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Sensors, Volume 22, Issue 14 (July-2 2022) – 400 articles

Cover Story (view full-size image): A robust perception system is crucial for natural human–robot interactions. Our proposal can provide a rich real-time representation of a robot's environment by using multiple sensory sources. This information allows a robot to react to external stimuli and user responses. This work presents the development of a perception architecture based on the bioinspired concept of endogenous attention integrated into a real social robot. The architecture defines mechanisms to establish the most salient stimulus, considering the current detections and tasks. Additionally, the architecture uses information from the robot's decision-making system, which provides the user's responses and the robot's latest decisions. This work also presents a series of case studies to validate the proposed architecture in a real robot. View this paper
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17 pages, 13619 KiB  
Article
Multiple Groups of Agents for Increased Movement Interference and Synchronization
by Alexis Meneses, Hamed Mahzoon, Yuichiro Yoshikawa and Hiroshi Ishiguro
Sensors 2022, 22(14), 5465; https://doi.org/10.3390/s22145465 - 21 Jul 2022
Viewed by 1891
Abstract
We examined the influence of groups of agents and the type of avatar on movement interference. In addition, we studied the synchronization of the subject with the agent. For that, we conducted experiments utilizing human subjects to examine the influence of one, two, [...] Read more.
We examined the influence of groups of agents and the type of avatar on movement interference. In addition, we studied the synchronization of the subject with the agent. For that, we conducted experiments utilizing human subjects to examine the influence of one, two, or three agents, as well as human or robot avatars, and finally, the agent moving biologically or linearly. We found the main effect on movement interference was the number of agents; namely, three agents had significantly more influence on movement interference than one agent. These results suggest that the number of agents is more influential on movement interference than other avatar characteristics. For the synchronization, the main effect of the type of the agent was revealed, showing that the human agent kept more synchronization compared to the robotic agent. In this experiment, we introduced an additional paradigm on the interference which we called synchronization, discovering that a group of agents is able to influence this behavioral level as well. Full article
(This article belongs to the Topic Human Movement Analysis)
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19 pages, 6265 KiB  
Article
EIEN: Endoscopic Image Enhancement Network Based on Retinex Theory
by Ziheng An, Chao Xu, Kai Qian, Jubao Han, Wei Tan, Dou Wang and Qianqian Fang
Sensors 2022, 22(14), 5464; https://doi.org/10.3390/s22145464 - 21 Jul 2022
Cited by 5 | Viewed by 2220
Abstract
In recent years, deep convolutional neural network (CNN)-based image enhancement has shown outstanding performance. However, due to the problems of uneven illumination and low contrast existing in endoscopic images, the implementation of medical endoscopic image enhancement using CNN is still an exploratory and [...] Read more.
In recent years, deep convolutional neural network (CNN)-based image enhancement has shown outstanding performance. However, due to the problems of uneven illumination and low contrast existing in endoscopic images, the implementation of medical endoscopic image enhancement using CNN is still an exploratory and challenging task. An endoscopic image enhancement network (EIEN) based on the Retinex theory is proposed in this paper to solve these problems. The structure consists of three parts: decomposition network, illumination correction network, and reflection component enhancement algorithm. First, the decomposition network model of pre-trained Retinex-Net is retrained on the endoscopic image dataset, and then the images are decomposed into illumination and reflection components by this decomposition network. Second, the illumination components are corrected by the proposed self-attention guided multi-scale pyramid structure. The pyramid structure is used to capture the multi-scale information of the image. The self-attention mechanism is based on the imaging nature of the endoscopic image, and the inverse image of the illumination component is fused with the features of the green and blue channels of the image to be enhanced to generate a weight map that reassigns weights to the spatial dimension of the feature map, to avoid the loss of details in the process of multi-scale feature fusion and image reconstruction by the network. The reflection component enhancement is achieved by sub-channel stretching and weighted fusion, which is used to enhance the vascular information and image contrast. Finally, the enhanced illumination and reflection components are multiplied to obtain the reconstructed image. We compare the results of the proposed method with six other methods on a test set. The experimental results show that EIEN enhances the brightness and contrast of endoscopic images and highlights vascular and tissue information. At the same time, the method in this paper obtained the best results in terms of visual perception and objective evaluation. Full article
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15 pages, 1964 KiB  
Article
Smart Immunosensors for Point-of-Care Serological Tests Aimed at Assessing Natural or Vaccine-Induced SARS-CoV-2 Immunity
by Simone Fortunati, Marco Giannetto, Chiara Giliberti, Angelo Bolchi, Davide Ferrari, Massimo Locatelli, Valentina Bianchi, Andrea Boni, Ilaria De Munari and Maria Careri
Sensors 2022, 22(14), 5463; https://doi.org/10.3390/s22145463 - 21 Jul 2022
Cited by 5 | Viewed by 3330
Abstract
Innovative and highly performing smart voltammetric immunosensors for rapid and effective serological tests aimed at the determination of SARS-CoV-2 antibodies were developed and validated in human serum matrix. Two immunosensors were developed for the determination of immunoglobulins directed against either the nucleocapsid or [...] Read more.
