% of the keystroke recognition accuracy. This new ring-type virtual keyboard input scheme has the advantages in portability, small volume, and lower cost over many existing human-computer interface methods.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Localization With Magnetic Field Distortions and Simultaneous Magnetometer
           Calibration
    • Pages: 3388 - 3397
      Abstract: Magnetic field localization utilizes position dependent and time persistent distortions of the earth magnetic field. These distortions are introduced by stationary ferromagnetic material in the environment and can be stored in a map to enable localization. Estimating the position of a magnetometer with these distortions requires a calibration of the sensor to enable the matching of the measurements to the map. Typically, the calibration is performed in a prior step and requires specific maneuvers like sensor rotations in a homogenous field. The goal of the maneuvers is to render the calibration parameters observable. For heavy platforms, e.g., cars, trains and driverless transport systems in factories, performing special maneuvers is cumbersome or even impossible. In addition they operate in an environment with an inhomogeneous magnetic field. To address this issue, this article proposes a novel method that exploits the magnetic field distortions to render the calibration parameters observable. To simplify the calibration process, the calibration parameters are estimated simultaneously with the position of the platform. The method employs a Rao-Blackwellized particle filter that reduces the computational complexity and enables real time processing. The feasibility of the method is shown in an evaluation with measurements of a magnetometer mounted on a model train. The results show a high accuracy of the position and calibration parameter estimation.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A New Defect Classification Approach Based on the Fusion Matrix of
           Multi-Eigenvalue
    • Pages: 3398 - 3407
      Abstract: Defect recognition plays an important part in the health monitoring of in-service equipment. Surface defects and sub-surface defects of key components have different effects on the safety of the equipment. Sub-surface defects are difficult to be identified, and it is more likely to cause unpredictable damage than surface defects. However, the existing characteristic cannot accurately identify the defect type. Therefore, it is critical to find features that are useful for defect classification. The fusion of multiple feature values facilitates defect classification. For obtaining multi-feature fusion eigenvalues, this article introduces a fusion matrix based on Fisher’s discriminant criterion and correlation analysis, which realizes the fusion of two dimensions of the whole and the local and different aspects. The BP neural network classifier is then used to discriminate and classify surface defects and sub-surface defects. The recognition accuracy of surface defects and sub-surface defects can reach 98.1%. The classification accuracy of this approach is significantly improved compared with the method based on a single feature value and other state-of-the-art methods. The approach can also be used to identify defects with different cross-sectional shapes, indicating that this approach is suited for nature defects.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic
           Approach for Suboptimal Parameters Tuning
    • Pages: 3408 - 3419
      Abstract: Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial-and-error (or grid-search) approach to find the optimal values. However, using trial-and-error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this article, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial-and-error were coincident, while in the other cases the maximum discrepancy amounted to 2.5 deg and less than 1.5 deg on average.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Defect Detection in the Dead Zone of Magnetostrictive Sensor for Pipe
           Monitoring
    • Pages: 3420 - 3428
      Abstract: Magnetostrictively transmitted and received guided waves can detect long distances from a single sensor position without a couplant. They are widely used in nondestructive testing and in testing the structural integrity of piping. If the guided waves reflected from a defect return to the sensor before the sensor excitation process has finished, the guided waves will then be submerged under the excited guided waves. This creates a dead zone in the guided wave detection. In this article, far-field eddy current testing is used to reduce the dead zone of the magnetostrictively excited guided wave detection. Both magnetostrictively excited guided waves and the far-field eddy current detection method are based on the principle of electromagnetic induction. The main components of the magnetostrictive sensor are a static magnetic field and coils; these components can also be used to realize far-field eddy current testing without the need for additional sensors. The defects located at the far end of the pipe are detected using the guided wave signal. The defects located in the guided wave testing dead zone are detected using the eddy current signal. Through a combination of simulation analysis and experimental verification, it is demonstrated that far-field eddy current detection can reduce the dead zone and enhance the reliability of the magnetostrictively excited guided wave detection.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • KNN-ST: Exploiting Spatio-Temporal Correlation for Missing Data Inference
           in Environmental Crowd Sensing
    • Pages: 3429 - 3436
      Abstract: Sparse mobile crowdsensing is a new crowdsensing paradigm which leverages the spatial and temporal correlation between data sensed at different locations over time to reduce the overall sensing cost by significantly reducing the number of sensing tasks. Consequently, only sparsely selected spatio-temporal cells would be reporting the sensed data, whereas data for the rest of the cells would have to be inferred from the sensed data. This process, which is largely known as missing data inference is the focus of this study. We examine the KNN (K-Nearest Neighbor) approach, which is known to be relatively faster and simpler. However, it is generally accepted to perform poorly when the sensed data is sparse. In the context of environmental crowd sensing, we examine whether it is a viable missing data inference approach if we incorporate the spatio-temporal correlation of data in the algorithm, instead of just exploiting either the spatial or the temporal correlation independently. Thus, we examine three variants of KNN: KNN-ST (KNN-Spatio-Temporal), KNN-S (KNN-Spatial), and KNN-T (KNN-Temporal) on sparse data. Besides, we find that voxelization is a natural way of exploiting the spatio-temporal properties of sensed data and thereby the spatio-temporal correlation between them. Interestingly, we find that KNN-ST indeed shows good performance (normalized absolute error of about 0.1) even when the loss probability is as high as 0.9. Additionally, we implement an existing method on the same experimental datasets and present corresponding comparative simulation results.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing
           Based on Complex-Valued ADMM-Net
    • Pages: 3437 - 3451
      Abstract: Sparse aperture radar imaging is generally achieved by methods of compressive sensing (CS), or, sparse signal recovery(SSR). However, most of the traditional SSR methods cannot produce focused image stably, which limits their applications. ${l}_{1}$ regularization and alternating direction method of multipliers(ADMM) are generally applied to the SSR problem, but its performance is sensitive to the selection of model parameters. This paper proposes a complex-valued ADMM-Net(CV-ADMMN) method to improve the stability of ADMM, and utilize it to achieve sparse aperture ISAR imaging and autofocusing. Firstly, the iterative procedure of ADMM is unrolled to be a deep network structure. Then, the parameters of the model are learned from a training dataset by utilizing an ${l}_{1}$ regularized loss function. Finally, an autofocusing module based on entropy-minimization is plugged into the trained model to compensate the phase error. Experimental results based on both simulated and measured data validate the superiority of the proposed method over ADMM.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • ZIZO: A Zoom-In Zoom-Out Mechanism for Minimizing Redundancy and Saving
           Energy in Wireless Sensor Networks
    • Pages: 3452 - 3462
      Abstract: In modern life, there is invisible data being continuously generated, data that if collected and processed can detect risks and changes and enable us to mitigate their effect. Thus, there is an essential need to sense everything around us in order to make better use of it. This lead us to an era known as “sensing-era” in which wireless sensor networks (WSN) play a vital role in the monitoring of natural and artificial environments. Indeed, the collection and transmission of huge amounts of redundant data by sensor nodes will lead to a faster consumption of their limited battery power, which is sometimes difficult to replace or recharge, reducing the overall lifetime of the network. Therefore, an effective way to increase lifetime by saving energy is to reduce the amount of transmitted data by eliminating redundancy along the path to the sink. In this paper, we propose a Zoom-In Zoom-Out (ZIZO) mechanism aimed to minimize data transmission in WSN. ZIZO works on two WSN levels: on the sensor level where we propose a compression method called index-bit-encoding (IBE) in order to aggregate similar readings before sending them to the second network level, e.g. cluster-head (CH). The CH searches then for correlation among node data in order to optimize the sampling rate of the sensors in the cluster through a process called sampling rate adjustment (SRA). We evaluate the performance of our mechanism based on both simulations and experiments while the obtained results are compared to other existing techniques, and we show reduced energy consumption by up to 90% in some cases.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Identification of Human Breathing-States Using Cardiac-Vibrational Signal
           for m-Health Applications
    • Pages: 3463 - 3470
      Abstract: In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspace projection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory-rates. Stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG-beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Self-Calibration Imaging Method for Microwave Staring Correlated Imaging
           Radar With Time Synchronization Errors
    • Pages: 3471 - 3485
      Abstract: Microwave Staring Correlated Imaging (MSCI) technology has attracted increasing attentions for its ability to acquire high-resolution images in staring imaging geometry. The key point of MSCI is to construct the temporal—spatial stochastic radiation field which can be realized by multi-transmitter systems emitting frequency-hopping (FH) waveforms. However, time synchronization errors always exist in practice, which heavily deteriorates the imaging results. Therefore in this article, a two–step self–calibration imaging method for MSCI transmitting frequency-hopping waveforms is proposed to directly estimate the time synchronization errors. At the first step, a sparsity driven algorithm is proposed which utilizes a novel cluster sparse prior to improve the imaging performance. At the second step, a self–calibration operation is proposed the first time to directly compensate the time synchronization errors in MSCI. By alternately reconstructing the targets and estimating the time synchronization errors, the proposed method can reconstruct the target with high imaging quality and effectively compensate the time synchronization errors. Numerical simulations demonstrate the potential advantages of the proposed method to enhance the imaging quality and improve the time synchronization error estimation performance.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Robust and Low-Complexity Time-Reversal Subspace Decomposition Methods for
           Acoustic Emission Imaging and Localization
    • Pages: 3486 - 3496
      Abstract: This article focuses on the acoustic emission (AE) localization problem in the complex and unknown probed domain. The schemes based on time-reversal (TR) theorem are proposed to achieve accurate imaging and localization, even when there is no accurate priori knowledge of the Green’s functions. In the proposed strategies, a signal enhancement method is proposed to de-noise sensor wavefields from single or multiple sources firstly. Then the wideband signal matrix is condensed into a single frequency matrix by coherent processing. The steering vectors are estimated based on minimizing noise variance. And by incorporating the steering vector estimation algorithm into the subspace decomposition-based methods, two novel robust imaging methods, robust coherent Decomposition of the time-reversal operator (R-DORT) and robust coherent time-reversal Multiple signal classification (R-TR-MUSIC), are presented. The performance of the proposed methods is evaluated using simulation studies that consider two background mismatches and different levels of signal-to-noise ratio (SNR) respectively. The simulation results demonstrate the effectiveness and the superiority of the presented methods in locating AE sources under the complex and uncertain circumstances. Moreover, the localization results based on experimental data show the potential of the proposed methods in practical applications.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Just-in-Time Fine-Tuning Framework for Deep Learning of SAE in Adaptive
           Data-Driven Modeling of Time-Varying Industrial Processes
    • Pages: 3497 - 3505
      Abstract: In modern industrial processes, soft sensors have played increasingly important roles for effective process monitoring, control and optimization. Deep learning has shown excellent ability for hierarchical nonlinear feature representation in soft sensors. However, the existing deep learning based soft sensors are mostly trained offline and applied online without updating mechanism. This may cause their performance degradation in time-varying processes. To deal with this problem, an adaptive updating framework is proposed for deep learning, which is based on just-in-time fine-tuning of stacked autoencoder (JIT-SAE). In JIT-SAE, an offline SAE model is first trained with layer-wise unsupervised pre-training and supervised fine tuning. For online prediction, the network is dynamically fine-tuned upon the query sample. For each query sample, the most relevant labeled samples are selected to form a fine-tuning dataset from the historical labeled database, which is regularly augmented once new labeled samples are available from laboratory analysis. Moreover, each relevant sample is assigned with a weight according to its similarity with the query sample. Then, the deep network is fine-tuned with these relevant labeled samples by designing a weighted loss function. Thus, JIT-SAE is able to track the newest process running state timely and match the data pattern accurately. Case study on an industrial hydrocracking process is provided to demonstrate the effectiveness of the JIT-SAE framework.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An Accurate Noninvasive Blood Glucose Measurement System Using Portable
           Near-Infrared Spectrometer and Transfer Learning Framework
    • Pages: 3506 - 3519
      Abstract: Diabetes is considered one of the life-threatening diseases in the world, which needs regular monitoring of blood glucose levels. In this article, we developed a portable system that makes near-infrared spectroscopy (NIRS) technology available to non-professionals through a mobile application and a specially-made enclosure. It overcomes the shortcomings of traditional spectroscopy systems, such as large volume, high cost, complicated operation, and difficulty in online detection. To verify the feasibility of NIRS in noninvasive blood glucose concentration detection, after the pretreatment of the acquired original spectra, we compared two different feature extraction algorithms of synergy interval (Si) and genetic algorithm (GA). On this basis, two quantitative prediction models of partial least squares (PLS) and extreme learning machine (ELM) were established. The experimental results showed the model based on the combination of Si and GA and ELM (i.e., Si-GA-ELM model) as the most accurate among the selected models. At the same time, the prediction accuracy of the spectral waveband was higher than that of the full. To further overcome the difficulty of establishing a finite sample data model and reduce the influence of individual differences, the model transfer method TrAdaBoost was used to enhance the accuracy and stability of our model. The final experimental results show that the NIR spectrometer used is portable and light and can be encased as a handheld device form. Computation models combining machine learning and chemometric methods make the estimated blood glucose more feasible, which is an innovative work in noninvasive blood glucose measurement fields.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Scene Segmentation of Multi-Band ISAR Fusion Imaging Based on MB-PCSBL
    • Pages: 3520 - 3532
      Abstract: We consider the problem of achieving multi-band inverse synthetic aperture radar (ISAR) fusion imaging of block structure targets with unknown block partition and develop a block-sparse recovering method based on matrix block pattern-coupled sparse Bayesian learning algorithm. Based on the sparse representation of multi-band ISAR fusion imaging model, a pattern-coupled hierarchical Gaussian prior is proposed to characterize the pattern relevance of scattering coefficients. The sparsity of each coefficient is controlled not only by its own hyperparameter, but also by the hyperparameters corresponding to its eight neighboring coefficients in the data matrix. The correlations between the coefficients in rows and columns are determined by different parameters, respectively. The proposed prior model can increase the model flexibility and promote the generation of block structures. Moreover, the whole observation scene is segmented into multiple sub-scenes to reduce the memory storage space and the computational complexity. Parameters and the fusion image result of each sub-scene are derived by the expectation-maximization method. The multi-band ISAR fusion image result of the whole scene is obtained through the stitching of the sub-scenes imaging results. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Multi-Feature Fusion Approach for Epileptic Seizure Detection From EEG
           Signals
    • Pages: 3533 - 3543
      Abstract: In this article, a new fusion scheme based on the Dempster–Shafer Evidence Theory (DSET) is introduced for Epileptic Seizure Detection (ESD) in brain disorders. Firstly, various features in temporal, spectral, and temporal-spectral domains are extracted from Electroencephalogram (EEG) signals. Afterward, a Correlation analysis via the Pearson Correlation Coefficient (PCC) is conducted on the extracted features to select and remove highly correlated features. It leads to the second feature set with about half numbers of the first feature set. Next, three separate filter-type feature selection techniques, including Relief-F (RF), Compensation Distance Evaluation Technique (CDET), and Fisher Score (FS), are conducted to this second feature set for ranking features. Following that, a feature fusion is engaged by the DSET through the individual feature ranking results to generate high qualified feature sets. Indeed, the DSET-based feature fusion is devoted to enhancing the feature selection confidence using the least superb ranked features. In the classification stage, an Ensemble Decision Tree (EDT) classifier, along with two common validation procedures, including hold out and 10-fold cross-validation, is appropriated to classify the selected features from the EEG signals as normal, pre-ictal (epileptic background), and ictal (epileptic seizure) classes. Finally, several test scenarios are investigated using experimental data of Bonn University to evaluate the proposed ESD performance. Moreover, a comparison with other research works on the same dataset and classes is accomplished. The obtained results indicate the effectiveness of the proposed feature fusion approach and superior accuracy compared to the traditional methods.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Elman Neural Network Soft-Sensor Model of PVC Polymerization Process
           Optimized by Chaos Beetle Antennae Search Algorithm
    • Pages: 3544 - 3551
      Abstract: The conversion rate of vinyl chloride monomer (VCM) is an important product quality indicator in the process of Polyvinyl chloride (PVC) polymerization. Due to the complexity of the PVC polymerization process and the limitation of site conditions, it is difficult to obtain the VCM conversion rate online in real time.Therefore, this article puts forward a soft-sensor model based on Beetle Antennae Search Algorithm (BAS) to optimize Elman neural network(Elman). Firstly, Multi-Cluster Feature Selection (MCFS) is used to reduce the dimensionality of the high-dimensional input variables, so that we get auxiliary variables of the soft-sensor model. Then, using Elman neural network as a soft-sensor model, and it is trained by the proposed optimization algorithm, which combines the chaotic map and the Beetle Antennae Search Algorithm (CBAS). The simulation results show that the model can significantly improve the prediction accuracy of the VCM conversion rate while realizing the real-time control of the PVC polymerization production process.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Classification of Alcoholic EEG Signals Using a Deep Learning Method
    • Pages: 3552 - 3560
      Abstract: Most of the traditional alcoholism detection methods are developed based on machine learning based methods that cannot extract the deep concealed characteristics of Electroencephalogram (EEG) signals from different layers. Hence, this study aims to introduce a deep leaning-based method that can automatically identify alcoholic EEG signals. It also explores if a hand-crafted feature extraction method is worth applying to deep learning techniques for classification of alcoholism. To investigate this, this paper presents two deep learning-based algorithms for classification of alcoholic EEG signals for comparison. In Algorithm 1, Principal Component Analysis (PCA) based feature extraction technique has been applied to extract representative components and then the extracted features are used as input to Artificial neural network (ANN) for classification. In Algorithm 2, the raw EEG data are directly used as inputs to a deep learning method: ‘long short-term memory (LSTM)’ for detection of alcoholism. The proposed algorithms were tested on a publicly available UCI Alcoholic EEG dataset. The experimental results show that the proposed Algorithm 2 could achieve an average classification accuracy of 93% while this accuracy is 86% for the proposed Algorithm 1. The comparative evaluations with the state-of-the-art algorithms indicate that Algorithm 2 also outperforms other competing algorithms in the literature. Thus deep learning algorithm when applied to raw data, can produce better performance than the combination of the hand-crafted feature method and the deep leaning algorithm. Our proposed system can be used to determine the extent of alcoholism-related changes in EEG signals and the effectiveness of therapeutic plans.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Robust Error-State Kalman Filter for Estimating IMU Orientation
    • Pages: 3561 - 3569
      Abstract: Inertial measurement units (IMUs) are increasingly utilized as motion capture devices in human movement studies. Given their high portability, IMUs can be deployed in any environment, importantly those outside of the laboratory. However, a significant challenge limits the adoption of this technology; namely estimating the orientation of the IMUs to a common world frame, which is essential to estimating the rotations across skeletal joints. Common (probabilistic) methods for estimating IMU orientation rely on the ability to update the current orientation estimate using data provided by the IMU. The objective of this work is to present a novel error-state Kalman filter that yields highly accurate estimates of IMU orientation that are robust to poor measurement updates from fluctuations in the local magnetic field and/or highly dynamic movements. The method is validated with ground truth data collected with highly accurate orientation measurements provided by a coordinate measurement machine. As an example, the method yields IMU-estimated orientations that remain within 3.7 degrees (RMS error) over relatively long (25 cumulative minutes) trials even in the presence of large fluctuations in the local magnetic field. For comparison, ignoring the magnetic interference increases the RMS error to 12.8 degrees, more than a threefold increase.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Deep Learning-Based Speed Bump Detection Model for Intelligent Vehicle
           System Using Raspberry Pi
    • Pages: 3570 - 3578
      Abstract: Artificial intelligence in vision based approaches have proven to be effective in various phases of intelligent vehicle system (IVS). An IVS has to intelligently take many critical decisions in heterogeneous environment. Speed bump detection is one such issue in real world due to its varying appearance in dynamic scene. The major issue is the scaling appearance of such objects from far distance and often viewed as small entity. In the proposed article, deep learning and computer vision based speed bump detection model is proposed, which assist and control the driving behavior of an IVS before it reaches to speed bump. The behavior of IVS has been explored and tested by incorporating the proposed method with a real time embedded prototype and found to be more efficient and comparable with state-of-art techniques. The overall performance of the proposed model has been achieved in terms of accuracy, precision and F-Measure as 98.54%, 99.05% and 97.89% respectively in the prepared real time environment.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Novel Multivariate-Multiscale Approach for Computing EEG Spectral and
           Temporal Complexity for Human Emotion Recognition
    • Pages: 3579 - 3591
      Abstract: This work proposes a novel multivariate-multiscale approach for computing the spectral and temporal entropies from the multichannel electroencephalogram (EEG) signal. This facilitates the recognition of three human emotions: positive, neutral, and negative. The proposed approach is based on the application of the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT). We have extended the existing FBSE-EWT method for multichannel signals and derived FBSE-EWT based multivariate Hilbert marginal spectrum (MHMS) for computing spectral Shannon and K-nearest neighbor (K-NN) entropies. The multivariate FBSE-EWT decomposes the multichannel EEG signals into narrow band subband signals. The multiscaling operation adapted in the spectral domain is based on the selection of successive joint instantaneous amplitude and frequency functions of the subband signals. On the other hand, the time domain multiscale K-NN entropy is computed from the cumulatively added multidimensional subband signals. The extracted spectral and temporal entropy features are smoothed and fed to the sparse autoencoder based random forest (ARF) classifier architecture for emotion classification. The proposed approach is tested using multichannel EEG signals available in a public database (SJTU emotion EEG dataset (SEED)). The bivariate EEG signals from different channel pairs with distinct spatial locations over the scalp are considered as input to our proposed system. The obtained overall classification accuracy of 94.4% reveals that the proposed approach is useful in classifying human emotions. The method is also validated using DREAMER emotion EEG public database. The method outperforms the existing state-of-the-art methods evaluated in these databases.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Rotor Blades Micro-Doppler Feature Analysis and Extraction of Small
           Unmanned Rotorcraft
    • Pages: 3592 - 3601
      Abstract: In this article, the micro-Doppler feature analysis and extraction of small unmanned rotorcraft (SUR) is considered. To be specific, the radar returns from the rotor blades are first modeled as sinusoidal frequency-modulated (SFM) signals. Then, the Gabor transform is utilized to obtain the time-frequency distribution (TFD). In order to solve the problem of limited TF analysis resolutions, high carrier frequency of radar is employed for the sake of separating different sinusoidal curves from TFD. After that, the Hough-Radon transform (HRT) is introduced to detect the sinusoidal curves from the TFD. Finally, based on the relationship between the SFM signal and the rotating blade, the micro-Doppler parameters which can reflect the threat level of the SUR to a large extend are estimated. Compared with other existing methods, the proposed method presents the relationships between the carrier frequency and the rotating blade parameters and it can be employed to extract the micro-Doppler feature of SUR with multiple rotor hubs. Simulation results demonstrate the effectiveness of the proposed micro-Doppler feature extraction method.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Geometric, Electronic and Optical Properties of Pt-Doped C3N Monolayer
           Upon NOxAdsorption: A DFT Study
    • Pages: 3602 - 3608
      Abstract: C3N monolayer is reported with comparable and even more desirable sensing behavior upon small gas molecules in comparison to graphene. In this work, we proposed Pt-doped C3N monolayer as a desirable 2D sensing nanomaterials for NOx detection by DFT method. The Pt atom tends to be doped on the $text{B}_{C-C}$ site of C3N surface, making little effect on the bandgap but changing the indirect semiconducting property on the other hand. Pt-C3N monolayer behaves more admirable performance upon NO2 adsorption than NO, but both systems are identified as chemisorption with $E_{ad}$ of −2.25 and −1.99 eV, and $Q_{T}$ of −0.344 and −0.083 e, respectively. The metallic property is determined in both systems given the calculated zero bandgap, which will increase the electrical conductivity of Pt-C3N monolayer largely after adsorption of NOx gases. This is the basic sensing mechanism for Pt-C3N monolayer upon NOx detection. In the meanwhile, the desirable changes of WF and dielectric function in NOx systems verify the potential of Pt-C3N monolayer for NOx detection through field effect transit or optical devices. Our calculations could be meaningful to exploit novel 2D nanomaterial for sensing NOx in order to monitor toxic gases in our environment.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Active Shooter Detection in Multiple-Person Scenario Using RF-Based
           Machine Vision
    • Pages: 3609 - 3622
      Abstract: Emerging applications of radio frequency (RF) vision sensors for security and gesture recognition primarily target single individual scenarios which restricts potential applications. In this article, we present the design of a cyber-physical framework that analyzes RF micro-Doppler signatures for individual anomaly detection, such as a hidden rifle among multiple individuals. RF avoids certain limitations of video surveillance, such as recognizing concealed objects and privacy concerns. Current RF-based approaches for human activity detection or gesture recognition usually consider single individual scenarios, and the features extracted for such scenarios are not applicable for multi-person cases. From a machine learning perspective, the RF sensor spectrogram images are conducible for training using deep convolutional neural networks. However, generating a large labeled training dataset with an exhaustive variety of multi-person scenarios is extremely time consuming and nearly impossible due to the wide range of combinations possible. We present approaches for multi-person spectrogram generation based on individual person spectrograms that can augment the training dataset and increase the accuracy of prediction. Our results show that the spectrogram generated by RF sensors can be harnessed by artificial intelligence algorithms to detect anomalies such as a concealed weapon for single and multiple people scenarios. The proposed system can aid as a standalone tool, or be complemented by video surveillance for anomaly detection, in scenarios involving single or multiple individuals.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • CNN-Based Multistage Gated Average Fusion (MGAF) for Human Action
           Recognition Using Depth and Inertial Sensors
    • Pages: 3623 - 3634
      Abstract: Convolutional Neural Network (CNN) provides leverage to extract and fuse features from all layers of its architecture. However, extracting and fusing intermediate features from different layers of CNN structure is still uninvestigated for Human Action Recognition (HAR) using depth and inertial sensors. To get maximum benefit of accessing all the CNN’s layers, in this paper, we propose novel Multistage Gated Average Fusion (MGAF) network which extracts and fuses features from all layers of CNN using our novel and computationally efficient Gated Average Fusion (GAF) network, a decisive integral element of MGAF. At the input of the proposed MGAF, we transform the depth and inertial sensor data into depth images called sequential front view images (SFI) and signal images (SI) respectively. These SFI are formed from the front view information generated by depth data. CNN is employed to extract feature maps from both input modalities. GAF network fuses the extracted features effectively while preserving the dimensionality of fused feature as well. The proposed MGAF network has structural extensibility and can be unfolded to more than two modalities. Experiments on three publicly available multimodal HAR datasets demonstrate that the proposed MGAF outperforms the previous state-of-the-art fusion methods for depth-inertial HAR in terms of recognition accuracy while being computationally much more efficient. We increase the accuracy by an average of 1.5% while reducing the computational cost by approximately 50% over the previous state-of-art.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An ELM-Based Semi-Supervised Indoor Localization Technique With Clustering
           Analysis and Feature Extraction
    • Pages: 3635 - 3644
      Abstract: In this article, a new indoor localization technique with extreme learning machine (ELM) is proposed where only a small number of received signal strength indicator (RSSI) measurements are labeled. In the off-line learning phase, the iterative self organizing data analysis techniques algorithm (ISODATA) is used to divide the RSSI measurements into some measurement data subsets. Then the multi-kernel ELM (MK-ELM) method is utilized to perform classification learning and obtain the RSSI measurement classification function. For each RSSI measurement subset, a two-stage feature extraction algorithm using the kernel principal component analysis, the deep learning network and ELM method is proposed for RSSI measurement feature extraction. At last, the position regression function of each subset is obtained by the semi-supervised regression learning. In the on-line position estimation phase, using the measurement classification and feature extraction of the received RSSI measurement, the position is estimated based on the corresponding position regression function. The field tests show that the proposed algorithm can obtain more accurate position estimation than other existing ELM based localization approaches do.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Strategies of Pen Tip Path Estimation and of Workload Comparison for
           Handwriting Tasks
    • Pages: 3645 - 3652
      Abstract: The magnitude of impulse and work at pen tip, which are related to the fingertip force, may be indicators of fatigue and pain during writing. Previous methods used specially prepared pens for testing which have different sizes and weights from those commercially available; however, they are not suitable for evaluating the operation and workload of ordinary handwriting tasks. This article proposes a data processing method of a six-axis force-torque sensor for determining the contact state of the pen tip and calculating the position and work of the pen tip without adding anything to a commercially available pen. The proposed method consists of kinematic calculation of the pen tip position as the observation and probabilistic determination of the contact state of the pen tip. The performance of the proposed method is presented by applying to the time-series data of force vector and moment vector during handwriting tasks of symbols. The data was measured by a low-resolution high-frequency 6-DoF force-torque sensor. Multiple handwriting tasks with the same or different load at one time were set. It was confirmed statistically whether the work amount with one pen tip can be evaluated appropriately if the work amount is the same or different. The experimental results demonstrate that the proposed method can compare the movement distance and work of the pen tip, depending on the severity of the writing task.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Development of a Portable Electrical Impedance Tomography Device for
           Online Thrombus Detection in Extracorporeal-Circulation Equipment
    • Pages: 3653 - 3659
      Abstract: A portable Electrical Impedance Tomography (EIT) device has been developed for online thrombus detection in extracorporeal-circulation equipment. For the portable EIT device, circuits of multiplexer module are designed to expend channels, pipeline processing mechanism is introduced to increase processing speed, and data acquisition rate reaches 50 Frames Per Second (FPS) and the online tomography rate reaches 40 FPS with 8-electrode sensor. To test the characteristics of the proposed EIT device, a series of experiments are conducted. Firstly, small sensors with different size and plastic phantoms are used to test the static properties of the device. Testing results show that the quality of the reconstructed images is related to the diameter of sensor, and Image Correlation (IC) reaches 0.75 when diameter of sensor is 30 mm. Secondly, swine blood is used for making thrombus to verify the EIT device’s image reconstruction ability of thrombus in stationary conditions instead of human blood. Experiments show that the reconstructed image error is 8.07% when diameter of the sensor is 10 mm, and it reduces to 1.49% when diameter is 30 mm. Finally, experiments are conducted to measure the position of flowing thrombus. Euclidean distance between voltage data is used to judge the occurrence of thrombus. Experimental results indicate that thrombus is clearly distinguished from the background solution in fluids. As a conclusion, the proposed EIT device is of great significance for online thrombus detection in extracorporeal-circulation system.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Non-Imaging Digital CMOS-SOI-MEMS Uncooled Passive Infra-Red Sensing
           Systems
    • Pages: 3660 - 3668
      Abstract: This paper presents a novel non-imaging digital passive Infra-Red (PIR) sensing system using a CMOS-SOI-MEMS transistor as the thermal sensor, which replaces the traditional pyroelectric sensors and outperforms thermopiles. The mosaic sensors, which are manufactured by nano-fabrication methods in standard FABs, exhibit enhanced performance and robust manufacturing on wafer level processing and vacuum packaging. Mirror optics instead of Fresnel plastic lenses provides enhanced performance at low-cost. The essential aspects of the design of the mirrors for curtain sensors and presence sensors are presented. The overall measured performance for detecting human targets at extended ranges and hot spots detection are reported. The overall performance of the sensing systems indicate why they are most suitable for consumer electronics, including smart homes, wearables, Internet of Things (IoT) and mobile applications.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using
           Modified Logistic and Dynamic Newton Leipnik Maps
    • Pages: 3669 - 3679
      Abstract: The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the ’unstable incapacity’. This health status is essentially determined by the apparent decline of independence in Activities of Daily Living (ADLs). Detecting ADLs provide possibilities of improving the home life of elderly people as it can be applied to fall detection systems.. This article looks at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this article were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this article is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the ava-lable data.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Generalized Dynamic Fuzzy NN Model Based on Multiple Fading Factors SCKF
           and its Application in Integrated Navigation
    • Pages: 3680 - 3693
      Abstract: Traditional neural network (NN)’s generalization ability is weak, and its prediction accuracy depends heavily on the selection of network structure and training samples, so it cannot be directedly applied to the strapdown inertial navigation system (SINS) and global navigation satellite system (GNSS) integrated navigation system in varied environment. Aiming at these two problems, based on the fuzzy neural network (FNN) model, a new neuron growth-attenuation mechanism is established by introducing the dynamic adjustment idea of network structure. Therefore, the network has a compact structure and good performance, which prevents the network from over-training and over-fitting. Besides, the theory of strong tracking filter (STF) is introduced into the nonlinear filter to design the multiple fading factor square root cubature kalman filter (MSCKF) method for neural network parameter training, which shortens the training time and improves the convergence speed. Results of the simulation and physical experiment verification demonstrate that the generalization ability of proposed model is enhanced and prediction accuracy is improved during the GNSS signal loss. Compared with the pure inertial navigation method, the position errors in latitude and longitude are reduced by 85.00%, 89.71% and the velocity errors in east and north are reduced by 94.57%, 83.11%, respectively by the proposed generalized dynamic fuzzy NN Model Based on MSCKF(MSCKF-GDFNN).
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • MoS2-Based Multifunctional Sensor for Both Chemical and Physical Stimuli
           and Their Classification Using Machine Learning Algorithms
    • Pages: 3694 - 3701
      Abstract: This report demonstrates for the first time the direct growth of the molybdenum disulfide (MoS2) on flexible pencil eraser substrate by cost-effective hydrothermal synthesis procedure and its application as a multifunctional sensor to monitor human appendage movements and breath pattern for smart personal health care services. The gauge factor calculated was 7.91, which is comparable to the strain sensors fabricated with sophisticated techniques and relatively better when the cost is a major constraint. One of the important roadblocks towards the development of multifunctional sensor is the inability of the frontend to identify stimulus. To overcome this, the categorization of sensor output data for a reliable front-end processing of the sensor data was performed using various machine learning (ML) algorithms such as Logistic regression, naive Bayes, k-nearest neighbors, support vector machine, decision tree, and random forest. The performance evaluation of the algorithms was tested using 10-fold cross validation (CV) method. The decision tree algorithm was able to classify both breath and strain sensory data with 95.57% accuracy, followed by random forest algorithm with 94.77% without tuning parameters. Decision tree could classify with 95.96% accuracy using 10-fold CV without tuning parameters whereas k-nearest neighbors gives 96.63% with 10-fold CV and tuning parameters. Further, the fabricated sensor was integrated on different parts of human body and corresponding movement were monitored. After 500 bending cycle studies, negligible change in the sensor performance suggesting the excellent durability of the device.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Toward an Integrated Multi-Modal sEMG/MMG/NIRS Sensing System for
           Human–Machine Interface Robust to Muscular Fatigue
    • Pages: 3702 - 3712
      Abstract: The research on muscular activity based human-machine interface (HMI) is of great significance, such as controlling prosthetic hand to improve the life quality of amputee patients. However, the HMI performance is limited by muscular fatigue due to frequent muscle contraction. To overcome the drawback, this paper presents a multi-modal sensing system that can collect surface electromyography (sEMG), near-infrared spectroscopy (NIRS) and mechanomyography (MMG) simultaneously. To evaluate the performance of the multi-modal signal acquisition system, incremental isometric voluntary contractions experiment is carried out. The experimental results show that the proposed system can reliably obtain three kinds of muscle contraction information from the perspective of electrophysiology, oxygen metabolism and low-frequency vibration of myofiber. Furthermore, muscle fatigue induced experiment imitating HMI usage is performed, and it convincingly demonstrates a significantly ( ${p} < 0.01$ ) improved classification accuracy (CA) by using multi-modal features. The CA is compensated by 3.6% ~ 22.9% in the presence of muscular fatigue. These results suggest that multi-modal sensing can improve the HMI performance and robustness. The outcomes of this study have great potential to promote the biomedical and clinical applications of human-machine interaction.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • LSTM Model Based on Multi-Feature Extractor to Detect Flow Pattern Change
           Characteristics and Parameter Measurement
    • Pages: 3713 - 3721
      Abstract: In the field of petroleum multiphase flow research, accurate measurement of oil-gas two-phase flow parameters is the research focus. This paper analyzes the changing characteristics of the flow pattern of oil-gas two-phase flow and measures the parameters. The experimental phenomenon shows that under different oil and gas flow rate, the flow patterns upstream and downstream of the venturi tube will change to varying degrees. The Convolutional Neural Network often used in the study of flow patterns, but the flow pattern recognition by the CNN model cannot consider the time series relationship. Therefore, this paper proposes a multi-feature extractor-based LSTM (MFE-LSTM) model to predict the parameters. First, the flow pattern features are extracted through the CNN model, and then the features extracted from different CNN models are combined to form a sentence. The relationship between different words in a sentence and different sentences is explored through the LSTM model. The MFE-LSTM algorithm analyzes the changing relationship between flow patterns with time series and spatial position. In addition, in this paper, the flow pattern horizontal splicing model and the flow pattern channel superposition model are designed. In this experiment, the distribution of oil flow rate is 1- $10~ m^{3}/h $ , the distribution of gas flow rate is 20- $150~ m^{3}/h $ , and the distribution of GVF (gas void fraction) is 0.25-0.95. The experimental results show that the MFE-LSTM model has the best prediction effect. The average prediction relative error of gas flow rate is 0.387%. The average prediction relative error of oil flow rate is 2.081%.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Inertial Motion Capture Accuracy Improvement by Kalman Smoothing and
           Dynamic Networks
    • Pages: 3722 - 3729
      Abstract: Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynamic ones. This error growth may somewhat be mitigated by imposing biomechanical constraints in multi-sensor systems. Error reduction is also possible by optimization-based methods that incorporate the dynamic and static constraints governing the system behavior over a period of time (e.g. the dynamic network algorithm); when this period includes multiple static phases for a link, its estimation accuracy is greatly improved. The current study enhances the error control capabilities of an existing inertial motion capture algorithm by multi-stage smoothing. The base algorithm benefits from imposing biomechanical constraints and is self-calibrating with respect to body geometry and some sensor parameters. The smoothing process, conducted over the stepping periods of each foot, comprises two stages; Kalman smoothing followed by error minimization by dynamic networks. The performance of the algorithm, deployed using both extended and square-root unscented Kalman filtering schemes (EKF and SRUKF, respectively), is experimentally evaluated during a fast-paced walking test using a custom-made inertial motion capture system. A comparison with an optical motion capture system showed that the proposed method decreased pelvis position and attitude estimation errors by 19% and 29%, respectively. Furthermore, compared to the EKF-based smoothing algorithm, the SRUKF-based method proved to be more successful in error reduction and parameter estimation.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Cost Optimized Design of Multi-Camera Dome for Volumetric Surveillance
    • Pages: 3730 - 3737
      Abstract: A multi-camera dome consists of number of cameras arranged in layers to monitor a hemisphere around its center. In volumetric surveillance, a 3D space is required to be monitored which can be achieved by implementing number of multi-camera domes. A monitoring height is considered as a constraint to ensure full coverage of the space below it. Accordingly, the multi-camera dome can be redesigned into a cylinder such that each of its multiple layers has different coverage radius. Minimum monitoring constraints should be met at all layers. This work is presenting a cost optimized design for the multi-camera dome that maximizes its coverage. The cost per node and number of square meters per dollar of multiple configurations are calculated using a search space of cameras and considering a set of monitoring and coverage constraints. The proposed design is cost optimized per node and provides more coverage as compared to the hemispherical multi-camera dome.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Avoiding Misdiagnosis of Parkinson’s Disease With the Use of Wearable
           Sensors and Artificial Intelligence
    • Pages: 3738 - 3747
      Abstract: Parkinson’s Disease (PD) is a neurodegenerative disease associated with the extrapyramidal motor system disorder currently being the second most common neurodegenerative disorder. The first clinical symptoms can manifest themselves long before the retirement age and inevitably lead to reducing the possibility of continuing work. However, PD is sometimes misdiagnosed. In this article, we discuss the typical misdiagnosed cases and propose a second opinion system based on wearable sensors and artificial intelligence. For this reason, we designed a number of common tasks and recorded the movement data using wearable sensors worn by individuals with PD and other extrapyramidal disorders. PD patients are differentiated against other patients with similar diseases and not against healthy subjects. This allows one to measure the true specificity of wearable technologies with regard to detecting PD. Data analysis and classification of the types of tremor using machine learning methods (feature extraction, dimensionality reduction, classification) helps significantly improve the accuracy of PD diagnosis. Our results show that, when considering bradykinesia and tremor together, the accuracy of distinguishing PD from similar diseases increases (f1 score 0.88). This closed-loop configuration makes it possible to tune exercises to maximize the diagnostic value of the entire routine. We report approbation on 56 patients.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Global Estimation Method Based on Spatial–Temporal Kalman Filter
           for DPOS
    • Pages: 3748 - 3756
      Abstract: A distributed position and orientation system (DPOS) can provide abundant motion parameters for multi-task remote sensing loads to conduct its motion compensation. The motion parameters of imaging loads equipped with slave Inertial Measurement Units (IMUs) can be obtained by transfer alignment from master system to slave IMU. However, considering the volume, weight, and cost of the slave IMU, it is impossible to install IMUs near all the remote sensing loads in engineering practice. Aiming at the problem, a global estimation method based on spatial-temporal Kalman filter (STKF) for DPOS is proposed to estimate the motion parameters of remote sensing loads not equipped with slave IMUs. Combining kriging and Kalman filter, and taking into account the spatial-temporal correlation between points, spatial-temporal Kalman filter can make full use of the useful information of the data and enable us to obtain the optimal estimation in time and space. In order to evaluate the effectiveness of the proposed method, the semi-physical simulation based on flight experiment is conducted. The results show that the proposed method not only can realize the global estimation, but also provides us some new insights into the layout scheme of DPOS.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Using a Single Uniaxial Gyroscope to Detect Lateral Ankle Sprain Hazard
    • Pages: 3757 - 3762
      Abstract: Lateral ankle sprain is very common in sports. Recently, there was a wearable assistive technology that stimulates the peroneal muscles to prevent this injury, but it requires a monitoring system to detect injury hazards and actuate the protection. This study presents the feasibility of a uniaxial gyroscope to monitor the peak ankle inversion velocity during common sporting motion and simulated ankle sprain motion. Ten males performed walking, running, 45-deg cutting, vertical jump-landing, stepping-down from a block, 5 kinds of simulated ankle sprain motion on sprain simulators, and a manual ankle-twisting motion in a biomechanics laboratory. The peak ankle inversion velocity was collected by an optical motion analysis system at 120 Hz. Besides, a uniaxial gyroscope was attached to the heel to collect the peak twisting velocity at 500 Hz. Pearson test showed a strong or high positive correlation between the two parameters. Independent t-test showed no difference between the two parameters in all testing motions except the manual ankle-twisting test which the value was 82% of that from the optical motion capture system and both values are above the threshold, 300 deg/s. We concluded the method is applicable to detect the hazard of lateral ankle sprain injury.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • American Sign Language Recognition Using RF Sensing
    • Pages: 3763 - 3775
      Abstract: Many technologies for human-computer interaction have been designed for hearing individuals and depend upon vocalized speech, precluding users of American Sign Language (ASL) in the Deaf community from benefiting from these advancements. While great strides have been made in ASL recognition with video or wearable gloves, the use of video in homes has raised privacy concerns, while wearable gloves severely restrict movement and infringe on daily life. Methods: This article proposes the use of RF sensors for HCI applications serving the Deaf community. A multi-frequency RF sensor network is used to acquire non-invasive, non-contact measurements of ASL signing irrespective of lighting conditions. The unique patterns of motion present in the RF data due to the micro-Doppler effect are revealed using time-frequency analysis with the Short-Time Fourier Transform. Linguistic properties of RF ASL data are investigated using machine learning (ML). Results: The information content, measured by fractal complexity, of ASL signing is shown to be greater than that of other upper body activities encountered in daily living. This can be used to differentiate daily activities from signing, while features from RF data show that imitation signing by non-signers is 99% differentiable from native ASL signing. Feature-level fusion of RF sensor network data is used to achieve 72.5% accuracy in classification of 20 native ASL signs. Implications: RF sensing can be used to study dynamic linguistic properties of ASL and design Deaf-centric smart environments for non-invasive, remote recognition of ASL. ML algorithms should be benchmarked on native, not imitation, ASL data.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • An ANN-Based Single Calibration Impedance Measurement System for Skin
           Impedance Range
    • Pages: 3776 - 3783
      Abstract: Impedance measurement has been widely used as an effective indicator for characterizing samples. Traditional high accuracy impedance analyzers are complicated, expensive, and non-portable. Many kinds of research require low cost, portable, and high precision impedance analyzer devices. AD5933 impedance analyzer integrated circuit has been popularly used for fulfilling these requirements. There are many successful applications of the AD5933 integrated circuit; however, the most significant drawback is nonlinear calibration requirements for high precision measurement in a specified range. The calibration and unknown impedances must be close enough to each other for better measurement accuracy. In the literature, calibration impedance arrays increasing the complexity and processing time are commonly used for high accuracy measurements. In this study, an artificial neural network-based signal post-processing algorithm is proposed to overcome the calibration requirements of the AD5933 integrated circuit, which requires different impedances for different ranges. In the literature, a neural network-based solution has not been applied to this phenomenon. An application specific artificial neural network topology is developed and trained for high precision impedance measurement using a fixed calibration impedance. The proposed measurement system is designed for operating in the range of nominal skin impedance. The average mean square error of measurements is obtained as 0.206%. Although a fixed calibration resistance is used, the proposed signal post-processing approach significantly improved the measurement accuracy of the AD5933 integrated circuit. The high accuracy measurement results prove the effectiveness of the proposed measurement system. The developed system offers portable, simple, and low cost high precision impedance analyzer.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Metal Detector Based on Lorentz Dispersion
    • Pages: 3784 - 3790
      Abstract: Lorentz Dispersion regime is a narrowband spectral region characterized by a sharp resonance dip and an anomalous phase response. Due to its high dispersive nature, the Lorentz resonance is very sensitive to the changes in the physical dimensions of the resonators and the external perturbations approaching the resonant cavity. The proposed Lorentz structure consists of an open microstrip stub which contains a small gap. A metallic perturbation brought closer to the gap changes the electric field distribution leading to an effective increase in the stub’s electrical size and a resonance shift. An experimental demonstration in the frequency range 1 to 1.5 GHz show that the relative resonance spectral shift correspond to the proximity of the metallic object.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Multi-Frequency Impedance Myography: The PhaseX Effect
    • Pages: 3791 - 3798
      Abstract: Muscle contraction is often detected via EMG in prosthetics. However, signal disturbances due to electrode motions can lead to misinterpretations. Therefore, alternative measurement approaches are desired to increase the reliability of the results. In this work, a novel approach based on impedance myography is proposed. By means of an equivalent circuit of a muscle, its electrical characteristics during contractions are analyzed. In this analysis, a new biomedical marker named the PhaseX Effect is described. This effect is based on the specific behavior of the phase response when the muscle is contracted and is interesting due to its high signal robustness and low signal processing requirements. The resilience of this effect against electrode motion is also analyzed. Measurements of the complex impedance myography spectra are performed on the forearms of three subjects during relaxation and contraction of the corresponding muscle. The subject measurements show the expected behavior of the muscle model. Actual muscle contractions can easily be detected via a simple analysis of the phase response. For a better visualization, the measurements are repeated while acquiring a synchronized video of the moving forearm. The particular effect of the phase response during muscle contraction can be used as a new marker that can be beneficial in several applications such as prostheses control. The PhaseX Effect has high reliability and low signal processing requirements, making it advantageous over other muscle activity markers. The combination of a reliable marker and simple signal analysis promises to become a new method for prostheses control.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • IoT-Based Unobtrusive Sensing for Sleep Quality Monitoring and Assessment
    • Pages: 3799 - 3809
      Abstract: Aging population is a worldwide trend, which has created a societal crisis as many countries face the challenges of supporting an aging population with increasing costs of healthcare and decreasing numbers of caregivers. Sleep related disorders are common diseases, especially among the elderly. In this paper, we propose a simple and affordable unobtrusive sensing environment including a high-sensitive accelerometer on a bed and passive infrared (PIR) motion sensors in every room, following the generic framework of Internet of Health Things (IoHT) for monitoring the elderly’s sleep-wake conditions, to assess their sleep quality. The environment is nonintrusive, comfortable and can be used for long-term sleep monitoring, detecting early symptoms of sleep related disorders, and responding to caregivers. We implement and pilot test the environment under different daily living situations related to sleep quality. We develop a feature extraction algorithm and applied five popular data analytics models to assess their relative performance. Our study shows that all classifiers except Naïve Bayes can effectively detect sleep quality with the Area under ROC curve (AUC) performance higher than 90%. Among which multilayer feed-forward neural network achieved the best results, in which the detecting sensitivity is up to 96.61%, specificity is 91.81% and AUC performance is 94.21%.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • New Unobtrusive Tidal Volume Monitoring System Using Channel State
           Information in Wi-Fi Signal: Preliminary Result
    • Pages: 3810 - 3821
      Abstract: In this paper, we propose a respiration monitoring system based on the channel state information received from a commercial off-the-shelf Wi-Fi device. We develop data processing modules for extracting respiration parameters from the Wi-Fi signal and estimate the relative tidal volume. In order to verify the performance of the proposed monitoring system, we use a human patient simulator and also test one real human subject in various experimental settings. With the human patient simulator, we evaluate the monitoring performance at various tidal volume levels and compare our monitoring results with the simulator settings. In testing with a human subject, we test our approach on different sleep postures and positions, Wi-Fi router’s positions, and monitoring environments and compare our performance with the spirometer measurement. The results with both the human patient simulator and real human subject demonstrate the strong and robust estimation performance of the proposed approach in various monitoring conditions, indicating the high promise for non-intrusive respiration monitoring in practice.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Design and Application of the Measuring System of Ice Sheet Profile Based
           on Thermal Conductivity Difference of Medium
    • Pages: 3822 - 3830
      Abstract: An automatic ice thickness measurement system was designed based on the temperature and thermal conductivity of air, ice and water. By studying the resistance temperature relationship of different types of platinum resistors, the platinum resistor (HM222) was selected with better performance, and a sensor was designed suitable for the temperature detection of the ice sheet profile. After calibration of platinum resistors, the accuracy of temperature measurement at 0.1°C was ensured within the range of −50°C to +20°C. The ice sheet was heated briefly by periodic self-heating units, and the interface was accurately judged according to the temperature rise difference caused by heating. Ultrasonic sensors were used to identify the ice sheet surface. In order to evaluate the performance of the system, an in-situ observation experiment was conducted in early January 2020 at the station of Yellow River’s Toudaoguai hydrological.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Piezoelectric Energy Harvesting From a Magnetically Coupled Vibrational
           Source
    • Pages: 3831 - 3838
      Abstract: This article presents a new magnetically coupled method to efficiently transfer vibrational energy from a source to a piezoelectric energy harvester. This method enables energy harvesting from vibration sources to which direct physical contact is not feasible or not preferred. The proposed harvester uses the piezoelectric cantilever in combination with two permanent magnets; one attached to the free end of the cantilever while the other magnet is firmly attached to the vibrational source facing the magnet on the cantilever. These magnets are kept in repulsive mode. As the source oscillates, the attached magnet follows the movement of the source. Due to this, the magnet on the cantilever gets perturbed and follows the movement of the source, bending the cantilever cyclically and generating a voltage output. This proposed arrangement not only (a) helps to scavenge energy without directly fitting the piezoelectric element and associated wiring on the vibrating source but also (b) provides more flexibility to adjust the resonant frequency, by varying the air gap between the magnets, (c) gives higher bandwidth compared to the conventional piezoelectric harvesters, ensuring less effect due to small mismatch between the frequency of the source and resonant frequency of the harvester. This is automatically achieved due to the nonlinearity of the system, introduced by the magnets. Prototypes of the proposed harvester and an equivalent conventional harvester are developed and tested. Results proved the functionality of the proposed harvester and showed superior performance in terms of gain, bandwidth, and charging time of a storage capacitor.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Mannequin-Based Training System With Integrated Sensors for Ophthalmic
           Sub-Tenon Anesthesia
    • Pages: 3839 - 3848
      Abstract: Sub-tenon ophthalmic blocks are gaining popularity for ophthalmic anesthesia but have no suitable systems available for training. In this article, the design and development of the first mannequin-based system to train and evaluate an ophthalmic anesthesiologist to perform the sub-tenon block correctly is presented. The system has integrated sensors with new sensing schemes for needle tip tracking and detection of errors incurred by the trainee. An integrated, conductive silicone-based eye model sensor is presented in this article. The model replicates the human ocular anatomy, and the embedded sensors within the eye model help to classify, assess, and evaluate the performed procedure. The developed system was also validated in a pilot clinical study for naturality and usage in a clinical setting with 37 ophthalmic anesthetists. The system recorded 98.3 % accuracy assessing the performed procedure during the clinical study, and the participants rated the naturality of the system with a score of 8.0 ± 0.63 (on a scale of 10) ( ${p} < 0.01$ ). 89 % of the participants and experienced sub tenon block instructors who previously used provisional animal eye-based models for training preferred the training system for its usage of anatomically accurate models and effective warning systems that evaluate the performed procedure.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Cavity-Enhanced Measurements of Benzene for Environmental Monitoring
    • Pages: 3849 - 3859
      Abstract: A mid-infrared laser-based sensor is designed and demonstrated for trace detection of benzene at ambient conditions. The sensor is based on a distributed feedback inter-band cascade laser operating near $3.3~mu text{m}$ and off-axis cavity-enhanced absorption spectroscopy. A multidimensional linear regression algorithm was applied to enable benzene measurements in the presence of interfering species, such as ethylene, methane and water vapor. A minimum detection limit of 2 ppb was achieved for benzene at an integration time of 6 seconds; the detection limit reduces to 200 ppt in dry conditions. A cross-sensitivity analysis was performed to study the effect of interfering species on benzene measurements. The sensor can be used to detect tiny leaks of benzene in petrochemical facilities and to monitor air quality in residential and industrial areas. The sensor performance was demonstrated by measuring air samples collected from various locations such as a petrol station, parking garage and ambient air.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Fine Doppler Frequency Estimation of Windowed Complex Sinusoidal Signals
           for Radar Systems
    • Pages: 3860 - 3870
      Abstract: This article presents a new fine Doppler frequency estimation method of complex sinusoidal signals for radar systems. The proposed method can work with nonrectangular window functions to suppress the interference signal caused by the undesired spectral leakage. This method is based on an interpolation curve that uses the sum and difference of two discrete Fourier transform (DFT) magnitudes, and can estimate an accurate frequency between the interpolation curve samples through precise linear approximation. The proposed method exhibits lower computational complexity than the conventional method, which compensates the bias of an arbitrary window function and exhibits high performance. Thus, the proposed method is applicable to radar systems requiring fast processing time. Furthermore, the simulation results show that the proposed method outperforms the conventional method for various window functions.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Vibrating FRET-Based Nanomechanical Sensor Preparation and
           Characterization for Environmental Monitoring Applications
    • Pages: 3871 - 3878
      Abstract: In this study, the recently introduced graphene nanomechanical sensor design based on the vibrating Förster resonance energy transfer (VFRET) was experimentally demonstrated for the first time. VFRET was realized by the dynamic FRET between quantum dots on vibrating graphene layers. Graphene nanomechanical membranes were fabricated and experimentally analyzed with quantum dots donor and acceptor material loads. The loaded membranes were paired in the VFRET based sensor device structure. The FRET and the VFRET tests were applied to the prepared devices, experimental challenges and the device development solutions were utilized. Novel technology and the future applications of the VFRET based sensor examples were discussed with the advanced capabilities of sensing nano- and micro-scale particles around the device for the real-time point-of-care (PoC), and the environmental monitoring applications include imaging, monitoring and tracking of intra- or inter- cellular mechanical processes as the nanophotonic applications by exploiting the VFRET based nanoscale sensor design.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • SCLA-RTI: A Novel Device-Free Multi-Target Localization Method Based on
           Link Analysis in Passive UHF RFID Environment
    • Pages: 3879 - 3887
      Abstract: RFID, a key enabling technology of Internet of Things (IoT), has a broad development prospect in the device-free localization (DFL). The RFID-based radio tomographic imaging (RTI) method has received great attention from scholars, owing to its properties of good real-time performance, low computational complexity, high localization accuracy. However, RTI suffers from the influence of multipath and noise, which results in the artifacts and false targets appearing in the imaging result and, in turn, decreases the positioning performance significantly, especially when there are multiple targets. Therefore, how to recognize the locations and number of true targets from the imaging result becomes an urgent problem in multi-target positioning. To solve this problem, we propose a novel scanning circle link analysis (SCLA-RTI) method to eliminate false targets and locate real targets in UHF RFID scenario. First, scanning circle method is introduced to detect the line-of-sight (LOS) links through the detecting region of the local maximum pixel in the imaging result. After feature analysis and selection of the RSS change of links, the random forest classifier is used to determine the location and number of real targets. Experiments demonstrate that SCLA-RTI method has superior performance in an indoor environment, which can accurately identify true and false targets and obtain high localization accuracy.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • J-RCA: A Joint Routing and Charging Algorithm With WCE Assisted Data
           Gathering in Wireless Rechargeable Sensor Networks
    • Pages: 3888 - 3899
      Abstract: With the development of wireless charging technology, the wireless energy replenishment using mobile wireless charging equipment (WCE) has been a promising way to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). However, many existing researches have neglected some dynamic factors (e.g. topology changes in routing selection) which lead to low energy efficiency of charging tour. In this paper, a joint routing and charging algorithm (J-RCA) with WCE assisted data gathering is proposed to increase the network lifetime (NL). For routing, an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is presented to balance energy consumption of nodes, which can fully consider the difference between indexes to select the optimal next hop. In terms of charging, we present a two-step charging scheme that includes calculating the shortest Hamilton cycle of original charging nodes and selecting the other nodes called passer-by nodes. That is, when WCE moves along the shortest Hamilton cycle, it considers both routing requirements and spatial location to pick up some passer-by nodes for energy replenishment with the extra energy. In this way, the energy carried by WCE can be fully utilized and NL can be effectively extended. The simulation results demonstrate that the proposed algorithm has certain advantages in prolonging NL and improving transmission reliability.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Application of a Multi-Chamber Fusion Algorithm Based on the Fick’s
           Theorem to Quantify Soil Respiration
    • Pages: 3900 - 3915
      Abstract: Reliable, low-cost and accurate monitoring of soil respiration is an important challenge that must be solved to fully understand the contribution of soil dynamics to climate change; however, accuracy obtained by single-chamber is insufficient. This article proposes a multi-chamber fusion method for integrating multi-source information measured using low-cost sensors. The proposed algorithm initially uses Fick’s first diffusion law to calculate the soil carbon flux values for five chambers, followed by multi-layer decomposition of a wavelet packet transform (WPT) to eliminate high-frequency noise. Then, the basic probability assignment (BPA) of each sensor is calculated via the Biggest-smallest Approach Degree and used to assign the Dempster-Shafer (D-S) fusion subjected BPA to determine the distribution weight of each gas chamber. Finally, the decision layer fusion is defined as the product of the chamber weights and feature signals obtained by wavelet multi-layer decomposition. The performance of the proposed algorithm was evaluated against existing algorithms using real data collected using a low-cost prototype device in an evergreen broad-leaved forest environment and compared to the data generated by an expensive commercial device. The proposed algorithm significantly improved the accuracy of soil respiration monitoring for the low-cost prototype device.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • A Survey on MAC Protocol Approaches for Underwater Wireless Sensor
           Networks
    • Pages: 3916 - 3932
      Abstract: Since communications in the Underwater Wireless Sensor Networks (UWSNs) have limited resources and capabilities, designing an efficient and reliable Media Access Control (MAC) protocol for UWSNs faces many challenges. UWSNs have limited bandwidth, power, memory, long propagation delay, high Bit Error Rate (BER), and unreliable communication. Current MAC protocols that have been designed for Terrestrial Wireless Sensor Networks (TWSNs) are not suitable for UWSNs. While the software-based mechanisms promise many benefits to TWSN, few proposal designs introduced MAC protocols based on software-based mechanisms for UWSNs. The software-based approach is a flexible one that helps to change system parameters of all layers with no need for additional hardware. In this article, we focus on presenting a survey of the state of the art of the recent development of MAC protocols for UWSNs from recent literature followed by a discussion of the characteristics and limitations of each MAC protocol. This article also evaluates the channel transmission and compares the performance of four different MAC protocol approaches to identify the most suitable one for the underwater oil/gas pipelines application in terms of end-to-end delay, energy consumption, Packet Delivery Ratio (PDR), the total number of collisions, and throughput using different network loads.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Optimization of Energy Utilization in Cognitive UAV Systems
    • Pages: 3933 - 3943
      Abstract: Wireless communication with unmanned aerial vehicle (UAV) has attracted much attention due to its flexibility and low-cost in providing wireless connectivity. In this article, we investigate the spectrum sharing between UAV and terrestrial wireless communication. Two scenarios are considered in this work. Scenario 1: the UAV is energy sufficient to transmit the required data. In this case, our objective is to minimize the time that is needed to complete data transmission. Scenario 2: the battery of the UAV is energy insufficient. In this case, the goal becomes maximizing the size of data that can be transmitted. Different scenarios require different placement of the UAV. When the UAV flies towards the primary user (PU), the sensing result will be more accurate and more opportunities can be utilized to transmit data. However, the interference to primary receiver (PR) and the propulsion energy due to UAV’s movement will be increased. In this article, the sensing performance, the power allocation and the UAV’s position are jointly optimized to satisfy different requirements of the UAV system. To efficiently utilize the licensed spectrum, a hybrid transmission mode is proposed for the UAV system, in which sensing and power control are jointly employed to protect the PR. Then, efficient iterative algorithms are proposed to solve the optimization problems. Simulations results show that proposed designs can significantly reduce the time that is required to complete data transmission in energy sufficient scenario and enhance the size of data that can be transferred in energy insufficient scenario.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Architecture Design of Distributed Redundant Flight Control Computer Based
           on Time-Triggered Buses for UAVs
    • Pages: 3944 - 3954
      Abstract: With the rapid development of unmanned aerial vehicles (UAVs) performance and their increasingly complex tasks, the current centralized flight control computer (FCC) architecture based on the event-triggered mechanism cannot meet the requirements of openness, reliability, and real-time processing. In view of the development trends and technical requirements of future UAVs, a distributed redundant FCC architecture design method based on commercial off-the-shelf (COTS) technology and a set of dual-channel redundant time-triggered buses is proposed. With this architecture, new UAV flight control design methods are then proposed, such as distributed fault-tolerant management, Byzantine fault-tolerant design based on dual-core self-monitoring, and an open/integrated design method for airborne multi-sensor information processing and fusion. According to the characteristics of the redundant FCC based on the time-triggered buses, a distributed task scheduling and communication model is established, and an optimal static scheduling and real-time analysis algorithm of distributed tasks based on a search tree is proposed. Finally, the real-time performance and reliability of the FCC are analyzed and verified. The verification results show that, compared with the centralized FCC architecture based on the event-triggered mechanism, the proposed UAV FCC architecture has better task schedulability and system scalability. Moreover, it has a higher task reliability under the same redundancy configuration, which means that it can provide a distributed, synchronous fault-tolerant and redundant reconfigurable technology platform for future UAV FCCs.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Comments on “An Improved Sparse Regularization Method for Weak Fault
           Diagnosis of Rotating Machinery Based Upon Acceleration Signals”
    • Pages: 3955 - 3955
      Abstract: The aim of this note is to point out errors and provide enough credit to the original references in the article [1]. In the above article [1], Section III-C, the original references [49] and [51] should be cited on Lemma 1 and its proof, Theorem 1 and its proof. In Section III-B, the corrected title of the Table I is “The convex penalty and smoothing non-convex penalty functions.” In addition, for (9), $hat {x}=arg min limits _{x} left{{F(x)=frac {1}{2}left { {y-x} }right _{2}^{2} +sum limits _{i=1}^{N} {lambda _{0} phi (v(x)_{i};a_{0})} +sum limits _{i=1}^{N-1} {lambda _{1} phi left({left[{ boldsymbol {D}x}right]_{i};a_{1} }right)} }right}$ , the smoothing nonconvex penalty functions (e.g., $phi _{varepsilon } (x;a)=frac {1}{a}log (1+asqrt {x^{2}+varepsilon }),varepsilon>0$ in the third column of Table I) should be used for $phi left({left[{ boldsymbol {D}x}right]_{i};a_{1} }right)$ in (9) and $v(x)_{n} $ is the singular values of the low-rank matrix $x$ . To clarify the derivation procedures, the credit of original references [64] and [65] need to be cited on Section III-D, Theorem 2 and its proof, and Appendix, for better readability.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Corrections to “Enhanced Performance Love Wave Magnetic Field Sensors
           With Temperature Compensation”
    • Pages: 3956 - 3956
      Abstract: In the above article [1], by Yang et al., published in IEEE Sensors Journal, volume 20, issue 19, pp. 11292–11301, in 2020, we would like to correct the coauthor Daniel Lacour’s biography on page 11300.
      PubDate: Feb.1, 1 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • IEEE Sensor Journal cover/frontispiece competition
    • Pages: 3957 - 3957
      Abstract: Presents information on the IEEE Sensor Journal cover/frontispiece competition.
      PubDate: Feb.1, 2021
      Issue No: Vol. 21, No. 3 (2021)
       
  • Introducing IEEE Collabratec
    • Pages: 3958 - 3958
      Abstract: Advertisement, IEEE. IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical interests, or career pursuits. You can also create and share a professional identity that showcases key accomplishments and participate in groups focused around mutual interests, actively learning from and contributing to knowledgeable communities. All in one place! Learn about IEEE Collabratec at ieeecollabratec.org.
      PubDate: Feb.1, 2021
      Issue No: Vol. 21, No. 3 (2021)
       
 
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