Subjects -> INSTRUMENTS (Total: 63 journals)
Showing 1 - 16 of 16 Journals sorted by number of followers
International Journal of Remote Sensing     Hybrid Journal   (Followers: 151)
IEEE Sensors Journal     Hybrid Journal   (Followers: 111)
Remote Sensing of Environment     Hybrid Journal   (Followers: 96)
Journal of Applied Remote Sensing     Hybrid Journal   (Followers: 88)
Remote Sensing     Open Access   (Followers: 57)
Modern Instrumentation     Open Access   (Followers: 57)
International Journal of Remote Sensing Applications     Open Access   (Followers: 49)
International Journal of Instrumentation Science     Open Access   (Followers: 41)
Experimental Astronomy     Hybrid Journal   (Followers: 39)
Measurement and Control     Open Access   (Followers: 36)
Photogrammetric Engineering & Remote Sensing     Full-text available via subscription   (Followers: 33)
Journal of Instrumentation     Hybrid Journal   (Followers: 31)
Remote Sensing Science     Open Access   (Followers: 30)
Applied Mechanics Reviews     Full-text available via subscription   (Followers: 27)
Review of Scientific Instruments     Hybrid Journal   (Followers: 20)
European Journal of Remote Sensing     Open Access   (Followers: 18)
Flow Measurement and Instrumentation     Hybrid Journal   (Followers: 15)
Journal of Sensors and Sensor Systems     Open Access   (Followers: 12)
Transactions of the Institute of Measurement and Control     Hybrid Journal   (Followers: 12)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 9)
Videoscopy     Full-text available via subscription   (Followers: 9)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 8)
Metrology and Measurement Systems     Open Access   (Followers: 8)
Science of Remote Sensing     Open Access   (Followers: 7)
Imaging & Microscopy     Hybrid Journal   (Followers: 7)
Instrumentation Science & Technology     Hybrid Journal   (Followers: 7)
Microscopy     Hybrid Journal   (Followers: 7)
International Journal of Metrology and Quality Engineering     Full-text available via subscription   (Followers: 6)
Computational Visual Media     Open Access   (Followers: 5)
Measurement : Sensors     Open Access   (Followers: 5)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal   (Followers: 5)
Optoelectronics, Instrumentation and Data Processing     Hybrid Journal   (Followers: 5)
Journal of Astronomical Instrumentation     Open Access   (Followers: 4)
IEEE Sensors Letters     Hybrid Journal   (Followers: 4)
Sensors and Materials     Open Access   (Followers: 4)
Journal of Optical Technology     Full-text available via subscription   (Followers: 4)
Journal of Medical Devices     Full-text available via subscription   (Followers: 4)
Measurement Techniques     Hybrid Journal   (Followers: 3)
Sensors International     Open Access   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 3)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Journal of Instrumentation Technology & Innovations     Full-text available via subscription   (Followers: 2)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 2)
Geoscientific Instrumentation, Methods and Data Systems     Open Access   (Followers: 2)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Medical Devices & Sensors     Hybrid Journal   (Followers: 1)
Invention Disclosure     Open Access   (Followers: 1)
Journal of Research of NIST     Open Access   (Followers: 1)
Geoscientific Instrumentation, Methods and Data Systems Discussions     Open Access   (Followers: 1)
International Journal of Measurement Technologies and Instrumentation Engineering     Full-text available via subscription   (Followers: 1)
Journal of Medical Signals and Sensors     Open Access   (Followers: 1)
Instruments and Experimental Techniques     Hybrid Journal   (Followers: 1)
Journal of Vacuum Science & Technology B     Hybrid Journal   (Followers: 1)
Metrology and Instruments / Метрологія та прилади     Open Access  
Measurement Instruments for the Social Sciences     Open Access  
Труды СПИИРАН     Open Access  
Standards     Open Access  
Jurnal Informatika Upgris     Open Access  
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
Devices and Methods of Measurements     Open Access  
EPJ Techniques and Instrumentation     Open Access  
Documenta & Instrumenta - Documenta et Instrumenta     Open Access  
Similar Journals
Journal Cover
IEEE Sensors Journal
Journal Prestige (SJR): 0.619
Citation Impact (citeScore): 3
Number of Followers: 111  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1530-437X
Published by IEEE Homepage  [228 journals]
  • IEEE Sensors Council

    • Free pre-print version: Loading...

      Abstract: Presents an editorial which examines the current state of the journal and planned future directions for the publication.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • IEEE Sensors Journal

    • Free pre-print version: Loading...

      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: June15, 1 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Highly Stable and Flexible Ca2+ Ion-Selective Sensor Based on Treated
           PEDOT:PSS Transducing Layer

    • Free pre-print version: Loading...

      Authors: Chani Park;Hyosang Yoon;Md. Abu Zahed;Shipeng Zhang;Sanghyuk Yoon;Dongkyun Kim;Daeheum Kim;Jaeyeong Park;
      Pages: 11213 - 11221
      Abstract: In this work, a highly stable Ca2+ ion-selective sensor was successfully developed by depositing a poly(3,4-ethylenedioxythiophene): poly(styrene-sulfonate) (PEDOT:PSS) layer on top of a carbon paste electrode as an ion-electron transducing layer. The PEDOT:PSS was treated by a co-solvent of ethylene glycol (EG) using the bath-sonication technique to enhance electrical and electrochemical characteristics, and finally coated with Ca2+ ion-selective membrane cocktail. The developed Ca2+ ion-selective electrode (Ca2+-ISE) showed excellent electrochemical properties by much-improved charge transfer kinetics with remarkable electric conductivity. The fabricated Ca2+-ISE exhibited an excellent sensitivity of 37.7 mV/decade (n = 4) in the range from 10−4 to 10−1 M with a rapid response (< 20 seconds). Moreover, the EG-treated PEDOT:PSS (PEDOT:PSS-EG) based ISE showed negligible responses for primary interfering ions of human biofluid samples, proving significant potential selectivity. Furthermore, the negligible drift of open circuit potential ( $2.78~ { mu }text{V}$ /s) proved the stability of the PEDOT:PSS-EG compared to pristine PEDOT:PSS( $10.63~{ mu }text{V}$ /s) based ISE. Based on these analyses, it can be expected that the organic solvent treatment on PEDOT:PSS will pave the way for long-term monitoring of other biochemical compounds.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Guest Editorial Special Issue on Selected Papers From the IEEE Sensors
           2020 Conference

    • Free pre-print version: Loading...

      Authors: Gijs Krijnen;Rolland Vida;
      Pages: 11222 - 11222
      Abstract: As the flagship conference series of the IEEE Sensors Council, IEEE SENSORS JOURNAL receives each year several hundreds of paper submissions, on various topics related to sensing technologies, sensors devices, their communications and applications. The IEEE Sensors 2020 Conference was initially planned to be held as a traditional face-to-face event in Rotterdam, The Netherlands, between October 25 and 28, 2020. However, the Covid-19 pandemic, and the gathering and travel restrictions that were implemented all over the world obliged us to change the format of the conference, and make it a fully virtual, on-line event.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Development of a Flexible Wireless ECG Monitoring Device With Dry Fabric
           Electrodes for Wearable Applications

    • Free pre-print version: Loading...

      Authors: Simin Masihi;Masoud Panahi;Dinesh Maddipatla;Anthony J. Hanson;Stephen Fenech;Lucas Bonek;Nicholas Sapoznik;Paul D. Fleming;Bradley J. Bazuin;Massood Z. Atashbar;
      Pages: 11223 - 11232
      Abstract: A flexible wireless electrocardiogram (ECG) device, integrated with fabric was fabricated for monitoring physiological signals in wearable biomedical applications. The ECG device consists of dry electrodes and a readout module. The dry electrode was fabricated by depositing multi-walled carbon nanotube (MWCNTs)/polydimethyl-siloxane (PDMS) composite on a thermoplastic polyurethane (TPU) substrate with screen printed silver (Ag) layer. The readout module with wireless data transmission capability was designed and fabricated on a flexible polyimide substrate. The ECG device was attached to fabric, and its performance was investigated by measuring the ECG signals and comparing them with the results of conventional wet Ag/AgCl electrode. It was observed that the device demonstrated similar performance in terms of signal intensity and correlation when compared to the conventional wet ECG electrodes. Signal-to-noise-ratio (SNR) analysis clearly showed that the dry electrodes with an average SNR of 23.1 dB, perform better than the commercially available Ag/AgCl electrodes (SNR of 21.2 dB). In addition, the capability of the fabric based dry electrodes to measure ECG signals during the physiological activities under exercise motions was studied. This enabled understanding the effect of motion artifacts on the dry electrode response and allowed comparing the obtained ECG signals with the responses of conventional wet ECG electrodes (subjected to similar conditions). It was observed that, even though dry electrodes didn’t use any kind of adhesive gel for measuring the ECG signals, they performed very similar to conventional wet electrodes. The results obtained clearly demonstrated the feasibility of employing fabric based flexible dry ECG electrodes for continuous monitoring of ECG signals in health care applications and can potentially replace the wet electrodes.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Carbon Nanotube-Functional Polymer Composite Film for Low-Power Indoor
           CO₂ Monitoring

    • Free pre-print version: Loading...

      Authors: Zachary A. Siefker;Xikang Zhao;Nikhil Bajaj;Abhi Boyina;James E. Braun;George T.-C. Chiu;Bryan W. Boudouris;Jeffrey F. Rhoads;
      Pages: 11233 - 11240
      Abstract: Indoor air quality (IAQ) has been a growing concern in recent years, only to be expedited by the COVID-19 pandemic. A common provisional measure for IAQ is carbon dioxide (CO2), which is commonly used to inform the ventilation control of buildings. However, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. This work presents a polymer composite-based chemiresistive CO2 sensor that leverages branched poly(ethylenimine) (PEI) and poly(ethylene glycol) (PEG) as the CO2 absorbing layer. This polymer blend was incorporated with single wall carbon nanotubes (CNT), which serve as the charge carriers. Prototype sensors were assessed in a bench-top environmental test chamber which varied temperature (22–26 °C), relative humidity level (20–80%), CO2 concentration (400–20,000 ppm), as well as other gas constituents to simulate typical and extreme indoor conditions. The results indicate that the proposed system could ultimately serve as a low-power alternative to current commercially available technologies for indoor CO2 monitoring.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Biodegradable, Flexible and Transparent Tactile Pressure Sensor Based on
           Rubber Leaf Skeletons

    • Free pre-print version: Loading...

      Authors: Anastasia Koivikko;Vilma Lampinen;Kyriacos Yiannacou;Vipul Sharma;Veikko Sariola;
      Pages: 11241 - 11247
      Abstract: Capacitive sensors have many applications in tactile sensing, human-machine interfaces, on-body sensors, and patient monitoring. Particularly in biomedical applications, it would be beneficial if the sensor is disposable and readily degradable for efficient recycling. In this study, we report a biodegradable capacitive tactile pressure sensor based on sustainable and bio resourced materials. Silver-nanowire-coated rubber tree leaf skeletons are used as transparent and flexible electrodes while a biodegradable clear tape is used as the dielectric layer. The fabricated sensor is sensitive and can respond to low pressures (7.9 mN when pressed with a probe with a surface area of 79 mm2 / 0.1 kPa) ranging to relatively high pressures (37 kPa), with a sensitivity up to $approx ,,4.5times 10 ^{-3}$ kPa−1. Owing to all bio resourced constituents, the sensor is biodegradable and does not create electronic waste.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • High Temperature Gradient Pirani Micro-Sensor Designed and Tested for
           Aerodynamic Wall Pressure Measurement

    • Free pre-print version: Loading...

      Authors: Cecile Ghouila-Houri;Abdelkrim Talbi;Romain Viard;Thomas Arnoult;Munique Kazar-Mendes;Aurélien Mazzamurro Mazzamurro;Quentin Gallas;Eric Garnier;Alain Merlen;Philippe Pernod;
      Pages: 11248 - 11255
      Abstract: This paper presents and discusses the first demonstration of aerodynamic wall pressure measurement using a thermal Pirani effect based micro-sensor. The thermal micro-sensor is designed as a suspended micro-hot-wire separated from the substrate by a nanoscale gap, and mechanically supported by perpendicular micro-bridges. The micro-sensor was calibrated for pressure measurement from 10 kPa to 800 kPa. It was then used as aerodynamic wall pressure sensor in a turbulent boundary layer wind tunnel and successfully measured the 1.1 kPa depression occurring in the wind tunnel when the flow velocity goes up to 40 m/s.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Modular Three-Dimensional Hall Effect Sensor for Performance
           Optimization

    • Free pre-print version: Loading...

      Authors: Eng-Huat Toh;Yongshun Sun;Ping Zheng;Mathew Shajan;Patrick Cao;Mohd Nurul Islam;Jian-Yi Wong;Praveen Arikath;Ruchil Jain;Tam Lyn Tan;Elgin Quek;
      Pages: 11256 - 11263
      Abstract: This paper presents three dimensional (3-D) Hall sensors that are capable of detecting magnetic fields in three axis directions and are realized on 180BCDLite Ⓡ technology with one or two mask adders. The 3-D Hall sensor architecture adopts a modular approach that enables optimization of the planar and vertical Hall devices. The geometries and doping profiles of the Hall devices can be designed for various performance specifications. The planar Hall devices are engineered independently to achieve either a high current–related sensitivity ${S}_{I} >385$ V/(A.T) or a high voltage–related sensitivity ${S}_{V} >50$ mV/(V.T). For the vertical Hall devices, ${S}_{V}$ up to 50 mV/(V.T) are demonstrated. The modular design enables flexibility for designers to provide a System-on-Chip (SoC) to meet the needs for a variety of magnetic sensing applications at a low-cost.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Lossy Mode Resonances Generated in Planar Configuration for Two-Parameter
           Sensing

    • Free pre-print version: Loading...

      Authors: Omar Fuentes;Jesus M. Corres;Ismel Domínguez;Ignacio Del Villar;Ignacio R. Matias;
      Pages: 11264 - 11270
      Abstract: This work shows a new sensor structure for simultaneous measurement of two parameters, temperature and refractive index. The optical configuration consists of incidence of light on the edge of a soda-lime coverslip fully coated with a CuO thin film and partially coated with a PDMS thick layer. This planar configuration permitted to generate two separated lossy mode resonances (LMRs): one centered at 600 nm and the other at 1000 nm. The second resonance is induced by the PDMS layer and it can be used to measure the temperature due to its high thermo-optic coefficient (the sensitivity is −1.75 nm/°C in the temperature range from 20 to 40 °C), whereas the first resonance is used for sensing refractive index with sensitivity of 1460 nm/RIU in the refractive index range from 1.3328 and 1.37. Finally, a calibration test was carried out using a calibrated oil series with refractive index ranging from 1.33 to 1.36. This work demonstrates the possibility of generating multiples resonances in a single structure as simple as a coverslip, which can be used as a multi-parameter interchangeable sensor, especially suitable for biological applications or the detection of heavy metals in water.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Near-Infrared, Depth, Material: Towards a Trimodal Time-of-Flight Camera

    • Free pre-print version: Loading...

      Authors: Miguel Heredia Conde;Thomas Kerstein;Bernd Buxbaum;Otmar Loffeld;
      Pages: 11271 - 11279
      Abstract: Time-of-Flight cameras are active sensors that are able to capture both the light intensity reflected by each observed point in the scene and the distance between these points and the camera. Enhancing intensity images with a depth modality enables capturing surfaces in 3D and boosts the applicability of these sensors. Nevertheless, high-level information still needs to be extracted from the data stream in order to accomplish high-level tasks, like recognition or classification. Ideally, the semantic gap between sensor output and high-level requirements should be as small as possible, in order to reduce both computational cost and failure probability. An additional depth modality helps in this regard, but there are further cues that can be seen by a ToF sensor that have remained underexploited so far. In this paper we take the first steps towards a trimodal ToF camera, which adds a valuable material modality to the classical intensity and depth modalities. To this end, ToF raw data is used to obtain Fourier samples of the material impulse response function (MIRF) to modulated illumination. The MIRF depends on the surface- and sub-surface-level scattering mechanisms of the material and, thus, can be used to identify materials of different nature. Consequently, distinctive feature vectors can be obtained from the Fourier measurements. Additionally, including the incidence angle in the feature vectors allows capturing the MIRF dependency on this parameter. Experimental validation confirms the feasibility of this approach. We also constructed a live demonstrator of our trimodal ToF camera.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Optical Sensing of Chlorophyll(in) With Dual-Spectrum Si LEDs in SOI-CMOS
           Technology

    • Free pre-print version: Loading...

      Authors: Satadal Dutta;Peter G. Steeneken;Gerard J. Verbiest;
      Pages: 11280 - 11289
      Abstract: Small and low-cost chlorophyll sensors are popular in agricultural sector and food-quality control.Combining such sensors with silicon CMOS electronics is challenged by the absence of silicon-integrated light-sources.We experimentally achieve optical absorption sensing of chlorophyll based pigments with silicon (Si) micro light-emitting diodes (LED) as light-source, fabricated in a standard SOI-CMOS technology.By driving a Si LED in both forward and avalanche modes of operation,we steer its electroluminescentspectrum between visible (400–900 nm) and near-infrared (~1120 nm). For detection of chlorophyll in solution phase, the dualspectrum light from the LED propagates vertically through glycerol micro-droplets containing sodium copper chlorophyllin at varying relative concentrations. The transmitted light is detected via an off-chip Si photodiode. The visible to near-infrared color ratio (COR) of the photocurrent yields the effective absorption coefficient. We introduce the LED-specificmolar absorption coefficient as ametric to compute the absolute pigment concentration (~0.019 ± 0.006 mol L−1) and validate the results by measurements with a hybrid spectrophotometer. With the same sensor, we also show noninvasive monitoring of chlorophyll in plant leaves. COR sensitivities $sim 3.9 times 10^{4}$ mol−1L and $sim 5.3 times 10^{4}$ mol−1L are obtained for two leaf species, where light from the LED propagates diffusely through the thickness of the leaf prior to detection by the photodiode. Our work demonstrates the feasibility of realizing fully CMOS-integrated optical sensors for biochemical analyses in food sector and plant/human health.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A CDM-WDM Interrogation Scheme for Massive Serial FBG Sensor Networks

    • Free pre-print version: Loading...

      Authors: Marek Götten;Steffen Lochmann;Andreas Ahrens;Eric Lindner;Johan Vlekken;Jan Van Roosbroeck;
      Pages: 11290 - 11296
      Abstract: Structural health monitoring and other smart structures gain an increased attention which can be satisfied by the quasi-distributed optical sensing approach. Serial fiber optic sensors, such as fiber-Bragg gratings (FBGs) provide among others small size, immunity to electro-magnetic interference, an accurate sensing accuracy and a high multiplexing capability to increase the amount of sensing points in an optical sensor network. Different multiplexing approaches demonstrated a limited number of FBGs, such as time-division multiplex, optical frequency domain refractometry or frequency shifted interferometry. This work introduces a code-division multiplex (CDM) - wavelength-division multiplex (WDM) interrogator for massive serial FBG sensor networks. The interrogation of 4000 serial sensors in a network with a length of 113m, 200 identical WDM sections over a length of 200m and a network with 1000 sensors and a length of 1.6km show a massive multiplexing capability of up to 16000 sensors and possible network lengths of several kilometers. Strain measurements with FBGs in rear sections prove the sensing applicability of the CDM-WDM scheme.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Fiber Bragg Grating Sensors-Based Thermometry of Gold Nanorod-Enhanced
           Photothermal Therapy in Tumor Model

    • Free pre-print version: Loading...

      Authors: Leonardo Bianchi;Rachael Mooney;Yvonne Cornejo;Caitlyn Hyde;Emiliano Schena;Jacob M. Berlin;Karen Aboody;Paola Saccomandi;
      Pages: 11297 - 11306
      Abstract: This work proposes the use of femtosecond laser-written fiber Bragg grating (FBG) sensors for internal temperature monitoring of tumors undergoing gold nanorod (AuNR)-mediated photothermal therapy (PTT). Arrays of sub-millimetric FBGs enabled an accurate and quasi-distributed temperature measurement within subcutaneous breast tumors in mice. Furthermore, FBGs permitted to investigate the laser-tissue interaction and AuNR-assisted photothermal enhancement on cancerous tissue exposed to 940 nm and 1064 nm radiations. The introduction of the tumor-localized AuNRs resulted in an overall increase of 13 °C of the mean temperature change, compared to control, in case of 1064 nm, while ~6 °C in case of 940 nm. This sensing solution allows the minimally invasive measurement of the internal tumor temperature under AuNR-assisted PTT. This feasibility study sets the basis for the evaluation of the thermal outcome mediated by nanoparticles under different laser sources.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Toward a Stochastic Drift Simulation Model for Graphene-Based Gas Sensors

    • Free pre-print version: Loading...

      Authors: Sebastian A. Schober;Cecilia Carbonelli;Alexandra Roth;Alexander Zoepfl;Caterina Travan;Robert Wille;
      Pages: 11307 - 11316
      Abstract: Monitoring air quality in cities as well as indoors has become an important topic in recent years. Being easily and densely deployable and rather low-cost, chemiresistive gas sensors indicate a feasible technology for this problem, especially if they are assembled as e-noses tracking multiple gases in one device. Nonetheless, the long-term stability of such sensors poses a severe problem for their measurement accuracy, which substantiates the need for drift compensation procedures as well as robust algorithms in the gas prediction process. In order to test such drift compensation methods and to generate synthetic data for algorithm training, a simulation model would be highly useful. In this paper, we present our investigations towards a simulation framework aiming at generating typical sensor responses of a graphene-based gas sensor with an emphasis on its drift behavior. Different drift models are studied, implemented and compared to real measurement data from a lab setup. The evaluation shows promising results when compared to experiments from a real chemiresistive sensor to which the model was parametrized. By using a second setup with fundamentally different heating characteristics we observed that recalibration is a necessary step to generalize to different sensing modes and to ensure the overall quality of the simulation data. Overall, these investigations provide a proper basis towards tackling the challenges in drift simulation.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Energy Efficient Pixel-Parallel Read-Out Circuits for Digital Image
           Sensors Using Cross-Layer Pixel Depth Control

    • Free pre-print version: Loading...

      Authors: Mandovi Mukherjee;Burhan Ahmad Mudassar;Minah Lee;Edward Lee;Saibal Mukhopadhyay;
      Pages: 11317 - 11327
      Abstract: Digital pixel sensors with pixel-parallel readout provide opportunities for high frame rate and high resolution in 3D integrated camera platforms with in-sensor machine learning. The active energy consumption of the digital pixels at the sensor front end is a key performance parameter for applications involving smart cameras. This work proposes a novel digital pixel design that utilizes cross-layer feedback to improve active energy efficiency of pixels in image sensors. An algorithm-circuit co-simulation of $336times256$ image sensor in 65nm CMOS for an object detection task shows 58% active energy saving in digital pixel circuits, while maintaining accuracy of end task.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Artefact-Suppressing Analog Spike Detection Circuit for Firing-Rate
           Measurements in Closed-Loop Retinal Neurostimulators

    • Free pre-print version: Loading...

      Authors: Andreas Erbslöh;Reinhard Viga;Karsten Seidl;Rainer Kokozinski;
      Pages: 11328 - 11335
      Abstract: The aim of this research is to investigate low-power circuit concepts for the hardware implementation of an adaptively controlled stimulator for future retinal implants. For this specific application purpose, the circuit complexity must be low, while at the same time the functionality is extended. This paper presents the implementation of an analog spike detection circuit to detect spikes from extracellular recordings and to perform electrode individual firing-rate measurements in a spatially high-density electrode array, which has a reduced circuit complexity compared to the widely-used nonlinear energy operator (NEO) and allows stronger suppression of local oscillations following the retinal remodeling. The module is verified by emulating extracellular activities using the Hodgkin-Huxley model. This recording-unit is integrated into an eight-channel closed-loop-neurostimulator prototype. It dissipates $11.4 ~mu text{W}$ and requires an area of 0.066 mm2 by using a 350 nm CMOS process.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Indoor Object Sensing Using Radio-Frequency Identification With Inverse
           Methods

    • Free pre-print version: Loading...

      Authors: Guoyi Xu;Pragya Sharma;David Lee Hysell;Edwin Chihchuan Kan;
      Pages: 11336 - 11344
      Abstract: Indoor device-free object sensing can be utilized in many applications such as occupant-centered control of building utilities and assisted living. To satisfy the mathematical requirement of many observation points for arbitrary indoor layout and furnishing, radio-frequency identification (RFID) offers a low-cost solution with a plethora of maintenance-free passive tags. Both the received signal strength indicator and carrier phase from tag backscattering can be assembled to generate the voxel reflectivity distribution by the inverse method. We adopt the regularized truncated pseudo-inverse method, and devise the strategies for optimal selection of the critical parameters. Compared with conventional matched filtering, our method is more robust against random noises in the collected data. An experimental prototype was established to evaluate the system robustness and performance, and the dipole antennas were used to replace patch antennas to enhance the system signal-to-noise ratio (SNR). The regularized truncated pseudo-inverse method together with the improved system SNR has successfully shown higher locating accuracy and lower computational cost.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Unconventional Protocol for SAW Sensor: Multi-Physic Response Enrichment
           in Liquid Medium

    • Free pre-print version: Loading...

      Authors: Maxence Rube;Ollivier Tamarin;Martine Sebeloue;Idris Sadli;Hamida Hallil;Laurent Linguet;Dominique Rebière;Corinne Dejous;
      Pages: 11345 - 11354
      Abstract: This paper presents a new paradigm in the analysis and methodology for Surface Acoustic Wave (SAW) devices for sensing application in a liquid medium. This new protocol encourages the analysis of the electrical and electro-acoustic behavior of the sensor rather than limiting the measurement interest to mechanical interactions between the sensor and the adjacent medium. This work focuses on a “holistic” approach, mainly seeking to achieve an improved understanding of the sensing phenomena and to enrich the global response and its analysis. Even though this work does not aim to enhance the general sensitivity, it could be used to improve the design of the sensor for a specific target. Our methodology is based on the separation of electrical and mechanical mechanisms with post-treatment analysis of experimental responses, using jointly frequency and time domains analysis. It is supported by an unconventional energy flow chart of the acoustic wave propagation in the SAW device which helped us extracting a few hypotheses. This new paradigm allows us to get information related to both dielectric and mechanical parameters of a liquid sample using a single sensor, thus enriching the response at no cost. In this paper, we applied the method to Love wave sensors with different technological characteristics influencing the electric and acoustic response, and numerous fluid solutions for a large diversity of dielectric constants and mechanical properties. The large set of measurement allows the extraction of both dielectric and mechanical parameters of a liquid medium on the Love wave sensor surface.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • RESPIRE++: Robust Indoor Sensor Placement Optimization Under
           Distance Uncertainty

    • Free pre-print version: Loading...

      Authors: Onat Gungor;Tajana S. Rosing;Baris Aksanli;
      Pages: 11355 - 11363
      Abstract: Sensor placement in wireless sensor networks (WSN) aims to maximize coverage while minimizing total deployment cost. However, existing coverage-only approaches do not consider the robustness of the entire system where sensors may break down or malfunction. In this paper, we first propose a robustness-aware sensor placement approach by constructing a multi-objective optimization model. Our experiments demonstrate that this method increases the robustness of a WSN by up to 50%, with 201% higher probability of monitoring the entire environment as compared to the state-of-the-art coverage-only approach. The paper further improves the proposed method by introducing a robust optimization based sensor placement approach which considers the distance uncertainty between a sensor and a target. We show that this improved model increases the probability of target detection by up to 77% compared to state-of-the-art coverage-only approach.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Monitoring of Electric Buses Within an Urban Smart City Environment

    • Free pre-print version: Loading...

      Authors: José Javier Astrain;Francisco Falcone;Antonio J. Lopez-Martin;Pablo Sanchis;Jesús Villadangos;Ignacio R. Matias;
      Pages: 11364 - 11372
      Abstract: A practical experience on monitoring the data generated by electric buses is presented, focusing on energy consumption, charge and state of the batteries. The work is carried out in the framework of a global smart city strategy developed by the H2020 Smart City Lighthouse STARDUST project. The crucial role of the data collection and transmission from electric buses has become evident in this work, so the adopted solutions are covered in detail. A practical electric bus charging station configuration is considered, operating within the city of Pamplona, Spain, with an urban route setting in which electric charging is performed. Various key factors for the practical implementation of the necessary communication infrastructure, including wireless Low Power Wide Area connectivity challenges within the urban scenario settings, based in LoRa/LoRaWAN communication system nodes. The monitoring system architecture is also presented, in which specific machine learning modules in order to collect patterns and visualization of data to enhance planning, operation and maintenance procedures.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Multi-Frequency RF Sensor Fusion for Word-Level Fluent ASL Recognition

    • Free pre-print version: Loading...

      Authors: Sevgi Z. Gurbuz;M. Mahbubur Rahman;Emre Kurtoglu;Evie Malaia;Ali Cafer Gurbuz;Darrin J. Griffin;Chris Crawford;
      Pages: 11373 - 11381
      Abstract: Deaf spaces are unique indoor environments designed to optimize visual communication and Deaf cultural expression. However, much of the technological research geared towards the deaf involve use of video or wearables for American sign language (ASL) translation, with little consideration for Deaf perspective on privacy and usability of the technology. In contrast to video, RF sensors offer the avenue for ambient ASL recognition while also preserving privacy for Deaf signers. Methods: This paper investigates the RF transmit waveform parameters required for effective measurement of ASL signs and their effect on word-level classification accuracy attained with transfer learning and convolutional autoencoders (CAE). A multi-frequency fusion network is proposed to exploit data from all sensors in an RF sensor network and improve the recognition accuracy of fluent ASL signing. Results: For fluent signers, CAEs yield a 20-sign classification accuracy of %76 at 77 GHz and %73 at 24 GHz, while at X-band (10 Ghz) accuracy drops to 67%. For hearing imitation signers, signs are more separable, resulting in a 96% accuracy with CAEs. Further, fluent ASL recognition accuracy is significantly increased with use of the multi-frequency fusion network, which boosts the 20-sign fluent ASL recognition accuracy to 95%, surpassing conventional feature level fusion by 12%. Implications: Signing involves finer spatiotemporal dynamics than typical hand gestures, and thus requires interrogation with a transmit waveform that has a rapid succession of pulses and high bandwidth. Millimeter wave RF frequencies also yield greater accuracy due to the increased Doppler spread of the radar backscatter. Comparative analysis of articulation dynamics also shows that imitation signing is not representative of fluent signing, and not effective in pre-training networks for fluent ASL classific-tion. Deep neural networks employing multi-frequency fusion capture both shared, as well as sensor-specific features and thus offer significant performance gains in comparison to using a single sensor or feature-level fusion.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Autonomous Sensor System for Wind Turbine Blade Collision Detection

    • Free pre-print version: Loading...

      Authors: Kyle Clocker;Congcong Hu;Jason Roadman;Roberto Albertani;Matthew L. Johnston;
      Pages: 11382 - 11392
      Abstract: This paper presents an automated blade collision detection system for use on wind turbines, toward the goal of supporting monitoring and quantitative assessment of wind energy impacts on wildlife. A wireless, multisensor module mounted at the blade root measures surface vibrations, and a blade-mounted camera provides image capture of colliding objects. Using sensor data recorded during field testing of the system on an operational wind turbine, we present the development, training, and testing of automated detection algorithms for collision detection using machine-learning approaches. In particular, we compare the use of a new two-step, anomaly-based classification algorithm with conventional adaptive boosting and amplitude-based detection techniques, where the two-step approach improves average precision for the experimental data set. This integrated sensor and classification systems demonstrates a new approach for automated, on-blade collision detection for wind turbines, with broad utility across structural health monitoring applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Two-Phase Sensor Decision: Machine-Learning for Bird Sound Recognition and
           Vineyard Protection

    • Free pre-print version: Loading...

      Authors: Tibor Cinkler;Kristóf Nagy;Csaba Simon;Rolland Vida;Husam Rajab;
      Pages: 11393 - 11404
      Abstract: For a wireless sensor network consisting of numerous sensors, spread over a large area with no direct power supply, energy efficiency is of paramount importance. As most power is consumed by the communication module, special attention has to be paid to reduce communication needs as much as possible. The more data is sent, the larger the power requirement of the sensor module. Preprocessing can help in reducing the amount of data to send. However, it also consumes energy. This paper focuses on this tradeoff between preprocessing, pre-filtering and preselecting of sensor data on one hand, and uploading of unprocessed and unfiltered raw data on the other hand, for the special case of protecting vineyards from starlings. The paper proposes a two-phase decision mechanism based on machine learning: the less complex first phase is executed on the microcontroller of the sensor module, while the more complex, more accurate second phase is performed in the cloud. Individual noise sensors monitor the environment, and try to detect starling songs, using a simple, SVM-based classification. These sensors are grouped into clusters, through a mechanism similar to the well-known LEACH protocol, and signal to the current cluster-head the likelihood of starling presence. If several alerts are received to justify further investigation, the cluster-head asks the node with highest starling detection likelihood to upload a one second sound sample to the cloud. There, the more complex and more accurate second phase sound matching is performed, and the actuators deployed in the field are remotely triggered, if needed.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • OFET and OECT, Two Types of Organic Thin-Film Transistor Used in Glucose
           and DNA Biosensors: A Review

    • Free pre-print version: Loading...

      Authors: Xin Ma;Hongquan Chen;Peiwen Zhang;Martin C. Hartel;Xiaona Cao;Sibel Emir Diltemiz;Qinglei Zhang;Javed Iqbal;Natan Roberto de Barros;Liyan Liu;Hao Liu;
      Pages: 11405 - 11414
      Abstract: Due to their advantages of low cost, flexibility, ease of manufacture and biocompatibility, organic thin film transistor (OTFT) hold great commercial potential. Specifically, there are two types of OTFT, OFET and OECT, which are widely used in the field of flexible biological sensors and have great ability for glucose and DNA detection of diabetes. In this paper, we describe the working principles of OFET and OECT, and compare the differences between them. Some examples are given and clarified, including the materials, fabrication and chemical reactions. There is still a lot of space to be explored in OTFT for other biomarker sensing applications. With the emergence of new materials and fabrication techniques, OTFT-based biosensor would be more widely used in diagnostic equipment to improve patient outcomes.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Recent Advances in K-SPR Sensors for the Detection of Biomolecules and
           Microorganisms: A Review

    • Free pre-print version: Loading...

      Authors: Shikha Uniyal;Kuldeep Choudhary;Surbhi Sachdev;Santosh Kumar;
      Pages: 11415 - 11426
      Abstract: This article summarizes the application of various optical techniques for biological sensing. The presence of abnormal quantities of biomolecules (such as glucose, haemoglobin, etc.) or pathogens (such as viruses or bacteria in body cells) can be used to diagnose any ailment or malfunction of any part of the body. Additionally, the concentration of biomolecules such as glucose and haemoglobin in blood serves as an indicator of the body’s normalcy. Thus, the ability to detect abnormalities in blood or human cells more easily and rapidly can be a boon to society. Optical techniques are one such approach for developing biosensors. The extremely sensitive, label-free, real-time dynamic monitoring capabilities of optical techniques-based sensors have piqued the interest of a large number of researchers. In this paper, we have reviewed the present advancements in the field of prism based SPR biosensors i.e., the Kretschmann SPR (K-SPR) configuration-based biosensors for sensing vital biomolecules in human body like glucose and haemoglobin and microorganism like Pseudomonas bacteria.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Sensors and Techniques for Creatinine Detection: A Review

    • Free pre-print version: Loading...

      Authors: Sumedha Nitin Prabhu;Subhas Chandra Mukhopadhyay;Guozhen Liu;
      Pages: 11427 - 11438
      Abstract: The use of biochemical markers in the kidney healthcare has advanced dramatically in recent years. Creatinine and Blood Urea Nitrogen (BUN) are two of the most essential biochemical markers that can help a concerned physician, nephrologist, or oncologist to understand the qualitative and quantitative functions of the kidneys. Monitoring serum creatinine levels is critical for renal health indicators such as creatinine and BUN. When people are diagnosed with kidney illness, they are usually prescribed with a regular blood test, ultrasound, MRI, X-ray, and/or CT scan, as well as hybrid radionucleotide scans like SPET/PET. These are conventional approaches that are commercially available. The blood test allows for a precise knowledge of serum creatinine and BUN levels, which aids in determining the blood’s waste content and kidney functioning levels. However, these approaches are expensive, time intensive, and necessitate the use of technical professionals to undertake medical analysis on patients. As a result, new biosensing approaches for detecting creatinine as a biomarker have been developed by several research groups. It is hoped that the development of a quick and low-cost Point-of-Care (PoC) device would aid in more regular monitoring of renal healthcare, which will be useful in detecting early stages of kidney function loss.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • On the Detection of Unauthorized Drones—Techniques and Future
           Perspectives: A Review

    • Free pre-print version: Loading...

      Authors: Muhammad Asif Khan;Hamid Menouar;Aisha Eldeeb;Adnan Abu-Dayya;Flora D. Salim;
      Pages: 11439 - 11455
      Abstract: The market size of civilian drones is tremendously increasing and is expected to reach 1.66 million by the end of 2023. The increase in number of civilian drones poses several privacy and security threats. To safeguard critical assets and infrastructure and to protect privacy of people from the illegitimate uses of commercial drones, a drone detection system is inevitable. In particular, there is a need for a drone detection system that is efficient, accurate, robust, cost-effective and scalable. Recognizing the importance of the problem, several drone detection approaches have been proposed over time. However, none of these provides sufficient performance due to the inherited limitations of the underlying detection technology. More specifically, there are trade-offs among various performance metrics e.g., accuracy, detection range, and robustness against environmental conditions etc. This motivates an in-depth study and critical analysis of the existing approaches, highlighting their potential benefits and limitations. In this paper, we provide a rigorous overview of the existing drone detection techniques and a critical review of the state-of-the-art. Based on the review, we provide key insights on the future drone detection systems. We believe these insights will provide researchers and practicing engineers a holistic view to understand the broader context of the drone detection problem.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Nb2CTx MXene Integrated Tapered Microfiber Based on Light-Controlled Light
           for Ultra-Sensitive and Wide-Range Hemoglobin Detection

    • Free pre-print version: Loading...

      Authors: Wenjie Li;Yinping Miao;Yibo Zheng;Kailiang Zhang;Jianquan Yao;
      Pages: 11456 - 11462
      Abstract: Hemoglobin is an indicator of various diseases in humans, and shows excellent photothermal properties. Here, a biosensor combining the molecular photothermal effect with the Nb2CTx MXene integrated microfiber is proposed for the specific detection of human hemoglobin concentration by light-controlled light technique. The Nb2CTx MXene with a large specific surface and hydrophilicity is deposited on the surface of the microfiber through an optical deposition method, which makes hemoglobin molecules adsorbed on the surface of the microfiber and enhance the interaction between light and matter. Under the irradiation of excitation light, the photothermal effect of the molecules amplifies the optical signal to be measured, which further improves the sensitivity and selectivity of detection. The experimental results show that the sensitivity reaches 7.581 nm/(g•dL−1) with a limit of detection of 0.0026 g/dL, which is far below the blood hemoglobin level of humans (0–13 g/dL). It has been also confirmed that the sensor exhibits good selectivity. This method has the advantages of being resistant to electromagnetic interference, low cost, and is applicable for in-situ label-free detection, therefore, being potentially useful in the fields of disease diagnosis, food safety monitoring, and environmental pollution detection.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Lightweight Residual Convolutional Neural Network for Soybean
           Classification Combined With Electronic Nose

    • Free pre-print version: Loading...

      Authors: Hualing Lin;Haoming Chen;Chongbo Yin;Qinglun Zhang;Ziyang Li;Yan Shi;Hong Men;
      Pages: 11463 - 11473
      Abstract: The quality of soybeans from different growing areas is different. It is common for low-quality soybeans to fake high-quality soybeans. This paper proposes a lightweight residual convolutional neural network (LRCNN) combined with an electronic nose (e-nose) to realize soybean quality traceability. Firstly, obtain soybean gas information from different growing areas through the e-nose. Then, according to the characteristics of e-nose detection data, the grouped heterogeneous kernel-based convolution (GHConv) is proposed, which effectively reduces the number of parameters through the combination of grouping and heterogeneous convolution. Finally, the LRCNN is proposed, which reduces the number of network parameters and avoids feature degradation, realizing the high-precision identification of soybean quality differences. In the multi-model comparison, the classification accuracy of the network is 98.37%, recall is 98.20%, and precision is 98.49%. The results show that the LRCNN combined with the e-nose can effectively identify the gas information of soybeans from different growing areas, providing a new method for soybean quality traceability.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Solid-State Ion-Selective pH Sensor

    • Free pre-print version: Loading...

      Authors: Yu Chen;Musafargani Sikkandhar;Ming-Yuan Cheng;
      Pages: 11474 - 11479
      Abstract: pH sensors are widely used in various applications such as agriculture, wastewater monitoring, and biomedical engineering. Solid-state ion-selective electrodes (ISEs) have been developed to enable the miniaturization of pH sensors. This paper evaluated the pH sensing electrodes based on hydrogen ionophores or pH-sensitive emeraldine-polyaniline. A miniaturized leak-free reference electrode was integrated with pH sensing electrodes. A multiple-channel electromotive force recording unit was built to facilitate the measurement of multiple ISEs. The sensitivity and dynamic range were assessed using the pH solutions from pH 1.3 to pH 10. The conductance and capacitance of different ISEs were measured and compared. We evaluated the longevity and stability of the emeraldine-polyaniline-based pH sensor, and the drifting was less than 4 mV/day.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A 0.6V–1.8V Compact Temperature Sensor With 0.24 °C Resolution, ±1.4
           °C Inaccuracy and 1.06nJ per Conversion

    • Free pre-print version: Loading...

      Authors: Benjamin Zambrano;Esteban Garzón;Sebastiano Strangio;Giuseppe Iannaccone;Marco Lanuzza;
      Pages: 11480 - 11488
      Abstract: This paper presents a fully-integrated CMOS temperature sensor for densely-distributed thermal monitoring in systems on chip supporting dynamic voltage and frequency scaling. The sensor front-end exploits a sub-threshold PMOS-based circuit to convert the local temperature into two biasing currents. These are then used to define two oscillation frequencies, whose ratio is proportional to absolute-temperature. Finally, the sensor back-end translates such frequency ratio into the digital temperature code. Thanks to its low-complexity architecture, the proposed design achieves a very compact footprint along with low-power consumption and high accuracy in a wide temperature range. Moreover, thanks to a simple embedded line regulation mechanism, our sensor supports voltage-scalability. The design was prototyped in a 180nm CMOS technology with a 0 $^{circ }text{C},,-100,,^{circ }text{C}$ temperature detection range, a very wide supply voltage operating range from 0.6V up to 1.8V and very small silicon area occupation of just 0.021mm2. Experimental measurements performed on 20 test chips have shown very competitive figures of merit, including a resolution of 0.24 °C, an inaccuracy of $boldsymbol {pm }1.4~^{circ }text{C}$ , a sampling rate of about 1.5kHz and an energy per conversion of 1.06nJ at 30 °C.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • The Use of Thermal Cameras for Pedestrian Detection

    • Free pre-print version: Loading...

      Authors: Fatih Altay;Senem Velipasalar;
      Pages: 11489 - 11498
      Abstract: Visible-range camera sensors have been widely used for pedestrian detection. However, most of the methods, which employ visible-range color cameras, do not perform well under low-light and no-light conditions, e.g. during night time. Since the working principle of thermal camera sensors is mainly based on temperature and not light, they have been employed for person detection to overcome the drawbacks of visible-range sensors under these conditions. Every object gives off thermal energy, which is captured by a thermal camera sensor. When an object becomes hotter, it emits more thermal energy, and is therefore captured as much brighter or vice versa. Yet, compared to visible-range cameras, there are many additional challenges that need to be addressed when detecting pedestrians from thermal camera images. These challenges include bright hot objects close to humans, similar pixel values in an image due to weather conditions, or objects that block thermal cameras such as concrete or glass. Glass acts like a mirror for infrared radiation and reflects whatever is in front of the camera. Thus, novel methods are still required to accomplish pedestrian detection task from thermal camera images. To contribute to these efforts, we propose a new method and a modified object detection network incorporating saliency maps of thermal camera images. The features obtained from thermal images and their corresponding saliency maps are combined to obtain richer representations of pedestrian regions, and better detection performance. We perform extensive evaluations on five different datasets to compare the performance of the proposed approach with two baselines. Moreover, we evaluate and compare the transferability of these approaches by doing leave-one-out cross validation across different datasets. The results show that the proposed approach outperforms the baselines, and has better transferability properties across different thermal image datasets.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Evaluation of a 3D Printed Soft Sensor for Measuring Fingertip Interaction
           Forces

    • Free pre-print version: Loading...

      Authors: Gerjan Wolterink;Dimitrios Kosmas;Martijn Schouten;Bert-Jan F. van Beijnum;Peter H. Veltink;Gijs Krijnen;
      Pages: 11499 - 11508
      Abstract: Current force sensors used to capture fingertip interaction forces lack compliance to the fingertip tissue resulting in the loss of touch sensation of the user. 3D printing offers the possibility to create personalized soft sensing structures. This work evaluates a 3D printed soft sensor that measures normal and shear interactions forces based on the deformations of the thumb and index fingertips of 7 subjects using an instrumented object. Due to the use of (carbon doped) thermoplastic materials, the signals provided by these sensing structures suffer from nonlinearities. Therefore, two compensation models, based on a neural network and recurrent neural network analogous to an electrical model are used to compensate for the nonlinear effects. The performance of the sensors was analysed using the normalized cross-correlation and the root-mean-square error. The output of the force sensors are highly correlated with the applied shear and normal force components. When paired with compensation models the correlation and error of the sensor output can be further improved. These results indicate that the proposed flexible fingertip interaction force sensors have a high potential for future applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Surface Acoustic Wave Strain Sensor With Ultra-Thin Langasite

    • Free pre-print version: Loading...

      Authors: Jikai Zhang;Hao Jin;Shurong Dong;Rui Ding;Jinkai Chen;Weipeng Xuan;Feng Gao;Jikui Luo;
      Pages: 11509 - 11516
      Abstract: Langasite (LGS) surface acoustic wave (SAW) sensor is considered as an ideal wireless passive sensing technology application in complex environments. However, existing LGS-based SAW strain sensor has low strain range and its sensing accuracy is affected by temperature greatly as well as strain, which can not meet the application requirements. By exploiting ultra-thin substrate and a custom calibration algorithm, this paper proposes an ultra-thin 100 $mu text{m}$ LGS SAW with (0°, 138.5°, 72°) Euler angle to extend strain range to 1200 $mu varepsilon $ at $500^{circ }text{C}$ , which is twice higher than the state-of-the-art. The ultra-thin SAW sensor has lower temperature coefficient and hysteresis loop effect, compared with thick LGS SAW. Aiming to improve sensing accuracy, the mechanism of temperature effects on strain sensitivity is investigated in different temperature stages, including thermal expansion mismatch effect and high-temperature glue strain transfer ratio effect. The mechanism can explain strain sensitivity curve under various temperature nicely. Based on the mechanism, a new calibration method is also developed to eliminate temperature effects. The testing results show that this calibration method can improve the measured strain accuracy effectively.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • TiB₂/SiCN Thin-Film Strain Gauges Fabricated by Direct Writing for
           High-Temperature Application

    • Free pre-print version: Loading...

      Authors: Chao Wu;Xiaochuan Pan;Fan Lin;Zaifu Cui;Yingping He;Guochun Chen;Yingjun Zeng;Xianlong Liu;Qinnan Chen;Daoheng Sun;Zhenyin Hai;
      Pages: 11517 - 11525
      Abstract: The in situ strain/stress monitoring of hot components in harsh environments remains a challenging task. In this study, TiB2/SiCN thin-film strain gauges were fabricated on nickel base alloy substrates via direct writing. The static and dynamic strain responses were investigated from 25 °C up to 800 °C. The results show that this thin-film strain gauge fabricated by ceramic-based materials exhibits excellent thermal stability and strain response. Without any anti-oxidative protective layer deposited, its operating temperature is as high as $700^{circ }text{C}$ , which is 200 °C higher than that of the high-temperature piezoresistive thin-film strain gauges that have been developed and comparable to thin-film strain gauges with protective layers. The gauge factor of theTiB2/SiCN high-temperature thin-film strain gauge is 7.12, which is higher than that of most high-temperature thin-film strain gauges. In addition, the strain gauge exhibits excellent resistance stability with a mechanical hysteresis of $3~mu varepsilon $ and a resistance drift of 0.0008/h at room temperature. Therefore, TiB2/SiCN thin-film strain gauges provide an effective approach for the measurement of in-situ static and dynamic strain of hot components in harsh environments.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Fetal Movement Detection by Wearable Accelerometer Duo Based on Machine
           Learning

    • Free pre-print version: Loading...

      Authors: Jingyi Xu;Chao Zhao;Bo Ding;Xiaoxia Gu;Wenru Zeng;Liang Qiu;Hong Yu;Yang Shen;Hong Liu;
      Pages: 11526 - 11534
      Abstract: Objective: Fetal movement counting is one of the most important indices reflecting the health of the fetus. In the hospital, ultrasound method serves as gold standard but could only be used within a short time because of the potential of tissue damage. Out of the hospital, maternal perception is recommended by obstetrician-gynecologist while its accuracy varies between pregnant women and typically is low. This study aims to develop a wearable device for out-of-hospital monitoring of fetal movement with acceptable accuracy. Methods: In this work, a wearable device with two accelerometers was developed for accurate and continuous fetal movement monitoring. Nine different machine learning algorithms with optimized hyperparameters and features were compared and analyzed, and the ensemble method was found to have the best performance compared to ultrasound as gold standard on 20 pregnant women. Feature importance factor and optimized number of accelerometers were also studied. Conclusions: The high-performance metrics indicate that our system could potentially replace maternal perception for accurate out-of-hospital fetal movement detection. Furthermore, because it’s intrinsically passive and wearable, it could broaden the application horizon of fetal movement monitoring, and be applied to discover more rich biological profile of fetus during perinatal period.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Piezoresistive 4H-SiC Pressure Sensor With Diaphragm Realized by
           Femtosecond Laser

    • Free pre-print version: Loading...

      Authors: Lukang Wang;You Zhao;Yu Yang;Xing Pang;Le Hao;Yulong Zhao;
      Pages: 11535 - 11542
      Abstract: The widespread application of bulk silicon carbide in pressure sensing has been largely limited by the slow etching rate of traditional micromachining processes. This paper proposes a piezoresistive bulk 4H-SiC pressure sensor with diaphragm which has controllable thickness realized by femtosecond laser. A modified stress model was established by finite element analysis to fit the SiC circular diaphragm prepared by laser micromachining. The test results proved that the fabricated sensor with diaphragm thickness of $60 mu text{m}$ had sensitivity of 1.42 mV $/text{V}/$ MPa under the applied pressure of up to 5 MPa at room temperature. Small hysterisis error of 0.17 %/FSO and nonlinearity of 0.20 % $/$ FSO was achieved. The sensor was able to work in a broad temperature range with the temperature coefficient of sensitivity of −0.23% FSO/°C at −50 °C and −0.10% FSO/°C at 300 °C. The dynamic response results exhibited that the resonance frequency of the sensor was 60.11 kHz and the rise time was $18 mu text{s}$ . The SiC pressure sensor also had the noise-limited resolution of 0.42 kPa and the dynamic range of 44.9 dB. The research demonstrates the prospect of employing the femtosecond laser technology to prepare bulk SiC pressure sensors for extreme temperature environment.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Structural Engineering Approach for Designing Foil-Based Flexible
           Capacitive Pressure Sensors

    • Free pre-print version: Loading...

      Authors: Rishabh B. Mishra;Fhad Al-Modaf;Wedyan Babatain;Aftab M. Hussain;Nazek El-Atab;
      Pages: 11543 - 11551
      Abstract: Structural engineering plays an essential role in designing, improving, and optimizing an electromechanical system, instinctively affecting its performance. In this study, design optimization, finite element analysis, and experimental evaluation of capacitive pressure sensors were conducted. The air pressure sensing application was demonstrated to characterize different sensors, which include a combination of multiple rectangular cantilevers and diaphragms (square and circular-shaped). After the design improvement, we found that the square and circular diaphragms each with two trapezoidal cantilevers exhibited highest sensitivity to air pressure monitoring among the different investigated designs which combine the square and circular diaphragms with cantilevers. These designs were then selected for further analysis for acoustic pressure monitoring. The sensors were fabricated using the do-it-yourself technique with household materials such as post-it paper, posted tape, and foil. Our approach offers an alternative to the conventional cleanroom fabrication technique and uses easily available materials to fabricate affordable sensors. Therefore, this is the first step toward the development of democratized and sustainable electronic devices that are affordable and available to everyone on the internet.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Molding-Free Fully-Printed Flexible Tactile Sensors With Performance-
           Enhancing Microstructures

    • Free pre-print version: Loading...

      Authors: Qifan Ding;Huyue Chen;Jiahao Wu;Tianxiang Zheng;Wen-Ming Zhang;Lei Shao;
      Pages: 11552 - 11561
      Abstract: Flexible tactile sensors have attracted a lot of attention due to their potential applications in biomedical monitoring and electronic skins. In this paper, we propose and verify a molding-free full-printing method for such sensors using a single microelectronic printer. The sensors are capacitive or piezoresistive transduced, and they consist of two inkjet printed electrodes with direct-ink-writing (DIW) printed dielectric or conductive microstructures sandwiched in between. The complete printing process for one device, taking only several minutes with an additional couple of hours for curing, is extremely fast and low-price while also being highly reliable. The fabricated capacitive sensors mainly show two linear regimes of sensitivity, one as high as 1.0 kPa−1 for a loaded pressure below 200 Pa, and the other much lower but for a wide pressure range up to 200 kPa. This performance results from careful optimization of the printing resolution and coverage ratio for the dielectric layer and is comparable to and with some parameters better than those related microstructured sensors of the same mechanism fabricated with slow and expensive molding processes. In addition, the piezoresistive sensors also prove the diversity of flexible devices that can be fabricated using the same process flow. Such sensors are harnessed to demonstrate their effectiveness in detecting both gentle and heavy contacts, and in artificial tactile feedback for robotics.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Design and Experiment of Mass Warning Resonant Sensor Induced by Modal
           Coupling

    • Free pre-print version: Loading...

      Authors: Lei Li;Hanbiao Liu;Wenming Zhang;
      Pages: 11562 - 11574
      Abstract: A new mass warning scheme via integrating modal coupled vibration of T-type resonant sensor is proposed in this paper, which can utilize the amplitude jump induced by mass perturbation to achieve the first and second mass warnings. A T-type resonant sensing structure with 1:2 internal modal coupling is designed and its coupling dynamic behavior is experimentally measured. Then, the theoretical expression is established and derived by Hamilton’s principle and multi-scale method to predict the amplitude-frequency curve with respect to the effect of mass perturbation. Besides, the physical conditions of modal coupled vibration under different perturbation masses are obtained. The working range of the resonant mass sensor is investigated by bifurcation analysis. Both theoretical and experimental results show that when the driving frequency is slightly greater than the upper critical frequency of the modal coupled vibration of the resonator, the resonant sensor has the first mass warning and the second mass warning with the increase of the perturbation mass.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Density Functional Theory Study of Oil-Immersed Transformers
           Characteristic Decomposition Products Adsorption on n ZrO2 (n = 1~2)
           Modified GaN Nanotubes

    • Free pre-print version: Loading...

      Authors: Yunfan Liu;Li Cheng;Rongxin Chen;Tao Li;Sida Zhang;Ruijin Liao;
      Pages: 11575 - 11583
      Abstract: Oil-immersed transformers, with safe reliability and high utilization, plays an irreplaceable role in power transmission and transformation. Unavoidable insulation defects can occur that affect the safety of the power system and socio-economic, thus timely online monitoring becomes a focus of attention in the power sector. In this study, n ZrO2 (n = 1-2) modified GaN nanotubes are firstly proposed as a novel gas-sensing material for estimating the discharge faults in oil-immersed transformers through dissolved gas analysis. Density functional theory calculations are carried out to deeply explore the characteristic dissolved gases adsorption and sensing mechanisms. The density of states, charge density difference, band structures, and theoretical recovery time are analyzed to evaluate the feasibility and applicability of the gas sensor. The data shows that ZrO2 dopant provides active sites for gas adsorption. n ZrO2-GaNNTs (n = 1-2) has limited gas sensitivity to H2 and CH4 but demonstrates considerable ability to capture C2H2. Moreover, all adsorption has excellent gas-sensitive recovery properties, aiding in the rapid recycling of sensors. Meanwhile, the verification is carried out by experiments. Results in this work can be employed for practical production of n ZrO2 (n = 1-2) modified GaN nanotubes gas sensors, ultimately realizing online monitoring of oil-immersed transformer.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Single Aerosol Particle Detection by Acoustic Impaction

    • Free pre-print version: Loading...

      Authors: Nadine Karlen;Tobias Rüggeberg;Bradley Visser;Jana Hoffmann;Daniel A. Weiss;Ernest Weingartner;
      Pages: 11584 - 11593
      Abstract: A new measurement method has been developed that enables an acoustic detection of individual coarse mode particles (aerodynamic particle diameter > $1 boldsymbol~ mu text{m}$ ) by impaction on a piezo transducer. The aerosol is accelerated and each momentum transfer by a particle is measured as a characteristic pulse in the transducer signal whose amplitude is directly proportional to the particle mass. The current single particle mass detection limit is approximately 50 picograms, which corresponds to an aerodynamic particle diameter of $boldsymbol sim 5 boldsymbol~ mu text{m}$ . The measurement technique allows a direct in-situ mass measurement of single coarse mode particles that is unique because it is based on first principles, is relatively simple and less prone to measurement artefacts compared to other methods. This particle mass measurement is independent of assumptions on particle properties like shape, density or refractive index. This technology is of interest for scientific, industrial and health-related monitoring applications as different sources of atmospheric aerosols can be identified via size-resolved mass measurements. Challenges to be overcome include a further lowering of the detection limit, eliminating systematic errors from bouncing phenomena, optimizing the correct mass assignment as well as improving the robustness against sensor vibrations and acoustic noise. In addition, the complete sensor should be portable and affordable. With the future goal to detect submicron particles, coincidence and a reduction of the impactor’s cutoff diameter additionally become important issues.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Immunoglobulin-Immobilized Quartz Crystal Microbalance for Staphylococcus
           Aureus Real-Time Detection

    • Free pre-print version: Loading...

      Authors: Zhangliang Xu;Yang Luo;
      Pages: 11594 - 11601
      Abstract: Quartz crystal microbalance (QCM) sensor is still a high-precision surface sensing technique. Here, we described an immunoglobulin G (IgG) biofunctionalized QCM sensor for Staphylococcus aureus (S. aureus) sensing application. In the case of the QCM sensor, a gold-titanium (Au/Ti) electrode layer was fabricated by magnetron sputtering and sequentially immobilized with thoil self-assembled monolayer (SAM) and IgG gripper. The selectivity test showed that the rabbit IgG-based QCM sensor exhibited a good sensitivity performance. IgG immobilization optimal experiment was conducted by adjusting parameters including immobilization time, temperature, buffer pH and IgG concentration. The lower limit of detection and sensitivity of the IgG-based QCM biosensor toward S. aureus were suggested to be $1.0times {10}^{{4}}$ CFU/mL and 562.5 Hz/ng, respectively. Furthermore, the real-time monitoring and frequency response of IgG immobilization process and its sensitive adsorption behavior to S. aureus were performed in droplet environment. By comparison, the average sensitivity of IgG-immobilized QCM sensor in liquid phase is 2-3.5 times higher than that in air. Overall, it is indicative that IgG-based QCM sensors do have great potential for biological monitoring applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Multiple Resonant Frequencies Sensor for Measuring Permittivity Based on
           Stepped Impedance Resonator

    • Free pre-print version: Loading...

      Authors: Xingye Fan;Yong Cheng;Ying Yu;Zhengwei Huang;
      Pages: 11602 - 11614
      Abstract: This paper presented a novel sensor based on a stepped impedance resonator, which can measure the permittivities of materials at different resonant frequencies simultaneously. The multiple resonant frequencies are generated by the fundamental frequency and harmonics. The sensitivity of designed sensor at different resonant frequencies is also analyzed in detail. For verifying the feasibility of the sensor, three different kinds of materials under test are measured. The experimental results demonstrated that the tested resonant frequencies show a multiplicative growth and are consistent with our theoretical analysis. Besides, the higher the tested resonant frequency, the greater the sensitivity. The permittivity of the material under test is derived from these measured resonant frequencies by simulation and calculation. The maximum relative error through simulation and calculation are 3.4% and 26.67%, respectively. Moreover, the proposed sensor also has the advantages of being simple to fabricate, inexpensive to manufacture, and compact in volume.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Differential ECT Probe Design and Investigation for Detection of Rolling
           Contact Fatigue Cracks With Different Orientations

    • Free pre-print version: Loading...

      Authors: Xin Li;Guiyun Tian;Kaiyu Li;Haitao Wang;Qing Zhang;
      Pages: 11615 - 11625
      Abstract: Eddy current testing(ECT) is widely applied to detect surface defects on metallic materials. In ECT for cracks on complex curved surfaces, immunity to lift-off and the detection ability of the eddy current probe are critical challenges. In this study, we designed and developed a novel differential transformer ECT probe using AD698 signal conditioning to detect cracks with different orientations in rail treads. It adopts flexible printed circuit board (FPCB) technology for complex curved surfaces. The new ECT probe consists of four-square driver coils and two 8-shaped pick-up coils with antiserial connections that can sense opposite magnetic field changes in different directions. The proposed probe can solve the detection ability and lift-off problem of crack with different orientation on complex curved surfaces. The theoretical analysis which is based on transformer principles, is described in this paper. The detection ability of the new probe at different frequencies was evaluated by numerical simulation. Upon completion, the numerical simulations analyzed the effects of crack with different orientation and lift-off on the detection ability of the proposed probe. The experimental system was built to verify the detection ability of different crack with different orientations and immunity to lift-off by the proposed probe. The results of the simulation and experiment indicated that the proposed probe has a good ability to detect crack with different orientation whereas its performance decreases as crack orientation increasing. The proposed probe can also substantially suppress lift-off noise.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Electromagnetic Angular Position Sensing Using High-Magnetic-Permeability
           Materials

    • Free pre-print version: Loading...

      Authors: Heng Wang;Shuangyi Wang;Rajesh Rajamani;
      Pages: 11626 - 11636
      Abstract: In this paper, a new electromagnetic angular position sensing method using high-magnetic-permeability metal (e.g., mu-metal) is proposed for measurement of joint angles of rotational mechanisms. An electromagnet and magnetic sensors are both located on one mechanical part and the mu-metal element is located on the other mechanical part with relative rotation to the first part around the joint. The mu-metal with high magnetic permeability is easily magnetized by the electromagnet and it exerts an additional magnetic field on the magnetic sensors dependent on the rotation angle nonlinearly. This influence is utilized for angle estimation using an extended Kalman filter. Two configurations of the angle sensing system are designed and modeled using numerical simulation. Experimental results show that the angle measurement error for both configurations is under 1% over their respective sensing range. The mu-metal based angle sensing system is also validated to be immune to ferromagnetic disturbances due to the use of an alternating magnetic field. The proposed angle sensing method is non-contacting and non-intrusive, and its off-joint configuration can measure joint angle without needing to be installed in the joint.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Magnetic Anomaly Detection Using One-Dimensional Convolutional Neural
           Network With Multi-Feature Fusion

    • Free pre-print version: Loading...

      Authors: Liming Fan;Hao Hu;Xiaojun Zhang;Huigang Wang;Chong Kang;
      Pages: 11637 - 11643
      Abstract: In order to improve the detection performance of magnetic anomaly signal with low signal-to-noise ratio (SNR), we develop an effective method using one-dimensional convolutional neural network (1D CNN) model with multi-feature fusion. In the method, the magnetic signal is processed by Hilbert-Huang transform and discrete wavelet transform to obtain its information as pre-feature in different dimensions. The 1D CNN model with three processing blocks is used to further extract features from pre-features and identified whether the anomaly signal exists or not based on multi-feature fusion. To train the model, the positive sample set is generated by simulated signals and the measured magnetic noise, while the negative sample set is only the measured magnetic noise. Simulation results show that the proposed method has high accuracies in training and test set. A field experiment is conducted to examine the detection performance of proposed method using real data. Results show that the proposed method has good detection performances in low SNR.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Absorption Improvement of the Anapole Metastructure for Sensing
           Applications

    • Free pre-print version: Loading...

      Authors: Li Zeng;Hai-Feng Zhang;
      Pages: 11644 - 11652
      Abstract: In this paper, a metastructure absorber based on the non-radiating anapole mode is firstly proposed and theoretically demonstrated. The combination of four split rings leads to a classical resonant anapole behavior upon the normally illuminating of the ${y}$ -polarized wave, which is originated from the destructive interference of the toroidal and electric dipoles. The results show that a point electric field hotspot with up to 20 times enhancement is strictly confined at the geometric center of the proposed structure in the near field and the absorption in the far-field reaches 50.18% at 1.25 THz. To further effectively enhance the absorption efficiency, an improved version is further presented by inserting two vertical asymmetric split rings on both sides of the first design, in which the strengthened magnetic dipole moments reconfigure the mode coupling of multipolar excitations and greatly enhance the absorption to 98.51% at 1.2 THz, while the confinement of the electric field amplifies to nearly 200 times. In addition, the sensing performance of this improved design for measuring the relative permittivity of the background surrounding from 1 to 1.5 has also been explored with the sensitivity, maximum FOM, minimum detection limit and minimum resolution of 341 GHz/RIU, 11.37, 0.00438 and 0.02481. Such an implementation of the absorption enhancement mechanism based on anapole mode enriches the theoretical framework and provides a new platform for multipole electrodynamics in the fields of ultra-sensitive sensing, label-free detection, optical switch, optical modulation, and so on.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • SpO2 Measurement: Non-Idealities and Ways to Improve Estimation Accuracy
           in Wearable Pulse Oximeters

    • Free pre-print version: Loading...

      Authors: Deepak Berwal;Ajay Kuruba;Aatha Mohin Shaikh;Anand Udupa;Maryam Shojaei Baghini;
      Pages: 11653 - 11664
      Abstract: The blood oxygen saturation level (SpO2) has become one of the vital body parameters for the early detection, monitoring, and tracking of the symptoms of coronavirus diseases 2019 (COVID-19) and is clinically accepted for patient care and diagnostics. Pulse oximetry provides non-invasive SpO2 monitoring at home and ICUs without the need of a physician/doctor. However, the accuracy of SpO2 estimation in wearable pulse oximeters remains a challenge due to various non-idealities. We propose a method to improve the estimation accuracy by denoising the red and IR signals, detecting the signal quality, and providing feedback to hardware to adjust the signal chain parameters like LED current or transimpedance amplifier gain and enhance the signal quality. SpO2 is calculated using the red and infrared photoplethysmogram (PPG) signals acquired from the wrist using Texas Instruments AFE4950EVM. We introduce the green PPG signal as a reference to obtain the window size of the moving average filter for baseline wander removal and as a timing reference for peak and valley detection in the red and infrared PPG signals. We propose the improved peak and valley detection algorithm based on the incremental merge segmentation algorithm. Kurtosis, entropy, and Signal-to-noise ratio (SNR) are used as signal quality parameters, and SNR is further related to the variance in the SpO2 measurement. A closed-loop implementation is performed to enhance signal quality based on the signal quality parameters of the recorded PPG signals. The proposed algorithm aims to estimate SpO2 with a variance of 1% for the pulse oximetry devices.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Investigation of Sensing Properties of NOx Adsorbed Gas Molecules on
           Fe-Doped MoSe₂ Monolayer

    • Free pre-print version: Loading...

      Authors: Neha Mishra;Bramha P. Pandey;Brijesh Kumar;Vinay K. Tomar;Santosh Kumar;
      Pages: 11665 - 11672
      Abstract: In this paper, the authors explore the impact of NOx (NO, NO2, N2O) gas molecule’s adsorption on the sensing properties of Fe-doped MoSe2 monolayer. Formation and binding energy confirm the electronic stability for pristine and doped monolayers. In terms of electronic parameters of interest, the sensing is adsorption energy, charge transfer, recovery time, the density of states (TDOS), conductivity, and sensitivity. Firstly, the adsorption energy (Eads) is analyzed in the sequence of 1.37, 2.00, 0.07 eV. Secondly, the study of charge transfer is in order of 3.67e, 5.91e, 5.90e. It is observed that charge transfer aligns with the trend of adsorption energy. Third, recovery time as a parameter of reusability of Fe-doped MoSe2 monolayer by a detachment of gas molecules is estimated. The N2O gas molecule takes minimum time for separation with a value of 0.15 pico sec. Fourth, adsorbed systems’ density of states (DOS) (NO/Fe-doped MoSe2, NO2/Fe-doped MoSe2, N2O/Fe-doped MoSe2) accounts for the population of electrons present in different energy levels showing impurities in the Fermi level region. These impurities arouse to asymmetrical nature of the TDOS inducing magnetic domains in it. Fifth, conductivity as an application of modulated bandgap is studied, and NO2 gives the highest conductivity of 0.49. Lastly, sensitivity as a practical sensing parameter concludes the three adsorbed configurations with a maximum sensing response of 89.69% for NO2 gas molecules. Therefore, analyzing three NOx gas molecules regarding electronic properties concludes that the Fe-doped MoSe2 monolayer more suitably senses NO2 gas molecules
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Novel Optical Accelerometer Based on Slant-Ended Fiber

    • Free pre-print version: Loading...

      Authors: Mingze Wu;Lianjin Hong;Yue Li;
      Pages: 11673 - 11681
      Abstract: We demonstrate a novel optical accelerometer based on slant-ended fiber. The accelerometer has an ingenious structure and is easy to manufacture, which consists of single mode fiber (SMF), slant-ended fiber, ceramic ferrule and ceramic sleeve. The SMF plays a role of cantilever beam in the work of the accelerometer, and the light emitted from the SMF is received by the slant-ended fiber. The slant-ended fiber is provided by a fiber angled physical contact (APC) connector, which not only reduces the cost but also greatly simplifies the manufacturing process. The acceleration can be directly obtained by monitoring the transmission intensity of the sensor. The experimental results show that the prototype of the optical accelerometer has a sensitivity of 0.475 V/g at the working frequency from 30 Hz to 700 Hz and a signal-to-noise ratio (SNR) of 58 dB. Further, the transverse sensitivity is 4 mV/g and the self-noise is $6.77 ~ mu mathrm{V} / sqrt{mathrm{Hz}}$ at 500 Hz. In addition, it is found that the optical accelerometer has good stability, repeatability and phase-frequency characteristic. The ingenious structure and simple fabrication make it have the potential for cost-effective application.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Laser-Crystallized Epitaxial Germanium on Silicon-Based Near-Infrared
           Photodetector

    • Free pre-print version: Loading...

      Authors: Khushboo Kumari;Sandeep Kumar;Mahek Mehta;Avijit Chatterjee;Shankar Kumar Selvaraja;Sushobhan Avasthi;
      Pages: 11682 - 11689
      Abstract: This work reports the fabrication of near-infrared (NIR) photodetectors based on laser-crystallized epitaxial germanium (100 nm) on silicon (001). The laser crystallized epitaxial germanium has an extracted dislocation density of $approx 10$ 9 cm−2. Metal-Semiconductor-Metal photodetectors fabricated on these films using Cr/Au as contact showed remarkable responsivity values of 0.64 A/W at 1550 nm and 0.71 A/W at 1310 nm for a 1V bias. Au/Cr-Ge contact exhibited ohmic characteristics with a contact resistance of $approx 340$ ± $13~Omega $ . The devices showed a time response with a rise time (10%-90%) of 3 s and fall time (90%-10%) of 5.2 s. The obtained high value of dark current is reduced by one order after passivating the Ge layer below contact with TiO2 (5 nm). TiO2 based devices showed a contact resistance of 3400± $283Omega $ . Kelvin probe force microscopy (KPFM) was employed to map the surface potential across the MSM photodiodes to understand the current transport mechanisms. KPFM yields an Ohmic and Schottky contact in MSM diodes without and with TiO2 passivation layer, respectively. With further improvement in the device architecture; and optimization of the laser crystallization conditions of Ge film; these devices can be used as a potential candidate for various low-cost NIR applications, like on-chip integration of photonic circuits.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Liquid Permittivity Sensing Using Teeth Gear-Circular Substrate Integrated
           Waveguide

    • Free pre-print version: Loading...

      Authors: Norhanani Abd Rahman;Zahriladha Zakaria;Rosemizi Abd Rahim;Rammah A. Alahnomi;Ahmed Jamal Abdullah Al-Gburi;Ammar Alhegazi;Wan Norhisyam Abd Rashid;Amyrul Azuan Mohd Bahar;
      Pages: 11690 - 11697
      Abstract: A new design of teeth gear-circular substrate integrated waveguide (TG-CSIW) sensor to extract complex permittivity of liquids is suggested in this study. The proposed sensor size is decreased up to 20% after the Angle Between Input and Output Ports (ABIOP) was introduced. As the outcome, there was a very excellent quality factor of 700 unloaded. The sensor operated at 2.45GHz resonant frequency, and it is progressively designed by the ANSYS program HFSS. The sensor’s simulated response was evaluated by different permittivity of liquid under test (LUT) with an amount of 0.11mL aqueous. A strong correlation is obtained between the simulated and experimental findings of complex permittivity of LUTs with a minimal error of less than 0.4%. In addition, the modelled sensor was employed to estimate the complex permittivity of unknown specimens. It has contributed to a miniaturized, low-cost, secure, non-contact, fast-detection system utilizing limited liquid volumes filled into Polypropylene tubes. The proposed sensor is practically useful for food or beverage products, fluids/tissues of organic and natural herbs industry applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Study of Ultra High Frequency Measurement Techniques for Online Monitoring
           of Partial Discharges in High Voltage Systems

    • Free pre-print version: Loading...

      Authors: Krishna Chaitanya Ghanakota;Yugandhara Rao Yadam;Sarathi Ramanujan;Vishnu Prasad V. J.;Kavitha Arunachalam;
      Pages: 11698 - 11709
      Abstract: Partial Discharge (PD) activity is a pre-cursor for insulation degradation which may eventually lead to catastrophic failure of the electrical equipment with severe social and economic consequences. It is therefore imperative that PD is detected at its early stages to permit repair or replacement, prior to total failure. The bulky non-planar construction of existing ultra-high frequency (UHF) sensors coupled with the lack of proper feeding techniques and the ultrawide signal bandwidth of PD signals which necessitates the use of digital storage oscilloscope have restricted field deployment of UHF technique for online PD detection. In this work, we present a compact ( $0.58 boldsymbol {lambda } $ ) wideband (0.5-3 GHz) dual arm Archimedean slot spiral with coplanar waveguide feed for PD detection, where $boldsymbol {lambda } $ is the wavelength at center frequency. Numerical simulations of the antenna were compared with the measurements of the fabricated antenna. The fabricated wideband antenna was used to detect PD emissions from a mineral oil test cell connected to 10 kVA 50 Hz transformer. We also present different UHF signal measurement techniques ranging from PD pulse detection to reconstruction of the bandlimited PD signal in the digital domain for online structural health monitoring of high voltage systems. The proposed UHF PD measurement techniques are low cost compared to the conventional approach and indicate the feasibility of an embedded system for online PD monitoring using the fabricated UHF sensor.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Optical Fiber Sensor With Double Tubes for Accurate Strain and Temperature
           Measurement Under High Temperature up to 1000 °C

    • Free pre-print version: Loading...

      Authors: Qin Tian;Guoguo Xin;Kok-Sing Lim;Yen-Jie Ee;Fengfeng Xue;Yudong He;Jiajie Zhu;Harith Ahmad;Xiaochong Liu;Hangzhou Yang;
      Pages: 11710 - 11716
      Abstract: In this work, we present a discriminative optical fiber sensor for temperature and strain measurement. The sensor comprises of two cascaded thermal regenerated Fiber Bragg gratings (RFBGs) incorporated with two fused silica capillary tubes. The RFBG2 is loosely enclosed in a fine fused silica tube and made solely sensitivity to temperature whereas the RFBG1 sensor still retains its sensitivity to both strain and temperature. These properties have made the discriminative measurement accurate and directive. The experimental results indicate that temperature response is linear in the range of 100 – 1000°C with the sensitivity of ~15.7 pm/°C. Besides, it presents good repeatability in strain detection at high temperatures (300 °C – 900 °C). The incorporation of the two fine glass tubes has enhanced the modified RFBG’s strain sensitivity to as high as ~5.46 pm/ $mu varepsilon $ in the measurement range of $0~mu varepsilon $ to 120 $mu varepsilon $ at 600 °C, which is about five times higher than that of common RFBG strain sensors. The sensitivity can be further enhanced by manipulating the parameters of the sensor’s structure.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Quantum-Enhanced Fiber Curvature Sensor Exploiting Intensity-Correlated
           Pulse Twin Beams

    • Free pre-print version: Loading...

      Authors: Hailong Wang;Zhihao Ni;Jun Chen;Yan Shi;Shangzhong Jin;Yajuan Zhang;Chunliu Zhao;
      Pages: 11717 - 11724
      Abstract: A quantum-enhanced fiber curvature sensor based on single-mode fiber (SMF) taper exploiting intensity-correlated pulse twin beams has been proposed and experimentally realized. The curvature can be measured by intensity difference noise power and degree of intensity difference squeezing which are tailored by the bending-induced loss of one beam’s SMF taper. In addition, the three different waist diameters of SMF tapers, i. e., $52.12~ boldsymbol {mu }text{m}$ , $61.68~ boldsymbol {mu }text{m}$ , and 67.54 $boldsymbol {mu }text{m}$ respectively have been prepared using a fusion splicer. Finally, the maximum measured sensitivity enhancement of 4.42 dB/m −1 and signal-to-noise ratio enhancement of 2.0 dB have been experimentally implemented through intensity difference measurement technique compared with the corresponding classical counterpart (double-channel light source). In contrast to other curvature sensors, this proposal has the advantages of high sensitivity, ease of fabrication, simple structure, and low cost.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Temperature-Insensitive Curvature Sensor With Plane-by-Plane Inscription
           of Off-Center Tilted Bragg Gratings in CYTOP Fibers

    • Free pre-print version: Loading...

      Authors: Arnaldo Leal-Junior;Antreas Theodosiou;Vitorino Biazi;Leandro Macedo;Carlos Marques;Kyriacos Kalli;Anselmo Frizera;
      Pages: 11725 - 11731
      Abstract: We demonstrate of an off-centered tilted fiber Bragg grating (FBG) inscribed in cyclic transparent optical polymer (CYTOP) fiber as a curvature sensor. The gratings were inscribed using a femtosecond (fs) laser with the direct write, plane-by-plane method due to its high flexibility and suitability for customization, particularly for the development of a single-peak spectrum in multimode CYTOP fiber. The grating is applied to curvature sensing, where the spectral features, namely the wavelength shift and reflected optical power, are analyzed with respect to the curvature angle on the optical fiber. The off-center and tilted FBG response as a function of the angle is compared with a centered and uniform FBG inscribed in CYTOP fiber using the same inscription method and laser parameters. Results show a significant improvement for the off-center grating when the wavelength shift information is considered and lower optical power sensitivity as a function of the angle with respect to the centered FBG. In addition, the temperature sensitivity differences of the two gratings, enables the development of temperature-insensitive angle sensor using a single off-center tilted FBG.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Simple Optical Fiber Interferometer for Dynamic Measurement of Refractive
           Index and Thickness of Polymer Films

    • Free pre-print version: Loading...

      Authors: Bernardo Dias;João Pedro Sampaio Mendes;José Manuel Marques Martins de Almeida;Luís Carlos Costa Coelho;
      Pages: 11732 - 11739
      Abstract: Fiber optic-based refractometers is a thoroughly researched field, with many different configurations being used. However, most designs require external calibration using substances of known refractive index (RI) and their fabrication process might be impractical and time consuming, creating the need for a quick and accurate method of measuring RI of different substances. A simple method for simultaneous measurement in real-time of RI and thickness of polymer thin films is presented, allowing dynamic measurements in the presence of changing environmental parameters, such as temperature or humidity. This method, which does not require previous calibration, is based on an inline Fabry-Perot (FP) cavity, created by dipping the tip of a cleaved optical fiber (OF) in a polymer solution. The procedure consists of using the equations of the low finesse FP interferometers to directly extract information from the structure created, such as RI and cavity length, by working in the spectral window from 1500 to 1600nm. The method was validated by creating FP cavities with liquids of known RI, for which a typical precision of ${3}times {10}^{-{3}}$ was achieved, along with errors lower than 0.6% and 1% for RI and cavity length determination, respectively. The procedure was then used to monitor three different curing processes, namely the temperature curing of Sylgard™ 184, the UV curing of Norland Optical Adhesives™ 65 and the mixing and curing of Ceys™ Araldite epoxy glue. Both RI and cavity length were compared to reference values, showing excellent agreement with the experimental results for a method that does not require external calibration.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Research on Vector Bending SPR Sensor Based on V-Groove Fiber

    • Free pre-print version: Loading...

      Authors: Yong Wei;Lingling Li;Chunlan Liu;Xiaoling Zhao;Tianci Jiang;Rui Wang;Chen Shi;Chunbiao Liu;
      Pages: 11740 - 11746
      Abstract: Bending curvature measurement and bending direction identification are very important in the field of mechanical engineering. This study aimed to realize the high sensitivity direction recognition and curvature sensing based on SPR mechanism by a V-groove fiber SPR vector bending sensor. By CO2laser, multiple evenly spaced V-grooves were fabricated on the step multimode fiber. The V-groove fiber coupled the core mode to the cladding mode. By rotatably coating 50nm gold film on the cladding behind the V-groove area, and splicing with a multimode fiber with larger diameter to receive the light with SPR signal, the fiber SPR sensor was realized. When the sensing probe was concave inward, the V-groove was squeezed, the angle became smaller, and the excited cladding mode changed with it, resulting in the increase of SPR incident angle, the resonance valley blue shifted, and the sensitivity of curvature sensing wavelength is −5.98nm/m−1. Similarly, when the sensing probe was convex outward, the angle became larger, resulting in the decrease of SPR incident angle, the resonance valley red shifted, and the sensitivity of curvature sensing wavelength is 1.42nm/m−1. The proposed V-groove fiber SPR sensor realized the bending curvature measurement and bending direction identification. The manufacturing method is simple and the sensitivity is high, which opens up a new way for application of the SPR technology in bending measurement.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • DFB Fiber Laser Based on External Optical Feedback Theory Applied in
           Long-Distance Power Transformer Partial Discharge Detection

    • Free pre-print version: Loading...

      Authors: Jixian Qiao;Weichao Zhang;Shuai Liu;Hao Wu;Hong Zhao;
      Pages: 11747 - 11755
      Abstract: Active fiber optic acoustic emission (AE) sensors cause external optical feedback (EOF) in engineering applications, resulting in performance loss when used for long-distance partial discharge detection in power transformers. This work employs a distributed feedback fiber laser (DFB-FL) as a sensor to construct a partial discharge detection system. Furthermore, two schemes that improve the long-distance detection performance of DFB-FL are proposed by Suppressing EOF (S-EOF) and Introducing EOF (I-EOF). Then, partial discharge tests in an 80 kVA power transformer validate that both schemes can implement long-distance partial discharge detection. Experimental results indicate that the S-EOF system has a significant signal-to-noise ratio, which is 6.01 dB higher than the piezoelectric transducer (PZT). The S-EOF scheme is suitable for long-term online partial discharge monitoring. The I-EOF system has a high sensitivity for partial discharge detection, and its detectable discharge magnitude is lower than 65.8 pC. The I-EOF scheme is applicable for short-term systemic sampling and daily inspections.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • An Improved Strain Sensor Based on Long-Period Fiber Grating With a Local
           Ellipse-Core Structure

    • Free pre-print version: Loading...

      Authors: Cuiting Sun;Chupeng Lu;Yiwei Ma;Xudong Chen;Zeju Rui;Zihang Xiang;Xiren Jin;Yongyao Chen;Tao Geng;Weimin Sun;Libo Yuan;
      Pages: 11756 - 11762
      Abstract: In this paper, we proposed a novel long-period fiber grating strain sensor based on local ellipse-core fiber (LEC-LPFG). The LEC-LPFG was fabricated by laser polishing and local heating methods, which could efficiently transform a single-mode fiber (SMF) into a periodic ellipse-core fiber. The effects associated with the fiber elliptical core shape were shown to significantly influence the strain sensitivity of the LEC-LPFG. Further, it was demonstrated that the LEC-LPFG has a high strain sensitivity of −20.2 pm/ $mu varepsilon $ and a temperature sensitivity of 68.6 pm/°C. Our work provides a new technology for straightforward, low-cost, and large-scale productions of premium fiber optical strain sensors which are desirable in many practical applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Study of Tensile and Fatigue Loading Effects on the Performance of
           Metal-Packaged FBG Strain Sensor Developed for Cryogenic Applications

    • Free pre-print version: Loading...

      Authors: Ya-Li Wang;Yun Tu;Shan-Tung Tu;
      Pages: 11763 - 11774
      Abstract: Strain measurements are the fundamental task for structural health monitoring of critical equipment for cryogenic applications. However, the failure of strain sensors has been inevitable due to cyclic loading and/or over-loading. Therefore, a durable metal-packaged FBG sensor for strain measurements at cryogenic temperatures is successfully developed by combining magnetron sputtering and electroplating processes. Tensile and fatigue loading effects on the performance of the sensors are evaluated for the first time by uniaxial tensile testing at constant cryogenic temperatures down to 80 K and tension-tension fatigue testing at 133 K to verify their durability and reliability. The sensors which have high-quality multilayer metallic layers and good interfacial bonding exhibit markedly higher sensitivity than that of the bare FBG, with good linearity, stability, and repeatability and without obvious hysteresis. Fatigue failure of the sensors may occur due to fatigue fracture of the optical fibers with the gratings at a certain high strain level and due to zero shift caused by interface debonding or cracking at a relatively lower strain level. As the strain level is lowered, the sensor fatigue life is increased. There is at least one substantially lower strain level at which the sensors with good linearity, repeatability, and stability do not fail at the maximum number of test cycles. These results indicate that the metal-packaged FBG sensors are promising candidates for long-term measurement of strains, and thus monitoring structural health of critical equipment at cryogenic temperatures.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Sensitive Fiber Optic Sensor for Rapid Hot-Spot Detection at Cryogenic
           Temperatures

    • Free pre-print version: Loading...

      Authors: Xiyong Huang;Mike Davies;Dominic A. Moseley;Jofferson T. Gonzales;Hubertus W. Weijers;Rodney A Badcock;
      Pages: 11775 - 11782
      Abstract: The incredible current density and high magnetic field generation possible with high-temperature superconductors (HTS) have the potential to revolutionize energy generation, e.g. high-power generators, and compact fusion energy devices. However, an open issue that limits the applications of this class of superconductors is the challenge of rapidly detecting a hot spot which can lead to a quench. Owing to the inherent advantages of fibre optic sensors, they are promising candidates to be integrated in HTS magnets for hot-spot detection. In this paper, closely spaced fibre Bragg gratings (FBG) with the same Bragg wavelength are used to establish an ultra-long FBG (ULFBG) for distributed hot-spot monitoring. We investigate the capability of a 10m ULFBG to detect a small temperature rise at the end of the sensor. The results show that a 10 m long ULFBG can rapidly detect a small hot spot within 1 K temperature rise at 80 K. It is expected that ULFBG can be wavelength-division multiplexed and integrated to superconducting coils to achieve long-distance hot-spot monitoring with extremely high spatial resolution and fast response.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Magnesium-Doped ZnO Nanorod Electrolyte–Insulator–Semiconductor (EIS)
           Sensor for Detecting Organic Solvents

    • Free pre-print version: Loading...

      Authors: Ensaf Mohammed. Al-Khalqi;Muhammad Azmi Abdul Hamid;Naif H. Al-Hardan;Lim Kar Keng;Azman Jalar;
      Pages: 11783 - 11790
      Abstract: Electrolyte–Insulator–Semiconductor (EIS) chemical sensor based on vertically aligned Mg doped ZnO nanorod is fabricated to detect different organic solutions at room temperature. The Mg doped ZnO nanorod was prepared by hydrothermal method over Si substrate. The X-ray diffraction (XRD) patterns and field emission scanning electron microscopy (FESEM) images confirm the Mg doped ZnO nanorods have good crystal quality and grow along with the (0 0 2) direction. Additionally, X-ray photoelectron spectroscopy (XPS) results suggest the existence of the Mg into ZnO crystal lattice. The sensing characteristics of Mg-doped ZnO nanorod toward detecting different organic solvents exhibit an excellent response to 2-methoxyethanol with a high sensitivity of 694 $mu text{V}$ /ppm, low drift rate of 0.062 mV/h and low limit of detection of 185 ppm. The selectivity results shows that the Mg doped ZnO nanorod is most selective to 2-methoxyethanol against acetone, ethanol and methanol.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Low-Cost Formaldehyde Sensor Evaluation and Calibration in a Controlled
           Environment

    • Free pre-print version: Loading...

      Authors: Arnab Chattopadhyay;Andres Huertas;Andrew Rebeiro-Hargrave;Pak Lun Fung;Samu Varjonen;Tuomas Hieta;Sasu Tarkoma;Tuukka Petäjä;
      Pages: 11791 - 11802
      Abstract: Formaldehyde is a carcinogenic indoor air pollutant emitted from common wood-based materials. Low-cost sensing of formaldehyde is difficult due to inaccuracies in measuring low concentrations and susceptibility of sensors to changing indoor environmental conditions. Currently gas sensors are calibrated by manufacturers using simplistic models which fail to capture their complex behaviour. We evaluated different low-cost gas sensors to ascertain a suitable component to create a mobile sensing node and built a calibration algorithm to correct it. We compared the performance of 2 electrochemical sensors and 3 metal oxide sensors in a controlled chamber against a photo-acoustic reference device. In the chamber the formaldehyde concentrations, temperature and humidity were varied to assess the sensors in diverse environments. Pre-calibration, the electrochemical sensors (mean absolute error (MAE) = 70.8 ppb) outperformed the best performing metal oxide sensor (MAE = 335 ppb). A two-stage calibration model was built, using linear regression followed by random forest, where the residual of the first stage acted as a input for the second. Post-calibration, the metal oxide sensors (MAE = 154 ppb) improved compared to their electrochemical counterparts (MAE = 78.8 ppb). Nevertheless, the uncalibrated electrochemical sensor showed overall superior performance hence was selected for the mobile sensing node.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Novel Capacitive Microwave Power Sensor Based on Double MEMS Cantilever
           Beams

    • Free pre-print version: Loading...

      Authors: Chen Li;Jijun Xiong;Debo Wang;
      Pages: 11803 - 11809
      Abstract: In order to improve the sensitivity characteristic and the fabrication reliability, a novel capacitive power sensor based on double MEMS cantilever beams is proposed in this work. It is designed and fabricated using GaAs MMIC process and MEMS technology. A lumped circuit model is built to study the microwave characteristic of this capacitive microwave power sensor. The influence of impedance value and electric length of the coplanar waveguide on the microwave performance of the double MEMS beams are studied. The microwave characteristic of both ports are measured. The return loss of port A is from −11.5dB to −14.6dB, and the return loss of port B is from −12.1dB to −14.3dB at 8-12GHz. The insertion loss of port A is from −3.6 dB to −2.8dB, and the insertion loss of port B is from −3.7 dB to −2.9dB at 8-12GHz. The measured results show that this capacitive power sensor has a good microwave characteristic. The measured sensitivity of this capacitive microwave power sensor is about 51.6 fF/W @10 GHz, and the theoretical sensitivity is 55.97 fF/W. The relative error is 7.8%. Compared with the capacitive microwave power detection system based on single cantilever beam, the sensitivity characteristics have been greatly improved. It is valuable to realize the multiple beams detection technology and enable further interesting possibilities in the field of microwave power detection.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Equivalent Circuit Models for SAW Delay Line Sensors

    • Free pre-print version: Loading...

      Authors: Raphael C. O. Jesus;Elyson A. N. Carvalho;Ollivier Tamarin;Raimundo C. S. Freire;Corinne Dejous;
      Pages: 11810 - 11818
      Abstract: Surface acoustic wave (SAW) devices are widely used as high quality factor filters for telecommunications. In recent years they have been investigated as sensors, which calls for application-specific electronic systems that interact with them. To design such circuits, it is important to understand the peculiarities of the devices, which may be a barrier for system designers. In this work, we explore different models that can be simulated in any circuit simulation environment, facilitating the creation of systems that interact with SAW delay lines, devices in which a signal is delayed by a constant time between piezoelectric transducers. The equivalent circuits of acoustic delay lines can be divided in two categories: a lumped-element model, based on frequency-dependent impedances implemented through the Foster and Mittag-Leffler theorems, and a distributed-element model, based on transmission lines. We compare qualitative frequency and time-domain characteristics among these categories so their limitations are clear, with the lumped-element model not being usable in the time domain due to its singularity at the center frequency. We also describe the process of implementation to achieve the model, while proposing a simple circuit block that can be included to represent perturbations on the device, which allows adjusting of the model response to different perturbations. Our model is also suitable for tests in distinct liquid media, provided that the parallel capacitor is fitted accordingly to model the electromagnetic wave induced in the device.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • T-ZnO/AlGaN/GaN HEMT Uric Acid Sensor-Sensitivity Analysis and Effect of
           Surface Wettability for Improved Performance

    • Free pre-print version: Loading...

      Authors: Praveen Pal;Yogesh Pratap;Sneha Kabra;
      Pages: 11819 - 11826
      Abstract: In this work, a ZnO-Tetrapod (T-ZnO) bio-functionalized AlGaN/GaN HEMT based biosensor has been designed for detection of uric acid (UA) present in human serum. The proposed device is easy to fabricate and offers maximum drain current sensitivity of $92.5 mu text{A}$ . $mu text{M}$ −1 and maximum threshold voltage sensitivity of 0.0229 mV. $mu text{M}$ −1 at UA concentration of $55 mu text{M}$ . Sensitivity has been evaluated by normalizing the drain current, transconductance and channel conductance with respect to gate width. Maximum transconductance and channel conductance sensitivity obtained are 0.0151 mS/ $mu text{M}$ and 0.0521 mS/ $mu text{M}$ respectively at $55mu text{M}$ concentration. An analytical model has also been developed for drain current which shows good match with the simulated results and previously reported experimental data. Due to hydrophilic nature of AlGaN barrier layer the sensitivity of the sensor changes rapidly with change in surface wettability conditions thus the impact of variation of aluminum (Al) composition in AlGaN barrier layer and surface wettability on device performance has also been studied. The sensor exhibits a very low threshold voltage ( $text{V}_{th}$ ) hysteresis of 1.9 mV at $text{V}_{G-sweep}=$ (- 8V, 0V) w-th a low response time of $260 mu text{s}$ .
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Metal Particles Velocity Measurement Based on Capacitance Sensor With
           Double Triangular-Shaped Electrodes

    • Free pre-print version: Loading...

      Authors: Heming Gao;Yingxing Min;Qi Chang;
      Pages: 11827 - 11834
      Abstract: How to measure the flow velocity of metal particles flowing in the pipeline is a urgent problem in mechanical manufacturing industry due to the complex flow state of metal particles. A novel capacitance sensor with double triangular-shaped electrodes (CSDTE) is presented for measuring the average velocity of metal particles flowing within a small-diameter pipeline. Firstly, the operating principle of CSDTE for measuring metal particles is theoretically deduced. Then the 3D simulation model of CSDTE is built by COMSOL software, and its axial sensitivity distribution is analyzed. On this basis, a quantitative mathematical relationship for velocity measurement is derived by analyzing spatial filtering effect of CSDTE. Finally, a CSDTE-based metal particles velocity measurement system is further developed and a gravity-fed particle flow experimental rig is designed to verify the measurement method. Experimental results indicate that the measurement method is feasible, and its repeatability error is less than 7% over the range of 2.44-5.34 m/s.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Pre-Detection Sensing With Multistage Low-Pass Type Negative Group Delay
           Circuit

    • Free pre-print version: Loading...

      Authors: Blaise Ravelo;Fayu Wan;Zhongzhu Yuan;Lala Rajaoarisoa;
      Pages: 11835 - 11846
      Abstract: This paper reveals a fascinating pre-detection technique of sensor signals by means of counterintuitive negative group delay (NGD) electronic function. The sensor signal pre-detection technique is unconventionally developed thanks to the engineering of low-pass (LP) type NGD circuit. The NGD pre-detection basic principle is introduced. After topological description, the LP-NGD cell design method in function of negative time delay specification is formulated. An innovative analysis of time-advance property related to multi-stage LP-NGD cell is elaborated. To demonstrate the LP-NGD pre-detection technique feasibility, a proof of concept (POC) is designed with fabricated 3-stage LP-NGD printed circuit board (PCB) prototype integrating 4 LED detectors. The NGD pre-detection experimental setup is described. Extraordinary pre-detection with −0.5 s negative time propagation visible with LED on-lightning is demonstrated by considering a sinc pulse input signal. The developed NGD pre-detection technique can be useful in the future for the anticipation of industrial equipment damages and failure pre-detection sensor system in real-time.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Cross-Domain Few-Shot Learning Approach for Lithium-Ion Battery Surface
           Defects Classification Using an Improved Siamese Network

    • Free pre-print version: Loading...

      Authors: Ke Wu;Jie Tan;Chengbao Liu;
      Pages: 11847 - 11856
      Abstract: It is difficult to detect the surface defects of a lithium battery with an aluminum/steel shell. The reflectivity, lack of 3D information on the battery surface, and the shortage of many datasets make the 2D detection method hard to apply in this field. In this paper, a cross-domain few-shot learning (FSL) approach for lithium-ion battery defect classification using an improved siamese network (BSR-SNet) is proposed. To obtain the critical 3D surface of the lithium-ion battery, a multiexposure-based structured light method is utilized. Then, the heights of the 3D cloud points are transferred to grayscale information and are saved as 8-bit 2D images. For the FSL task, the DAGM 2007 datasets are used as the source domain to pre-train the improved siamese model. To avoid negative mitigation in the target domain, batch spectral regularization (BSR) is added as a penalizer in the loss function. The accuracies of the experimental results are 93.3% for 10-shot batteries and 91.0% for 5-shot batteries, which means that our method can be used to classify the surface defects of lithium batteries well.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A GNSS/IMU/Vision Ultra-Tightly Integrated Navigation System for Low
           Altitude Aircraft

    • Free pre-print version: Loading...

      Authors: Zhaoyang Zuo;Bo Yang;Ze Li;Tianhao Zhang;
      Pages: 11857 - 11864
      Abstract: In order to solve the problem of poor anti-interference ability of GNSS/IMU ultra-tight integrated navigation system under high dynamic and harsh environments, a visual assisted GNSS/IMU ultra-tight integrated navigation method is studied. The attitude and position information provided by binocular vision are introduced into the GNSS/IMU ultra-tight integrated navigation system. The state equation of the traditional ultra-tight integrated navigation system is used as the state equation of the visual assisted GNSS/ IMU ultra-tight integrated navigation system. Meanwhile, the pseudorange of SINS and GNSS, the difference of pseudorange rate, the platform misalignment angle of SINS, the position and attitude error of SINS and vision are used as the measurement information of the ultra-tight combination of GNSS/IMU assisted by vision. On this basis, the fuzzy control method is used to replace the traditional federated Kalman filter to fuse the information of the navigation structure of the two self-filters, and the navigation method is simulated and verified by C #. The simulation results show that the computational system performance is improved 52% by using the fuzzy control method compared with the traditional federal Kalman filter method, and the attitude error is significantly reduced in the ultra-tight integrated navigation system with visual assistance. When the GNSS signal is interfered by strong noise, the tracking accuracy of GNSS/IMU ultra-tight integrated navigation system assisted by binocular vision can effectively reduce the navigation error. And the position and velocity errors of the system are kept within 5.0m/s and 0.3m/s, respectively, which effectively solves the navigation problem of low altitudeaircraft in the case of GNSS signal occlusion or interference.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Fault Diagnosis for Limited Annotation Signals and Strong Noise Based on
           Interpretable Attention Mechanism

    • Free pre-print version: Loading...

      Authors: Biao Chen;Tingting Liu;Chao He;Zecheng Liu;Li Zhang;
      Pages: 11865 - 11880
      Abstract: Deep learning methods based on vibration signals of rotating machinery have been continuously developed in fault diagnosis. However, there are still three challenges in intelligent fault diagnosis: (1) Limited annotation data; (2) Interference of strong noise; (3) Continuous changes of signals due to working conditions. To solve the problems above, a method based on dual-path convolution with attention mechanism and capsule network (WDACN) is established for efficient diagnosis, where the more dominant informative segments of vibration signal are focused by a novel attention mechanism, namely, Multi-branch Parallelized Attention Mechanism (MBPAM). Besides, an improved visualization method—Gradient Score Class Activation Mapping (GS-CAM) is proposed to analyze the attention distribution on time domain signals from the perspective of interpretability. Experiments are conducted on the data of bearings and gearbox, which prove that WDACN has excellent capacities of generalization and robustness.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • LJDA-Net: A Low-Rank Joint Domain Adaptation Network for Industrial Sample
           Enhancement

    • Free pre-print version: Loading...

      Authors: Yifu Ren;Jinhai Liu;Yingjiao Chen;Wei Wang;
      Pages: 11881 - 11891
      Abstract: The small sample problem is a challenge for data-driven industrial fault diagnosis. The available sample enhancement works have limited application in real industry, due to the difficulty in achieving the expected efficiency and accuracy performance of modeling. To address this issue, this paper proposes a low-rank joint domain adaptation network (LJDA-Net). First, we design the low-rank feature extraction subnet to effectively alleviate the limitation of feature redundancy and improve model training efficiency while ensuring feature accuracy. Second, we design the adaptively joint distribution alignment subnet, in which the internal multi-source domains and each source-target domain are jointly aligned, meanwhile, the correlation of each source-target domain is embedded in the alignment as the adaptive term, thus the performance of sample distribution alignment can be improved. By doing so, fault samples in other working conditions can be employed to enhance the small samples in current working conditions. Finally, benchmark simulated experiments and actual application experiments are conducted to evaluate the proposed method. All the results demonstrate that our LJDA-Net performs favorably against the state-of-the-art methods.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Deep Learning Approach for Detecting Work-Related Stress Using Multimodal
           Signals

    • Free pre-print version: Loading...

      Authors: Wonju Seo;Namho Kim;Cheolsoo Park;Sung-Min Park;
      Pages: 11892 - 11902
      Abstract: Work-related stress causes serious negative physiological and socioeconomic effects on employees. Detecting stress levels in a timely manner is important for appropriate stress management; therefore, this study proposes a deep learning (DL) approach that accurately detects work-related stress by using multimodal signals. We designed a protocol that simulates stressful situations and recruited 24 subjects for the experiments. Then, we collected electrocardiogram (ECG), respiration (RESP), and video data. The datasets were pre-processed and 10-s ECG and RESP signals and a sequence of facial features were fed into our deep neural network. Sixty-eight facial landmarks’ coordinates were extracted, and facial textures were extracted from a pre-trained network based on facial expression recognition. Each signal was processed by each of its network branch, and data were fused at two different levels: 1) feature-level and 2) decision-level. The feature-level fusion that used RESP and facial landmarks’ coordinates showed average accuracy of 73.3%, AUC of 0.822, and F1 score of 0.700 in two-level stress classification, and the feature-level fusion that used ECG, RESP, and the coordinates showed average accuracy of 54.4%, AUC of 0.727, and F1 score of 0.508 in three-level stress classification. When analyzing the weights in the decision-level fusion, we found that the importance of each information item varied according to the stress classification problem. When comparing t-stochastic neighbor embedding results, we observed that overlapped samples of different classes caused performance degradation in both classifications. Our findings suggest that the proposed DL approach fusing multimodal and heterogeneous signals can enhance stress detection.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Traffic Awareness Through Multiple Mobile Sensor Fusion

    • Free pre-print version: Loading...

      Authors: Elnaz Namazi;Rudolf Mester;Jingyue Li;Chaoru Lu;Meng Tang;Ying Xiong;
      Pages: 11903 - 11914
      Abstract: An intelligent traffic management system (ITMS) benefits from an accurate and global awareness of the traffic status obtained through traffic data. A growing body of literature recognizes the importance of modern vehicles (MVs) equipped with sensing devices to estimate the traffic data to generate knowledge about the traffic scene. One critical issue is how to use a low-cost sensor mounted on an MV with the purpose of increasing generalizability related to the used sensor and provide the required traffic data. In addition, the sensing coverage of a single vehicle is limited to a specific range. Multiple sensor fusion can be used to overcome these issues. This paper focuses on analyzing target vehicle’s geolocation estimated by multiple vehicles equipped with a low-cost monocular camera and proposes a new methodology for fusing those data by considering sensor estimation uncertainties. Our objective is to use the sensor fusion technique to improve the target vehicle’s geolocalization estimation accuracy and provide a more comprehensive picture of the traffic status than what can be obtained by using data from only one vehicle. Our proposed methodology includes two primary steps: (1) target vehicle re-identification to determine whether observing vehicles are identifying the same target vehicle, and (2) multiple sensor fusion to dynamically integrate the estimated geolocation of the target vehicle. Our proposed methodology provides one of the first investigations into using data from multiple moving vehicles to estimate and fuse real traffic data. The experiments show that our proposed methodology performs well to enhance geolocation estimation accuracy.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Data Driven Calibration of Color-Sensitive Optical Sensor by Supervised
           Learning for Botanical Application

    • Free pre-print version: Loading...

      Authors: Shinya Ueno;Osamu Sakai;
      Pages: 11915 - 11927
      Abstract: Color is a fundamental and informative element in botanical applications, as it is in our various daily activities. The conventional ways of evaluating colors based on human visibility are person-specific and thus likely to be biased, whereas color sensing equipment with high sensitivity and accuracy is often a very expensive product. Recently, nonlinear data conversions using machine learning, such as supervised learning, have become available in various fields, and such techniques may be useful for calibrations of low-cost, poor-accuracy sensors. In this study, we propose a method for color-value calibration applicable to low-cost sensors by supervised learning. Specifically, after a supervised learning procedure using training data based on a large number of color samples positioned on a two-dimensional color plane, we improve calibration accuracy over mixed-color areas containing red, green and blue values. We apply this calibrated sensor to estimating the ripeness level of Japanese pears, and the results indicate that the chroma shown in a color coordinate is a good measure for this purpose. In another botanical case, temporal changes in color values of autumn tree leaves in chroma and hue could be successfully detected. This technology is aimed at use with low-cost consumer electronic devices, and it is applicable to color sensing in other areas beyond in the botanical field, thus showing high potential for other kinds of low-cost sensors.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Power Spectrum Pattern Difference-Based Time-Frequency Sub-Band
           Selection Method for MI-EEG Classification

    • Free pre-print version: Loading...

      Authors: Liangsheng Zheng;Yue Ma;Pengchen Lian;Yang Xiao;Zhengkun Yi;Qiuzhi Song;Wei Feng;Xinyu Wu;
      Pages: 11928 - 11939
      Abstract: Motor imagery-based brain-computer interface (MI-BCI) is considered to be the most promising technology, which can help patients with muscle disorders to carry out rehabilitation training and assist in activities of daily living. However, the time window and frequency band of the ERD/ERS pattern activated by motor imagery vary from person to person. In this study, we propose a power spectrum pattern difference-based time-frequency sub-band selection (PSPD) method, which can further improve the classification performance of MI EEG. To better analyze the sensorimotor rhythm, we divide the EEG channels and extract the average and maximum pattern difference of the power spectrum according to the division. Then, the difference metric of the power spectrum pattern difference of inter-sample sets is calculated by dynamic time warping distance, and the consistency metric of the power spectrum pattern difference of intra-sample sets is calculated by mean pooling, and the optimal time-frequency sub-band is selected based on these two metrics. Finally, the optimal time-frequency sub-bands are respectively used for the common spatial pattern feature extraction and the linear kernel support vector machine classification. The proposed PSPD method achieves an average classification accuracy of 84.51%, 84.10% and 73.21% in BCI Competition IV Dataset IIa, Dataset IIb and OpenBMI dataset, respectively. Compared with the state-of-the-art methods, the PSPD method achieves the highest classification accuracy and good stability on three datasets. Excellent experimental results show that the PSPD method has great potential in MI-BCI’s motion intention decoding.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Application of a Tactile Sensor for the Gait Phase Classification for an
           Exoskeleton

    • Free pre-print version: Loading...

      Authors: Ming-Chan Lee;Cheng-Hsin Chuang;Da-Huei Lee;Cheng-Tang Pan;Kuan-Ming Li;
      Pages: 11940 - 11953
      Abstract: This paper presents the design and implementation of fuzzy logic control for human walking using lab-developed sensors and the second lower limb robotic exoskeleton (LLRE-II) to detect gait phases. A piezoresistive tactile sensor (PRTS) was developed and used to measure the foot pressure to predict human walking behavior. The proposed sensor with grid structures was then fabricated using polymer composites of multi-walled carbon nanotubes (MWCNTs) combined with polydimethylsiloxane (PDMS). The doping ratio and design of the grid structure of the sensors were optimized to achieve a good sensing range. The foot reactive forces measured by the four sensors were calculated and converted to force ratio factors to improve the performance of gait phase detection by the fuzzy logic control. Based on the sensor tests, the most stable doping ratio was fixed as 7 wt%, and the ratio of the grid structure (line width:line spacing:thickness) was 1:1:1. Sensors were set in the soles of the shoes on the foot pads of the exoskeleton, and the foot reactive forces were captured for analysis. The performance of the proposed algorithm for gait phase detection was verified experimentally. The results indicated that both regular and irregular walking could be detected by the proposed method.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Feature-Fused Convolutional Neural Network for Emotion Recognition From
           Multichannel EEG Signals

    • Free pre-print version: Loading...

      Authors: Qunli Yao;Heng Gu;Shaodi Wang;Xiaoli Li;
      Pages: 11954 - 11964
      Abstract: Automatic emotion recognition based on multichannel electroencephalogram (EEG) data is a fundamental but challenging problem. Some previous researches ignore the correlation information of brain activity among the inter-channel and inter-frequency bands, which may provide potential information related to emotional states. In this work, we propose a 3-D feature construction method based on spatial-spectral information. First, power values per channel are arranged into a 2-D spatial feature representation according to the position of electrodes. Then, features from different frequency bands are arranged into a 3-D integration feature tensor to capture their complementary information. Simultaneously, we propose a novel framework based on feature fusion modules and dilated bottleneck-based convolutional neural networks (DBCN) which builds a more discriminative model to process the 3-D features for EEG emotion recognition. Both participant-dependent and participant-independent protocols are conducted to evaluate the performance of the proposed DBCN on the DEAP benchmark datasets. Mean 2-class classification accuracies of 89.67% / 90.93% (for participant-dependent) and 79.45% / 83.98% (for participant-independent) were respectively achieved for arousal / valence. These results suggest the proposed method based on the integration of spatial and spectral information could be extended to the assessment of mood disorder and human-computer interaction (HCI) applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Camera-Augmented Non-Contact Vital Sign Monitoring in Real Time

    • Free pre-print version: Loading...

      Authors: Arash Shokouhmand;Samuel Eckstrom;Behnood Gholami;Negar Tavassolian;
      Pages: 11965 - 11978
      Abstract: This study develops a camera-guided frequency-modulated continuous-wave (FMCW) radar to monitor vital signs. A red-green-blue-depth (RGB-D) camera estimates the human torso landmarks and a processing unit constantly adapts the radar beams to the direction of the subjects. To constantly optimize the regions of interest for monitoring respiratory rate (RR) and heart rate (HR), a novel method, coined “singular value-based point detection (SVPD),” is designed. Vital sign extraction is then followed as the last step. Experiments are conducted for the cases of single-subject (10 subjects, 31 scenarios, and 1550 repetitions) and dual-subject monitoring (6 subjects, 6 scenarios, and 90 repetitions). Average (RR, HR) accuracies of (97.68%, 85.88%), (90.02%, 86.05%), (96.71%, 89.50%), and (97.52%, 86.71%) are achieved for the range of distances (0.5-2.5 m), azimuth angles (0°–30°), elevation angles (−30°–+30°), and incident angles (−30°–+30°), respectively. The higher chest and upper abdomen are determined as the optimal regions for RR and HR estimation respectively, with average accuracies of 98.31% and 86.93%. Finally, the capability of dual-subject monitoring at various inter-subject distances (range of 20–70 cm) is confirmed with average accuracies of 92.26% and 73.23% for RR and HR respectively.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Attention-Based Deep Learning Framework for Hemiplegic Gait Prediction
           With Smartphone Sensors

    • Free pre-print version: Loading...

      Authors: Dipanwita Thakur;Suparna Biswas;
      Pages: 11979 - 11988
      Abstract: This research revealed a reliable hemiplegia gait monitoring strategy to help medical practitioners in keeping track of a patient’s status. Although numerous technologies have been utilized in the past to collect motion data from patients, the high costs and huge spaces required make them challenging to use in a home setting for rehabilitation. A telemedicine protocol requires a reliable patient monitoring technique that can automatically record and classify patient movements. To achieve this, we propose an attention-based deep learning framework for hemiplegia gait prediction with smartphone-based sensory data, i.e., accelerometer and gyroscope. Firstly, convolutional neural network long short-term memory (CNN-LSTM) architecture is proposed to automatically learn potential features from the high-frequency sensory data. Moreover, considering the effectiveness of the domain expert knowledge-based hand-engineered features for gait analysis, we combine the automatically learned features and the extracted hand-engineered features from sensory data. Secondly, an attention network is proposed to tune the significance of two different features, considering these two different sourced features may be complementary to each other. Finally, extensive experiments are carried out to establish the effectiveness of the suggested hemiplegia gait prediction method in the evaluation of $5times $ 2 fold cross-validation and leave-one-subject-out (LOSO) cross-validation, which is more difficult and practical.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Deep Learning Approaches for Air-Writing Using Single UWB Radar

    • Free pre-print version: Loading...

      Authors: Nermine Hendy;Haytham M. Fayek;Akram Al-Hourani;
      Pages: 11989 - 12001
      Abstract: Air-writing is an emerging and promising method for contactless human-machine interaction. This paper proposes a novel air-writing framework based on a single ultra wide-band radar (UWB). The framework employs a simple data capture and a processing pipeline facilitated by deep learning approaches, where a number of data representations and models are explored. Two different data representations are proposed, two-dimensional and three-dimensional range-Doppler spectrogram. The deep learning approaches include, fully connected neural networks, convolutional neural networks, three-dimensional convolutional neural networks, and hybrid two-dimensional and three-dimensional convolutional neural networks long short-term memory recurrent neural networks. A dataset of 1,800 samples containing 10 air-written numbers is collected to train, validate, and test the performance of the proposed methods. It is shown that hybrid convolutional neural networks long short-term memory recurrent neural networks architectures can effectively predict air-written numbers with an accuracy of 98.5%. The experimental results suggest the efficacy of the proposed approaches for practical and convenient air-writing applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • BPNN Based Indoor Fingerprinting Localization Algorithm Against
           Environmental Fluctuations

    • Free pre-print version: Loading...

      Authors: Yaqin Xie;Kailiang Wang;Hai Huan;
      Pages: 12002 - 12016
      Abstract: For mobile users, the ability to accurately acquire their own location is critical. By locating, mobile users can get information about their environment and access location-related services. RSS fingerprint-based indoor localization method collects a training database of measurement fingerprints and uses a machine learning classifier to determine a person’s location from a new fingerprint. However, as the environment changes over time due to furniture or other objects being moved, the new fingerprints diverge from those in the original database. Therefore, an RSS-difference based localization system is designed to deal with the above problem. This method combines back-propagation neural network (BPNN) and weighted K-Nearest Neighbor (WKNN) method to improve the fingerprint similarity based indoor location method (FSIL). We train BPNN in the off-line stage to obtain the optimal BPNN parameter settings. In the online stage, K nearest neighbor points are firstly selected based on the improved FSIL algorithm, and then the difference of signal strength values between the K nearest neighbor points and the target user is input into the BPNN network, to obtain the Euclidean distance between the K nearest neighbor points and the target user, and finally the WKNN algorithm is used to obtain the user’s ultimate location. Simulation experiments based on the LDPL model and the Wireless Insite software, as well as the test results based on the indoor localization dataset IPIN2016, show that the localization accuracy in complex indoor scenarios can be improved by at least 11% when using the method proposed in this paper.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Two-Stage DNN Model With Mask-Gated Convolution for Automotive Radar
           Interference Detection and Mitigation

    • Free pre-print version: Loading...

      Authors: Shengyi Chen;Jalal Taghia;Uwe Kühnau;Nils Pohl;Rainer Martin;
      Pages: 12017 - 12027
      Abstract: As the number of radar sensors on the road increases rapidly and many of these sensors share the same frequency spectrum, mutual interference cannot be avoided. This paper introduces a novel automotive radar interference mitigation approach using an autoencoder model which consists of separate neural networks for the detection and reconstruction steps. A mask-gated convolution is proposed to help the reconstruction neural network to learn the signal pattern from interference-free samples and to interpolate accordingly the signal segments at the disturbed positions. Through perturbation analysis it is shown that the reconstruction neural network can recover the distorted samples by utilizing their surrounding relevant samples. By exploiting the nature of interference in real-world scenarios, the proposed training approach does not need hand-labeled training data. Together with the proposed composite training loss, the neural network can recover the disturbed discrete beat signal with remarkable improvements in the signal-to-interference-plus-noise ratio (SINR) and the mean absolute percentage error (MAPE). Moreover, despite the use of a purely simulated training data set, the autoencoder can deal with real-world radar measurements which are more complex than the training data set.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Equivalent Pipeline Processing for IR-UWB and FMCW Radar Comparison in
           Vital Signs Monitoring Applications

    • Free pre-print version: Loading...

      Authors: Alexandra Lopes;Daniel F. Noronha Osório;Hugo Silva;Hugo Gamboa;
      Pages: 12028 - 12035
      Abstract: Several studies have been showing radars as promising contactless vital signs monitoring tools, with IR-UWB and FMCW, being the most popular radar architectures implemented for vital signs remote sensing applications. However, most of the studies focus on developing algorithms for processing data extracted from one only type of radar and consequently, neglecting the effect that hardware choices have on their results and algorithms performance. To carry out a more complete pilot study and deal with the algorithms and hardware influences, we use two off-the-shelf radars with IR-UWB and FMCW characteristics and make them agnostic through the implementation of a data equivalent processing pipeline. To test it, a pilot test was developed from where the vital signs of 10 healthy participants were recorded with both radars and compared with “ground truth” data. Up to four radar-algorithms pairs were compared where an absolute minimum error of 0.3 respiration per minute for respiratory rate and 1.8 beats per minute for heartbeat rate were obtained among the four configurations. From our results, IR-UWB architecture was seen as the best option to be implemented in a bio-radar application. We recommend our equivalent processing pipeline method to assess bio-radar systems in a more realistic way.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • DSCNN: Dilated Shuffle CNN Model for SSVEP Signal Classification

    • Free pre-print version: Loading...

      Authors: Mengyu Li;Chao Ma;Weidong Dang;Ruiqi Wang;Yong Liu;Zhongke Gao;
      Pages: 12036 - 12043
      Abstract: The steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) system has attracted a lot of attention. It is a great challenge to increase the classification accuracy of SSVEP, especially in fatigue state. In this paper, we propose a dilated shuffle convolutional neural network (DSCNN) model to realize EEG-based SSVEP signal classification. Firstly, we conduct experiments to obtain SSVEP recordings in normal and fatigue states. Then combining continuous wavelet transform (CWT) and DSCNN, we construct a framework for realizing the SSVEP detection. In DSCNN, the signals are processed by three parallel dilated convolution layers firstly, then we extract the characteristics of the signals through channel shuffle and group convolution, while reducing the computational load. For normal condition, we reach an average accuracy rate of 96.75%, and for the data under fatigue state, the average accuracy of this method increases to 77.52%. Through the comparison with the existing methods, the effectiveness and advance of our method are proved, and the effect of channel shuffle on signal extraction is also demonstrated by comparison.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Rolling Bearing Fault Diagnosis Method Base on Periodic Sparse Attention
           and LSTM

    • Free pre-print version: Loading...

      Authors: Yiyao An;Ke Zhang;Qie Liu;Yi Chai;Xinghua Huang;
      Pages: 12044 - 12053
      Abstract: The rolling bearing fault signals are complex time series with complex dynamic characteristics and non-uniform periodicity due to the influence of random interference, such as random impulse noise and equipment vibration. This will affect the accuracy of the diagnostis method. This paper analyses the characteristics of bearing fault signals and discussed the basic ideas of current diagnosis methods. In order to decrease the computational complexity in time series analysis process and reduce the influence of random interference in subsequent feature extraction, this paper proposes a rolling bearing fault diagnosis method base on periodic sparse attention and LSTM (PSAL) for non-uniform bearing vibration signals. According to the periodic characteristics of bearing fault, a periodic sparse attention network is proposed to decrease the time consumption in the process of reducing the influence of random interference and enhancing the feature. Then, LSTM is used to extract long-term dependence features in the fault signals. Finally, two sets of rolling bearing datasets are adopted to verify the validity and superiority of the proposed method by comparing with other methods.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Novel Suppression Method of Height Drift for Pedestrian Navigation With
           a Circular Hypotheses on Terrain Slope

    • Free pre-print version: Loading...

      Authors: Jiasheng Wang;Xiangbo Xu;Zhibin Yu;Zhe Li;Siheng Liu;
      Pages: 12054 - 12063
      Abstract: The inertial navigation system (INS) based on zero velocity update (ZUPT) is a traditional method for pedestrian positioning. The height information of INS will drift over time. A common solution is to use external height information, such as barometer output, to compensate the drift. However, this method is easily affected by air temperature, wind speed, etc. A new geometric model method is proposed in this study to suppress the height drift. The inertial sensor is mounted at the shoe. The natural terrain with continuous slope changing is assumed to be circular arc-shaped every single step. A geometric model of three-dimensional step length is established using the horizontal step length and the pitch angle of the shoe. Four experiments were designed on flat, sloped, and natural terrains, respectively. The experimental results show that the accuracy of the proposed method is improved by more than 90% and 70% compared to the classical INS method and the INS method incorporating a barometer, respectively. The proposed method needs no external signal and is not influenced by the external environment. Furthermore, lightweight calculations make it appropriate for real-time applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A New Induced GNSS Spoofing Detection Method Based on Weighted
           Second-Order Central Moment

    • Free pre-print version: Loading...

      Authors: Wenlong Zhou;Zhiwei Lv;Xu Deng;Ye Ke;
      Pages: 12064 - 12078
      Abstract: Global navigation satellite system (GNSS) spoofing causes the victim receiver to deduce false positioning and timing data; this notably threatens navigational safety. Thus, anti-spoofing techniques that improve the reliability of GNSS systems, for which interference detection is critical, are essential. Based on the distortion of tracking loop correlation function symmetry of the target receiver caused by gradual adjustment of induced spoofing signals, we proposed a new induced spoofing detection method that uses the weighted second-order central moment (WSCM) difference in the time-domain transient response of multiple correlators of the left and right peaks to obtain the test statistic, theoretically proving that the test statistic follows Gaussian distribution. The Neyman-Pearson hypothesis test method is used to determine the optimal test threshold and determine whether the receiver is being spoofed. The proposed WSCM-based method for spoofing detection was compared with three conventional methods in Scenarios 4 and 7 of the Texas Spoofing Test Battery database, showing that the detection probability of the proposed method is at least 24.15% higher at a false alarm rate of 10% and is more advantageous at lower false alarm rates and the alert time is shortened by at least 30 seconds, enabling at least a 20% faster detection efficiency. The proposed method overcomes the problem of existing methods, which are associated with difficulties in capturing the subtle time-varying effects of the relative carrier phase between the spoofing and authentic signals; thus, it provides excellent detection accuracy and effectiveness, showing broad potential applicability in GNSS spoofing detection.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Fusion Map Matching Method for Low-Cost Ground Autonomic Positioning
           System

    • Free pre-print version: Loading...

      Authors: Tianxiao Song;Yu Chen;Xianmu Li;Zhaoyang Li;Li Xing;
      Pages: 12079 - 12085
      Abstract: Map matching technology is an effective way to improve the accuracy of ground inertial positioning systems by similarity comparison of vehicle trajectory and road shape characteristics. Traditional point matching algorithm can correct the inertial positioning errors in time through continuous projection operation, but it is prone to mismatch in urban complex road networks with similar shape features. Line matching algorithm can reduce the probability of false matching, but it demands to waiting obvious road bending characteristics, leading to the problem of untimely error correction. Therefore, an innovative fusion map matching method is proposed in this paper, aiming to make an organic combination of the advantages of point and line map matching algorithms, especially for accuracy improvement of low-cost ground autonomic positioning systems. Moreover, the paper discusses the influence of inertial positioning error characteristics on map matching accuracy, pointing out the original positioning errors with gentle fluctuation is more conducive to further improve map matching performance. The correctness and feasibility of proposed fusion matching method are demonstrated by a vehicle experiment, proving that positioning accuracy could be improved by 22.5% compared with traditional line map matching method.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Pair-Wise Orthogonal Classifier Based Domain Adaptation Network for Fault
           Diagnosis in Rotating Machinery

    • Free pre-print version: Loading...

      Authors: Zixu Chen;Wennian Yu;Xiaoxi Ding;Yimin Shao;Chris K. Mechefske;
      Pages: 12086 - 12097
      Abstract: Although machine learning methods have demonstrated their effectiveness in fault diagnosis in rotating machinery, there is a major assumption that the training data (source domain) and testing data (target domain) should share the same distribution. However, this assumption is difficult to hold in real scenarios considering the variable working conditions, and it recasts the fault diagnosis problem in a cross-domain manner. Recently, the adversarial domain adaptation methods have become a hot research topic, since they aim to address cross-domain issues and can be well embedded into convolutional neural networks. Most previous studies aimed to achieve the optimal alignment of data in a global view. Unfortunately, they may affect the data which are originally well aligned in the local view between the source domain and the target domain, thus leading to diminished diagnosis performance. In this paper, a pair-wise orthogonal classifier based domain adaptation network is proposed to address this issue. A feature extractor together with a pair-wise orthogonal classifier is designed to learn domain-invariant features from the source domain and the target domain. Then, based on the outputs of the pair-wise classifier, a dynamic weighted domain discriminator is designed to form an adversarial framework with a feature extractor. It considers the sample-level alignment in the domain adaptation process and enables the global alignment without sacrificing the original well-aligned data. Cross-domain experiments via two datasets are carried out to validate the performance of the proposed network. Performance comparisons with state-of-the-art methods are also made. The results have demonstrated the effectiveness and novelty of the proposed network.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Multisynchrosqueezing Transform Based Improved Time-Frequency
           Representation for Automated Contamination Severity Estimation of Overhead
           Line Insulator

    • Free pre-print version: Loading...

      Authors: Arup Kumar Das;Suhas Deb;Debangshu Dey;Biswendu Chatterjee;Sovan Dalai;
      Pages: 12098 - 12106
      Abstract: Contamination flashover of overhead line insulator is a serious problem which interrupts the power flow and affect reliability of transmission and distribution system. Therefore, timely and accurately estimation of contamination severity is a key to prevent contamination flashover henceforth enhancement of the reliability of transmission and distribution system. This paper presents an innovative and automated framework to estimate contamination level of overhead line insulator in service accurately employing surface leakage current (SLC) signal. In this framework, SLC signal procured at different contamination level has been analyzed in a joint time-frequency plane through Multisynchrosqueezing Transform. Thereafter, time-frequency spectrogram image obtained through Multisynchrosqueezing Transform has been fed to a configured CNN model for automated feature extraction and classification of SLC signals. Experimental results revealed that proposed framework is highly accurate and delivered better performance compared to other time-frequency spectrogram-based approach.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Vector Miss Distance Parameter Estimation Based on Direct Position
           Determination

    • Free pre-print version: Loading...

      Authors: Jinlong Ren;Guohua Wei;Xu Wang;
      Pages: 12107 - 12116
      Abstract: The vector miss distance measurement (VMDM) plays a key role in the scoring system. In the traditional two-step method, the estimation accuracy of intermediate measurements is limited by the nonstationary characteristics of echoes present in the rendezvous process. To address this problem, an innovative vector miss distance parameter estimation method based on direct position determination (DPD) is proposed in this study. The proposed method can directly estimate vector miss distance parameters with higher accuracy from the echoes received, in which the intermediate measurements need not be estimated in advance. The closed-form expressions of the Cramér-Rao bound (CRB) for vector miss distance parameters are also derived. The simulation results show that the proposed method outperforms the traditional two-step method in terms of the estimation accuracy of vector miss distance parameters. Moreover, the proposed method is more robust to the high noise levels and inconsistency of the receiving channels. The experimental data further validated the efficiency of the proposed method.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Human Activity Recognition Using Wearable Sensors Based on Image
           Classification

    • Free pre-print version: Loading...

      Authors: Saeedeh Zebhi;
      Pages: 12117 - 12126
      Abstract: Two-Dimensional Fast Fourier Transform (2-D FFT) and Wigner-Ville Transform (WVT) are two popular transforms applied to find frequency and time-frequency representations, respectively. By using them, signals acquired from different axes or sensors are mapped to these representations, considered 2D images. Based on this opinion, three novel methods are presented in this paper. The first two methods are called basic methods. The 2D images based on the magnitude of the 2-D FFT are constructed in method 1 and a fine-tuned CNN is also applied for classifying. WVT is used for constructing 2D compressed images in method 2, and classifying is done like method 1. Fusing two basic methods is presented as the proposed method. It attains the accuracies of 93.45%, 96.47%, 99.00%, and 98.20% for UCI HAR, MOTIONSENSE, MHEALTH, and WISDM datasets, respectively. Achieved results show the superiority of the proposed method compared with the state-of-the-art approaches.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • An IMU Fault Diagnosis and Information Reconstruction Method Based on
           Analytical Redundancy for Autonomous Underwater Vehicle

    • Free pre-print version: Loading...

      Authors: Xiaoxiong Liu;Yuting Ju;Xuhang Liu;Sheng Miao;Weiguo Zhang;
      Pages: 12127 - 12138
      Abstract: IMU fault diagnosis and information reconstruction are important to the safety of the autonomous underwater vehicle. To improve the fault tolerance of AUV to IMU faults, an IMU fault diagnosis and information reconstruction method based on the analytical redundancy is proposed in this paper. First, the IMU analytical method is constructed with the help of the autonomous underwater vehicle kinematic model, and the analytical relationship between IMU signal and innovation is given. Next, the t-test method and decoupling matrix method are designed to get fault diagnosis results for gyroscopes and accelerometers. Furthermore, the IMU fault estimation method based on innovation has been designed to estimate IMU faults and realize IMU signal reconstruction. The experiment results demonstrate the effectiveness and efficiency of the proposed method, showing its feasibility for IMU faults.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Balanced Adaptation Regularization Based Transfer Learning for
           Unsupervised Cross-Domain Fault Diagnosis

    • Free pre-print version: Loading...

      Authors: Qin Hu;Xiaosheng Si;Aisong Qin;Yunrong Lv;Mei Liu;
      Pages: 12139 - 12151
      Abstract: In fault diagnosis field, inconsistent distribution between training and testing data, resulted from variable working conditions of rotating machinery, inevitably leads to degradation of diagnostic performance. To address this issue, this study proposes a novel fault diagnosis method based on enhanced multi-scale sample entropies and balanced adaptation regularization based transfer learning. Specifically, different statistics-based multi-scale sample entropies are used to improve feature discriminability for different fault patterns under each working condition and enhance similarity of fault information between different working conditions. Then, based on these hand-crafted features, an improved transfer learning algorithm, referred to as balanced adaptation regularization based transfer learning, simultaneously exploring balanced distribution adaptation and balanced label propagation, is utilized to learn an adaptive classifier to perform cross-domain fault diagnosis. Finally, two public rolling bearing datasets verify that the proposed method can achieve an accurate diagnosis and outperform several existing transfer learning methods.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Fingerprint Augment Based on Super-Resolution for WiFi Fingerprint Based
           Indoor Localization

    • Free pre-print version: Loading...

      Authors: Tian Lan;Xianmin Wang;Zhikun Chen;Jinkang Zhu;Sihai Zhang;
      Pages: 12152 - 12162
      Abstract: WiFi fingerprint based indoor localization has become a key research direction in the field of indoor localization due to its high positioning accuracy and low equipment deployment cost. Increasing the number of reference point collected offline can improve the positioning accuracy, however it yields excessive cost of offline collection. Fingerprint augment is an effective solution to reduce the cost while ensuring the positioning accuracy. In this paper, we are pioneering to propose a fingerprint augment framework based on super-resolution (FASR), which achieves the fusion of fingerprint augment and super-resolution based on mutual conversion between fingerprint data and fingerprint image. The processing framework of FASR is formulated and the implementation of three modules in FASR are given, including Fingerprint-To-Image Conversion module, Super-Resolution module and Image-To-Fingerprint Conversion module. Simulated and real data experiments reveal the feasibility and effectiveness of the FASR. In addition, we explore the impact of two key engineering parameters on the performance of the FASR method. Our work demonstrates the new application of super-resolution in image processing field in wireless indoor localization topics.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • TSF: Two-Stage Sequential Fusion for 3D Object Detection

    • Free pre-print version: Loading...

      Authors: Heng Qi;Peicheng Shi;Zhiqiang Liu;Aixi Yang;
      Pages: 12163 - 12172
      Abstract: There have been significant advances in 3D object detection using LiDAR and camera fusion for autonomous driving. However, it is surprisingly difficult to effectively design fusion location and fusion strategies for point cloud-based 3D object detection networks. In this paper, we propose a novel two-stage sequential fusion (TSF) method. In the first stage of fusion, TSF generates the enhanced point cloud by combining the raw point cloud and semantic information of image instance segmentation. In the second stage, the proposals generated by LiDAR baseline is used to complete the No-Maximum Suppression (NMS) together with the 2D object detection results. Numerous experiments on the KITTI validation set show that our method outperforms state-of-the-art multimodal fusion-based methods on the three classes in 3D performance (Easy, Moderate, Hard): cars (89.94%, 82.76%, 76.04), pedestrians (70.74%, 63.47%, 56.56%), and cyclists (84.72%, 64.22%, 56.78%). In ablation, we analyze the augmented effect of fusion module on the LiDAR baseline detection capability, and study the best trade-off between running time and accuracy.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Angle-Insensitive Human Motion and Posture Recognition Based on 4D Imaging
           Radar and Deep Learning Classifiers

    • Free pre-print version: Loading...

      Authors: Yubin Zhao;Alexander Yarovoy;Francesco Fioranelli;
      Pages: 12173 - 12182
      Abstract: The need for technologies for Human Activity Recognition (HAR) in home environments is becoming more and more urgent because of the aging population worldwide. Radar-based HAR is typically using micro-Doppler signatures as one of the main data representations, in conjunction with classification algorithms often inspired from deep learning methods. One of the limitations of this approach is the challenging classification of movements at unfavorable aspect angles (i.e., close to 90°) and of static postures in between continuous sequences of activities. To address this problem, a hierarchical processing and classification pipeline is proposed to fully exploit all the information available from millimeter-wave (mm-wave) 4D imaging radars, specifically the azimuth and elevation information in conjunction to the more conventional range, Doppler, received power, and time features. The proposed pipeline uses the two complementary data representations of Point Cloud (PC) and spectrogram, and its performance is validated using an experimental dataset with 6 activities performed by 8 participants. The results show good performance of the proposed pipeline compared with alternative baseline approaches in the literature, and the effect of key parameters such as the amount of training data, signal-to-noise levels, and virtual aperture size is investigated. Leave-one-subject-out test is also applied to study the impact of body characteristics on the generalizability of the trained classifiers.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Intelligent Health Monitoring of Machine Tools Using a Bayesian
           Multibranch Neural Network

    • Free pre-print version: Loading...

      Authors: Rong Zhu;Weiwen Peng;Yu Han;Cheng-Geng Huang;
      Pages: 12183 - 12196
      Abstract: Online health monitoring of machine tools is an essential technique for tool life extension, manufacturing productivity improvement and product quality improvement. In the era of industrial big data, numerous deep learning (DL)-based methods have been proposed to achieve these goals. However, in complex and dynamic manufacturing processes, practical concerns such as uncertainty quantification and anti-noise capabilities of the DL-based methods are rarely considered. Thus, in this study, a novel multi-branch Bayesian Neural Network (BNN) is developed for the reliable and robust online health monitoring of Computer Numerical Control (CNC) machine tools. With the proposed model, the heterogeneous fault information extracted from multiple sensors can be simultaneously integrated in a deep convolutional neural network (DCNN)-multiple layer perceptron (MLP)-based multi-branch neural network to enhance the health monitoring accuracy and robustness. Furthermore, the proposed multi-branch neural network is extended into a BNN to improve its uncertainty quantification capabilities. The proposed method is evaluated on the tool wear tests of three cutting tools. Tool wear estimation results indicate that the proposed method outperforms comparative methods and achieves the best prediction accuracy and robustness on all three health monitoring tasks investigated in this study. We also found that the proposed method can accurately classify tool wear stages and reach up to 95% mean classification accuracy, which is the best among comparative methods. Also, measures, such as coverage probability of estimation interval (EICP) and normalized mean estimation interval width (NMEIW), are used to assess the capability of quantifying the confidence intervals (CIs) of the tool wear estimations. Results show that the proposed method achieves superior CIs quantification performance with the average EICP and NMEIW values of 95.77% and 0.27 on all three he-lth monitoring tasks.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware
           Wheeled Odometer

    • Free pre-print version: Loading...

      Authors: Hailiang Tang;Xiaoji Niu;Tisheng Zhang;You Li;Jingnan Liu;
      Pages: 12197 - 12208
      Abstract: Odometer has been proven to significantly improve the robustness and accuracy of the Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated vehicle navigation in GNSS-denied environments. However, odometer is inaccessible in many applications, especially for aftermarket devices and smartphones. To apply forward speed aiding without hardware wheeled odometer, we propose OdoNet, an untethered one-dimensional Convolution Neural Network (CNN)-based pseudo-odometer model learning from a single Inertial Measurement Unit (IMU). Dedicated experiments have been conducted to verify the generalization capability and the precision of the OdoNet. The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet. Hence, a data-cleaning procedure is adopted to effectively mitigate the impacts of the IMU biases and the mounting angles. Compared to the processing mode using only non-holonomic constraint (NHC), by employing the pseudo-odometer, the positioning error is reduced by around 68%, while the percentage is around 74% for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be employed as an untethered pseudo-odometer for vehicle navigation.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Data and Decision Level Fusion-Based Crack Detection for Compressor Blade
           Using Acoustic and Vibration Signal

    • Free pre-print version: Loading...

      Authors: Di Song;Tianchi Ma;Yang Li;Feiyun Xu;
      Pages: 12209 - 12218
      Abstract: Crack detection is important for compressor blades. Due to the interference of strong noise, the existing methods based on single signal reach unsatisfactory performance. To improve the reliability and accuracy, a novel crack detection method is proposed by fusing the information from acoustic and vibration signals (AVS) in data and decision level. Firstly, the data-level fusion method is proposed by calculating Hoyer and improved cosine similarity, which is applied for vibration signals fusion based on its sparsity and similarity. Then, the raw AVS and data-level fusion samples are trained in the one-dimensional convolutional neural network (1D CNN), and the preliminary classification is obtained based on the probabilities of different cracks. Finally, the decision-level fusion method with changed reliability assignment is proposed through modifying and correcting the preliminary results of 1D CNN, which can reach reliable decisions and realize crack detection for compressor blade. The proposed method is tested by compressor experiments with three cracked blades under four single and one mixed working conditions. The results illustrate that the proposed method can make full use of AVS and detect cracks reliably under five working conditions. Furtherly, the superiority of the proposed method is validated by comparing with other crack detection approaches.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Distinguishing Medication ON and OFF for Parkinson’s Disease Through
           Repetitive Foot Motion Recognition and Analysis

    • Free pre-print version: Loading...

      Authors: Rui Hua;Ya Wang;
      Pages: 12219 - 12227
      Abstract: As Parkinson’s disease (PD) progresses, patients experience difficult times to distinguish their medication effective (ON) and non-effective (OFF) periods, due to subjective judgements on observations of motor symptoms and self-recording in diaries. Thus, a simple-to-use tool to objectively identify and record ON and OFF in daily life is in great need. This study proposes a method to distinguish medication ON and OFF for PD patients through 4 repetitive foot motions– toe tapping, heel tapping, toe pivoting and heel pivoting by wearing a pair of monitoring insoles (MONI). After collecting data from 16 patients and 9 age-matched healthy controls, we extract over 1200 features from all repetitive foot motions and provides comprehensive analytical results of feature importance with Analysis on Variance (ANOVA). Then, we use Random Forest Classifier to first identify foot motions through motion recognition and then extract features to distinguish ON and OFF. Through evaluation on 2-month long data collected from 5 individual patients, we identify motions performed mistakenly by one patient during an experimental session and show 85% ON $/$ OFF distinguishment accuracy for the rest 20 sessions, compared to 60% accuracy from a baseline model referencing to observation judgement in clinical tests. The proposed method and experimental procedures can be transformed into an assistant tool, including an application (APP) and MONI, as a potential solution to help patients with Parkinson’s disease understand and objectively record their medication effectiveness in daily life.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Highly Reliable Passive RFID-Based Inductor–Capacitor Sensory System
           

    • Free pre-print version: Loading...

      Authors: Md. Rajibur Rahaman Khan;Alireza Khalilian;Jungyoon Seo;Seungtaek Oh;Atul Thakre;Tae Kyu An;Hwa Sung Lee;
      Pages: 12228 - 12236
      Abstract: A passive radio frequency-identification-based inductor-capacitor (LC) lactate sensory system with a specific dye-containing interdigitated capacitor (IDC) in which the sensing signal is amplified by the solvatochromic effect is proposed. When a lactate solution contacts the IDC of the LC lactate sensor, the capacitance of the IDC changes, changing the resonance frequency of the sensor. This changes the oscillation frequency of the Colpitts oscillator in the readout circuit. By analyzing the frequency changes, the concentration of the lactate solution can be measured quickly and accurately over a wide range. To our knowledge, the proposed device is the first passive, battery-free LC lactate sensor that uses solvatochromic dye-containing IDC sensing elements to detect lactate solution concentrations. Four solvatochromic dyes were tested and incorporated into a polymer as the lactate-sensitive membranes of the IDCs. The proposed LC sensor tag offers excellent sensitivity and linearity over a wide lactate concentration range of about $10 mu text{M}$ to 1 M. The response and recovery times of our LC sensory system were significantly shorter than those of previously reported lactate sensors. Our results are useful for the development of reliable wearable devices capable of real-time lactate detection at a low cost.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • A Flexible Thin-Film Magnetostrictive Patch Guided-Wave Transducer for
           Structural Health Monitoring

    • Free pre-print version: Loading...

      Authors: Chenxi Xie;Tianhao Liu;Cuixiang Pei;Zhenmao Chen;
      Pages: 12237 - 12244
      Abstract: Structural health monitoring (SHM) technology has attracted more and more attention in aerospace and energy industries as it may detect and monitor early degradation and damage in the structures and thus avoid life and financial loss. Guided wave (GW) based inspection is very useful for the SHM of thin-walled structures due to its capability for long-distance and wide-range inspection with high sensitivity. However, the lack of small and high-performance transducers seriously limits the development of GW-based SHM technology. In this paper, a thin-film magnetostrictive patch transducer (MPT) that can generate and receive single-mode and almost nondispersive GW is proposed. The new transducer is composed of an alloy patch and a double-layer flexible printed circuit (FPC). Firstly, the basic structure and principle of the proposed transducer are presented and analyzed. Compared with current single-mode GW transducers, the main advantages of the proposed transducer are lightweight, flexibility, and high transduction efficiency. Both the numerical simulation and experimental results validate that the new transducer has good performance in generating and receiving single and almost nondispersive S0 mode GW in an aluminum plate. It is very promising to be applied for the SHM in curved thin-walled structures.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Integration of Spatial Modulation Scheme With Code Division Multiple
           Access for VIVO Based Frequency Selective Nano Sensor Networks

    • Free pre-print version: Loading...

      Authors: Fadila Berrahma;Khalida Ghanem;Hicham Boussbia-Salah;Akram Alomainy;Muhammad Ali Imran;Qammer Hussain Abbasi;
      Pages: 12245 - 12252
      Abstract: Due to their suitability for human-body based communications, in-VIVO nano sensor networks are envisioned to use the promising Terahertz signals in order to ensure the forthcoming high-rate communication needs of the modern medicine. However, the propagation losses at these frequency bands are quite significant and dependent on the operation frequency as well as on the physiological characteristics, thus impeding the use of Terahertz rates to their utmost benefit. Using numerous emitting elements is likely to improve the quality of the received signal, but gives rise to multi-channel interference (MCI) emanating from multi-antenna signaling reception, which necessitates a relatively complex signal processing to mitigate it. When multiple physiological signals are of interest, detecting them necessitates to mitigate multiple-access interference (MAI). In this perspective, orthogonal frequency division multiplexing (OFDM) allows to combat the channel frequency selectivity, whereas code division multiple access (CDMA) scheme cancels MAI. In this paper, we propose to embed the novel spatial modulation technique with CDMA architecture in an OFDM framework to ensure a viable communication in in-VIVO frequency selective Nano channels. The immunity of our proposed solution to such an interference is confirmed, since less than −2 dB in SNR level is required to support 5 users simultaneously communicating with a BER which is inferior to ${10}^{-{3}}$ when the operating frequency is equal to 1THz. This hybrid scheme is shown to efficiently combat the MCI while enabling a safe retrieval of the useful signal at the very-high data rate communications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Ballpoint-Pen Like Probes for Multipoint Dynamic Pulse Diagnosis System

    • Free pre-print version: Loading...

      Authors: Ming Xin;Ning Lu;Dejian Yin;Ruowen Tao;Jiean Li;Le Liu;Hongxing Liu;Yi Shi;Lijia Pan;
      Pages: 12253 - 12259
      Abstract: The radial artery pulse on the wrist is a non-invasive and reliable index for health monitoring, and is important diagnosis basis for traditional Chinese medicine and Ayurvedic medicine. Digitalization of pulse signals is an important way to develop healthcare big data and implement traditional Chinese medicine theories. However, collecting all the characteristics of human pulse with high fidelity remains a challenge. In this paper, we proposed a capacitive pulse diagnosis system with a novel ballpoint pen-like probe structure, and electromechanical control modules to effectively collect pulse condition characteristics. The ballpoint pen-like probe has low-friction contact with the skin surface, which can precisely locate the pulse points of maximum intensity with the assistance of the x-y stage, and accurately record the pulse waveform with excellent signal quality. This design provided a new approach to digitalize the cognitive category of traditional Chinese medicine, and opportunities to better understand and analyze the physiological signals in health monitoring and disease treatment applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Robust Adaptive Beamforming Based on Subspace Decomposition, Steering
           Vector Estimation and Correction

    • Free pre-print version: Loading...

      Authors: Jian Yang;Yuwei Tu;Jian Lu;Zhiwei Yang;
      Pages: 12260 - 12268
      Abstract: Considering that the performance of adaptive arrays is sensitive to any type of mismatches, an innovative robust adaptive beamforming method based on covariance matrix reconstruction, subspace decomposition, steering vector estimation and correction is proposed. Based on Capon spatial spectrum, a group of angle sets containing all interfering signals are determined, and the interference covariance matrix can be reconstructed with a smaller integration interval. On the other hand, the sample covariance matrix can be decomposed into signal subspace and interference-plus-noise by using the principle of maximum correlation. Based on the interference-plus-noise subspace and the reconstructed signal-plus-noise covariance matrix, a new convex optimization model is built to estimate the steering vector of the desired signal. Then, an improved projection approach based on signal subspace is designed for correction to improve the robustness against the nominal direction vector mismatches. Simulation results demonstrate that the proposed method achieves better overall performance under multiple mismatches over a wide range of input signal-to-noise ratios.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • AFSense-ECG: Atrial Fibrillation Condition Sensing From Single Lead
           Electrocardiogram (ECG) Signals

    • Free pre-print version: Loading...

      Authors: Arijit Ukil;Leandro Marin;Subhas Chandra Mukhopadhyay;Antonio J. Jara;
      Pages: 12269 - 12277
      Abstract: In this paper, we propose AFSense-ECG, an intelligence-embedded single lead ECG sensor that is enabled with the ability of accurate detection of Atrial Fibrillation (AF) condition, which is the most common sustained cardiac arrhythmia and increased risk of stroke is higher with sub-clinical AF patients. AFSense-ECG acts like an early-warning sensor for AF condition detection. A processing unit (e.g., ESP32WROVERE microcontroller) integrated with off-the-shelf single lead ECG sensor like Alivecor or AD8232 embeds intelligence to the sensing system to augment for inferential sensing for empowering automated decision-making. AFSense-ECG captures the quasi-periodic nature of typical ECG signals with repetitive P-wave, QRS complex and T-wave patterns into its feature extraction and the representation learning process of model construction and learning rate optimization. Our empirical study validates the superiority of proposed ECG signal characteristics-based hyperparameter tuned ECG classification model construction. AFSense-ECG demonstrates F1-measure of 86.13%, where the current state-of-the-art methods report F1-measures of 83.70%, 83.10%, 82.90%, 82.60%, 82.50%, 81.00% over publicly available single lead ECG datasets of Physionet 2017 Challenge. Further, the proposed learning model for the inferential sensing is lean (approximately 25 times simpler in terms of total number of trainable parameters with reduced model size than relevant state-of-the-art model, where the state-of-the-art method with 83.70% F1-measure consists of 10474607 trainable parameters, and our proposed model consists of 433675 trainable parameters) and more effective (better F1-measure than the state-of-the-art methods), which enables us to construct affordable intelligent sensing system.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Highly Sensitive Undersea Corrosion Monitoring System

    • Free pre-print version: Loading...

      Authors: Javier Alonso-Valdesueiro;Iñaki Madinabeitia;Iñigo Santos-Pereda;Jean-Baptiste Jorcin;Esther Acha-Peña;
      Pages: 12278 - 12287
      Abstract: Corrosion monitoring of undersea metallic structures has become one of the major challenges for the energy industry in the last two decades. Reliability, autonomy and high grade of accuracy is expected from a network of sensors distributed along subsea distribution grids and maintenance equipment. Despite the many techniques proposed by the scientific literature, the most extended techniques rely on the corrosion of low and ultra-low resistive elements with degradation rates similar to the metallic structures under monitoring, more commonly known as corrosion probes. However, the sensitivity of resistive sensors is limited to $25,{mu m}/{yr}$ in a time frame of 10 days which reduces the time response of the sensor to fast corrosion process. The most extended solution is to decrease the thickness of the resistive element. This solution increases the resolution by increasing the resistance of the resistive element and the influence of any material reduction due to corrosion. However, a decrease in thickness leads to shorter lifetimes and a higher influence of thermal variations. In this work, a highly sensitive resistive corrosion sensor and its performance are presented. The functionality of a highly sensitive $mu $ Ohm-meter as a corrosion sensor and its uncertainty in corrosion rate are analyzed. Different sensor prototypes for laboratory and field deployment are described thoroughly and tested for calibration purposes in corrosive environments. The final prototype is connected to an autonomous platform and deployed in an offshore platform located at the Cantabrian Sea resulting in a corrosion rate sensitivity of ~ $1.1,{mu m}/{yr}$ in 4hours time frame.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Smartstep: A Robust STEP Detection Method Based on SMARTphone Inertial
           Signals Driven by Gait Learning

    • Free pre-print version: Loading...

      Authors: Nahime Al Abiad;Yacouba Kone;Valerie Renaudin;Thomas Robert;
      Pages: 12288 - 12297
      Abstract: Step detection is critical for many applications including health and indoor navigation. However, it remains challenging to achieve robust step detection for all types of human gait and sensors locations on the user’s body. The challenge increases for blind people whose gait is different from sighted and affected by the use of navigation aids. In this study, we propose and evaluate a new machine-learning-based step detection method: Smartstep. The advantages of this method are that it does not rely on any sensor-position, step-mode, and hand motion mode pre-classifications, nor on any threshold calibration. The method had already shown a promising performance with 99% recall and precision when applied in challenging conditions on young adults’ gait. In this study, the ability of this method to generalize to blind gait is put to question. The performance is assessed on two different blind people walking datasets including various challenging conditions (different walking speeds, smartphone placements, hand motion modes, sensor types, and navigation aids). Smartstep achieves a 99% precision or 1% overcount rate and a 90% recall or 10% undercount rate. This study demonstrates the robustness of the method and encourages its usage for other applications and populations.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Dead-Reckoning Configurations Analysis for Marine Turtle Context in a
           Controlled Environment

    • Free pre-print version: Loading...

      Authors: Pierre Gogendeau;Sylvain Bonhommeau;Hassen Fourati;Denis De Oliveira;Virgil Taillandier;Andrea Goharzadeh;Serge Bernard;
      Pages: 12298 - 12306
      Abstract: In the past few years, dead-reckoning (DR) has been frequently used to estimate the trajectory of marine animals at a fine temporal scale using bio-logger devices. The precision of the swim sequence trajectory estimation depends on various accumulated errors from external forces, sensors and computation. Trajectory accuracy is hard to estimate due to the difficulty of collecting precisely-known underwater positions. In this paper, we aim at estimating this accuracy at a fine temporal scale using a reference system for positioning. This work focuses on how each sensor frequency and algorithm used for the DR affect trajectory accuracy and the global power consumption of the bio-logger. We develop a dual GPS Real Time Kinematic (RTK) system offering us reference trajectories with 2 cm accuracy on position and 1.6° on heading. The DR algorithms use 3-axis Inertial Measurement Unit (IMU), depth and speed sensor data for orientation and speed determination. For the experimental tests, the GPS module and the bio-logger are attached to a swimmer doing breaststroke imitating turtle movement for different swim sequences between 15 and 40 minutes. Power consumption of the electronics is measured during laboratory tests. Results show that using an adapted speed sensor and correcting for marine current, even roughly, provide us with the best gain in accuracy. The use of the gyroscope or high-frequency sampling of sensors does not increase the accuracy of the trajectory reconstruction to a level that would be critical for slow moving marine animal applications.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • GaN-HEMT on Si as a Robust Visible-Blind UV Detector With High
           Responsivity

    • Free pre-print version: Loading...

      Authors: Arathy Varghese;Abdalla Eblabla;Zehao Wu;Seyed Urman Ghozati;Khaled Elgaid;
      Pages: 12307 - 12313
      Abstract: This work presents performance evaluation of GaN High Electron Mobility Transistor (HEMT) based ultraviolet (UV) detector on Si substrate. In addition to the fabrication and characterization, a systematic study is presented here using simulations extensively to investigate the UV detection mechanism. Output current has been chosen as the sensing metric, the fabricated device exhibits a high UV responsivity of $1.62times 10^{{7}}$ A/W at $2.5times 10^{-{10}}$ W, $text{V}_{{text {GS}}}={0.5}$ V. Simulations have been done using optical modules available in Silvaco ATLAS TCAD to analyze the energy band bending, Two-Dimensional Electron Gas (2DEG), channel potentials and electric fields in the device. This model can aid in systematic study of HEMT based detectors in terms of dimensional and epi layer design optimizations for sensitivity enhancements. The UV response of the device is found to decrease as the wavelength approaches the visible light wavelength. This makes the photodetectors blind to visible light ensuring selective detection of UV wavelengths. It has been observed that as the area for UV absorption is increased by increasing the W/L ratio, the increases. For a W/L ratio of 100, the detector exhibits a responsivity of $1.86times 10^{{7}}$ A/W.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Monitoring of Cylindrical Plunge Grinding Process by Electromechanical
           Impedance

    • Free pre-print version: Loading...

      Authors: Fabio Isaac Ferreira;Paulo Roberto de Aguiar;Rosemar Batista da Silva;Mark James Jackson;Fabricio Guimaraes Baptista;Eduardo Carlos Bianchi;
      Pages: 12314 - 12322
      Abstract: One of the most promising monitoring techniques is based on electromechanical impedance (EMI) transducers, which are low-cost components and allow easy implementation. EMI signal features showed good results for some applications in previous studies, but it is still unexplored for machining processes, which is essential for metal-cutting industries. This paper presents a new approach to verify the applicability of EMI measurements to monitor surface quality after the plunge cylindrical grinding of non-flat parts, which require low roughness and tight dimensional tolerances. Tests were carried out in a camshaft grinder and two low-cost piezoelectric diaphragms were attached to each workpiece to guarantee redundancy. Roughness $text{R}_{mathbf {a}}$ was also monitored to check existence of correlation with the EMI signals and relevant discussions regarding monitoring by EMI are presented. Results showed a great correlation between EMI measurement and surface roughness: 0.94 and 0.80 for both diaphragms. Since these diaphragms cost less than 1 US dollar and good correlation was verified, this monitoring system is promising to replace the traditional ones.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Fast LiDAR R-CNN: Residual Relation-Aware Region Proposal Networks for
           Multiclass 3-D Object Detection

    • Free pre-print version: Loading...

      Authors: Lihua Wen;Kang-Hyun Jo;
      Pages: 12323 - 12331
      Abstract: Three-dimensional (3-D) object detection from Light Detection and Ranging (LiDAR) point clouds is the most challenging problem in practical 3-D scene understanding. This paper presents a fast two-stage 3-D object detection framework that jointly integrates voxel and point feature representations. Specifically, the first stage takes the voxel features from raw point clouds as inputs and then outputs bird eye’s view (BEV) feature maps and structured voxel center points. The BEV feature map and objects’ empirical sizes are used for generating 3-D proposals. The second stage extracts region pointwise features for the final object prediction using the 3-D proposals generated in the first stage. The proposed framework runs at 30 frames per second (FPS) with high performance. To improve the performance of the pedestrian class, we propose a dual-path feature module (DFM) to learn and pass features from BEV feature maps. Moreover, we propose a lightweight relation-aware module (LRAM) for sparse point clouds to enhance the attention ability of region proposal networks by exploring the relationships between pixels and between channels. On the KITTI benchmark suite, performed experiments show that the proposed LiDAR-based method achieves a new state-of-the-art on the three classes in 3-D performance (Easy, Moderate, Hard): car (92.53%, 84.70%, 82.32%), pedestrian (68.30%, 61.20%, 55.17%), cyclist (91.73%, 72.61%, 68.24%).
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Electro-Textile UHF-RFID Compression Sensor for Health-Caring Applications

    • Free pre-print version: Loading...

      Authors: Chengyang Luo;Ignacio Gil;Raúl Fernández-García;
      Pages: 12332 - 12338
      Abstract: Electro-textile Ultra High Frequency (UHF, 865–868 MHz) Radio Frequency Identification (RFID) devices have great potential to be explored as sensors due to the features of fabric materials. In this work, an electro-textile UHF-RFID compression sensor base on T-match structure with a corresponding interface are developed and evaluated for two application scenarios. For accurate textile UHF-RFID antenna design and maximize the read range, the impedance of the electro-textile based on snap buttons is modelled and characterized an a measured read range of 5.22m is experimentally obtained. If the distance of the RFID reader and RFID sensor remain constant at 1 m. The experimental results show that RSSI range change from −42 dBm to −58 dBm as a quadratic function in terms of the knee angle bending and from −45 dBm to −40 dBm during expiration and inspiration phase when the sensor is located on the chest, which validated the usefulness of the proposed sensor.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Bi-Directional Style Adaptation Network for Person Re-Identification

    • Free pre-print version: Loading...

      Authors: Hangying Wang;Jian Lu;Feifei Pang;Jian Zhou;Kaibing Zhang;
      Pages: 12339 - 12347
      Abstract: Large-scale surveillance camera system can provide multiple visual information. Nevertheless, the images exhibit diverse appearance features due to different parameters and installation positions of the cameras. This camera style variation deteriorates its benefit from capturing identity features in person re-identification (Re-Id). The existing methods for filtering shallow appearance information through the Instance Normalization (IN) layer are extremely unfavorable to supervised tasks. To mitigate this problem, a simplified and valid Bidirectional Style Adaptation Network (BSA-Net) is presented to incorporate a new branch containing IN layer to learn invariant features with changing appearance. For these two branches, the structure is completely independent and the parameters are partially shared. BAS-Net is able to focus on the extraction of identity information while preserving appearance features. Specially, this new branch is removed during the testing phase, which significantly facilitates performance without introducing new computation. The superiority of the model is confirmed in extensive experiments on widely used benchmarks.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Time-Resolved Dosimetry of Pulsed Photon Beams for Radiotherapy Based on
           Diamond Detector

    • Free pre-print version: Loading...

      Authors: Sara Pettinato;Marco Girolami;Riccardo Olivieri;Antonella Stravato;Cristina Caruso;Stefano Salvatori;
      Pages: 12348 - 12356
      Abstract: The widespread diffusion of precision radiotherapy techniques, geared toward the release of larger dose gradients in shorter time frames, is leading to new challenges in dosimetry. Accurate dose measurements are essential to check for beam anomalies and inaccuracies to ensure treatment efficacy and patient safety during radiotherapy. This work describes the main features of a diamond dosimeter coupled to an extremely compact front-end electronics. The detection system was tested under the X-ray pulses generated by a medical LINAC for both the 6 MV and the 18 MV accelerating voltages. Located in the LINAC’s bunker, it eliminates the need for a long cable connection between the detector and the electronics, detrimental for the system response speed. Signal acquisition was performed synchronously with the impinging X-ray pulses with a sampling period as low as $20 mu text{s}$ , allowing for a real-time beam monitoring. The dosimeter demonstrated a very good stability despite the high value of the absorbed dose during the performed experiments (~100 Gy). The measured dose-per-pulse values of $278 mu $ Gy and $556 mu $ Gy at 6 MV and 18 MV, respectively, are in excellent agreement with the nominal values expected for the LINAC apparatus used for the tests. In addition to single-pulse measurements, fundamental for dynamic radiotherapy, the proposed system also allows for the calculation of both the total collected charge and the photocurrent generated by the detector. In this regard, despite the compactness, it demonstrates its effectiveness as a tool for source diagnostics in terms of both beam intensity and emission timing.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Optimization of Energy Efficiency in UAV-Enabled Cognitive IoT With Short
           Packet Communication

    • Free pre-print version: Loading...

      Authors: Hang Hu;Yangchao Huang;Guobing Cheng;Qiaoyan Kang;Hang Zhang;Yu Pan;
      Pages: 12357 - 12368
      Abstract: The unmanned aerial vehicle (UAV) assisted communication can significantly improve the coverage and spectrum efficiency of the Internet of Things (IoT). One main feature of IoT is short packet communication (SPC), in which the data transmission uses finite block-length codewords. The transmission rate and the packet error rate will affect the effective throughput of the IoT. In this paper, we investigate the impact of SPC on the performance of UAV-enabled spectrum sharing network. Specifically, the optimization of energy efficiency (EE) in the UAV communication system is considered since the battery of the UAV is usually limited. We design the packet error rate, the sensing duration, the normalized sensing threshold and the UAV’s transmit power to maximize the EE under the constraint that the primary user is sufficiently protected. Since the system parameters are intertwined with each other, we employ a successive optimization algorithm to solve the optimization problem by dividing it into three subproblems. Then, an efficient iterative algorithm is proposed to obtain the optimal parameters. Simulation results reveal that the proposed algorithm can converge to global optimal value and has better EE performance than other benchmark schemes. And there is a fundamental tradeoff between the throughput and the EE.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Adaptive Advertising Interval for Electronic Shelf Label System Based on
           Bluetooth Low Energy

    • Free pre-print version: Loading...

      Authors: Hsueh-Wen Tseng;Hao Kao;Chin-Fu Kuo;
      Pages: 12369 - 12385
      Abstract: With the advent of the IoT age, an increasing number of items can connect to the Internet, making life more convenient. Bluetooth Low Energy (BLE) is a popular wireless protocol used in the IoT. Because of its low price and low energy consumption, BLE is applied in numerous fields. In this paper, we use BLE technology in an electronic shelf label system. In accordance with the 80/20 principle, most customers are interested in only a small percentage of a store’s merchandise. If resources can be allocated to the most suitable places, then more benefits can be obtained. Therefore, we consider the popularity of all products and allocate more resources to the electronic shelf labels (ESLs) of the most popular products. According to the neighbor discovery process (NDP) delay model and the popularity of each product, a gradient-based method is employed to calculate the advertising intervals for the ESLs. Our method reduces the average delay under the condition that the total power consumed does not increase. Besides, we propose a power-saving method that adaptively adjusts the advertising intervals to reduce the power consumed by popular ESLs depending on the environment. We also propose the neighbor discovery process delay model, which considers the failure to transmit advertising packets. The capability of the proposed scheme is evaluated by a series of experiments, for which we have encouraging results.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Energy-Efficient Dynamic and Adaptive State-Based Scheduling (EDASS)
           Scheme for Wireless Sensor Networks

    • Free pre-print version: Loading...

      Authors: Muhammad Nawaz Khan;Haseeb Ur Rahman;Muhammad Zahid Khan;Gulzar Mehmood;Adel Sulaiman;Asadullah Shaikh;Abdulmajeed Alqhatani;
      Pages: 12386 - 12403
      Abstract: Today is the era of super-connectivity, where real-world things connect, gather real-time data from their surroundings and disseminate the recorded data into the environment. The users can access services without understanding the basic composite structure of heterogeneous devices and hybrid IoT infrastructure. Data is collected, managed, and processed by minute and plug-able sensors for the IoT paradigm. Due to the resource-constrained nature of these sensors, massive and recurrent tasks create congestion in the network and drain the energy of sensors. Sending unnecessary and redundant data packets is life-threatening and affects the availability of other resources. This paper proposes a novel scheme, an “Energy-Efficient Dynamic and Adaptive State-based Scheduling” (EDASS) for Wireless Sensor Network. The suggested method switches nodes between states dynamically and adapts to new states based on the contents of sensed data packets. Four distinct states of energy are derived from a combination of internal modules of the sensor. The typical sequence of operation is abruptly changed, and all sensors become active when a new event occurs. EDASS decreases energy consumption by 29% in live nodes, 41% in message overhead and 33% in the cluster head selection process. At the same time, the average delay in EDASS increases from 1.26 ms to 1.39 ms due to control message overhead.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Joint Design of Multicast Transmission and In-Network Caching for Green
           Internet of Things

    • Free pre-print version: Loading...

      Authors: Xuewei Zhang;Yuan Ren;Junxuan Wang;Tiejun Lv;
      Pages: 12404 - 12414
      Abstract: In this paper, the joint design of energy-efficient multicast beamforming and in-network caching in Internet of Things is investigated. We propose the fractional in-network caching scheme. Specifically, parts of the sensed data resources are cached in the helpers, such as small-cell base stations and device-to-device equipments, and the caching fraction for a certain resource is the same among the helpers. The remaining data resources are kept in the sensor nodes. Employing multicast transmission at the helpers, the cached data can be cooperatively transmitted to the clients belonging to multiple multicast groups, where the data requests are identical among the clients in the same multicast group. Based on the proposed fractional caching and multicast transmission protocols, the power consumption model is established, and then we formulate the energy efficiency (EE) optimization problem, which is challenging to solve due to its non-convexity. Thereafter, by exploiting the Dinkelbach ’ s method, semi-definite relaxation, successive convex approximation and other approximation techniques, the original problem is converted into a second-order cone programming problem, which is convex and can be efficiently solved by off-the-shelf CVX solvers. Numerical results exhibit the fast convergence of the proposed algorithm, and demonstrate the superior EE performance of the proposed fractional caching scheme, compared to the benchmark strategies.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Upper Confidence Bound Based Communication Parameters Selection to Improve
           Scalability of LoRa@FIIT Communication

    • Free pre-print version: Loading...

      Authors: Alexander Valach;Dominik Macko;
      Pages: 12415 - 12427
      Abstract: Number of embedded devices connected to the Internet is rapidly increasing, especially in the era of the Internet of Things (IoT). The growing number of IoT devices communicating wirelessly causes a communication-parameters selection problem, implying the increasing number of communication collisions. Multiple factors of IoT networks signify this problem, such as inability of communication-channel listening prior to the transmission (due to longer distances), energy constrains (due to inability of powering devices from the grid), or limitation of duty cycle and high interference (due to usage of an unlicensed band in communication technologies). This article is focused on alleviating this problem in LoRa networks, which is one of the most promising technologies for long-range and low-power communication. We utilize the existing LoRa@FIIT protocol to achieve ultra energy-efficient communication. In the proposed architecture, the scalability of the LoRa network is increased by modifying the communication-parameters selection algorithm. The original adaptive data rate algorithm is efficient in static environments. However, when considering mobile nodes, upper confidence bound algorithm can provide more energy-efficient communication, as shown in this work. The end nodes are partially independent from the network server, able to modify their communication parameters on their own. The adaptation to network changes is thus faster. Also, by ensuring the quality of service mechanism at each node in the infrastructure, the application domain of the proposed architecture is widened. The simulation-based experimental results showed a significantly reduced number of collisions for mobile nodes, which reduces the channel congestion and the wasted energy by retransmissions.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Analysis and Optimization of Multihop Broadcast Communication in the
           Internet of Vehicles Based on C-V2X Mode 4

    • Free pre-print version: Loading...

      Authors: Zhonghui Pei;Wei Chen;Changzhen Li;Luyao Du;Huiheng Liu;Xiaojun Wang;
      Pages: 12428 - 12443
      Abstract: Cellular Vehicle-to-Everything (C-V2X) Mode 4, developed by 3GPP, supports vehicle-to-vehicle communication without a cellular base station via LTE sidelink. The messages transmitted in the Internet of Vehicles (IoV) include periodic single-hop broadcast Beacon messages and multi-hop broadcast Emergency messages triggered by events. In this paper, we analyze the impact of multi-hop broadcast of Emergency messages in C-V2X Mode 4 on the performance of Beacon messages and propose collision probability models for Beacon messages and Emergency messages. The non-periodicity nature and lower broadcast latency requirement of the multi-hop Emergency messages may cause resource allocation conflicts in the resource selection process of C-V2X Mode 4, thus result in forwarding collision of Emergency messages and collision of subsequent Beacon messages. Therefore, we propose several multi-hop broadcast schemes for Emergency messages to reduce the collision probability by assigning independent resource grants for Emergency messages and adjusting the number of forwarding nodes. The performance of Beacon messages and Emergency messages of several different forwarding schemes proposed are simulated. The simulation results show that the proposed broadcasting schemes significantly improve the performance of Beacon messages and Emergency messages.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • IEEE Sensor Journal cover/frontispiece competition

    • Free pre-print version: Loading...

      Pages: 12444 - 12444
      Abstract: Presents information on the IEEE Sensor Journal cover/frontispiece competition.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
  • Introducing IEEE Collabratec

    • Free pre-print version: Loading...

      Pages: 12445 - 12445
      Abstract: Advertisement, IEEE.
      PubDate: June15, 15 2022
      Issue No: Vol. 22, No. 12 (2022)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.238.125.76
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-