Innovative and highly performing smart voltammetric immunosensors for rapid and effective serological tests aimed at the determination of SARS-CoV-2 antibodies were developed and validated in human serum matrix. Two immunosensors were developed for the determination of immunoglobulins directed against either the nucleocapsid or the spike viral antigen proteins. The immunosensors were realized using disposable screen-printed electrodes modified with nanostructured materials for the immobilization of the antigens. Fast quantitative detection was achieved, with analysis duration being around 1 h. Signal readout was carried out through a smart, compact and battery-powered potentiostat, based on a Wi-Fi protocol and devised for the Internet of Things (IoT) paradigm. This device is used for the acquisition, storage and sharing of clinical data. Outstanding immunosensors’ sensitivity, specificity and accuracy (100%) were assessed, according to the diagnostic guidelines for epidemiological data. The overall performance of the sensing devices, combined with the portability of the IoT-based device, enables their suitability as a high-throughput diagnostic tool. Both of the immunosensors were validated using clinical human serum specimens from SARS-CoV-2 infected patients, provided by IRCCS Ospedale San Raffaele. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Italy 2023)
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14 pages, 7650 KiB  
Article
Gaze Estimation Approach Using Deep Differential Residual Network
by Longzhao Huang, Yujie Li, Xu Wang, Haoyu Wang, Ahmed Bouridane and Ahmad Chaddad
Sensors 2022, 22(14), 5462; https://doi.org/10.3390/s22145462 - 21 Jul 2022
Cited by 7 | Viewed by 3345
Abstract
Gaze estimation, which is a method to determine where a person is looking at given the person’s full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the [...] Read more.
Gaze estimation, which is a method to determine where a person is looking at given the person’s full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with angular-error of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images. Full article
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8 pages, 3995 KiB  
Communication
A High-Power 3P3T Cross Antenna Switch with Low Harmonic Distortion and Enhanced Isolation Using T-Type Pull-Down Path for Cellular Mobile Devices
by Arash Hejazi, Reza E. Rad, S. A. Hosseini Asl, Kyung-Duk Choi, Joon-Mo Yoo, Hyungki Huh, Seokkee Kim, Yeonjae Jung and Kang-Yoon Lee
Sensors 2022, 22(14), 5461; https://doi.org/10.3390/s22145461 - 21 Jul 2022
Viewed by 2003
Abstract
This paper presents a radio frequency (RF) triple pole triple throw 3P3T cross antenna switch for cellular mobile devices. The negative biasing scheme was applied to improve the power-handling capability and linearity of the switch by increasing the maximum tolerable voltage drop across [...] Read more.
This paper presents a radio frequency (RF) triple pole triple throw 3P3T cross antenna switch for cellular mobile devices. The negative biasing scheme was applied to improve the power-handling capability and linearity of the switch by increasing the maximum tolerable voltage drop across the drain and source and reverse biasing the parasitic junction diodes. To avoid signal reflection through the antenna in off-state, all the antenna ports were equipped with 50-ohm termination to provide the pull-down path. Considering the simultaneous operation of antenna ports in different switch cases, the presented T-type pull-down path demonstrated improvement of isolation by over 15 dB. Using stacked switches, the 3P3T handled the input power level of over 35 dBm. The chip was manufactured in 65 nm complementary metal oxide semiconductor (CMOS) silicon on insulator (SOI) technology with a die size of 790 × 730 µm. The proposed structure achieved insertion loss, isolation, and voltage standing wave ratio (VSWR) of less than −0.9 dB, −40 dB, and 1.6, respectively, when the input signal was 3.8 GHz. The measured results prove the implemented switch shows the second and third harmonic distortion performances of less than −60 dBm when the input power level and frequency are 25 dBm and 3.8 GHz, respectively. Full article
(This article belongs to the Section Communications)
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14 pages, 3576 KiB  
Article
BREEZE—Boundary Red Emission Zone Estimation Using Unmanned Aerial Vehicles
by Oren Elmakis, Tom Shaked, Barak Fishbain and Amir Degani
Sensors 2022, 22(14), 5460; https://doi.org/10.3390/s22145460 - 21 Jul 2022
Cited by 3 | Viewed by 1874
Abstract
Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These [...] Read more.
Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These teams risk their lives by manually mapping the gas dispersion. This process is currently performed using hand-held gas detectors and requires dense and exhaustive monitoring to achieve reliable maps. However, the conventional mapping process is impaired due to limited human mobility and monitoring capacities. In this context, this paper presents a method for gas sensing using unmanned aerial vehicles. The research focuses on developing a custom path planner—Boundary Red Emission Zone Estimation (BREEZE). BREEZE is an estimation approach that allows efficient red zone delineation by following its boundary. The presented approach improves the gas dispersion mapping process by performing adaptive path planning, monitoring gas dispersion in real time, and analyzing the measurements online. This approach was examined by simulating a cluttered urban site in different environmental conditions. The simulation results show the ability to autonomously perform red zone estimation faster than methods that rely on predetermined paths and with a precision higher than ninety percent. Full article
(This article belongs to the Special Issue The Sensor Location-Allocation Problem for Environmental Sensing)
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15 pages, 5990 KiB  
Article
Two-Dimensional Position Tracking Using Gradient Magnetic Fields
by Xuan Thang Trinh, Jen-Tzong Jeng, Huu-Thang Nguyen, Van Su Luong and Chih-Cheng Lu
Sensors 2022, 22(14), 5459; https://doi.org/10.3390/s22145459 - 21 Jul 2022
Cited by 2 | Viewed by 2095
Abstract
In this work, a two-dimensional (2D) position-detection device using a single axis magnetic sensor combined with orthogonal gradient coils was designed and fabricated. The sensors used were an induction coil and a GMR spin-valve sensor GF807 from Sensitec Inc. The field profiles generated [...] Read more.
In this work, a two-dimensional (2D) position-detection device using a single axis magnetic sensor combined with orthogonal gradient coils was designed and fabricated. The sensors used were an induction coil and a GMR spin-valve sensor GF807 from Sensitec Inc. The field profiles generated by the two orthogonal gradient coils were analyzed numerically to achieve the maximum linear range, which corresponded to the detection area of the tracking system. The two coils were driven by 1-kHz sine wave currents with a 90° phase difference to generate the fields with uniform gradients along the x- and y-axis in the plane of the tracking stage. The gradient fields were detected by a single-axis sensor incorporated with a digital dual-phase lock-in detector to retrieve the position information. A linearity correction algorithm was used to improve the location accuracy and to extend the linear range for position sensing. The mean positioning error was found to be 0.417 mm, corresponding to the relative error of 0.21% in the working range of 200 mm × 200 mm, indicating that the proposed tracking system is promising for applications requiring accurate control of the two-dimensional position. Full article
(This article belongs to the Special Issue Advanced Sensors for Intelligent Control Systems)
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20 pages, 1003 KiB  
Article
Dynamic Segmentation of Sensor Events for Real-Time Human Activity Recognition in a Smart Home Context
by Houda Najeh, Christophe Lohr and Benoit Leduc
Sensors 2022, 22(14), 5458; https://doi.org/10.3390/s22145458 - 21 Jul 2022
Cited by 8 | Viewed by 2320
Abstract
Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly [...] Read more.
Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly focused on recognition through pre-segmented sensor data. In this paper, real-time human activity recognition based on streaming sensors is investigated. The proposed methodology incorporates dynamic event windowing based on spatio-temporal correlation and the knowledge of activity trigger sensor to recognize activities and record new events. The objective is to determine whether the last event that just happened belongs to the current activity, or if it is the sign of the start of a new activity. For this, we consider the correlation between sensors in view of what can be seen in the history of past events. The proposed algorithm contains three steps: verification of sensor correlation (SC), verification of temporal correlation (TC), and determination of the activity triggering the sensor. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database. F1 score is used to assess the quality of the segmentation. The results show that the proposed approach segments several activities (sleeping, bed to toilet, meal preparation, eating, housekeeping, working, entering home, and leaving home) with an F1 score of 0.63–0.99. Full article
(This article belongs to the Special Issue Metrology for Living Environment)
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13 pages, 3945 KiB  
Article
PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
by Zhangzhen Zhao, Tao Song, Bin Xing, Yu Lei and Ziqin Wang
Sensors 2022, 22(14), 5457; https://doi.org/10.3390/s22145457 - 21 Jul 2022
Cited by 8 | Viewed by 2402
Abstract
In indoor low-texture environments, the point feature-based visual SLAM system has poor robustness and low trajectory accuracy. Therefore, we propose a visual inertial SLAM algorithm based on point-line feature fusion. Firstly, in order to improve the quality of the extracted line segment, a [...] Read more.
In indoor low-texture environments, the point feature-based visual SLAM system has poor robustness and low trajectory accuracy. Therefore, we propose a visual inertial SLAM algorithm based on point-line feature fusion. Firstly, in order to improve the quality of the extracted line segment, a line segment extraction algorithm with adaptive threshold value is proposed. By constructing the adjacent matrix of the line segment and judging the direction of the line segment, it can decide whether to merge or eliminate other line segments. At the same time, geometric constraint line feature matching is considered to improve the efficiency of processing line features. Compared with the traditional algorithm, the processing efficiency of our proposed method is greatly improved. Then, point, line, and inertial data are effectively fused in a sliding window to achieve high-accuracy pose estimation. Finally, experiments on the EuRoC dataset show that the proposed PLI-VINS performs better than the traditional visual inertial SLAM system using point features and point line features. Full article
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17 pages, 4095 KiB  
Article
Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm
by Seyoung Kang, Seonkyo Kim, Cheolsun Park and Wonzoo Chung
Sensors 2022, 22(14), 5456; https://doi.org/10.3390/s22145456 - 21 Jul 2022
Cited by 1 | Viewed by 1630
Abstract
In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting [...] Read more.
In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting limits only on the beam width (BW) and side-lobe level (SLL) in order to satisfy the desired BW and SLL in the wide band. We added the scan angle condition to the cost function to design the scanned beam pattern, as the ability to scan a beam in the desired direction is important in various applications. In order to prevent possible pointing angle errors for asymmetric NUSLA, we employed a penalty function to ensure the peak at the desired direction. Modified reinforcement learning algorithm (MORELA), which is a reinforcement learning-based algorithm used to determine a global optimum of the cost function, is applied to optimize the spacing and weights of the NUSLA by minimizing the proposed cost function. The performance of the proposed scheme was verified by comparing it with that of existing heuristic optimization algorithms via computer simulations. Full article
(This article belongs to the Section Remote Sensors)
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8 pages, 2020 KiB  
Communication
Raman Scattering Study of Amino Acids Adsorbed on a Silver Nanoisland Film
by Alexey Skvortsov, Ekaterina Babich, Andrey Lipovskii, Alexey Redkov, Guang Yang and Valentina Zhurikhina
Sensors 2022, 22(14), 5455; https://doi.org/10.3390/s22145455 - 21 Jul 2022
Cited by 4 | Viewed by 1942
Abstract
We studied the surface-enhanced Raman spectra of amino acids D-alanine and DL-serine and their mixture on silver nanoisland films (SNF) immersed in phosphate-buffered saline (PBS) solution at millimolar amino acid concentrations. It is shown that the spectra from the amino acid [...] Read more.
We studied the surface-enhanced Raman spectra of amino acids D-alanine and DL-serine and their mixture on silver nanoisland films (SNF) immersed in phosphate-buffered saline (PBS) solution at millimolar amino acid concentrations. It is shown that the spectra from the amino acid solutions differ from the reference spectra for microcrystallites due to the electrostatic orientation of amino acid zwitterions by the metal nanoisland film. Moreover, non-additive peaks are observed in the spectrum of the mixture of amino acids adsorbed on SNF, which means that intermolecular interactions between adsorbed amino acids are very significant. The results indicate the need for a thorough analysis of the Raman spectra from amino acid solutions, particularly, in PBS, in the presence of a nanostructured silver surface, and may also be of interest for studying molecular properties and intermolecular interactions. Full article
(This article belongs to the Special Issue Optical Imaging, Optical Sensing and Devices)
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44 pages, 2531 KiB  
Review
Wearable Travel Aids for Blind and Partially Sighted People: A Review with a Focus on Design Issues
by Marion Hersh
Sensors 2022, 22(14), 5454; https://doi.org/10.3390/s22145454 - 21 Jul 2022
Cited by 4 | Viewed by 11982
Abstract
The ability to travel (independently) is very important for participation in education, work, leisure activities, and all other aspects of modern life. Blind and partially sighted people experience a number of barriers to travel, including inaccessible information and environments, and consequently require support [...] Read more.
The ability to travel (independently) is very important for participation in education, work, leisure activities, and all other aspects of modern life. Blind and partially sighted people experience a number of barriers to travel, including inaccessible information and environments, and consequently require support from technology or other people to overcome them. Despite the potential of advanced technologies and the development of electronic travel aids, the long cane and guide dog remains the most commonly used solutions. Wearable technologies are becoming increasingly popular. They have the particular advantage of keeping the hands free, thereby facilitating the use of a long cane, guide dog or another device at the same time. They also have the potential to change the ways in which users interact with the environment. The main contributions of this paper are surveying the current state-of-the-art of travel aids from a design perspective and investigating the following issues: (1) The important design issues in wearable travel aids and the extent to which they are taken into account in different devices; (2) The relationship, if any, between where and how travel aids are worn and their design, features and functions; (3) Limitations of existing devices, gaps in provision and future research directions, particularly with regard to meeting potential users’ needs. Full article
(This article belongs to the Special Issue Wearable Assistive Devices for Disabled and Older People)
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16 pages, 1652 KiB  
Article
Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose
by Malgorzata Labanska, Sarah van Amsterdam, Sascha Jenkins, John P. Clarkson and James A. Covington
Sensors 2022, 22(14), 5453; https://doi.org/10.3390/s22145453 - 21 Jul 2022
Cited by 15 | Viewed by 2695
Abstract
The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, [...] Read more.
The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops. Full article
(This article belongs to the Special Issue Multi-Sensor Systems for Food and Agricultural Applications)
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14 pages, 8298 KiB  
Article
Single Fusion Image from Collections of Fruit Views for Defect Detection and Classification
by Antonio Albiol, Carlos Sánchez de Merás, Alberto Albiol and Sara Hinojosa
Sensors 2022, 22(14), 5452; https://doi.org/10.3390/s22145452 - 21 Jul 2022
Viewed by 2219
Abstract
Quality assessment is one of the most common processes in the agri-food industry. Typically, this task involves the analysis of multiple views of the fruit. Generally speaking, analyzing these single views is a highly time-consuming operation. Moreover, there is usually significant overlap between [...] Read more.
Quality assessment is one of the most common processes in the agri-food industry. Typically, this task involves the analysis of multiple views of the fruit. Generally speaking, analyzing these single views is a highly time-consuming operation. Moreover, there is usually significant overlap between consecutive views, so it might be necessary to provide a mechanism to cope with the redundancy and prevent the multiple counting of defect points. This paper presents a method to create surface maps of fruit from collections of views obtained when the piece is rotating. This single image map combines the information contained in the views, thus reducing the number of analysis operations and avoiding possible miscounts in the number of defects. After assigning each piece with a simple geometrical model, 3D rotation between consecutive views is estimated only from the captured images, without any further need for sensors or information about the conveyor. The fact that rotation is estimated directly from the views makes this novel methodology readily usable in high-throughput industrial inspection machines without any special hardware modification. As proof of this technique’s usefulness, an application is shown where maps have been used as input to a CNN to classify oranges into different categories. Full article
(This article belongs to the Section Sensing and Imaging)
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12 pages, 1960 KiB  
Article
Virtual Spectral Selectivity in a Modulated Thermal Infrared Emitter with Lock-In Detection
by David Santalices, Juan Meléndez and Susana Briz
Sensors 2022, 22(14), 5451; https://doi.org/10.3390/s22145451 - 21 Jul 2022
Cited by 1 | