Subjects -> INSTRUMENTS (Total: 63 journals)
Showing 1 - 16 of 16 Journals sorted alphabetically
Applied Mechanics Reviews     Full-text available via subscription   (Followers: 27)
Bulletin of Social Informatics Theory and Application     Open Access   (Followers: 1)
Computational Visual Media     Open Access   (Followers: 4)
Devices and Methods of Measurements     Open Access  
Documenta & Instrumenta - Documenta et Instrumenta     Open Access  
EPJ Techniques and Instrumentation     Open Access  
European Journal of Remote Sensing     Open Access   (Followers: 9)
Experimental Astronomy     Hybrid Journal   (Followers: 39)
Flow Measurement and Instrumentation     Hybrid Journal   (Followers: 18)
Geoscientific Instrumentation, Methods and Data Systems     Open Access   (Followers: 4)
Geoscientific Instrumentation, Methods and Data Systems Discussions     Open Access   (Followers: 1)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 2)
IEEE Sensors Journal     Hybrid Journal   (Followers: 103)
IEEE Sensors Letters     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Imaging & Microscopy     Hybrid Journal   (Followers: 9)
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
Instrumentation Science & Technology     Hybrid Journal   (Followers: 6)
Instruments and Experimental Techniques     Hybrid Journal   (Followers: 1)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 7)
International Journal of Instrumentation Science     Open Access   (Followers: 40)
International Journal of Measurement Technologies and Instrumentation Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Metrology and Quality Engineering     Full-text available via subscription   (Followers: 4)
International Journal of Remote Sensing     Hybrid Journal   (Followers: 282)
International Journal of Remote Sensing Applications     Open Access   (Followers: 45)
International Journal of Sensor Networks     Hybrid Journal   (Followers: 4)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Journal of Applied Remote Sensing     Hybrid Journal   (Followers: 83)
Journal of Astronomical Instrumentation     Open Access   (Followers: 3)
Journal of Instrumentation     Hybrid Journal   (Followers: 32)
Journal of Instrumentation Technology & Innovations     Full-text available via subscription   (Followers: 2)
Journal of Medical Devices     Full-text available via subscription   (Followers: 5)
Journal of Medical Signals and Sensors     Open Access   (Followers: 3)
Journal of Optical Technology     Full-text available via subscription   (Followers: 5)
Journal of Sensors and Sensor Systems     Open Access   (Followers: 11)
Journal of Vacuum Science & Technology B     Hybrid Journal   (Followers: 3)
Jurnal Informatika Upgris     Open Access  
Measurement : Sensors     Open Access   (Followers: 3)
Measurement and Control     Open Access   (Followers: 36)
Measurement Instruments for the Social Sciences     Open Access  
Measurement Science and Technology     Hybrid Journal   (Followers: 7)
Measurement Techniques     Hybrid Journal   (Followers: 3)
Medical Devices & Sensors     Hybrid Journal  
Medical Instrumentation     Open Access  
Metrology and Instruments / Метрологія та прилади     Open Access  
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microscopy     Hybrid Journal   (Followers: 8)
Modern Instrumentation     Open Access   (Followers: 50)
Optoelectronics, Instrumentation and Data Processing     Hybrid Journal   (Followers: 4)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal  
Photogrammetric Engineering & Remote Sensing     Full-text available via subscription   (Followers: 29)
Remote Sensing     Open Access   (Followers: 55)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 8)
Remote Sensing of Environment     Hybrid Journal   (Followers: 93)
Remote Sensing Science     Open Access   (Followers: 24)
Review of Scientific Instruments     Hybrid Journal   (Followers: 23)
Science of Remote Sensing     Open Access  
Sensors and Materials     Open Access   (Followers: 2)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Standards     Open Access  
Transactions of the Institute of Measurement and Control     Hybrid Journal   (Followers: 13)
Труды СПИИРАН     Open Access  
Similar Journals
Journal Cover
IEEE Sensors Journal
Journal Prestige (SJR): 0.619
Citation Impact (citeScore): 3
Number of Followers: 103  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1530-437X
Published by IEEE Homepage  [229 journals]
  • IEEE Sensors Journal publication information
    • 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: May15, 2021
      Issue No: Vol. 21, No. 10 (2021)
  • IEEE Sensors Council Information
    • 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: May15, 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Integrated Bionic Polarized Vision/VINS for Goal-Directed Navigation and
           Homing in Unmanned Ground Vehicle
    • Authors: Wenzhou Zhou;Chen Fan;Xiaofeng He;Xiaoping Hu;Ying Fan;Xuesong Wu;Hang Shang;
      Pages: 11232 - 11241
      Abstract: In this paper we present a bionic multi-sensor anavigation and control system for unmanned ground vehicles to complete the homing task. The system consists of the pixelated polarized sensor, a Micro Inertial Measurement Unit (MIMU), and a monocular camera. To compensate for the installation error, we provide a joint calibration method for multiple sensors. Utilizing the measurements of pixelated polarized vision sensor, we firstly propose an adaptive integrated method with the Visual-Inertial System. The integrated algorithm can not only solve the ambiguity problem of polarized orientation and reduce the cumulative error of the system, but also increase the navigation output rate and enhance the robustness of the system. We present a homevector-based strategy for the goal-directed navigation and control of the unmanned ground vehicles. When the external data link is interrupted, the unmanned vehicles can return to the starting point autonomously, which benefits for improving the survivability of the system. Finally, we design various experiments to verify the algorithm proposed in this paper. The experimental results of the calibration demonstrate that the RMSE of the orientation after calibration is only 0.014°. In the navigation and homing experiment, the RMSE of the position error is 0.64m, and the minimum homing error is only 0.49m (1.09% of the travelled distance). Finally, we discuss interesting insights gained with respect to future work in multi-sensor integration and robot control strategies.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Guest Editorial Special Issue on Sensors in Machine Vision of Automated
    • Authors: Oleg Sergiyenko;Wendy Flores-Fuentes;Paolo Mercorelli;Julio C. Rodriguez-Quiñonez;Tohru Kawabe;
      Pages: 11242 - 11243
      Abstract: Sensors in machine vision of automated systems significantly improve the quality and reliability of all those processes and products related with such automated systems.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Probability of Detecting the Deep Defects in Steel Sample Using Frequency
           Modulated Independent Component Thermography
    • Authors: Javed Ahmad;Aparna Akula;Ravibabu Mulaveesala;H. K. Sardana;
      Pages: 11244 - 11252
      Abstract: Active thermography is a widely used non-destructive testing and evaluation technique (NDT&E) for evaluating the properties of materials without impairing its future usefulness. In this work, a mild steel sample made of artificial flat-bottom holes at varied depths, was examined with the emerging non-stationary thermal wave imaging (NSTWI) technique, i.e. frequency modulated thermal wave imaging (FMTWI). The pulse compression favorable of NSTWI technique is eminent for compressing the applied thermal energy into a narrow-compressed pulseto enhance the depth resolution and sensitivity. In this work, pulse compressed thermographic data generated from FMTWI experimentation is analyzed with the unsupervised learning approach independent component analysis (ICA) to test their mutual return in the detection of the deep defects in a mild steel sample and this proposed technique was referred to as frequency modulated independent component thermography (FMICT). In comparison, the effect of FMICT was contrasted with othermethodsi.e. pulse compression of time domain and ICA of feature space by considering the signal-to-noise ratio (SNR) as a figure of merit. Furthermore, a probability of detection (POD) analysis framework based on the minimum threshold SNR criteria for apparent visibility of the defects has been presented to assess the probability of identifying defects at various depths using such approaches. The influence of the SNR threshold value for the above strategies on the POD curves has also been presented.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • High-Speed Optical 3D Measurement Sensor for Industrial Application
    • Authors: Congyi Lyu;Peng Li;Daochuan Wang;Shanshan Yang;Yinping Lai;Congying Sui;
      Pages: 11253 - 11261
      Abstract: 3D optical sensors are becoming more and more popular in vision guidance of industrial robots and other industrial automation applications. Although research on high-speed 3D shape measurement (or imaging) has experienced tremendous growth over the past decades, simultaneously achieving high-speed and high-accuracy performance remains a major challenge in industrial practice. This paper presents a new FPGA architecture for the PMP algorithm and designs an high-speed embedded binocular structured light 3D measurement sensor. The sensor mainly contains a DLP projector, two cameras and a Xilinx Zynq-7000 UltraScale which make real-time computation possible. Experiments showed the time consuming of processing a set of ${9} times {2}$ images with a resolution of ${2048} times {1536}$ using the sensor proposed in this paper is 31.47 ms that has a better performance than 255ms on GPU. To the best of our knowledge, this is the first high-speed FPGA-based embedded binocular structured light 3D measurement sensor. This proposed 3D sensor can obtain ultra high quality 3D information in real time, it can be widely applied to robot random pin-picking and other industrial applications.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • 3D Optical Machine Vision Sensors With Intelligent Data Management for
           Robotic Swarm Navigation Improvement
    • Authors: Oleg Yu Sergiyenko;Vera V. Tyrsa;
      Pages: 11262 - 11274
      Abstract: the optimized communication within robotic swarm, or group (RG) in a tightly obstacled ambient is crucial point to optimize group navigation for efficient sector trespass and monitoring. In the present work the main set of problems for multi-objective optimization in a non-stationary environment is described. It is presented the algorithm of data transfer from 3D optical sensor, based on the principle of dynamic triangulation. It uses the distributed scalable big data storage and artificial intelligence in automated 3D metrology. Two different simulations in order to optimize the fused data base for better path planning aiming the improvement of electric wheeled mobile robots group navigation in unknown cluttered terrain is presented. The optical laser scanning sensor combined with Intelligent Data Management permits more efficient dead-reckoning of the RG.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Facile Zinc Oxide Nanoparticle Green Synthesis Using Citrus reticulata
           Extract for Use in Optoelectronic Sensors
    • Authors: Priscy A. Luque;Osvaldo Nava;Gerardo Romo-Cardenas;Juan Ivan Nieto-Hipolito;Alfredo R. Vilchis-Nestor;Karla Valdez;Juan de Dios Sánchez-López;Fabian N. Murrieta-Rico;
      Pages: 11275 - 11282
      Abstract: Waveguides are structures that can be used as sensors. After adding a cladding of a proper material, waveguides can be used for detecting specific chemical species. In particular, high band gap oxide semiconductors can be used as cladding for optical waveguides. In these materials, the synthesis processes are expensive or contaminant generators, for this reason novel approaches are required. This work addresses a simple synthesis method for the preparation of zinc oxide nanoparticles using Citrus reticulata extract. XRD presented only the wurtzite structure for the zinc oxide nanoparticles, while TEM showed size and shape homogeneity. The ZnO band gap was lower than commercial ZnO. Using a computer simulation and frequency dependent electrical characteristics, a waveguide model was tested. The resulting characteristics presented great parameters for using in optoelectronic sensors. Resulting ZnO NPs have negligible amounts of green matter afterwards, making this a non-toxic method. As a result, herein presented synthesis is a promising method for developing new optoelectronic sensors. These devices can be used for automatic detection of chemical species by automated systems.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Frequency Shifts Estimation for Sensors Based on Optoelectronic
    • Authors: Fabian N. Murrieta-Rico;Vitalii Petranovskii;Donald H. Galván;Joel Antúnez-García;Rosario I. Yocupicio-Gaxiola;Vera Tyrsa;
      Pages: 11283 - 11290
      Abstract: Optoelectronic oscillators (OEO) based sensors generate low-phase-noise signals, whose frequency is in the order of GHz. These sensors are highly sensitive to parameters like strain, transverse load or temperature. Particularly, an OEO transforms a wavelength change into a frequency shift in the order of MHz. This creates a particular challenge for measuring instruments used in OEO, where novel mathematical methods for advanced processing of their signals is required. The principle of rational approximations is a frequency measurement technique based on number theory, with the fundamental property of measurement time dependent on the frequency value of input signals. This means that if the frequency of input signals increases, less time for measuring is required. This is quite useful for time-frequency measurement systems, where if more accuracy is needed, more time is required for measuring. This paper shows how the principle of rational approximations can be used in OEO based sensors, this allows to estimate the frequency shifts in a bandwidth from 6 to 600 MHz, with a maximum uncertainty of 2.5 MHz. Such an accuracy is achieved in a time as short as $0.2~mu text{s}$ .
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Machine Vision System Based on Driving Recorder for Automatic Inspection
           of Rail Curvature
    • Authors: Su-Mei Wang;Ching-Lung Liao;Yi-Qing Ni;
      Pages: 11291 - 11300
      Abstract: Because of long distance of railway lines, it is difficult to find an appropriate method to inspect the rail track condition efficiently and accurately. In this article, a machine vision system based on driving recorder and image signal processing is proposed to evaluate the rail curvature automatically. The proposed machine vision system consists of four modules including the video acquisition module, the image extraction module, the image processing module, and the track condition assessment module. Three classic edge detection methods are adopted and compared for rail edge detection. In line with the videos of driving recorder, coordinate systems for train and rail are defined in the Lagrangian space, and the track curvature is estimated using the proposed chord offset method and double measurement method. For evaluating the track condition, an index describing the concordance between the train and track is defined. In the case study, a set of videos from the driving recorders of trains during their in-service operations are analyzed by the proposed technique, and the obtained results are verified by comparison with those obtained by a track geometry inspection vehicle. It is shown that the proposed technique can evaluate the track curvature accurately. Moreover, the influence of the position of deployed driving recorder, the focal length and anti-shake of camera on the accuracy of evaluation results is discussed. It is testified that the proposed technique provides a simple and reliable way to inspect the track curvature.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Method of Classifying Railway Sleepers and Surface Defects in Real
    • Authors: André Stanzani Franca;Raquel Frizera Vassallo;
      Pages: 11301 - 11309
      Abstract: Rail transport is an efficient and safe way to move large quantities of goods and people over long distances but it still suffers from maintenance issues, mainly due to assets of great extent, quantity, weight, and geographic dispersion. Because of this, some initiatives in automatic inspection of railway assets have been developed in recent years/in the last decade. In particular, the automatic inspection of railway sleepers still needs improvement and consolidation. This work presents a method for sleepers inventorying, identification of the type and defects based on image processing, heuristics and feature fusion in an unsupervised way. The Haar transform and integral images are used, as well as other image processing techniques such as edge detection, and entropy computation along with aspects of railroad topology. The algorithm was developed using real images of daily railway, previously unclassified, and that were subject to various noises and variations of a real railway operation. The method was validated through experiments with an image set comprising 32,917 sleepers in 10,116 images. The results are promising in which 97% accuracy is reached, for the identification of the type of sleepers, and 93% accuracy for the identification of visible defects in sleepers.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Photometric-Planner for Visual Path Following
    • Authors: Eder A. Rodríguez Martínez;Guillaume Caron;Claude Pégard;David Lara-Alabazares;
      Pages: 11310 - 11317
      Abstract: Robotic navigation is the aspect of cognition related to robot robust mobility. It combines perception, some knowledge on the environment and a set of goal poses to reliably control the robot during a mission that involves displacement. Vision-based autonomous navigation is an instantiation of the latter discipline where visual perception is used to control the robot and to represent the environment. This article presents a vision-based navigation system that uses its onboard camera to navigate and a visual path that represents the scene with a set of images. Being a memory-based system, the navigation is conceived as a concatenation of positioning tasks in the visual servoing scheme. The novelty on the proposed system relies on the generation of the images that compose the visual path. These images are rendered from a preobtained model of the scene. The experiments evaluate the performance of the system over three different scenes, contemplating indoor and outdoor environments, and using an Unmanned Aerial Vehicle as testbed.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Novel Sensing Approaches for Structural Deformation Monitoring and 3D
    • Authors: Moises J. Castro-Toscano;Julio C. Rodríguez-Quiñonez;Oleg Sergiyenko;Wendy Flores-Fuentes;Luis Roberto Ramírez-Hernández;Daniel Hernández-Balbuena;Lars Lindner;Raúl Rascón;
      Pages: 11318 - 11328
      Abstract: Nowadays, laser vision systems have allowed the development of different applications such as reverse engineering, manufacturing, navigation systems and, structural health monitoring (SHM). However, most of the machine vision systems for structural behavior analysis have restricted field of view, consume high levels of computational resources for image processing and require special illumination conditions to achieve lower error rates. Therefore, the purpose of this paper is to present a technical vision system (TVS) for structural behavior analysis using dynamic laser triangulation and k- Nearest Neighbor (k-NN) machine learning regression algorithm. The proposed vision system was tested in order to demonstrate the practicality of it, different deformations and displacements were analyzed over real structures in controlled laboratory conditions to assure the reproducibility of the experimentation. The TVS prototype proved to be a reliable option on SHM tasks, presenting balance between precision and operating ranges, without the issues aforementioned.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • DCFNet++: More Advanced Correlation Filters Network for
           Real-Time Object Tracking
    • Authors: Lang Tian;Pingmu Huang;Zhipeng Lin;Tiejun Lv;
      Pages: 11329 - 11338
      Abstract: Visual object tracking has been widely addressed in Siamese networks, where accurate and fast object tracking can be achieved. However, it is challenging to discriminate foregrounds from semantic backgrounds, because the semantic backgrounds are always considered as distractors, which would hinder the robustness of Siamese trackers. In this paper, we address the visual object tracking problem in complex scenarios, including occlusions, out-of-view, deformation, background cluttering, and other variations. We introduce multiple combinations in the training set to improve the discriminative ability of the learned features. As a result, the robustness of the model can be improved. We also propose an advanced method to fuse multi-layer features, so that the feature representation can be enhanced. Finally, we develop a flow-based tracking method, which makes the tracker very robust to occlusion scenarios. Extensive evaluations on OTB-2015, VOT2018, UAV123, and LaSOT benchmarks demonstrate that the proposed DCFNet++ has strong robustness when facing challenging scenarios. Without bells and whistles, our proposed tracker can run at more than 56 FPS during test time.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Frontiers in Photosensor Materials and Designs for New Image Sensor
    • Authors: Ross D. Jansen van Vuuren;Jean-Michel Nunzi;Sidney N. Givigi;
      Pages: 11339 - 11348
      Abstract: Certain applications of image sensors require capabilities that are beyond the technology of current image sensors, such as automated color-based quality inspection systems operating under varying levels of illumination. This paper serves to provide a brief overview of image sensor technologies involving the use of alternative photosensor materials (organic semiconductors and perovskites) being developed to meet these needs. The discussion around such developments is typically confined to chemistry and physics “silos”; by publishing in this special edition, the authors hope to bridge the knowledge gap between scientists developing new photosensor materials and engineers developing image sensors for new machine vision systems.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Concrete Surface Damage Volume Measurement Based on Three-Dimensional
           Reconstruction by Smartphones
    • Authors: Chengcheng Liu;Lei Zhou;Weiwei Wang;Xuefeng Zhao;
      Pages: 11349 - 11360
      Abstract: Surface damage of structures is an important indicator that further inspection of the structure is needed. However, existing detection methods rarely detect three-dimensional data of damage. Aiming at the inspection of structural surface damage, this study proposes a method based on 3-D structural surface damage reconstruction techniques for reconstructing and extracting data for damage volume calculation. The surface damage of concrete specimen is three-dimensionally reconstructed using multi-view smartphone-taken images and compared with a depth camera. The point cloud data was obtained, and then the damage plane was fitted and removed by a Random Sample Consensus algorithm to obtain the damage site data, finally, the damage volume was calculated. In the experimental process, accuracy and post-processing difficulty, equipment cost, data acquisition efficiency, and overall applicability of the two methods were compared and analyzed. In conclusion, it was determined that the 3-D damage parameters obtained through the smartphone multi-view stereo reconstruction performs better, at a lower cost and is more convenient for use in inspection work. Finally, the practicability of the method was proved by the damage detection experiment of real building.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A 64 × 64 SPAD Flash LIDAR Sensor Using a Triple Integration Timing
           Technique With 1.95 mm Depth Resolution
    • Authors: Daniel Morrison;Simon Kennedy;Dennis Delic;Mehmet Rasit Yuce;Jean-Michel Redouté;
      Pages: 11361 - 11373
      Abstract: This paper presents a $64times64$ dual mode flash LIDAR sensor that utilizes a triple integration timing technique. The sensor, fabricated in 130 nm HV CMOS, is capable of operating in both a direct time-of-flight (ToF) mode where the timestamp of the first arriving photon is recorded per-pixel, as well as a photon counting mode where the number of photons is recorded over a time interval. The timing technique utilizes both a time-to-digital converter (TDC) and a time-to-amplitude converter (TAC) with a counter measuring global clock cycles and the triple integration interpolator (TII) measuring between clock cycles. The TII uses an analog integration with an additional two reference integrations allowing the time measurement to be resistant to PVT variation and in turn, allowing the circuit to be miniaturized without causing a large timing non-uniformity across the array. Utilizing the TII, the sensor achieves a state-of-the-art timing performance, with a resolution of 13 ps (1.95 mm depth resolution), a maximum range of 220 $mu text{s}$ (32 km), a single-shot jitter of 233 ps, and a differential non-linearity (DNL) of 6 ps (0.47 LSB). The sensor captures at a maximum frame rate of 8,300 fps and consumes 733 mW during operation. Experimental scenarios demonstrating the operation of the sensor are also provided.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A New Approach to Polyp Detection by Pre-Processing of Images and Enhanced
           Faster R-CNN
    • Authors: Zhiqin Qian;Yi Lv;Dongyuan Lv;Huijun Gu;Kunyu Wang;Wenjun Zhang;Madan M. Gupta;
      Pages: 11374 - 11381
      Abstract: Colon cancer is the third most common cancer in the world, and it is increasingly threatening people’s health. Early diagnosis is crucial to reducing the threat; however, the chance of missed polyps in today’s colonoscopy examination is still high (about 10%) due to limitations in diagnosis techniques and data analysis methods. The colonoscope is a kind of robot and on its tip there is a camera to acquire images. This paper presents a study aimed to improve the rate of successful diagnosis with a new image data analysis approach based on the faster regional convolutional neural network (faster R-CNN). This new approach has two steps for data analysis: (i) pre-processing of images to characterize polyps, and (ii) incorporating of the result of the pre-processing into the faster R-CNN. Specifically, the pre-processing of colonoscopy was expected to reduce the influence of specular reflections, resulting in an improved image, upon which the faster R-CNN algorithm was aplied. There are several improvements of the faster r-CNN tailoring to the task of colon polyps detection. To confirm the superiority of this new approach, the mean average precision (mAP) was used to compare the results obtained with the new approach and the faster R-CNN algorithm. The experimental result shows that the mAP of the new approach is 91.43%, as opposed to 90.57% with the faster R-CNN, which shows a significant improvement.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Cascaded Feature Pyramid Network With Non-Backward Propagation for
           Facial Expression Recognition
    • Authors: Wei Yang;Hongwei Gao;Yueqiu Jiang;Jiahui Yu;Jian Sun;Jinguo Liu;Zhaojie Ju;
      Pages: 11382 - 11392
      Abstract: In this work we propose a novel cascaded feature pyramid network with non-backward propagation (CFPN-NBP) for facial expression recognition (FER) that addresses the problems inherent in traditional backward propagation (BP) algorithms in the training process by using the Hilbert-Schmidt independence criterion (HSIC) bottleneck. The proposed algorithm is developed at two different levels. At the first level, a novel training method HSIC bottleneck is considered as an alternative to traditional BP optimization, where the correlation between the output of the hidden layers and the input, and the correlation between the output of the hidden layers and its label are calculated to reduce redundant information; hence, the least information is used to predict the results. At the second level, a novel architecture is designed in the feature extraction process. The convolutional layers with the same resolutions are densely connected and introduced into the attention mechanism, so that the model can focus on more important information. The convolutional layers with different resolutions are combined by three cascaded pyramid networks; in this way, the shallow features and the deep features can be further fused, and; therefore, the semantic information and the content information can both be reserved. To further reduce the number of parameters, the operation of separable convolution instead of traditional convolution is utilized. Experiments on the challenging FER2013 dataset show that the proposed CFPN-NBP algorithm improves the accuracy of the FER task and outperforms the related state-of-the-art methods.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Novel Thermgoraphic Methodology to Predict Damage Evolution of Impacted
           CFRP Laminates Under Compression-Compression Fatigue Based on Inverted
           Weibull Model
    • Authors: Yin Li;Yuan-Jia Song;
      Pages: 11393 - 11400
      Abstract: A novel thermographic methodology is proposed in this work to predict damage evolution of impacted CFRP laminates under compression-compression fatigue loading based on inverted weibull model. To this goal, the inverted weibull model is firstly mathematically proposed. Then, several specimens are subjected to impact testing, followed by fatigue testing and monitored by infrared thermography. Afterwards, the thermal image and the surface temperature of specimens are analyzed. Finally, taking the surface temperature as damage variables, the inverted weibull model is introduced to predict the damage evolution. The obtained results show that the inverted weibull model can be used for damage evolution prediction with the predicted error less than 10%.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Multi-Modal, Silicon Retina Technique for Detecting the Presence of
           Reflective and Transparent Barriers
    • Authors: Andre Green;Kaleb Kleine;Isaiah Acevedo;Dustan Kraus;Charles R. Farrar;David D. L. Mascareñas;
      Pages: 11401 - 11416
      Abstract: There is currently great interest in enhancing the ability of aerial robots to navigate indoors. Navigating a building under various lighting and environmental conditions would have application in disaster response, infrastructure inspections, as well as a wide variety of commercial applications. In order to achieve this goal, one common feature of indoor environments that must be addressed is the detection of transparent/reflective barriers. Transparent/reflective barriers as they pertain to structures generally take the form of a window, office divider, or storefront. Human tele-operators of aerial robots in environments such as malls, airports, office buildings, and museums that feature transparent barriers will need some means to enhance their situational awareness so they can recognize the presence of transparent/reflective barriers, distinguish between the two, and have some idea of the distance and pose relative to the transparent/reflective barrier. The ability to detect and localize transparent barriers will also be important for autonomous navigation. The focus of this work is to develop a multi-modal sensing solution that can successfully identify transparent/reflective barriers, distinguish between the two, and provide information on pose and distance to the barrier at time-scales exceeding human response in order to facilitate navigation of indoor spaces. The sensing solution relies on using an imager to measure the differences in the interactions of actively visible light illumination of transparent/reflective barriers. A silicon retina event-driven imager is used in this work to provide a path to obtaining information on transparent/reflective barriers at high speeds, while requiring very little communications bandwidth.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Invariant Feature-Based Darknet Architecture for Moving Object
    • Authors: S. Vasavi;N. Kanthi Priyadarshini;Koneru Harshavaradhan;
      Pages: 11417 - 11426
      Abstract: Object detection and classification is important for video surveillance applications. Counting vehicles like cars, truck and vans is useful for intelligent transportation systems to identify dense and sparse roads, track loaded vehicles at the country borders. Even though many solutions such as appearance-based (Multi-block Local Binary Pattern) and model-based ((DATMO) algorithm) are proposed to classify the moving objects within the satellite images using machine learning and deep learning techniques, they either have over fitting problems or low performance. Hence these challenges have to be addressed during detecting and classifying the objects. Instead of training the classifiers with hand-crafted features, this paper uses neural network based object detection and classification to achieve promising accuracy better than the humans. Invariant feature concept is added to the existing Darknet Architecture of You Only Look Once (YOLO) and is combined with Faster Region-Based Convolutional Neural Networks (Faster R-CNN) to count the number of vehicles with different spatial locations. This combined model improves feature extraction step and vehicle classification process. The proposed system is tested on two benchmark datasets Cars Overhead with Context (COWC) and Vehicle Detection in Aerial Imagery (VEDAI) for counting the cars and trucks. Experimental results prove that the proposed system is better by 9% in detecting smaller objects than existing works.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • WideSegNeXt: Semantic Image Segmentation Using Wide Residual Network and
           NeXt Dilated Unit
    • Authors: Yoshiki Nakayama;Huimin Lu;Yujie Li;Tohru Kamiya;
      Pages: 11427 - 11434
      Abstract: Semantic segmentation is widely applied in autonomous driving, in robotic picking, and for medical purposes. Due to the breakthrough of deep learning in recent years, the fully convolutional network (FCN)-based method has become the de facto standard in semantic segmentation. However, the simple FCN has difficulty in capturing global context information, since the local receptive field is small. Furthermore, there is a problem of low image resolution because of the existence of the pooling layer. In this paper, we address the shortcomings of the FCN by proposing a new architecture called WideSegNeXt, which captures the image context on various spatial scales and is effective in identifying small objects. In addition, there is little loss of position information, since there are no pooling layers in the structure. The proposed method achieves a mean intersection over union (MIoU) of 72.5% and a global accuracy (GA) of 92.4% on the CamVid dataset and achieves higher performance than previous methods without additional input datasets.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Improved Change Detector Using Dual-Camera Sensors for Intelligent
           Surveillance Systems
    • Authors: Ajmal Shahbaz;Kang-Hyun Jo;
      Pages: 11435 - 11442
      Abstract: Unauthorized entrance in a prohibited area might create a security risk. An intelligent surveillance system should be able to mitigate such a problem by incorporating a sterile zone monitoring algorithm. The algorithm is challenged by a dual-camera sensors (color/IR), dynamic backgrounds, illumination changes, camouflaged, and static foreground objects, etc. This paper proposes an improved change detector (ICD) to mitigate the above-mentioned challenges. It employs a novel statistical decision criterion (SDC) based on skewness patterns. The SDC helps to differentiate time of day using the camera sensors (color/IR). The input frames are processed according to the time of day. For instance, IR input is image-enhanced to differentiate between camouflaged intruders from the background. Then input goes through Gaussian Mixture Models (GMM) based change detector to segment foreground (intruder). The foreground object is further cleansed using morphological operations for possible isolated noise and holes. The ICD was tested on three datasets and outperformed top-ranked change detection algorithms.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Detection of Landmarks by Autonomous Mobile Robots Using Camera-Based
           Sensors in Outdoor Environments
    • Authors: Oleksandr Poliarus;Yevhen Poliakov;Andrii Lebedynskyi;
      Pages: 11443 - 11450
      Abstract: This paper presents the methods of increasing the probability of landmark detection using the features of the color parameters (coordinates) of the image averaged over each column of the camera matrix pixels. The first method involves selecting a jump (dip) of color parameters distribution and averaging these parameters in the background areas using shifted similar distributions for images obtained by scanning the surrounding space. This possibility occurs if the correlation radius of the color parameters on the image is several times smaller than the horizontal size of the image. The second method uses a color parameter jump on the first modes of the Hilbert-Huang transform in the location of a landmark image.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Calibration Method for Mobile Omnidirectional Vision Based on Structured
    • Authors: Ling Meng;Yuan Li;Qing Lin Wang;
      Pages: 11451 - 11460
      Abstract: Mobile omnidirectional structured light vision is increasingly used in scene perception and robot navigation. A wide range of information is obtained by means of the vision system by only one image and laser image features are detected and extracted easily and quickly. In this paper a novel calibration method for mobile omnidirectional camera based on structured light is presented. Firstly, a set of parallel laser planes is emitted on the walls of corridor as auxiliary targets by structured light and intersects with wall orthogonally. Secondly, the constraint relationship is analyzed between the vanishing points in fisheye images and intrinsic parameters of imaging model. Finally, effects of the laser stripes’ interval and the angle between the wall which contains laser stripes and ground on calibration results are evaluated. Compared to Scaramuzza method, the calibration method shows its superiority in terms of both feasibility and efficiency. The method with the characteristic of self-calibration since the planar target is replaced by actively projected laser stripes. The result illustrates that our method has the advantages of simple and feasible operation, but result is effective and accurate. The calibration parameters are independent of the laser stripes’ interval and the angle between the wall and ground. Therefore, the method of the mobile omnidirectional structured light vision presented in this paper can be applied to many areas.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A New Framework for Smartphone Sensor-Based Human Activity Recognition
           Using Graph Neural Network
    • Authors: Riktim Mondal;Debadyuti Mukherjee;Pawan Kumar Singh;Vikrant Bhateja;Ram Sarkar;
      Pages: 11461 - 11468
      Abstract: Automatic human activity recognition (HAR) through computing devices is a challenging research topic in the domain of computer vision. It has widespread applications in various fields such as sports, healthcare, criminal investigation and so on. With the advent of smart devices like smartphones, availability of inertial sensors like accelerometer and gyroscope can easily be used to track our daily physical movements. State-of-the-art deep neural network models like Convolutional Neural Network (CNN) do not need any additional feature extraction for such applications. However, it requires huge amount of data for training which is time consuming, and requires ample resource. Another limiting factor of CNN is that it considers only the features of an individual sample for learning without considering any structural information among the samples. To address the aforesaid issues, we propose an end-to-end fast Graph Neural Network (GNN) which not only captures the individual sample information efficiently but also the relationship with other samples in the form of an undirected graph structure. To the best of our knowledge, this is the first work where the time series data are transformed into a structural representation of graph for the purpose of HAR using sensor data. Proposed model has been evaluated on 6 publicly available datasets, and it achieves nearly 100% recognition accuracy for all the 6 datasets. Source code of this work is available at
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • An Improved Encoder–Decoder Network for Ore Image Segmentation
    • Authors: Hao Yang;Chao Huang;Long Wang;Xiong Luo;
      Pages: 11469 - 11475
      Abstract: Accurate segmentation of ore images plays a significant role in automatic geometric parameter detecting and composition analyzing of ore dressing progress. Semantic segmentation based on deep learning is a promising method for accurate ore image segmentation. However, the similar appearance with low contrast and blurry boundary of ores in image hamper segmentation accuracy. Moreover, it is difficult to train a deep network due to limited available ore images. In this work, an improved encoder-decoder network based on U-Net is proposed to handle above challenges. A contour awareness loss (CAL) is proposed to improve model sensitivity to misclassified pixels, pixels of similar appearance, and pixels near the boundary. The proposed scheme is verified on ore images by benchmarking against state-of-the-art segmentation methods. Experiment results show that the proposed scheme achieves 88.6% pixel-wise accuracy (PA) and 66.0% mean intersection over union (mIoU).
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Automatic and Efficient Metallic Surface Defect Detection Based on Key
           Pixel Point Locations
    • Authors: Jiahui Yu;Hongwei Gao;Jian Sun;Wei Yang;Yueqiu Jiang;Zhaojie Ju;
      Pages: 11476 - 11487
      Abstract: Surface defect detection aims to accurately recognize and distinguish types of defects and plays a key role in many applications. However, most of the recent studies focus on specific scenario detection and do not fairly consider the balance between the speed and accuracy. In the paper, we propose a key pixel points location-oriented method to identify multiscale defects, with several important properties: 1) A real-time template matching-based model is designed to speed up the process by introducing the Gaussian operator; 2) An improved Hough-based model is used to achieve a higher detection precision by deep mining both incremental properties and parallel properties; and 3) An adaptive filtering-based image preprocessing method is proposed to eliminate the interference of multiple types of clutters and noises. In the experiments, a mean average rate of 96% was achieved to detect and classify four types of common defects and the average time was reduced to 0.149 s. Furthermore, we fully evaluate the proposed method on two public datasets collected in real production lines and compare the results with other state-of-the-art methods. The results show that the proposed method achieved better balanced performance in many real application scenarios.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Attention! A Lightweight 2D Hand Pose Estimation Approach
    • Authors: Nicholas Santavas;Ioannis Kansizoglou;Loukas Bampis;Evangelos Karakasis;Antonios Gasteratos;
      Pages: 11488 - 11496
      Abstract: Vision based human pose estimation is an non-invasive technology for Human-Computer Interaction (HCI). The direct use of the hand as an input device provides an attractive interaction method, with no need for specialized sensing equipment, such as exoskeletons, gloves etc, but a camera. Traditionally, HCI is employed in various applications spreading in areas including manufacturing, surgery, entertainment industry and architecture, to mention a few. Deployment of vision based human pose estimation algorithms can give a breath of innovation to these applications. In this article, we present a novel Convolutional Neural Network architecture, reinforced with a Self-Attention module. Our proposed model can be deployed on an embedded system due to its lightweight nature with just 1.9 Million parameters. The source code and qualitative results are publicly available.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Global Localization With a Single-Line LiDAR by Dense 2D Signature and 1D
    • Authors: Zhong Wang;Lin Zhang;Shengjie Zhao;Shaoming Zhang;
      Pages: 11497 - 11506
      Abstract: Global localization is a key problem that needs to be solved for single-line LiDAR based robot navigation since it will directly affect the estimation accuracy of the robot’s initial pose and the success rate of recovering the robot’s state when it loses its local pose. Existing studies to deal with this problem usually extract feature points from laser beams and then resort to fast retrieval and registration methods to further determine the robot’s pose. Although these methods have achieved good results in specific scenes, they often fail to perform well when the robot is far away from the map-building trajectory. It is therefore highly desirable to develop more robust techniques for this problem. In this work, we propose a novel solution which is based on “Dense 2D Signature and 1D Registration”, D2S1R for short. By establishing a dense signature database for 2D locations and combining with the fast retrieval technology, the 2D search space is extremely compressed. Furthermore, fast yaw angle determination is achieved by converting scan points to 1D space and measuring the difference of scan contours based on relative entropy. Experimental results on several complex indoor scenes show that D2S1R can complete global localization within $0.03{s}$ on an ordinary CPU in an area of nearly $4,000{m}^{2}$ . Besides, on the premise of achieving a location accuracy of $0.08pm 0.04{m}$ and an orientation accuracy of 0.72±0.60°, it can achieve an average success rate of 95% on all test datasets.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Recursive Algorithms for Computing Sliding DCT With Arbitrary Step
    • Authors: Vitaly Kober;
      Pages: 11507 - 11513
      Abstract: Discrete cosine transform (DCT) is one of the most popular transforms in digital signal processing. Its close approximation to the Karhunen-Loeve transform implies a high degree of energy compaction. Therefore, it can be used in a wide range of applications such as sensor noise removal, spectral analysis, linear filtering, feature extraction and pattern recognition. When it is required to estimate the spectrum of a nonstationary process such as radar, speech, biomedical and communication signals, a short-time (sliding) transform can be used. Basically, the sliding transform means that the transform is calculated on a fixed-length window of the signal, which is constantly updated with new samples while the oldest ones are discarded. In some engineering applications, when the spectral content changes slowly, the window can slide more than one sample at a time to speed up the spectral analysis. In this paper, second-order recursive algorithms are proposed for fast computing the DCT in windows that are located at a given equal distance from each other. The computational complexity of the algorithms is compared with that of common fast and sliding DCT algorithms.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • k-Nearest Neighbor Classification for Pattern Recognition of a Reference
           Source Light for Machine Vision System
    • Authors: Jesús Elias Miranda-Vega;Moisés Rivas-López;Wendy Flores Fuentes;
      Pages: 11514 - 11521
      Abstract: The design of machine vision applications allows automatic inspection, measuring systems, and robot guidance. Typical applications of industrial robots are based on no-contact sensors to give the robot information about the environment. Robot’s machine vision requires photosensors or video cameras to make intelligent decisions about its localization. Video cameras used as image-capturing equipment are too costly in comparison with optical scanning systems (OSS). The OSS system provides spatial coordinates measurements that can be exploited to solve a wide variety of structural problems in real-time. Localization and guidance using machine learning (ML) techniques offer advantages due to signals captured can be transformed and be reduced for processing, storage, and displaying. The use of algorithms of ML enhances the performance of the optical system based on localization and guidance. Feature extraction represents an important part of ML techniques to transform the original raw data onto a low-dimensional subspace and holding relevant information. This work presents an improvement of an optical system based on $textit k$ -nearest neighbor ( $textit k$ -NN) technique to solve the object detection and localization problem. The utility of this improvement allows the optical system can discriminate between the reference source and the optical noise or interference. The OSS system presented in this article has been implemented in structural health monitoring to measure the angular position even under “lighting and weather conditions”. The feature extraction techniques used in this article were linear predictive coding (LPC), quartiles ( $textit Q_{iquartile}$ ), and autocorrelation coefficients (ACC)- The results of using $textit k$ -NN and autocorrelation coefficients and quartiles predicted more than 98% of correct classification by using a reference source light as a class 1 and a light bulb as an optical noise and called class 2.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Independent Moving Object Detection Based on a Vehicle Mounted Binocular
    • Authors: Jianying Yuan;Gexiang Zhang;Fengping Li;Jiajia Liu;Lin Xu;Sidong Wu;Tao Jiang;Dequan Guo;Yurui Xie;
      Pages: 11522 - 11531
      Abstract: Accurate detection of independent moving objects is the key to ensure the safety of driverless vehicles during travelling. In this study, a moving object detection method based on a vehicle-mounted binocular camera is proposed. Firstly, a feature matching points selection strategy is designed for high accuracy camera ego-motion estimation. Then, the residual optical flow filed generated only by moving objects is estimated by taking into account the camera ego-motion and global mixed optical flow. A dynamic threshold segmentation method based on disparity is proposed to separate the moving regions from the residual optical flow field. Finally, 3D and 2D information are combined for extracting each single moving object from moving regions. The innovation of the proposed method is that it can detect any type and any size of moving object theoretically by using the dense motion information of the images. By taking the data in KITTI database as the test samples, the detection precision of the moving objects using the proposed algorithm can reach up to 91% without considering tracking strategy.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • GA-SVM-Based Facial Emotion Recognition Using Facial Geometric Features
    • Authors: Xiao Liu;Xiangyi Cheng;Kiju Lee;
      Pages: 11532 - 11542
      Abstract: This paper presents a facial emotion recognition technique using two newly defined geometric features, landmark curvature and vectorized landmark. These features are extracted from facial landmarks associated with individual components of facial muscle movements. The presented method combines support vector machine (SVM) based classification with a genetic algorithm (GA) for a multi-attribute optimization problem of feature and parameter selection. Experimental evaluations were conducted on the extended Cohn-Kanade dataset (CK+) and the Multimedia Understanding Group (MUG) dataset. For 8-class CK+, 7-class CK+, and 7-class MUG, the validation accuracy was 93.57, 95.58, and 96.29%; and the test accuracy resulted in 95.85, 97.59, and 96.56%, respectively. Overall precision, recall, and F1-score were about 0.97, 0.95, and 0.96. For further evaluation, the presented technique was compared with a convolutional neural network (CNN), one of the widely adopted methods for facial emotion recognition. The presented method showed slightly higher test accuracy than CNN for 8-class CK+ (95.85% (SVM) vs. 95.43% (CNN)) and 7-class CK+ (97.59 vs. 97.34), while the CNN slightly outperformed on the 7-class MUG dataset (96.56 vs. 99.62). Compared to CNN-based approaches, this method employs less complicated models and thus shows potential for real-time machine vision applications in automated systems.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Contrast Enhancement Model for X-Ray Mammograms Using Modified Local
           s-Curve Transformation Based on Multi-Objective Optimization
    • Authors: Hamid El Malali;Abdelhadi Assir;Vikrant Bhateja;Azeddine Mouhsen;Mohammed Harmouchi;
      Pages: 11543 - 11554
      Abstract: The biased X-ray field and the subtle difference in X-ray attenuation between normal and abnormal breast tissues prevent the biomedical sensors to generate mammograms with good quality. These are the common imperfections in the acquisition systems (sensor) of Mammography which contribute towards the degradation of mammogram image quality. This paper aims to develop a contrast enhancement model for mammograms so that the human or machine vision can distinguish easily among the variants of breast lesions and predict their severity. The proposed mammogram enhancement model exploits locally the proprieties of the sigmoidal function based on multi-objective Genetic Algorithm for improving the contrast of lesions along with a simultaneous increment in various Image Quality Assessment (IQA) parameters like: $textit {EME}$ , $textit {EC}$ , $textit {AMBEn}$ and $textit {FSIM}$ . Herein, the multi-objective optimization problem is transformed into a single-objective optimization to find a unique solution for mammogram enhancement. An increase in these IQA metrics is indicative of better contrast, edge content, and conservation of brightness and other diagnostics information. Simulations of the proposed enhancement model are carried out on mammograms from the mini-MIAS database for benchmarking and validation of results.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Line Scanning Thermography Reconstruction Algorithm for Defects Inspection
           With Novel Velocity Estimation and Image Registration
    • Authors: Baoyuan Deng;Xiang Li;Hongjin Wang;Yunze He;Francesco Ciampa;Yiwen Li;Ke Zhou;
      Pages: 11555 - 11568
      Abstract: Line scanning thermography (LST) is a fast non-destructive inspection (NDI) method for large-scale components. However, the current reconstruction algorithms for LST require the match between the frame rate and the scanning velocity. Moreover, these algorithms bring in spatial distortions. This paper proposes a non-orthogonal reconstructed space for LST. In such a space, the excitation motion in the line scanning process is described as a function of time delay, the moving velocity is estimated by the derived position map, and the reconstruction is achieved by image registration. The LST data are registered spatially by allowing the temporal misalignment among the pixels within the same reconstructed frame. The effectiveness of the proposed reconstruction algorithm is validated by both numerical simulations and experiments. The reconstruction results of experimental data indicate that the velocity is finely estimated so that the temporal alignment error is controlled within one pixel. Besides, no spatial distortions occur no matter how the frame rate and scanning velocity change. Furthermore, the Fourier phase image of the reconstructed LST reaches the diameter-to-depth ratio of 1.25 when inspecting a planar specimen with flat-bottom holes.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • MMHAR-EnsemNet: A Multi-Modal Human Activity Recognition Model
    • Authors: Avigyan Das;Pritam Sil;Pawan Kumar Singh;Vikrant Bhateja;Ram Sarkar;
      Pages: 11569 - 11576
      Abstract: In this article, we propose a new deep learning model named as MMHAR-EnsemNet (Multi-Modal Human Activity Recognition Ensemble Network) which makes use of four different modalities to perform sensor-based Human Activity Recognition (HAR).Two separate Convolutional Neural Networks (CNNs) are made for skeleton data. While one CNN and one LSTM is trained for RGB images. For Accelerometer and Gyroscope data first it is converted to signal diagram then another CNN model is trained. Finally, all the outputs of the said models have been used to form an ensemble so that performance of the HAR model gets improved. The proposed model has been evaluated on two standard benchmark datasets namely UTD-MHAD and Berkeley-MHAD which contain four different modalities of input information. Experimental results confirm that the MMHAR-EnsemNet model has outperformed some recently proposed models considered here for comparison. Source code of this work can be found at:
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Supervised Scene Illumination Control in Stereo Arthroscopes for Robot
           Assisted Minimally Invasive Surgery
    • Authors: Shahnewaz Ali;Yaqub Jonmohamadi;Yu Takeda;Jonathan Roberts;Ross Crawford;Ajay K. Pandey;
      Pages: 11577 - 11587
      Abstract: Minimally invasive surgery (MIS) offers many advantages to patients but it also imposes limitations on surgeons ability, as no tactile or haptic feedback is available. From medical robotics perspective, visualizations issues specific to MIS such as limited field of view and the lack of automatic exposure control of the surgical area make it challenging when it comes to tracking tissue, tools and camera pose as well as in perceiving depth. Lighting plays an important role in 3D reconstruction and variations due to internal illumination conditions are known to degrade vital visual information. In this work, we describe a supervised adaptive light control system to solve some of the image visualization problems of MIS. Our proposed method is able to classify underexposed and over-exposed frames and adjust lighting condition automatically to enrich image quality. Our method uses support vector machines to classify different illumination conditions. Visual feedback is provided by gradient information to assess image quality and justify classifier decision. The output of this system has been tested against two cadaver knee experiment data with an overall accuracy of 97.75% for under-exposed and 89.11% for over-exposed classes. Hardware implementation of this classifier is expected to result in adaptive lighting for robot assisted surgery as well as in providing support to surgeons by freeing them from manual adjustments to lighting controls.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Application of Computer Vision for Estimation of Moving Vehicle Weight
    • Authors: Maria Q. Feng;Ryan Y. Leung;
      Pages: 11588 - 11597
      Abstract: Heavy vehicle weights need to be closely monitored for preventing fatigue-induced deterioration and critical fractures to highway infrastructure, among many other purposes, but development of a cost-effective weigh-in-motion (WIM) system remains challenging. This paper describes the creation and experimental validations of a computer vision-based non-contact WIM system. The underlining physics is that the force exerted by each tire onto the road is the product of the tire-road contact pressure and contact area. Computer vision is applied (1) to measure the tire deformation parameters so that the tire-roadway contact area can be accurately estimated; and (2) to recognize the marking texts on the tire sidewall so that the manufacturer-recommended tire inflation pressure can be found. In this research, a computer vision system is developed, which is comprised of a camera and computer vision software for measuring tire deformation parameters and recognizing the tire sidewall markings from images of individual tires of a moving vehicle. Computer vision techniques such as edge detection and optical character recognition are applied to enhance the measurement and recognition accuracy. Field experiments were conducted on fully loaded or empty concrete trucks and the truck weights estimated by this novel computer vision-based non-contact WIM system agreed well with the curb weights verified by static weighing. This research has demonstrated a novel application of the computer vision technology to solve a challenging vehicle WIM problem. Requiring no sensor installation on the roadway or the vehicle, this cost-effective non-contact computer vision system has demonstrated a great potential to be implemented.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Modeling for Tracking Micro Gap Weld Based on Magneto-Optical Sensing and
           Kalman Filtering
    • Authors: Yanfeng Li;Xiangdong Gao;Yuquan Chen;Xiaohu Zhou;Yanxi Zhang;Deyong You;
      Pages: 11598 - 11614
      Abstract: Seam detecting and micro weld tracking are difficult in the laser welding process. In this paper, a micro gap weld with a width of not more than 0.05 mm is detected and identified by magneto-optical (MO) imaging technology. The MO imaging law of the micro gap weld under direct-current (DC) magnetic field excitation is studied, and the relationship between the gray-scale characteristics of the weld’s MO image and the leakage magnetic field strength is analyzed based on the MO effect law. A finite element (FE) model of the micro gap weld is set up, and the leakage magnetic field distributions of different lift-off heights and different excitation voltages are analyzed, which is helpful in improving the detection accuracy of the center position of the micro gap weld. A MO imaging inspection system for micro gap weld excited by a DC magnetic field is established. According to the finite element simulation results and the MO imaging experiments, the optimal excitation voltage of the MO imaging device is 15 V, and the optimal lift-off height is 1.0 mm. To reduce the influence of noise coupled with the laser welding process, the measurement precision of the micro gap weld center was improved by the Kalman filtering (KF) method. The experimental results at different welding speeds indicated that MO imaging can be used for high-precision micro gap weld center identification under optimal detection parameters.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Local Feature Performance Evaluation for Structure-From-Motion and
           Multi-View Stereo Using Simulated City-Scale Aerial Imagery
    • Authors: Ke Gao;Hadi Aliakbarpour;Joshua Fraser;Koundinya Nouduri;Filiz Bunyak;Ricky Massaro;Guna Seetharaman;Kannappan Palaniappan;
      Pages: 11615 - 11627
      Abstract: Ubiquitous low cost multi-rotor and fixed wing drones or unmanned aerial vehicles (UAVs) have accelerated the need for reliable, robust, and scalable Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipelines suitable for a variety of flightpath trajectories especially in degraded environments. Feature tracking being a core part of SfM and MVS, is essential for multiview scene modeling and perception, but difficult to evaluate in large scale datasets due to the lack of sufficient ground-truth. For large-scale aerial imagery, accurate camera orientation and dense 3D point cloud accuracy can be used to assess the impact of accurate feature localization and track length. We propose a novel view simulation (or synthesis) framework which generates visually realistic new unseen camera views for feature detection using known high fidelity camera poses for modeling. Seven state-of-the-art local handcrafted and learning-based features are quantitatively evaluated for robustness and matchability within the SfM and MVS pipelines using the open source COLMAP software. Our experimental results provide performance rankings of each feature, using twelve different evaluation metrics across three synthetic city-wide aerial image sequences. We show that recent learned features, SuperPoint and LF-Net, have not only reached the quality of the best handcrafted features like SIFT and SURF, but now outperform them in terms of more accurate 3D camera pose estimates and longer feature tracks. SuperPoint produces 1.51 meter average position error and 0.03° average angular error, while SIFT remains competitive (second best for pose and overall) with 1.78 meter and 0.11° errors respectively.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Tactile Sensing Using Machine Learning-Driven Electrical Impedance
    • Authors: Zainab Husain;Nadya Abdel Madjid;Panos Liatsis;
      Pages: 11628 - 11642
      Abstract: Electrical Impedance Tomography (EIT) tactile sensors have limited success in equipping robots with tactile sensing capabilities due to the low spatial resolution of the resulting tactile images and the presence of image artifacts. To address these limitations, we propose a modular framework for invariant recognition of objects, within the context of an EIT artificial skin sensor. Three interconnected problems, i.e., EIT image reconstruction, segmentation and object recognition, are tackled in this work with the aid of machine learning. A novel conductivity surface decomposition approach, based on low order bivariate polynomials and RBF networks is introduced for the efficient solution of the EIT inverse problem. Next, segmentation of the reconstructed images is performed using a convolutional neural network and transfer learning. Finally, a subspace KNN ensemble classifier is trained on the set of object descriptors extracted from the segmented inhomogeneities to classify the objects. The proposed framework provides an accuracy of 97.5% on unseen data.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • SK-Unet: An Improved U-Net Model With Selective Kernel for the
           Segmentation of LGE Cardiac MR Images
    • Authors: Xiyue Wang;Sen Yang;Yuqi Fang;Yunpeng Wei;Minghui Wang;Jing Zhang;Xiao Han;
      Pages: 11643 - 11653
      Abstract: In the clinical environment, myocardial infarction (MI) as one common cardiovascular disease is mainly evaluated using late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). Accurate segmentation of the ventricles and the myocardium is a prerequisite for quantitative assessment of cardiac functions and disease progression. Performing the task using LGE images is, however, rather challenging due to heterogeneous image intensity distribution and lack of clear boundaries between adjacent organs and tissues. In this paper we propose a deep neural network method for automatic segmentation of the left ventricle (LV), right ventricle (RV), and left ventricular myocardium (LVM) from LGE CMRIs, which also leverages complementary information from cine and T2-weighted CMRIs if available. In the proposed method, termed SK-Unet, we augment the original U-Net model by adding a squeeze-and-excitation residual (SE-Res) module in the encoder and a selective kernel (SK) module in the decoder. The SE-Res module applies an attention mechanism to enhance informative feature extraction and suppress redundant ones. The SK module offers the ability to adaptively learn task-relevant multi-scale spatial features. We tested our method by participating in the MICCAI 2019 MS-CMRSeg challenge and achieved a mean dice score of 0.922 for LV segmentation, 0.827 for LVM, and 0.874 for RV. The results placed our method at the 1st place in the competition, and our accuracy of 0.827 also greatly surpasses the measured inter-observer agreement of 0.757 for manual segmentation of LVM in LGE CMRIs. The code accompanying our method is made available online at
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Sensor Placement and Structural Damage Evaluation by Improved Generalized
    • Authors: Xi Peng;Qiu-Wei Yang;
      Pages: 11654 - 11664
      Abstract: The technology of damage evaluation based on data collected by sensors has become an important topic in the field of structural engineering This paper presents an improved generalized flexibility method for sensor placement and damage evaluation. Compared with the existing methods, the main improvements of the proposed method lie in three aspects. The first one is to replace mass-normalized mode shapes with arbitrary-scaled mode shapes since that only the latter can be obtained in modal testing under ambient excitation. The second one is to combine the generalized flexibility sensitivity with the frequency sensitivity to obtain more equations. The third one is to use a new regularized generalized inverse technique for computing the unknowns more accurately. Sensor placement based on the improved generalized flexibility method can be divided into three steps. Firstly, the number of sensors can be predicted by a simple formula derived by the principle of the number of equations should close to the number of unknowns as far as possible. Secondly, the key components of the whole structure are determined based on their contributions to the global generalized flexibility change. Thirdly, the sensor positions can be obtained according to the common nodes of those key components. Overall, the proposed method is simple and convenient since it only needs environment excitation and a few sensors. The results of numerical and experimental examples show that the proposed method can successfully evaluate structural damage with a few sensor signals.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Enhanced Context Attention Network for Image Super Resolution
    • Authors: Wang Xu;Renwen Chen;Bin Huang;Qinbang Zhou;
      Pages: 11665 - 11673
      Abstract: The performance of image super-resolution (SR) have been greatly improved with deep convolution neural network (CNN). Despite image SR targets at recovering high-frequency details, most SR methods still focus on generating high-level features via a deep and wide network. They lack the discriminative ability of high-frequency information hidden in the abundant CNN features, thus hindering CNNs to yield better SR results. To tackle this issue, we propose two new attention mechanism: context weighted channel attention (CWCA) and persistent spatial attention (PSA). They modulate abundant features by suppressing the useless features and enhancing the interested ones in a channel-and-spatial manner. The network is then enabled to concentrate more on informative features closely related to the high-frequency components of an image. Furthermore, we propose enhanced attention residual groups with dense connection (EARG-D) to capture not only short-term information but also long-term information to maintain more useful features. Finally, we construct a deep enhanced context attention super resolution network (EASR) for better image reconstruction. Quantitative and qualitative experiments well demonstrate that our proposed method performs better than existing state-of-the-art SR methods.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Through-Wall Mapping Using Radar: Approaches to Handle Multipath
    • Authors: Sedat Dogru;Lino Marques;
      Pages: 11674 - 11683
      Abstract: Through-wall mapping is an emerging field of research with promising applications varying from search and rescue, to health care and to security. Radar is a valuable tool in this process, mainly due to its long wavelength, which can pass through construction materials. However, this capability comes at a price, namely multipath reflections caused by the environment, which can reduce the usability of the produced map considerably. This paper proposes methods to detect and isolate these multipath reflections, eventually leaving out a usable map of the enclosed environment. For this purpose, two different radars, one operating at 24GHz and the other at 76GHz, were evaluated in various wall configurations constructed inside an enclosed space using portable wall segments. The testing arena was probed from the outside with the radars mounted on the top of a differential drive mobile robot. The paper shows that the proposed methods are effective in eliminating multipath reflections and in building an accurate representation of the environment using the radars.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Static Model Assisted Stereo-Visual Shape Sensing of Flexible Manipulators
    • Authors: Xin Ma;Philip Wai-Yan Chiu;Zheng Li;
      Pages: 11684 - 11691
      Abstract: Wire-driven flexible manipulators are widely used in medical interventions and most of them are controlled in open loop. Their closed-loop control relies on shape sensing, which is challenging. In this paper, a shape sensing method for two-dimensional flexible manipulators based on Statics Model and Bezier curve is proposed. This method builds on the statics modeling of the flexible manipulator and the assumption that the deformed shape is close to a Bezier curve. By employing the motion and tension of the wires together with one or more joints’ locations measured by a stereo vision system, the manipulator shape can be well estimated. Compared with purely vision-based method, this method applies to wider conditions, including visual occlusion. The proposed method is experimentally validated. Results show that for both load-free and loaded conditions this method could well estimate the shape of the flexible manipulator.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Pose Estimation Based on Wheel Speed Anomaly Detection in Monocular
           Visual-Inertial SLAM
    • Authors: Gang Peng;Zezao Lu;Shanliang Chen;Dingxin He;Li Xinde;
      Pages: 11692 - 11703
      Abstract: Considering the adverse impact of speed measurement on the accuracy of pose estimation after a mobile robot slips, collides, or abducts, this paper proposes a monocular inertial simultaneous localization and mapping algorithm that includes wheel speed anomaly detection. The algorithm adds wheel speed measurement to the least squares problem in a tightly coupled manner and uses a nonlinear optimization method to maximize the posterior probability to solve the optimal state estimation. For the speed control of the Mecanum wheel, because the existing closed-loop speed control method cannot calculate the motion constraint error, this paper reports a design of a control method of the Mecanum wheel moving chassis based on torque control, which can use the motion constraint error to estimate the credibility of the wheel speed measurement to detect whether the chassis movement status is abnormal; meanwhile, to prevent the chassis speed measurement error from adversely affecting the robot pose estimation, this paper uses three methods to actively detect whether the chassis movement is abnormal, and analyze the chassis movement status in real time. When it is determined that the chassis has abnormal motion, the wheel odometer pre-integration measurement of the current frame is actively removed from the state estimation equation, thereby ensuring the accuracy of the pose estimation. Experimental results show the feasibility and effectiveness of the method proposed in this paper, and the algorithm is robust.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Sensor Initiated Healthcare Packet Priority in Congested IoT Networks
    • Authors: Kedir Mamo Besher;Sara Beitelspacher;Juan Ivan Nieto-Hipolito;Mohammed Zamshed Ali;
      Pages: 11704 - 11711
      Abstract: Currently healthcare data packets do not get any special priority while routing through the Internet of Things (IoT) networks. These data packets flow through routers using conventional QoS process which does not guarantee that a patient’s critical health data traveling in congested IoT network will actually be routed to doctors office on time. This leaves the remote medical treatment at risk with possible threat to patients’ lives. In this paper, we studied the current healthcare packet routing process in IoT and its performance issues in congested IoT networks, then proposed a sensor driven solution to prioritize healthcare data routing in congested IoT networks. In our proposed system, we add a healthcare data identifier in IP packet header at the sensor level, modify QoS software at the network router level, and provide the highest priority to healthcare data packet routing based on the healthcare data identifier seen at router QoS. A prototype has been built and tested by using TI Launchpads. Test data shows that healthcare packets with an identifier can route to doctors at 80% less latency than healthcare packets routed without an identifier. Additionally, the proposed system saves medical diagnostic cost, and more importantly, reduces the risk of losing human lives. A shorter version of this analysis has also been presented to IEEE conference. The detail analysis in this paper opens up multiple avenues for further research.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Augmented Perception for Agricultural Robots Navigation
    • Authors: Francisco Rovira-Más;Verónica Saiz-Rubio;Andrés Cuenca-Cuenca;
      Pages: 11712 - 11727
      Abstract: Producing food in a sustainable way is becoming very challenging today due to the lack of skilled labor, the unaffordable costs of labor when available, and the limited returns for growers as a result of low produce prices demanded by big supermarket chains in contrast to ever-increasing costs of inputs such as fuel, chemicals, seeds, or water. Robotics emerges as a technological advance that can counterweight some of these challenges, mainly in industrialized countries. However, the deployment of autonomous machines in open environments exposed to uncertainty and harsh ambient conditions poses an important defiance to reliability and safety. Consequently, a deep parametrization of the working environment in real time is necessary to achieve autonomous navigation. This article proposes a navigation strategy for guiding a robot along vineyard rows for field monitoring. Given that global positioning cannot be granted permanently in any vineyard, the strategy is based on local perception, and results from fusing three complementary technologies: 3D vision, lidar, and ultrasonics. Several perception-based navigation algorithms were developed between 2015 and 2019. After their comparison in real environments and conditions, results showed that the augmented perception derived from combining these three technologies provides a consistent basis for outlining the intelligent behavior of agricultural robots operating within orchards.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • An Improved Monocular Visual-Inertial Navigation System
    • Authors: Tian Sun;Yong Liu;Yujie Wang;Zhen Xiao;
      Pages: 11728 - 11739
      Abstract: Simultaneous Localization and Mapping (SLAM) combined with visual and inertial measurements has attained considerable consideration in the Robotics and Computer Vision society. Nevertheless, balancing between real-time needs and precision could be a difficult challenge. Thus, a new tightly-coupled visual-inertial concurrent localization and mapping approach is proposed with precise and real-time motion estimating and map reconstruction capabilities. The nonlinear optimization is based on the concept that the frontend and backend in the visual-inertial SLAM (VISLAM) system can enhance one another. Moreover, a new inertial measurement unit (IMU) initialization approach is employed for rapid calculation of the scale, the gravity orientation, velocity, and gyroscope and accelerometer biases with high precision. Besides, precise motion estimation of the frontend could be provided, which improves the backend optimization due to achieving a more precise initial state for the backend. Also, feedback-based relocalization and continued SLAM frameworks are designed for autonomous robot navigation or SLAM. The accuracy of the presented VISLAM system is investigated via experiments performed on the public EuRoC dataset and actual environments. According to the experiments, the presented VISLAM system is more accurate with lower computational cost compared with existing VISLAM systems.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Improving Dense Mapping for Mobile Robots in Dynamic Environments Based on
           Semantic Information
    • Authors: Jiyu Cheng;Chaoqun Wang;Xiaochun Mai;Zhe Min;Max Q.-H. Meng;
      Pages: 11740 - 11747
      Abstract: In recent decades, semantic mapping has become a hot topic benefited from the maturity of visual simultaneous localization and mapping (visual SLAM) and the success of deep learning. Despite the impressive performance of the current state-of-the-art systems, semantic mapping in dynamic environments is still a challenging task. To address this problem, we propose a framework that fuses geometric information, semantic information, and human activity into a 3D dense map. The accuracy of the map is guaranteed by the reliable camera trajectory estimation and the static pixels used for 3D reconstruction. With the proposed framework, we achieve two objectives. On the one hand, we accurately reconstruct the environment from both geometric and semantic perspectives. On the other hand, we record human activity by tracking the human trajectory during the mapping period. We conduct both qualitative and quantitative experiments on the public TUM dataset. The experimental results demonstrate the feasibility and effectiveness of the proposed framework.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • BiLuNetICP: A Deep Neural Network for Object Semantic Segmentation and 6D
           Pose Recognition
    • Authors: Luan Van Tran;Huei-Yung Lin;
      Pages: 11748 - 11757
      Abstract: The ability of understanding a scene and predicting the pose of objects has attracted significant interests in recent years. Specifically, it is used with visual sensors to provide the information for a robotic manipulator to interact with the target. Thus, 6D pose estimation and object recognition from point clouds or RGB-D images are important tasks for visual servoing. In this article, we propose a learning based approach to perform 6D pose estimation for robotic manipulation using a BiLuNetICP pipeline. It consists of a multi-path convolutional neural network (CNN) for semantic segmentation on RGB images. The network extracts the object mask and uses it to merge with the depth information to perform 6D pose estimation by the Iterative Closest Point (ICP) algorithm. We collected our own dataset for training and evaluate with Intersection over Union (IoU). The proposed method is able to provide better results compared with Unet++ using a small amount of training data. For the robotic grasping application, we test and evaluate our approach using a HIWIN 6-axis robot with Asus Xtion Live 3D camera and our structured-light depth camera. The experimental results demonstrate its efficiency in computation and the high success rate in grasping.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Sensored Semantic Annotation for Traffic Control Based on Knowledge
           Inference in Video
    • Authors: Chang Choi;Tian Wang;Christian Esposito;Brij Bhooshan Gupta;Kyungroul Lee;
      Pages: 11758 - 11768
      Abstract: Images and videos in multimedia data are typical representation methods that include various types of information, such as color, shape, texture, pattern, and other characteristics. Besides, in video data, information such as object movement is included. Objects may move with time, and spatial features can change, which is incorporated in spatio-temporal relations. Many research studies have been carried out over time on information recognition by computers using low-level data in this connection. There is a semantic gap between low-level and high-level information in vocabulary representing human thinking. A substantial amount of research has been conducted on reducing the semantic gap, and it is focused on representation methods of logic. The goal of this study is to understand object movement and define spatio-temporal relations through mapping between vocabulary and the object movements. Ontology mapping is a method used to bridge the gap between low-level and high-level information. In this case, the spatio-temporal relation consists of temporal relations obedient to the passage of time, directional relations obedient to changes in object movement direction, changes in object size relations, topological relations obedient to changes in object movement position, and velocity relations using concept relations between topology models. In this paper, an ontology is used to define the inference rules using the proposed spatio-temporal relations and the use of Markov Logic Networks (MLNs) for probabilistic reasoning. Finally, the performed experiment and evaluation prove the verification recognition and understanding of object movements based on video data. This paper can be extended to retrieval and comparison between object movements, automatic annotation, and video summarization. The contributions of this paper include definition of the spatio-temporal relations of a region-based object, recognition of the semantic movements of moving objects, designing and const-ucting a spatio-temporal ontology, and Understanding the semantic movement of moving objects.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • CEB-Map: Visual Localization Error Prediction for Safe Navigation
    • Authors: Weinan Chen;Lei Zhu;Chaoqun Wang;Li He;Max Q.-H. Meng;
      Pages: 11769 - 11780
      Abstract: For safe visual navigation, areas with high localization errors should be concentrated and could be further refined by additional mapping operations. Given an environment map, we propose to predict the visual localization error and hence to either improve the navigation performance or call an additional mapping to refine the built map. Previous work adopts the uncertainty of landmarks for the error prediction. In our work, we take into account both the spatial distribution of visual landmarks and the uncertainty of landmarks. Our main idea is that standing at one place, a good spatial distribution of landmarks means a sufficient enough visible landmarks from all views at that place, i.e., enough landmarks under arbitrary view-direction. Combining the spatial distribution and the uncertainty of landmarks, we propose a new framework to predict the error of visual localization. Furthermore, we show that additional mapping in the area with high predicted error can significantly improve the visual localization precision. Experimental results show that there is a strong relationship between the predicted error and the real error, of which the absolute value of correlation coefficient is between 0.707 to 0.915. We apply our method to conduct an optimal refining policy on the built map and the experimental results show the improved localization precision. Applications on navigation test verify the superiority of our proposed method.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Multi-Modal Sensor Fusion-Based Deep Neural Network for End-to-End
           Autonomous Driving With Scene Understanding
    • Authors: Zhiyu Huang;Chen Lv;Yang Xing;Jingda Wu;
      Pages: 11781 - 11790
      Abstract: This study aims to improve the performance and generalization capability of end-to-end autonomous driving with scene understanding leveraging deep learning and multimodal sensor fusion techniques. The designed end-to-end deep neural network takes as input the visual image and associated depth information in an early fusion level and outputs the pixel-wise semantic segmentation as scene understanding and vehicle control commands concurrently. The end-to-end deep learning-based autonomous driving model is tested in high-fidelity simulated urban driving conditions and compared with the benchmark of CoRL2017 and NoCrash. The testing results show that the proposed approach is of better performance and generalization ability, achieving a 100% success rate in static navigation tasks in both training and unobserved situations, as well as better success rates in other tasks than the prior models. A further ablation study shows that the model with the removal of multimodal sensor fusion or scene understanding pales in the new environment because of the false perception. The results verify that the performance of our model is improved by the synergy of multimodal sensor fusion with scene understanding subtask, demonstrating the feasibility and effectiveness of the developed deep neural network with multimodal sensor fusion.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • An Improved Variational Auto-Encoder With Reverse Supervision for the
           Obstacles Recognition of UGVs
    • Authors: Aijun Yin;Fenglei Zheng;Jian Tan;Yu Wang;
      Pages: 11791 - 11798
      Abstract: The obstacles detection plays an important role in the field of unmanned ground vehicle (UGV). This article proposes a semi-supervised learning model with reverse supervision based on Variational Auto-Encoder (VAE) to recognize the terrain obstacles of UGVs. The proposed model compresses terrain data to latent space and casts the abnormal observations to invalid white noise in order to perform more accurate fitting on marginal likelihood of normal observations. In addition, the proposed model adopts the convolutional layer instead of fully connected layer of VAE to extract data features. Gaussian Mixed Model (GMM) is used to fit the latent distribution of normal terrain data. The improved VAE could learn the actual potential distribution of target data with the reverse supervision of abnormal data, it can achieve better performance in generating ability and discriminating ability compared with existing generative models. The superiority and effectiveness of the proposed model are illustrated and validated by an application in the shooting range of UGVs. Besides, the proposed model has the promising potential for some other applications, it can be used for military operations, robot rescue, and terrain exploration in dangerous environment, etc.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • ABC-Net: Area-Boundary Constraint Network With Dynamical Feature Selection
           for Colorectal Polyp Segmentation
    • Authors: Yuqi Fang;Delong Zhu;Jianhua Yao;Yixuan Yuan;Kai-Yu Tong;
      Pages: 11799 - 11809
      Abstract: Untreated colorectal polyps can develop into colorectal cancer, which is a leading cause of cancer-related deaths. Colonoscopy is a commonly-used method for colorectal polyp scanning, but limited to the experience and subjectivity of clinicians, one out of four polyps cannot be correctly recognized. In this article, we propose an automatic colorectal polyp segmentation system based on the deep convolutional neural network, aiming to improve the accuracy of colorectal polyp scanning. The proposed ABC-Net is comprised of a shared encoder and two novel mutually-constrained decoders for simultaneous polyp area and boundary segmentation. To sufficiently exploit multi-scale image information, the selective feature modules are embedded into the network and used for dynamically learning and fusing multi-scale feature representations. Furthermore, a new boundary-sensitive loss is proposed to model the interdependencies between the area and boundary branches, the information of the two branches are reciprocally propagated and constrained, yielding a significant improvement in segmentation accuracy. Extensive experiments are conducted on three public colorectal polyp datasets, and the results, e.g., F1 scores are 0.866, 0.915, 0.874 in EndoScene, Kvasir-SEG, and ETIS-Larib datasets, demonstrate the advantages of the proposed method.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Sparse Visual Odometry Technique Based on Pose Adjustment With Keyframe
    • Authors: Huei-Yung Lin;Jhih-Lei Hsu;
      Pages: 11810 - 11821
      Abstract: In this article we propose a sparse visual odometry model for RGB-D images. The technique utilizes the minimization of the photometric errors obtained from the edge features for pose adjustment. Different from the conventional feature-based approaches, the features are extracted on the edge images. It makes the feature matching more robust and the computation more efficient. Moreover, we introduce a posterior probability based on the different degree of exposure for each edge point. The weight is then adjusted to improve the pose estimation by keyframe matching according to the probability. Since the sensor noise will affect the feature extraction results and cause the inaccurate estimation, the epipolar geometry and mixture distribution are used for the depth value updates. The experiments carried out using public datasets and our own image sequences have demonstrated the effectiveness of the proposed technique.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Novel Approach to Inspections of As-Built Reinforcement in Incrementally
           Launched Bridges by Means of Computer Vision-Based Point Cloud Data
    • Authors: Piotr Owerko;Tomasz Owerko;
      Pages: 11822 - 11833
      Abstract: The paper presents inspection and assessment of as-built reinforcement of a selected segment of an incrementally launched (IL) concrete bridge under construction. Two novel techniques were used for data acquisition: modified photogrammetry and High Definition Surveying - a combination of terrestrial-based laser scanning, computer technology and precision control networks. The main goal of this in-situ experiment was to develop a practical, effective, yet affordable methodology for inspecting reinforcement of the above mentioned structures. In order to maintain the requirements resulting from the specifics of IL technology (seven-day cycles, 24/7 operation, two concrete pours per week), the authors have limited the maximum allowed time for data acquisition, minimized the complexity of measurement data processing and related requirements for the software and computers, and replaced the professional equipment for photogrammetry with a commonly available SLR camera. Data obtained using this method was than referred to the point cloud model obtained with a precise, state-of-the-art 3D laser scanner. The adopted mathematical model for data post-processing turned out to be effective and correct both for the analysis of data from laser scanners and photogrammetry. Presented solution adopts practical workflow with camera calibration to simplify in-situ measurement procedure achieving high accuracy standards required for reinforcement inspection. The point cloud data model obtained from photogrammetry turned out to be insufficient to assess web reinforcement thoroughly. In turn, it was possible to inspect and evaluate the reinforcement of the bottom slab, with accuracy matching the laser scanning.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Visual Perception With Servoing for Assisting Micro Aerial Vehicle Through
           a Staircase
    • Authors: Cheng-Ming Huang;Ting-Wei Lin;
      Pages: 11834 - 11843
      Abstract: This article presents a visual perception system with servoing design for a micro aerial vehicle (MAV) flying through a staircase. Since space in an indoor environment is usually limited, the size of MAV employed here have to be small and the payload of MAV is limited. Two apinhole cameras mounted on the front and bottom of the MAV are utilized to take turns in observing the stairs. The captured images and the flying control commands are transmitted between a remote computational processing platform and the MAV through a wireless network. Visual features of the stairs, wall, railing, and the landing are described to estimate the location and orientation of the MAV in the staircase. This visual information refined through the probabilistic data association filter is then used by the MAV as a basis for navigating its environment. A system of visual servoing based on fuzzy logic is proposed to control the MAV’s flight through a set of straight stairs and align the entrance of the stairs. The MAV’s ability to fly online through the staircase is verified in several experiments to demonstrate the efficiency of the overall system.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Coarse-to-Fine Visual Object Catching Strategy Applied in Autonomous
           Airport Baggage Trolley Collection
    • Authors: Chaoqun Wang;Xiaochun Mai;Danny Ho;Tingting Liu;Chenming Li;Jin Pan;Max Q.-H. Meng;
      Pages: 11844 - 11857
      Abstract: In this study, a systematic solution for mobile manipulation with the application of autonomous trolley collection in the airport is presented. The developed system features a novel integrated hardware and software system, incorporating a specially designed manipulator capable of tightly catching the trolley. Particularly, we innovatively present a supervisory control guided coarse-to-fine visual trolley catching strategy in large-scale airport environments. At the coarsest level, an image-based visual servoing method is utilized to approach the trolley from far to near, where a learning-based object detection method is adopted to identify trolleys in the environment in real-time. At the finest level, we present an accurate point cloud registration based object pose estimation method to promote the object manipulation with a position-based visual servoing controller. The tightly integrated system and the subsystems are evaluated through conducting experiments in the trolley collection task and comparing the key module with the state of the art. Extensive experiments demonstrate that the proposed framework can find and fetch the trolley robustly. Notably, the proposed object detection and pose estimation methods exhibit higher accuracy, providing credible perceptual approaches for mobile manipulation. Moreover, the proposed framework can be easily extended to other mobile manipulation tasks for object search and retrieval.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Complete and Accurate Indoor Scene Capturing and Reconstruction Using a
           Drone and a Robot
    • Authors: Xiang Gao;Lingjie Zhu;Hainan Cui;Zhanyi Hu;Hongmin Liu;Shuhan Shen;
      Pages: 11858 - 11869
      Abstract: Completeness and accuracy are two important factors in image-based indoor scene 3D reconstruction. Thus, an efficient image capturing scheme that could completely cover the scene, and a robust reconstruction method that could accurately reconstruct the scene are required. To this end, in this article we propose a new pipeline for indoor scene capturing and reconstruction using a mini drone and a ground robot, which takes both capturing completeness and reconstruction accuracy into consideration. First, we use a mini drone to capture aerial video of the indoor scene, from which a 3D aerial map is reconstructed. Then, the robot moving path is planned and a set of ground-view reference images are synthesized from the aerial map. After that, the robot enters the scene and captures ground video autonomously while using the reference images to locate its position during the movement. Finally, the ground and aerial images, which are adaptively extracted from the captured videos, are merged to reconstruct a complete and accurate indoor scene model. Experimental results on two indoor scenes demonstrate the effectiveness and robustness of our proposed indoor scene capturing and reconstruction pipeline.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Robotic Grasping With Multi-View Image Acquisition and Model-Based Pose
    • Authors: Huei-Yung Lin;Shih-Cheng Liang;Yu-Kai Chen;
      Pages: 11870 - 11878
      Abstract: Due to recent advances on hardware and software technologies, industrial automation has been significantly improved in the past few decades. For random bin picking applications, it is a future trend to use machine vision based approaches to estimate the 3D poses of workpieces. In this work, we present a robotic grasping system with multi-view depth image acquisition. First, RANSAC and an outlier filter are adopted for noise removal and multi-object segmentation. A voting scheme is then used for preliminary pose computation, followed by the ICP algorithm to derive a more precise target orientation. A model-based registration approach using a genetic algorithm with parameter minimization is proposed for 6-DOF pose estimation. Finally, the grasping efficiency is increased by disturbance detection, which reduces the number of 3D data scanning for multiple operations. The experiments are carried out in the real scene environment, and the performance evaluation has demonstrated the feasibility of the proposed technique.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Sea Ice Classification via Deep Neural Network Semantic Segmentation
    • Authors: Benjamin Dowden;Oscar De Silva;Weimin Huang;Dan Oldford;
      Pages: 11879 - 11888
      Abstract: Sea ice monitoring plays a critical role in any icebreaker’s journey, where standard procedures are in place to document and report sea ice types and concentration. In this paper, we propose semantic segmentation for automated detection and classification of sea ice types using camera feeds onboard an ice breaker. For this purpose, we evaluate the SegNet and PSPNet101 neural network architectures, which have proven success in navigation and mapping applications such as self-driving cars, remote sensing, and medical imagery. The networks are used to segment images based on two custom datasets, one with four classes: ice, ocean, vessel, and sky, i.e., sea ice detection dataset, and the second with eight classes: ocean, vessel, sky, lens artifacts, first-year ice, new ice, grey ice, and multiyear ice, i.e., sea ice classification dataset. The Nathaniel B. Palmer imagery, which captured 2-month footage of the icebreaker completing an Antarctic expedition was used in the creation of both datasets. A subset of the dataset was labeled to generate a 240-image training set for sea ice detection achieving an accuracy of 98% classification for the 26-image test set. The sea ice classification dataset consists of 1,090 labeled images achieving accuracies of 98.3% or greater for all ice types for the 104-image test set. These results validate the applicability of deep learning methods for sea ice detection and classification using images captured onboard an ice breaker, which can be further enhanced by incorporating additional ice types and operational data to support marine navigation and mapping applications.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Novel Centralization Method for Pipe Image Stitching
    • Authors: Salaheddin Hosseinzadeh;William Jackson;Dayi Zhang;Liam McDonald;Gordon Dobie;Graeme West;Charles MacLeod;
      Pages: 11889 - 11898
      Abstract: The creation of unwrapped stitched images of pipework internal surfaces is being increasingly used to augment routine visual inspection. A significant challenge to the creation of these stitched images is the need to estimate the pose and position of the camera for each frame, which is often alleviated through the use of a mechanical centralizer to ensure the camera is held in the center of the pipe. This article proposes a novel method for image centralization and pose estimation, which is particularly beneficial to circumstances where mechanical centralization is impractical. The approach involves post-inspection centralization of the captured video, by first estimating the probe’s position relative to the pipe, using an integrated laser ring projector combined with the camera sensor, and then using this position to unwrap the image, so it produces an undistorted view of the pipe interior (equivalent to unwrapping a centralized view). These unwrapped images are then stacked to produce a stitched image of the pipe interior. In this paper pose estimation was successfully demonstrated to have a 90% confidence interval of ±0.5 mm and ±0.5° in translation and rotation changes. This pose estimation is then used to create stitched images for both a visual test card image mounted inside a pipe and an aluminum pipe sample with artificial defects, in both cases demonstrating near equivalent results to those obtained using traditional mechanical centralization.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Articulated Object Tracking by High-Speed Monocular RGB Camera
    • Authors: Yang Liu;Akio Namiki;
      Pages: 11899 - 11915
      Abstract: In recent years, tracking of articulated objects at high speed with monocular cameras has been gaining attention. This study presents a novel method for high-frame-rate articulated object tracking with a monocular camera. The method is an extended version of our previous research on high-speed monocular rigid object tracking. In this study, to realize tracking of an articulated object, we integrate dual-quaternion kinematics with our previous fast pixel-wise-posteriors (fast-PWP3D) tracking framework, and propose an auto-regressive (AR) process to encode the dynamic propagation of the estimated state vectors. We give a full three-dimensional derivation of the mathematical formulation of our method and show that our method is capable of tracking an articulated object having a large number of degrees of freedom with only a monocular camera, and is robust against dynamic environmental changes (e.g., illumination/partial occlusion). Moreover, we show an efficient implementation strategy of our method. The results of real-time experiments show that we achieved nearly 350 Hz performance when tracking a four degrees-of-freedom (4-DOF) articulated object with a monocular camera.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Distinguishing Between Parkinson’s Disease and Essential Tremor Through
           Video Analytics Using Machine Learning: A Pilot Study
    • Authors: Ekaterina Kovalenko;Aleksandr Talitckii;Anna Anikina;Aleksei Shcherbak;Olga Zimniakova;Maksim Semenov;Ekaterina Bril;Dmitry V. Dylov;Andrey Somov;
      Pages: 11916 - 11925
      Abstract: Parkinson’s Disease (PD) is currently the fastest growing neurodegenerative disease. It decreases the quality of life for patients, especially when not diagnosed properly and timely. Accurate diagnostic of PD is complicated by the fact that there exist several neurodegenerative diseases with similar motor symptoms, e.g. essential tremor. In this work, we report on a second opinion system based on the video analysis and classification of subjects using machine learning methods including feature extraction, dimensionality reduction and classification. Our approach serves for avoiding a typical misdiagnosis of PD by essential tremor. Consequently, we designed 15 common tasks and recorded the movement video. Video data was collected from 89 subjects at a medical center and labeled by doctors. We first demonstrate classification between the healthy subjects and subjects with PD suspected case followed by the classification between the subjects with true PD and the subjects with essential tremor. We achieved f1 score 0.90 for the first classification and f1 score 0.84 for the second classification. The proposed unobtrusive approach demonstrated its feasibility through a pilot study. It opens up wide vista for differentiating PD patients against other patients and not against a cohort of healthy subjects.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Stereo Vision Combined With Laser Profiling for Mapping of Pipeline
           Internal Defects
    • Authors: Amal Gunatilake;Lasitha Piyathilaka;Antony Tran;Vinoth Kumar Vishwanathan;Karthick Thiyagarajan;Sarath Kodagoda;
      Pages: 11926 - 11934
      Abstract: Underground potable water pipes are essential infrastructure assets for any country. A significant proportion of those assets are deteriorating due to pipe corrosion which results in premature failure of pipes causing enormous disruptions to the public and loss to the economy. To address such adverse effects, the water utilities in Australia exploit advanced pipelining technologies with a motive of extending the service life of their pipe assets. However, the linings are prone to defects due to improper liner application and unfavorable environmental conditions during the liner curing phase. To monitor the imperfections of the pipe linings, in this article, we propose a mobile robotic sensing system that can scan, detect, locate and measure pipeline internal defects by generating three-dimensional RGB-Depth maps using stereo camera vision combined with infrared laser profiling unit. The system does not require complex calibration procedures and it utilizes orientation correction to provide accurate real-time RGB-D maps. The defects are identified and color mapped for easier visualization. The robotic sensing system was extensively tested in laboratory conditions followed by field deployments in buried water pipes in Sydney, Australia. The experimental results show that the RGB-D maps were generated with millimeter (mm) level accuracy with demonstrated liner defect quantification.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Vision-Based System for 3D Tower Crane Monitoring
    • Authors: Ricardo Gutiérrez;Mónica Magallón;Danilo Cáceres Hernández;
      Pages: 11935 - 11945
      Abstract: Cranes play an important role in the industry sectors worldwide, such as construction, transport, shipping and cargo industry. For the safety of loading and unloading materials there are definitely ongoing improvements in the crane industry. However, there still remains issues related to the crane operation and control measures given by the pendulum swing while the crane is moving. To solve this problem a vision-based movement control of a 3 degrees of freedom (3 DOF) crane system, using both an analytical and experimental models based on displacements and adaptive optimization algorithms was proposed. The framework consists of three steps, more precise recognition, detection and tracking a set of targets within the image to compute the payload displacement. A prepossessing process was applied in order to enhance the image improving both the color information and the edge extraction task. Then, a set of targets within the image were detected to estimate the displacement. Then to solve the problem given by the displacement, a tracking task was implemented using a second order filter. This paper introduces a new strategy to determine the relationship between the movement of the spin in the jib and position of the markers within the image. Regression analysis was carried out to take into account the motion of the cart and the payload. In Results Section is shown a set of real-time experiment, obtaining euclidean errors of 1.41 pixel, 2 pixels and 3.16 pixels for Cart, Jib and Hoist, respectively.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Searching Space Constrained Partial to Full Registration Approach With
           Applications in Airport Trolley Deployment Robot
    • Authors: Jin Pan;Xiaochun Mai;Chaoqun Wang;Zhe Min;Jiankun Wang;Hu Cheng;Tingguang Li;Erli Lyu;Li Liu;Max Q.-H. Meng;
      Pages: 11946 - 11960
      Abstract: For airports with high passenger and luggage flows, a large number of staff members have to be hired to deploy the scattered passenger luggage trolleys. To release humans from the repetitive and laborious job, we develop an autonomous trolley deployment robot to detect, transport and collect the scattered idle trolleys to recycling points. This paper will firstly illustrate the entire collection pipeline of the deployment robot system and then address the key challenge: partial to full point set registration. With the perception framework, the robot can detect the idle trolleys and acquire the pose of the trolleys on the ground, and then capture the trolley from behind, along the same direction for subsequent grasping and manipulation. With RGB-D camera and a segmentation Convolutional Neural Network, the robot can generate a partial surface point cloud of the detected trolley. The resulting point cloud, data and a pre-scanned full trolley point cloud, model, are matched by an implicit pose. To tackle the low accuracy and long computation time issues, a novel searching space-constrained point set registration algorithm is proposed to register the two overlapping point sets. Based on Branch-and-Bound (BnB) mechanism, the error between data and model is iteratively optimized. The constraint of searching space speeds up the global searching of the optimal pose, by pruning the candidate spaces which is impossible to contain the optimal result. To evaluate the performance, an airport trolley segmentation dataset and a point cloud dataset for registration are constructed. Experimental results on the datasets and synthetic dataset show that our method achieves higher accuracy and success rate than the previous methods. The experiments demonstrated in video clips validate the developed system works in real-world applications.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Accurate Alignment Inspection System for Low-Resolution Automotive LiDAR
    • Authors: Seontaek Oh;Ji-Hwan You;Azim Eskandarian;Young-Keun Kim;
      Pages: 11961 - 11968
      Abstract: A misalignment of LiDAR as low as a few degrees could cause a significant error in obstacle detection and mapping that could cause safety and quality issues. In this paper, an accurate inspection system is proposed for estimating an automotive LiDAR alignment error after sensor attachment on a vehicle. The proposed method uses only a single target board at a close range to estimate the orientations and the horizontal position of the LiDAR attachment with sub-degree and millimeter level accuracy. It is a static method for the stationary target board and vehicle during the inspection. Also, it only requires one pose of the target board and does not utilize other sensors, such as a camera or GPS. The performance of the proposed method is evaluated using a test bench that can control the reference yaw and horizontal translation of LiDAR within ranges of 3 degrees and 30 millimeters, respectively. The experimental results for a low-resolution 16 channel LiDAR (Velodyne VLP-16) confirmed that misalignment could be estimated with accuracy within 0.2 degrees and 4 mm. The high accuracy and simplicity of the proposed system make it practical for large-scale industrial applications such as automobile manufacturing process that inspects the sensor attachment for the safety quality control.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Computer Vision for Sensed Images Approach in Extremely Harsh
           Environments: Blast Furnace Chute Wear Characterization
    • Authors: Aimé Lay-Ekuakille;Moïse Avoci Ugwiri;John Djungha Okitadiowo;Cosimo Chiffi;Antonio Pietrosanto;
      Pages: 11969 - 11976
      Abstract: Measurements and characterization for extremely harsh environments require accurate approach especially by means of image-based computer vision. Because of harsh conditions, such as high temperature, pollution, turbulences, radioactive exposure, high energy, direct measurements through conventional sensors are not easy even with recent sensing technologies. Live and/or shortest time-delayed sensing, by means of imaging, can come to help to overcome the aforementioned constraints. The paper outilines the use of sensed images for characterizing the effects of high temperatures, at the inlet of a blast furnace, during the discharge of materials using a chute. This latter is subject to wear due to chemico-physical reactions at around 350-450 °C. Given the specific application related to the harsh environment, two algorithms are comparatively proposed and updated for the purposes of the paper; they are based both on computer vision, namely monadic technique and conventional neural network. For the first technique, virtual sensors have been introduced within the image thanks to sinogram and backprojection subtechniques. The results highlight the effects of the environment on the layers of anti-wear compounds applied on the chute, then they permit to understand the chute life-cycle. Quantitative percentage of material detection has been included as well as specific metrics for machine learning expression.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • IoT Sensor Initiated Healthcare Data Security
    • Authors: Kedir Mamo Besher;Zareen Subah;Mohammed Zamshed Ali;
      Pages: 11977 - 11982
      Abstract: While the Internet of Things (IoT) has been instrumental in healthcare data transmission, it also presents vulnerabilities and security risks to patients’ personalized health information for remote medical treatment. Currently most published security solutions available for healthcare data are not focused on data flow all the way from IoT sensor devices placed on a patient’s body through network routers to doctor’s offices. In this paper, we studied how the IoT network facilitates healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, especially between IoT sensor devices and network routers, and then proposed an encrypted security solution initiated at IoT sensor devices. Our proposed solution provides a cryptography algorithm embedded into the sensor device such that the packets generated with patient’s health data are encrypted right at the sensor device before being transmitted. The proof of concept has been verified using a lab setup with two level encryption at the IoT sensor level and two level decryption at the receiving end at the doctor’s office. Test results are promising for an end-to-end security solution of healthcare data transmission in IoT. This paper also opens up further research avenues on IoT sensor driven security.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Pattern Recognition for Distributed Optical Fiber Vibration Sensing: A
    • Authors: Junchan Li;Yu Wang;Pengfei Wang;Qing Bai;Yan Gao;Hongjuan Zhang;Baoquan Jin;
      Pages: 11983 - 11998
      Abstract: In recent years, pattern recognition technologies for distributed optical fiber vibration sensing have attracted more and more attention, aiming to intelligently recognize vibration events along with the optical fiber. Firstly, distributed optical fiber sensors for vibration detection are introduced. Secondly, this paper presents the state of the art of pattern recognition models used in distributed optical fiber vibration sensing systems. The feature extraction method, the model structure, and the processing performance are reported. As the results of the comparison, the support vector machine is a small sample learning method with a solid theoretical foundation and it has excellent generalization ability. The artificial neural network is suitable for massive data learning and multi-classification problems. Also, deep learning can learn more features information by a deep nonlinear network structure in an automated way, and thus has better accuracy and robustness. Furthermore, different applications of pattern recognition for distributed optical fiber vibration sensing are provided, including perimeter security, pipeline monitoring, and railway safety monitoring. Finally, the prospects of pattern recognition for distributed optical fiber vibration sensing are discussed.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Acoustically Driven Manipulation of Microparticles and Cells on a
           Detachable Surface Micromachined Silicon Chip
    • Authors: Jingui Qian;Jifeng Ren;Wei Huang;Raymond H. W. Lam;Joshua E.-Y. Lee;
      Pages: 11999 - 12008
      Abstract: Particles and cells can be patterned and moved (i.e., manipulated) precisely using acoustically driven techniques. To date, application of acoustic particle manipulation has been limited to plain surfaces. There is much potential for applying acoustic manipulation techniques to surfaces with microfabricated structures for high-throughput sensing. But adding thin film structures could alter manipulation characteristics compared to a plain surface. Using a two-chip setup that allows the wave generating device to be reused, we study the feasibility of acoustofluidic micro-manipulation on a surface-micromachined silicon (SMS) chip. The SMS chip is a complex superstrate with generic thin-film structures fabricated by patterning and etching multiple layers of thin films, with properties meant to represent a broad range of microfabricated devices. We report notable alterations in the particle separation distances on the SMS chip compared to a bare silicon superstrate, which we attribute to a change in wave type through a comparison of different superstrates prepared. We demonstrate a high cell viability after acoustic manipulation of live cells on the SMS chip. The results herein demonstrate the possibility of integrating a suite of microfabricated sensors on a chip with acoustically driven manipulation capabilities for multiplexed sensing and analysis for bio-applications.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Rapid Electrochemical Impedance Spectroscopy and Sensor-Based Method for
           Monitoring Freeze-Damage in Tangerines
    • Authors: Pablo Albelda Aparisi;Elena Fortes Sánchez;Laura Contat Rodrigo;Rafael Masot Peris;Nicolás Laguarda-Miró;
      Pages: 12009 - 12018
      Abstract: This study focuses on the analysis and early detection of freeze-damage in tangerines using a specific double-needle sensor and Electrochemical Impedance Spectroscopy (EIS). Freeze damage may appear in citrus fruits both in the field and in postharvest processes resulting in quality loss and a difficult commercialization of the fruit. EIS has been used to test a set of homogeneous tangerine samples both fresh and later frozen to analyze electrochemical and biological differences. A double-needle electrode associated to a specifically designed electronic device and software has been designed and used to send an AC electric sinusoidal signal 1 V in amplitude and frequency range [100Hz to 1MHz] to the analyzed samples and then receive the electrochemical impedance response. EIS measurements lead to distinct values of both impedance module and phase of fresh and frozen samples over a wide frequency range. Statistical treatment of the received data set by Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) shows a clear classification of the samples depending on the experienced freeze phenomenon, with high sensitivity (1.00), specificity (≥ 0.95) and confidence level (95%). Later Artificial Neural Networks (ANN) analysis based on 20-3-1 architecture has allowed to create a mathematical prediction model able to correctly classify 100% of the analyzed samples (CCR =100% for training, validation and test phases, and overall classification), being fast, easy, robust and reliable, and an interesting alternative method to the traditional laboratory analyses.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Simple Nano Cerium Oxide Modified Graphite Electrode for Electrochemical
           Detection of Formaldehyde in Mushroom
    • Authors: Shreya Nag;Susmita Pradhan;Hemanta Naskar;Runu Banerjee Roy;Bipan Tudu;Panchanan Pramanik;Rajib Bandyopadhyay;
      Pages: 12019 - 12026
      Abstract: In this present work, electrochemical detection and quantification of formalin (FAL) trace present in mushroom (Agaricus bisporus) was premeditated using a cerium oxide nanoparticle modified graphite paste electrode (CeO2@GP). Cerium oxide (CeO2) nanoparticles (nps) were synthesized through the sol-gel technique from cerium nitrate hexahydrate using poly (ethylene glycol) as a capping agent. The prepared CeO2 nps were characterized using X-ray diffraction (XRD) and transmission electron microscopy (TEM) techniques which revealed the successful formation of the cubic phase of CeO2 having crystallite size 4.84 nm. The prepared CeO2 nps were used to modify the graphite paste electrode (bare GP). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques were utilized to study comparative electroanalytical features of the fabricated electrodes. Under optimized experimental conditions, CeO2@GP exhibited a wide linear range from $25~mu text{M}$ -1mM and a limit of detection of $1~mu text{M}$ . Moreover, CeO2@GP featured high repeatability, reproducibility, and long-term stability. The electrode exhibited high selectivity for FAL in the presence of interferences like ethanol, methanol, formic acid, benzaldehyde, and acetone. CeO2@GP demonstrated exceptional aptitudes in electrochemical behavior when subjected to mushroom extract.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Sensing of Yeast Inactivation by Electroporation
    • Authors: Guilherme B. Pintarelli;C. T. S. Ramos;J. R. da Silva;M. J. Rossi;D. O. H. Suzuki;
      Pages: 12027 - 12035
      Abstract: Irreversible electroporation is a treatment to control microorganisms in food without harming food palatable aspects. The treatment by electroporation is dependent on the electric field amplitude, pulse durations and pulse repetitions. This study was carried out as a further investigation on the effects of irreversible electroporation pulse amplitude on S. cerevisiae and methods to verify the occurrence of irreversible electroporation, such as cell viability hemocytometer, macroscopic impedance measurements, scanning electron microscopy and numerical simulations. Active yeasts in wine represent a small portion of the volume (< 10 8 cell/mL), which challenges macroscopic impedance analysis. Irreversible electroporation proved to reach active yeast limits established by standards within 500 kV/m. We detected a 15% increase in the in pulse current measurements for a 100-fold yeast viability decrease to 10 5 cells/ml. Scanning electron microscopic images show details of yeast surface damage which may be PEF-triggered. Electric fields above 1 MV/m on the cell wall and release of intracellular substance caused by membrane permeability increase may directly contribute to yeast inactivation. Electroporation combined with instantaneous current-voltage measurements may be an adequate procedure for reducing yeast numbers in the industry.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Polymorphic Measurement Method of FeO Content of Sinter Based on
           Heterogeneous Features of Infrared Thermal Images
    • Authors: Zhaohui Jiang;Yuhao Guo;Dong Pan;Weihua Gui;Xavier Maldague;
      Pages: 12036 - 12047
      Abstract: FeO content of sinter is an important indicator of the quality of sinter. Aiming to overcome the difficulty of detecting the FeO content of sinter in the sintering process in real-time, this paper proposes a polymorphic measurement method for sinter FeO content based on heterogeneous features of infrared thermal images. First, an infrared thermal imager is applied to capture the infrared thermal images of sinter cross section at the tail of the sintering machine, and key frame and region of interest extraction are adopted to reduce the data throughput. Then, the shallow features and deep features that are related to the FeO content are extracted based on the regions of interest. Next, a polymorphic mechanism model is established to obtain the preliminary FeO content, and the sinter quality is divided into three grades according to the preliminary FeO content. Finally, three intelligent models corresponding to the three sinter grades are established to achieve the FeO content prediction based on the extracted heterogeneous features. Results in a sintering plant show that the proposed method can measure the FeO content accurately and provide reliable FeO content data for sintering site.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Flexible Capacitive Pressure Sensor Based on Laser–Induced Graphene and
           Polydimethylsiloxane Foam
    • Authors: Lixiong Huang;Han Wang;Daohua Zhan;Feiyu Fang;
      Pages: 12048 - 12056
      Abstract: Flexible pressure sensors have been extensively employed in a range of fields, such as electronic skin (E–skin), humanoid robots, and personal health care. Laser–induced graphene (LIG) is an ideal active material to produce flexible sensors due to its advantages of one–step fabrication, excellent mechanical performance, and high conductivity. This paper presents a flexible capacitive pressure sensor (FCPS) consisting of LIG and polydimethylsiloxane (PDMS) foam. LIG can be fabricated by using a laser to directly write on polyimide (PI) film. By transferring the LIG to a porous PDMS foam, the FCPS acquired a plate–foam–plate integrated structure and it had high sensitivity ( $sim 0.026,textit {kPa}^{-1}$ in $15sim 40,textit {kPa}$ ) and a fast response time ( $sim 120,textit {ms}$ ). In dynamic testing, the FCPS exhibited a stable ( $delta _{r}=sim 1.785$ %) and low–hysteresis ( ${h}=sim 9.762$ %) response to pressure. Furthermore, no significant signal distortions were identified in 5000–cycle press/release testing, which demonstrated the long–term durability of the FCPS. The FCPS was capable of distinguishing between different external mechanical stimuli, including stretching, pressing, bending, and twisting by multiple responses (i.e., two electrode resistances and the capacitance between the electrodes). The FCPS was also employed to detect joint movements, body pressure, and arterial pulse. To study the spatial pr-ssure distribution in depth, an FCPS array was developed by designing a LIG pattern into an electrode array. As a result, there was a mapping between the measurements and the spatial pressure. In our study, FCPS and its array were prepared for multiple stimuli identification and tactile sensing using a simple, efficient, and low–cost technique. The results from this study demonstrated that the FCPS and its array demonstrated potential for being fabricated into wearable medical devices, virtual reality/augmented reality (VR/AR) devices, or E–skin.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Multi-Functional Module-Based Capsule Robot
    • Authors: Lingling Zheng;Shuxiang Guo;Zixu Wang;Takashi Tamiya;
      Pages: 12057 - 12067
      Abstract: This paper proposes a main-functional module working concept for capsule robots, and the capsule robots consist of a main module and functional modules based on this concept. The main module drives the functional modules and provides guidance and support for the functional modules while the functional modules perform the specific diagnosis or treatment. In addition, we propose a novel single-function design concept, which enables different functional modules to have different functions according to the medical requirements. The diagnosis and treatment functions are separated, and they will allow each module to work more specifically and efficiently. Various functional modules can be selected according to medical requirements, and thus it can improve treatment efficiency and reduce medical costs. The single-function design concept eliminates the need to integrate multiple functions into one robot and decrease manufacturing difficulty. Besides, we present a novel docking-separation method to realize effective docking and rapid separation for capsule robots. It can also enable the docked robot to work in bent parts of intestinal tracts easily. A multimodule capsule robot (MCR) was fabricated and the performance was evaluated through experiments. Experimental results demonstrated that the robot modules could be controlled independently and could dock reliably and separate easily. Moreover, the MCR can prevent accidental separation and has potential applications in the clinical practices of intestinal tracts.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Angular Rate Sensitive Method of Magnetically Suspended Control & Sensing
           Gyroscope Based on Deflection Current and Angle
    • Authors: Chunmiao Yu;Yuanwen Cai;Yuan Ren;Yahong Fan;Weijie Wang;Chen Nie;
      Pages: 12068 - 12076
      Abstract: A high-precision angular rate sensitive method of spacecraft based on deflection current and deflection angle measurement is proposed in this paper, in the view of this problem that the angular rate sensitive accuracy of spacecraft using Magnetically Suspended Control & Sensing Gyroscope (MSCSG) is affected by the deflection disturbance of the rotor relative to the stator. Firstly, the moment model of the rotor and the Lorentz force magnetic bearing (LFMB) is established, and the main factors affecting the sensitive accuracy of MSCSG are analyzed in detail; The inertial coupling terms which have great influence on the sensitivity accuracy are effectively retained by the rotor deflection angular velocity and angular acceleration estimation. The high precision analytical expression of attitude angular rate is derived and the error analysis is carried out; It is verified by simulation and experiment that the high precision sensitive method proposed in this paper has higher sensitive precision than the traditional measurement method, which greatly improves the zero bias stability index of MSCSG. The method proposed in this paper has important reference significance for improving the integration of magnetic suspension rotor control and sense performance.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Calibration of a Cryogenic Turbine-Based Volumetric Flow Meter (CTVFM)
           Using Sub-Cooled Liquid Nitrogen and Solution for Its Practical Issues
    • Authors: Isaac De Souza;Abhik Sarkar;Ankit Anand;Maalika Sarkar;J. Senthil Kumar;Abhay Singh Gour;Vutukuru Vasudeva Rao;
      Pages: 12077 - 12083
      Abstract: The application of cryogenic refrigeration in various applications of superconductivity such as HTS cables, HTS motors, SMES and so on, requires a controlled flow of the cryogens to the superconducting mass. The flow control of the cryogens necessitates the use of a pre-calibrated flow meter to monitor the flow rates and hence maintaining the necessary refrigeration. This paper describes a method to calibrate a Cryogenic Turbine based Volumetric Flow Meter (CTVFM) using sub-cooled liquid nitrogen. The experimental setup along with the procedure has been explained. The paper also discusses the practical issues faced during experiments and remedies adopted to overcome them. Further, the data acquisition (DAQ) system used during the experiment for precise logging of values is discussed. The numerical analysis of the calibration data is carried out and a suitable curve fit along with an equation is obtained to calculate the flow rate from output current of the flow meter transmitter.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Displacement Sensor With Nanometric Resolution Based on Magnetoelectric
    • Authors: Yikun Yang;Bintang Yang;
      Pages: 12084 - 12091
      Abstract: Micro-displacement measurement is essential in precision fields, such as precision positioning, micro-vibration control and biological engineering. Conventional sensors have disadvantages of susceptibility to environmental, high cost and difficulty in integration. To address these deficiencies, this paper developed a novel displacement sensor with nanometric resolution based on magnetoelectric effect. Combining equivalent magnetic circuit method and equivalent circuit method for magnetoelectric effect using the nonlinear constitutive parameters, an equivalent circuit model of the proposed sensor is established to analyse and predict the performance of displacement sensor. Then a prototype of sensor based on Terfenol-D/PZT composites is fabricated, and the performance of prototype for dynamic displacement amplitude and static position measurement are tested. The results not only validate the developed equivalent circuit model, but also evidence the potential of magnetoelectric displacement sensor. Especially for dynamic displacement amplitude measurement, the resolution of it is better than 13.27 nm, which is close to or even beyond commercial laser displacement sensor.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Time-Gated and Multi-Junction SPADs in Standard 65 nm CMOS Technology
    • Authors: Wei Jiang;Yamn Chalich;Ryan Scott;M. Jamal Deen;
      Pages: 12092 - 12103
      Abstract: SPADs (Single-Photon Avalanche Diodes) are important detectors for a wide range of applications including positron emission tomography, Raman spectroscopy, light detection and ranging, and quantum key distribution. For some applications, custom image sensor technologies are used, but at a higher cost and lower performance imagers when compared to implementation in a standard planar CMOS technology. In this paper, we explore time-gating and multi-junction techniques to improve the SPAD’s performance in smaller standard planar CMOS processes to take advantage of their potential for monolithic integration with other advanced, mixed-signal circuitry for simple, low-cost, high-performance imaging solutions. A passively quenched, unbuffered, triple-junction SPAD structure was designed in a standard 65 nm CMOS process from TSMC. The characterization of the SPAD junctions in this process is the first in literature and proves useful for SPAD designers aiming for advanced CMOS technology nodes. The time-gated (TG) pixel design used the top shallow junction. The potential for improved photon detection efficiency and wavelength distinction through a multi-junction design was investigated. Our testing demonstrated that the proposed implementation of the triple-junction SPAD in this technology node is not suitable for wavelength distinction. The TG design achieved a fill-factor of 28.6%, and at an excess voltage of 300 mV, it achieved a peak photon detection efficiency of ~2.1% at 440 nm, 22 ns, and
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Permittivity Estimation of Dielectric Substrate Materials via Enhanced SIW
    • Authors: Prashant Kumar Varshney;M. Jaleel Akhtar;
      Pages: 12104 - 12112
      Abstract: An enhanced substrate integrated waveguide (SIW) cavity sensor is proposed and designed in this work operating under sub −6 GHz 5G band suitable for the accurate permittivity estimation of low-loss dielectric materials. The proposed SIW sensor is designed for the fundamental TE $_{mathbf {101}}$ mode at the resonance frequency of 3 GHz. The designed sensor is excited by employing a newly optimized external coupling topology incorporating a transition offset, contrary to the conventional microstrip feed. Due to which, a fully planar SIW sensor is developed for the first time without incorporating any active component, exhibiting a substantially high unloaded quality factor of nearly 515 basically required for the accurate testing of low loss tangents. Besides, the designed sensor is yielding a high sensitivity of 20 MHz (equivalent to 0.67% in terms of normalized sensitivity) desirable for segregating nearly resembling dielectric constants. Several standard dielectric samples are tested for the performance validation revealing that the proposed sensor is efficiently differentiating between Plexiglass ( $boldsymbol {varepsilon }_{r} =2.6$ ) and PVC ( $boldsymbol {varepsilon }_{r} =2.65$ ). Simultaneously, the sensor is accurately determining even the low loss tangent of Teflon (tan $delta = 3.2times 10 ^{-4}$ ). The measurement results are in close agreement with the references available in the literature. A thorough comparison with the state of the art literature corroborates the advantages offered by the proposed sensor and hence, its usability. Apart from characterizing the commercial grade dielectrics, this resonator is also potentially ap-ropriate for designing high performance RF filters, oscillators and other devices.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Denoising Method of the Φ-OTDR System Based on EMD-PCC
    • Authors: Wei Chen;Xiaohui Ma;Qinglin Ma;Jiajie Wen;
      Pages: 12113 - 12118
      Abstract: To filter out the phase noise of the $Phi $ -OTDR system, a method based on empirical mode decomposition and Pearson correlation coefficient fusion (EMD-PCC) is proposed. First, the EMD-PCC method is simulated. The experimental results show that the SNR increases from 7.32 dB to 13.68 dB. Second, the beat signal detected by the $Phi $ -OTDR system is demodulated by the I/Q quadrature demodulation method. Finally, the phase signal is decomposed by empirical mode decomposition to obtain the modal function and residual component. The Pearson correlation coefficient with the phase signal is calculated. Then, the threshold value is 0.4–1.0. The modal function within the threshold is superimposed with the residual component signal. In this paper, the experimental verification of PZT analog disturbance signal frequencies of 200 Hz, 300 Hz, 400 Hz and 500 Hz is performed. Then, wavelet denoising and EMD-soft denoising are compared with EMD-PCC denoising. Experimental results show that the proposed method can accurately restore the disturbance signal. The experiment of sawtooth disturbance signal frequencies of 400 Hz and 500 Hz verifies the applicability of the algorithm and shows that the method is suitable for other arbitrary forms of disturbance signals. The $Phi $ -OTDR system has an important application in the field of oil and gas exploration. This method provides a good theoretical basis for the exploration field.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A High-Order Enhanced Attitude Algorithm Under Angular-Rate Input
    • Authors: Peng Ding;Xianghong Cheng;Yineng Wang;
      Pages: 12119 - 12129
      Abstract: With the development of high-precision strapdown inertial navigation systems (SINS), it is increasingly important to improve the accuracy of navigation algorithms to match high-precision inertial sensors and meet the navigation localization requirements in high dynamic environments. However, it is difficult to further improve the accuracy of traditional second-order angular rate-based attitude algorithms due to the neglect of the triple-cross-product term and the limitation of attitude updating frequency. In this paper, a high-order rotation vector-based coning algorithm with compressed form for high accuracy attitude computation of online processing systems is proposed. The theoretical third-order terms of rotation vector differential equation in pure coning motion are deduced in detail and the angular rate vectors of previous attitude updating cycle are additionally used to estimate angular increment, coning correction term and the third-order terms. The error analysis and optimization of the coefficients for proposed algorithm are conducted by utilizing the Taylor series expansions in powers of coning frequency. Simulations under coning vibration environment and turntable experiments in laboratory were performed to verify the performance of the algorithm. The results show that the proposed algorithm can obviously reduce the noncommutativity error and obtain higher accuracy compared with the existing angular rate-based algorithms.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Phase-Sensitive Optical Time-Domain Reflectometric System Based on Optical
           Synchronous Heterodyne
    • Authors: Ting-Ting Lin;Yu-Xin Bai;Zhi-Cheng Zhong;Xu Gao;
      Pages: 12130 - 12136
      Abstract: Aiming at the frequency drift of the laser and acousto-optic modulator in the traditional phase-sensitive optical time domain reflectometric ( $Phi $ -OTDR), a $Phi $ -OTDR system based on optical synchronous heterodyne is proposed and demonstrated. The method of optical synchronization heterodyne is used to follow the beat frequency signal to eliminate the interference of frequency shift fluctuation in the phase information and the ubiquitous residual frequency before the optical path heterodyne. The signal-to-noise ratio (SNR) is more than 31.4 dB on a 10 km sensing optical fiber with a probe light pulses of 5 kHz repetition rate and 100 ns pulse width. The error between the demodulated signal amplitude and the actual signal amplitude is less than 1%, and the maximum harmonic amplitude is about 28.3 dB less than the actual signal amplitude. While improving the real-time performance, this research greatly improves the demodulation characteristics of the system, which is of great significance to the advancement of the practical process of the phase-sensitive optical time domain reflectometric system.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • pH Sensor Through a Self-Assembled Multifunctional Layer With Clitoria
           Ternatea Based on Long-Period Fiber Gratings
    • Authors: Hsin-Yi Wen;Jian-Jie Weng;Chia-Chin Chiang;
      Pages: 12137 - 12145
      Abstract: In this study, Clitoria ternatea was used to provide a quantifiable method for pH measurement. The researchers proposed a new pH measurement method that involves the use of a KrF excimer laser to perform laser-assisted etching of the crown structure of long-period fiber gratings. Processing of the front surface of the optical fibers through grating resulted in periodically structured optical fibers with a sensor diameter of $55~mu text{m}$ and a period of $660~mu text{m}$ ; these structured optical fibers were encapsulated using glass slides. On the front surface of the optical fibers, functional group modification of the fiber surface was conducted and a nanogold coating was applied using the self-assembly method. Additionally, Clitoria ternatea was bonded to the surface of the optical fibers for the measurement of refractive index. In the pH sensing experiment, the average loss sensitivity detected by the sensors with no nanogold self-assembled coating was 0.0635 dB/pH. By contrast, the average resonant wavelength sensitivity of the sensors with a nanogold self-assembled coating under localized surface plasmon resonance was increased to 0.173 nm/pH. Compared with the sensors with no nanogold self-assembled coating, the sensors with this coating had a wavelength drift sensitivity enhanced by a factor of 37.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • In-Line Interferometric Temperature Sensor Based on Dual-Core Fiber
    • Authors: Haijin Chen;Xuehao Hu;Xiaoyong Chen;Qianqing Yu;Zhenggang Lian;Heng Wang;Hang Qu;
      Pages: 12146 - 12152
      Abstract: In this paper, we proposed an in-fiber Mach-Zehnder temperature sensor based on a dual-core fiber (DCF) in which one core, working as the sensing arm, is suspended in an embedded fluidic channel filled with silicone oil, while the other one, working as the reference arm, locates eccentrically in the DCF. Temperature variations would change the refractive index of silicone oil infiltrated as well as the effective index of the guided mode in the suspended core, thus shifting the interference spectra. Both experiments and numerical simulations were carried out to characterize the sensor. The spectrum shifts measured experimentally agreed well with the theoretical results. Experimental sensitivity of the sensor using a DCF infiltrated with ~20 cm-long silicone oil was found to be as high as −1.42 nm/°, comparable to those of the SPR fiber sensors and other interferometric sensors. The measuring range of the sensor was more than 120°. The proposed sensor could be easily fabricated with good robustness and stability, which makes the sensor promising for applications such as environment and architecture monitoring.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Tuning the Sensitivity of a Fiber-Optic Plasmonic Sensor: An Interplay
           Among Gold Thickness, Tapering Ratio and Surface Roughness
    • Authors: Aditi Chopra;Girish C. Mohanta;Bhargab Das;Randhir Bhatnagar;Sudipta Sarkar Pal;
      Pages: 12153 - 12161
      Abstract: The performance of a multimode fiber optic surface plasmon resonance (FO-SPR) sensor has been studied, both theoretically and experimentally, as a function of three crucial parameters - metal layer thickness, tapering ratio (TR) and fiber surface roughness. A combined approach is adopted to investigate the effect of these three parameters on FO-SPR sensor performance instead of considering them individually. Intriguingly, the three parameters are found to be highly correlated and their combined effect significantly influences the FO-SPR sensitivity. It is observed that optimum gold thickness to attain maximal sensitivity varies for different tapering ratios. Moreover, increasing the tapering ratio does not always increase the sensitivity as claimed in previous studies, but instead depends on the fiber surface roughness generated during chemical etching. The optimized FO-SPR sensor exhibits a high sensitivity of 4714 nm RIU−1 with standard glycerol solution in the refractive index (RI) range of 1.372-1.388. Furthermore, the optimized FO-SPR sensor has been employed for label-free detection of bovine serum albumin (BSA) and parathion pesticide. The system is able to detect BSA concentration as low as 1.5 pM and the parathion pesticide is detected at 0.1 ppb levels.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Using Sensor Network for Tracing and Locating Air Pollution Sources
    • Authors: Xiuhong Li;Meiying Sun;Yushuang Ma;Le Zhang;Yi Zhang;Rongjin Yang;Qiang Liu;
      Pages: 12162 - 12170
      Abstract: Atmospheric fine particulate matter (particulate matter with an aerodynamic diameter $le 2.5~mu text{m}$ in ambient air; PM2.5) is a major pollutant causing regional air pollution and harm to human health. To monitor PM2.5, Chinese industry authorities use data from a small number of fixed stations with uneven distribution. Pollution control is promoted through assessment and enforcement, which can include industry-wide shutdown measures harmful to local economic development. The objective of this study was to establish a fine particulate matter network (FPMN) of sensors based on the Internet of Things with low cost, high spatiotemporal resolution, flexible distribution points, large numbers, and high collection frequency. The FPMN-derived data, together with other multisource environment-related data, could be used to track and locate atmospheric pollutants and selectively identify source. This study adopted Chizhou, China as the research object. Specifically, the work included designing the FPMN, selecting locations for sensor placement on the basis of local weather, terrain, and land use, and using software/hardware collaborative calibration technology to ensure consistency between FPMN-derived data and National Control Station(NCS) data. The analysis revealed that FPMN data effectively reconstructed a reliable regional field of PM2.5 concentration with improved spatiotemporal accuracy. The research results will have great importance regarding the traceability of sources of PM2.5 pollution, analysis of pollution causes and transboundary pollution, optimization and adjustment of industrial layouts, and differentiation of the temporal and spatial control of pollution. Ultimately, the FPMN could directly support management and decision-making processes of local governments in relation to the environment and industry.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Measurement of Gas Holdup in Oil-Gas-Water Flows Using Combined
           Conductance Sensors
    • Authors: Dayang Wang;Ningde Jin;Lusheng Zhai;Yingyu Ren;
      Pages: 12171 - 12178
      Abstract: In this study, a new method for gas holdup measurement in water continuous oil-gas-water multiphase flow based on conductance sensors is proposed. This method includes the use of the combined conductance sensors and the establishment of gas holdup model. A cross-sectional type conductance sensor is applied to acquire the conductivity of oil-gas-water mixture. For the purpose to derive gas holdup from the mixture conductivity, a novel liquid conductivity sensor which has shallow detection depth is proposed and it is flush-mounted on the inner wall of pipe to detect the liquid conductivity. The performances of the cross-sectional type conductance sensor combined with the liquid conductivity sensor are evaluated by experiments. And the results indicate that the liquid conductivity sensor can online detect the liquid conductivity in the multiphase mixture effectively. Based on the measured conductivity of multiphase mixture and the conductivity of liquid, the normalized conductivity which is related to the gas holdup is defined. The gas holdup model based on flow structures is investigated and established, and finally the measurement of gas holdup independently of salinity in oil-gas-water multiphase flow based on the conductance method is achieved for the first time.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Fastener Inspection Method Based on Defective Sample Generation and Deep
           Convolutional Neural Network
    • Authors: Jianwei Liu;Yun Teng;Xuefeng Ni;Hongli Liu;
      Pages: 12179 - 12188
      Abstract: For the safety of railways, well-trained workers are required to check the fastener constantly, which shows the disadvantage of large time cost, huge labor cost and might being dangerous to workers. To address this and achieve automatic detection, an inspection model based on deep convolutional neural network (DCNN) is adopted in this paper. However, the inspection model suffering from the unbalanced training samples of defective vs normal due to defective fasteners are far less than normal fasteners in real railways. To tackle this problem, a novel sample generation method is proposed to generate defective fastener samples using the normal fasteners to realize sample augmentation. The comprehensive experiments are conducted on the collected real fastener samples and generated samples. The experimental results show that our method has good performance for fastener inspection on unbalanced samples and outperforms other state-of-the-art methods.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Single-Frequency Amplitude-Modulation Sensor for Dielectric
           Characterization of Solids and Microfluidics
    • Authors: Paris Vélez;Jonathan Muñoz-Enano;Amir Ebrahimi;Cristian Herrojo;Ferran Paredes;James Scott;Kamran Ghorbani;Ferran Martín;
      Pages: 12189 - 12201
      Abstract: A microfluidic sensor based on a microstrip line loaded with a composite resonator is reported in this paper. The composite resonator combines a shunt connected step impedance resonator (SIR) and a complementary split ring resonator CSRR) etched in the ground plane. By etching the CSRR beneath the patch of the SIR, the composite CSRR-loaded SIR resonator exhibits two transmission zeros and a pole in between. The operating principle of the sensor is the variation of the transmission coefficient at the pole frequency of the bare resonator, when a material or liquid under test (LUT) is in contact with the CSRR (the sensitive element). By designing the CSRR-loaded SIR resonator with closely spaced pole and transmission zeros, highly sensitive sensors are obtained. Despite the fact that the proposed sensor can also operate as a frequency variation sensor, using it as a single-frequency sensor based on the variation of the transmission coefficient (caused by the LUT) at a specific frequency reduces sensor costs. The reason is that a harmonic signal injected to the input port of the microstrip-based sensor plus a simple amplitude modulation (AM) detector (connected to the output port) suffices for measuring purposes. The proposed microfluidic sensor is applied to the characterization of volume fraction of solutions of isopropanol in deionized (DI) water. The sensor is able to resolve volume fractions as small as 5%, and the maximum measured sensitivity is as good as 4 mV/%.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Measurement-Based Extraction and Analysis of a Temperature-Dependent
           Equivalent-Circuit Model for a SAW Resonator: From Room Down to Cryogenic
    • Authors: Giovanni Crupi;Giovanni Gugliandolo;Giuseppe Campobello;Nicola Donato;
      Pages: 12202 - 12211
      Abstract: This article provides for the first time a very extensive experimental characterization coupled with a fully analytical modeling in order to investigate, in a systematic and comprehensive way, the sensing performance of a two-port surface acoustic wave (SAW) resonator from room down to cryogenic temperatures. The motivation behind this work is twofold: to quantitatively assess the temperature sensitivity of the SAW technology for cryogenic applications and to gain a better understanding of the underlying physics in terms of the equivalent-circuit elements. Although the measurement-based analysis is developed by focusing on a SAW from Murata as a case study, the developed investigation methodology is independent of the considered technology and extensible to other SAW types. A cryogenic system based on a closed-loop helium refrigerator is used to cool the tested SAW from 300 K down to 20 K with a step of 10 K. At each studied temperature, the scattering parameters are measured using a vector network analyzer over a narrow frequency band around the nominal resonant frequency of 423.2 MHz, spanning from 420 MHz to 425 MHz with a small step of 3.125 kHz. The measured scattering parameters are then transformed into the admittance ones, as they are more useful for sensing performance assessment and for equivalent-circuit model extraction. The extracted model is successfully validated through the achieved good agreement between measurements and simulations of the temperature- and frequency-dependent behavior of the studied resonator.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Energy Efficient Data Compression in Cloud Based IoT
    • Authors: Halah Mohammed Al-Kadhim;Hamed S. Al-Raweshidy;
      Pages: 12212 - 12219
      Abstract: In this study, an adaptive data compression scheme (ADCS) is proposed for efficiently controlling the IoT device compression rate and energy consumption in the cloud based IoT network. The ADCS consists of two data compression schemes, the sensor Lempel–Ziv–Welch (S-LZW) scheme and the sequential lossless entropy compression (S-LEC) scheme. In Auto state, the ADCS can select the appropriate energy efficient data compression scheme for each IoT device, while taking into consideration the IoT device’s processing capability, the available energy in each IoT device battery, and the amount of compression power. Our proposed scheme has been developed using mixed integer linear programming. The result verifies that the proposed ADCS scheme saves power by an average of 40% compared to the non-compression scheme (NCS) due to reducing the traffic load and the number of hops in the network, which leads to an ability to handle higher traffic demands and increasing the lifetime of IoT devices by 50% compared to NCS systems.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Efficient Parallel Branch Network With Multi-Scale Feature Fusion for
           Real-Time Overhead Power Line Segmentation
    • Authors: Zishu Gao;Guodong Yang;En Li;Zize Liang;Rui Guo;
      Pages: 12220 - 12227
      Abstract: Image-based segmentation of overhead power lines is critical for power line inspection. Real-time segmentation helps the inspection robot avoid obstacles or land on the wire during the inspection task. It is challenging for several studies to achieve real-time overhead power line segmentation with high accuracy. In addition, cluttered background brings great difficulties to overhead power lines segmentation. To address these issues, an efficient parallel branch network for real-time overhead power line segmentation is proposed. Our framework combines a context branch that generates useful global information with a spatial branch that preserves high-resolution segmentation details. The asymmetric factorized depth-wise bottleneck (AFDB) module is designed in the context branch to achieve more efficient short-range feature extraction and provide a large receptive field. Furthermore, the subnetwork-level skip connections in the classifier are proposed to fuse long-range features and lead to high accuracy. Experiments demonstrate that our framework achieves more than 90% segmentation accuracy.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • An Optimization-Based Multi-Sensor Fusion Approach Towards Global
           Drift-Free Motion Estimation
    • Authors: Ke Wang;Chuan Cao;Sai Ma;Fan Ren;
      Pages: 12228 - 12235
      Abstract: Precise and drift-free motion estimation is an essential technology for autonomous driving. Single-sensor methods such as laser-based or vision-based have proven to be inadequate. To solve the problem, we proposed an optimization-based fusion approach that incorporates information from complementary sensors to achieve high accuracy and global drift-free. The core idea is to construct a globally unified pose graph through a dual-layer optimization strategy. The local estimation layer obtains the relative pose through LiDAR odometry and visual-inertial odometry. Subsequently, by introducing the absolute geographic position information of GPS, the accumulated drifts are corrected in the global optimization layer. The performance of our approach has been evaluated both in real-world environments and public datasets. The result demonstrates that our approach outperforms other state-of-the-art algorithms, with an average translation error of 0.8045% and an average rotation error of 0.0043deg/m.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Sensing the Thermal Aging of Epoxy Alumina Nano-Composites Using Electric
    • Authors: Subhajit Maur;Soumya Chatterjee;Nasirul Haque;Preetha P.;Sovan Dalai;Biswendu Chatterjee;
      Pages: 12236 - 12244
      Abstract: In this contribution, a novel technique to accurately sense the thermal aging of the epoxy alumina nano-composites employing complex electric modulus is proposed. To sense the thermal ageing of epoxy nano-composites accurately, knowledge of relaxation behavior is necessary. However, relaxation behavior cannot be understood completely due to electrode polarization and charge transport effects at high temperatures and at low frequencies. To overcome this problem, electric modulus which is defined as the inverse of the complex permittivity, is being proposed in this study to analyze the aging behavior of the epoxy alumina nano-composites quantitatively. For this purpose, three epoxy resin samples mixed with alumina (Al2O3) nano-fillers with different filler concentrations were prepared and thermal aging of the same samples was done for 100 hours, 200 hours, 300 hours and 400 hours, respectively. For each sample, the complex dielectric modulus ( $M^{ast }$ ( $omega $ )) was computed using frequency domain spectroscopy measurement to observe their frequency dependent relaxation behaviors. The variation of the real ( $M^{prime }$ ( $omega $ )) and imaginary ( $M^{prime prime }$ ( $omega $ )) part of ( $M^{ast }$ ( $omega $ )) over the frequency range from 1 mHz to 10 kHz was further fitted using Cole-Cole (C-C) model. From the nature of variation of $M^{prime prime }$ ( $omega $ ) spectrum and the fitting parameters of the C-C model, two characteristic parameters were extracted to quantitatively describe the thermal aging of the epoxy nano-composite samples. Investigations revealed that the extracted parameters can be accurately used to sense the aging condition of the epoxy nano-composites.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Nonlinear Adaptive Harmonics Vibration Control for Active Magnetic Bearing
           System With Rotor Unbalance and Sensor Runout
    • Authors: Huijuan Zhang;Jianjuan Liu;Ruipu Zhu;Hongmei Chen;Hang Yuan;
      Pages: 12245 - 12254
      Abstract: Rotor unbalance (RUB) and Sensor Runout (SR) are two main vibration sources for active magnetic bearing (AMB) system, and they not only produce considerable harmonic vibration but also endanger the stability of AMB system. Therefore, a nonlinear adaptive algorithm, whose asymptotic stability is guaranteed by Lyapunov’s theory, is proposed in this paper to suppress the harmonic vibration. Nevertheless, RUB and the synchronous component of SR have the same frequency with the rotational frequency, and it is highly necessary to distinguish them from each other for the compensation of displacement stiffness force. Hence, the scheme of changing rotor speed is adopted for the permanent magnet biased magnetic bearing system. As a consequence, the harmonic current caused by RUB and SR is effectively attenuated and the displacement stiffness force is correctly compensated. The results of the numerical simulations and experiments demonstrate that the harmonic vibration is dramatically suppressed through the proposed method.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Flexible and Stretchable Dry Active Electrodes With PDMS and Silver Flakes
           for Bio-Potentials Sensing Systems
    • Authors: Yizhou Jiang;Lili Liu;Lin Chen;Yuanyuan Zhang;Zishang He;Wei Zhang;Jun Zhao;Danzhu Lu;Jie He;Hongda Zhu;Ye Gong;Li-Rong Zheng;Yuanyuan Wang;Zhuo Li;Yajie Qin;
      Pages: 12255 - 12268
      Abstract: Flexible and stretchable dry active electrodes for multi bio-potentials sensing based on Ag flakes / polydimethylsiloxane (PDMS) electrically conductive composite (ECC) are developed and characterized. The proposed dry active electrode consists of soft substrate, electrode, and simple circuits with an amplifier and a capacitor. The soft substrate is made of silicone with a molding process, while the electrode is fabricated by bar coating the ECC on the patterned substrate. Furthermore, circuits interconnect, soldering of chip and other components, and connection with flexible PCB (FPC) are all implemented with ECC directly, which simplifies the fabrication process. A portable bio-potential sensing system is also designed and implemented to work with the proposed electrodes. Various experiments were carried out to verify the proposed electrodes as well as the whole sensing system. The connectivity and proper functionality of the electrodes and cables remain stable during the stretching test. The system and the sensors yield good signal quality for multiple bio-potentials. Compared with conventional Ag/AgCl wet passive electrodes in electrocardiogram (ECG) sensing, the proposed dry active electrodes showed comparable noise floor and less sensitivity to power line interferences and motion artifacts. In electroencephalogram (EEG) sensing, the observed alpha rhythm was 12.66 dB higher than the baseline in the eye-close state. The proposed sensors exhibited potential applications in wearable systems in the electromyogram (EMG) grip force measurement and classification test. Overall, the proposed sensors could provide better comforts during long-time wearing according to its better matching modulus with skin and can meet the requirements of high quality multi bio-potential sensing.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Research on Measurement Method of Spherical Joint Rotation Angle Based on
           ELM Artificial Neural Network and Eddy Current Sensor
    • Authors: Penghao Hu;Chuxin Tang;Linchao Zhao;Shanlin Liu;Xueming Dang;
      Pages: 12269 - 12275
      Abstract: This paper proposes a measurement method for the rotation angle of the spherical joint based on the extreme learning machine (ELM) artificial neural network and four eddy current sensors. Aiming at the problems of small range and low accuracy in the early three-eddy-current angle measurement prototype, the position matching scheme of four eddy current sensors is researched, a new prototype is developed through simulation analysis, and ELM neural network substitutes the previous generalized regression neural network (GRNN) for building a new measurement model. The modelling training and comparison test are completed in the self-developed high-precision angle calibration device. Experimental results show that the new prototype not only covers a ±20° measurement range but also promotes measurement accuracy, and the standard deviation of the single-axis measurement drops to $3^{prime }$ within the range of 5°–15°. It provides a relatively high-precision measurement method for real-time, multi-axis active detection of spherical joint space rotation angle error.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • A Novel Adherence Sensor System for Valved Holding Chambers Suitable for
           Children and Adults With Asthma
    • Authors: Aobo Zhao;Christopher O’Callaghan;Xiao Liu;
      Pages: 12276 - 12283
      Abstract: Poor adherence to inhaled drug therapy is considered to be a major factor in asthma related morbidity and it has been shown, in a significant number of patients, that estimates of adherence by patients or parents of children is not accurate. Inaccurate reporting may lead to higher dose of drug or more potent drugs being prescribed. A number of adherence sensors are available for some commercial inhaler devices. However, existing adherence monitoring mainly focuses on how the hardware of an inhaler has been actuated and does not provide evidence of drug aerosol inhalation. Valved holding chambers are now first choice for aerosol drug delivery for young children, who inhale drug by breathing in and out of the device several times and are increasingly used by older children and adults who inhale drug aerosol by taking a single deep breath. To the best knowledge of the authors, there is no adherence sensor in the market that provides evidence of actuation of the drug into a valved holding chamber and evidence that it is inhaled by different age groups. We therefore developed a novel adherence sensor system embedded into a valved holding chambers to achieve this. Our customised algorithm ensures that correct use is recorded, and incorrect use flagged. It also distinguishes a deep breath from tidal breaths and in combination measurement of actuation into the device triggers different classifiers for registering usage as good technique or not. A good technique or poor technique is immediately fed back to users using a visual light signal, while the data recorded is Bluetoothed to a paired mobile device for historical recording and statistical analysis. The entire system, including battery, micro-controller, sensors and Bluetooth, is integrated on a $66 times 22$ mm2 printed circuit board and weighs only 8 grams. The system has been evaluated on a custom-made artificial lung which mimics t-e breathing patterns confirming recognition of tidal and deep breaths. The low-power device operates from a 3.3 V, 560 mAh battery and lasts 14 months if used daily.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Development of a Tactile Sensing Robot-Assisted System for Vascular
           Interventional Surgery
    • Authors: Xiaoliang Jin;Shuxiang Guo;Jian Guo;Peng Shi;Takashi Tamiya;Hideyuki Hirata;
      Pages: 12284 - 12294
      Abstract: The challenge of vascular interventional surgery is that surgeons require to be exposed to X-ray for a long time, operating guidewires and catheters to complete the treatment. To reduce the burden of the surgeons, it is of great significance to develop a tactile sensing robot-assisted system for vascular interventional surgery. Therefore, a slave manipulator with the function of collaborative operating guidewires and catheters was developed to replace doctors to perform the surgery in the operating room. In addition, a master manipulator based on magnetorheological fluids was located on the master side, and the haptic force feedback of the system was realized by generating the tactile force acting on the doctor’s hand. To verify the proposed system, a series of experiments were carried out, the results of experiments in “Vitro” indicated that the proposed system has good performance in collaborative operating and can accurately deliver a guidewire and a catheter to the target position. The maximum tracking error of the axial motion was less than 2 mm, and the maximum tracking error of the radial motion was less than 2 degrees, which is acceptable. And under the guidance of the force feedback, the safety of the system was obviously higher than that of without force feedback, after the experiment was completed by 5 participants, the safety increased by 4.32% on average. So, we can get the results that our system is feasible and effective.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • An Intelligent Multi-Sourced Sensing System to Study Driver’s
           Visual Behaviors
    • Authors: Josué S. Armenta;Ángel G. Andrade;Marcela D. Rodríguez;
      Pages: 12295 - 12305
      Abstract: Understanding how changes in visual and attentional behaviors impact driving as we age is still a subject studied by the research community. However, little attention has been paid to using sensing and AI techniques to conduct such studies. We present a multi-sourced intelligent sensing system that infers the visual point of attention (VPoA) associated with five vehicle’s cockpit zones with an accuracy of 98%. The VPoA is inferred from the pitch, yaw, and roll angles of head movements captured with inertial sensors and a facial recognition application. The system also includes a tablet-based application that automatically collects data from the driving context, e.g., speed and location. It also enables an annotator to add observed drivers’ actions, e.g., interactions with a passenger. We conducted a naturalistic study with 15 younger adults and 15 older adults to demonstrate the system’s efficacy to identify visual behavior patterns similar to those identified in previous studies that have used traditional data collection methods. A new finding is that the younger group looks more frequently at the lap than the elderly group, independently if a passenger was present. The Lap was the VPoA associated with using the cellphone.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Development of an Embedded UHF-RFID Corrosion Sensor for Monitoring
           Corrosion of Steel in Concrete
    • Authors: K. Bouzaffour;B. Lescop;P. Talbot;F. Gallée;S. Rioual;
      Pages: 12306 - 12312
      Abstract: Corrosion of reinforcing steel is the leading cause of deterioration in concrete and impacts strongly the safety and durability of civil infrastructures. It occurs in marine environment with the presence of chloride which breaks the thin oxide film passivation layer leading to dissolution. This research proposes an innovative embedded sensor in concrete for the detection of corrosion of steel. The autonomous UHF RFID (Ultra High Frequency Radio Frequency Identification) sensor is based on the coupling between the antenna of a dipolar RFID tag and a layer of steel exposed to chloride in concrete. Variation of the RSSI (Received Signal Strength Indication) measured by the reader localized in air has two origins, namely the degree of moisture of concrete and the presence of the steel layer. By minimizing the impact of seawater ingress on the antenna property, the presence of metallic films of few micrometers thickness can be detected. This authorizes the development of the proposed method for monitoring mass loss of steel in concrete.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Compressing the Index on Distributed Data of Sensors
    • Authors: Vandana Bhasin;P. C. Saxena;Sushil Kumar;
      Pages: 12313 - 12321
      Abstract: The most daunting and conflicting challenges accompanying the wireless sensor networks are energy and security. And data aggregation and compression techniques are two of the effective ways to reduce energy consumption. As it is known that the radio transceiver consumes energy which is proportional to the number of bits transmitted on the network; hence sending fewer bits on the communication channel implies lesser energy consumption. This paper works on compressing the index of secure index on distributed data (SIDD) technique; to reduce the number of bits of an index that is transmitted on the communication channel. The objective being to reduce energy consumption of SIDD. In this paper, we have worked to reduce the number of bits of the index sent on the communication channel, deploying difference encoding. The compression mechanism has established an upper bound on the energy consumption whilst all data items were unique. The scheme is scalable and can be deployed for saving energy consumption.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Sensing Physiological and Environmental Quantities to Measure Human
           Thermal Comfort Through Machine Learning Techniques
    • Authors: Nicole Morresi;Sara Casaccia;Matteo Sorcinelli;Marco Arnesano;Amaia Uriarte;J. Ignacio Torrens-Galdiz;Gian Marco Revel;
      Pages: 12322 - 12337
      Abstract: This paper presents the results from the experimental application of smartwatch sensors to predict occupants’ thermal comfort under varying environmental conditions. The goal is to investigate the measurement accuracy of smartwatches when used as thermal comfort sensors to be integrated into Heating, Ventilation and Air Conditioning (HVAC) control loops. Ten participants were exposed to various environmental conditions as well as warm - induced and cold-induced discomfort tests and 13 participants were exposed to a transient-condition while a network of sensors and a smartwatch collected both environmental parameters and heart rate variability (HRV). HRV features were used as input to Machine Learning (ML) classification algorithms to establish whether a user was in discomfort, providing an average accuracy of 92.2 %. ML and Deep Learning regression algorithms were trained to predict the thermal sensation vote (TSV) in a transient environment and the results show that the aggregation of environmental and physiological quantities provide a better TSV prediction in terms of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), 1.2 and 20% respectively, than just the HRV features used for the prediction. In conclusion, this experiment supports the assumption that physiological quantities related to thermal comfort can improve TSV prediction when combined with environmental quantities.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Corrections to “Smartphone Assisted Colourimetric Detection and
           Quantification of Pb²⁺ and Hg²⁺ Ions Using Ag Nanoparticles From
           Aqueous Medium”
    • Authors: Neethu Emmanuel;Reethu Haridas;Sanoop Chelakkara;Raji B. Nair;Arun Gopi;Manikantan Sajitha;Yoosaf Karuvath;
      Pages: 12338 - 12338
      Abstract: In the above article [1], the authors declare that the affiliation “Neethu Emmanuel, Reethu Haridas, Arun Gopi, Manikantan Sajitha, and Yoosaf Karuvath are with the Academy of Scientific and Innovative Research (AcSIR), New Delhi 110001, India” is to be correctly read as “Neethu Emmanuel, Reethu Haridas, Arun Gopi, Manikantan Sajitha, and Yoosaf Karuvath are with the Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
      PubDate: May15, 15 2021
      Issue No: Vol. 21, No. 10 (2021)
  • IEEE Sensor Journal cover/frontispiece competition
    • Pages: 12339 - 12339
      Abstract: Presents information on the IEEE Sensor Journal cover/frontispiece competition.
      PubDate: May15, 2021
      Issue No: Vol. 21, No. 10 (2021)
  • Introducing IEEE Collabratec
    • Pages: 12340 - 12340
      Abstract: Advertisement.
      PubDate: May15, 2021
      Issue No: Vol. 21, No. 10 (2021)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762

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

JournalTOCs © 2009-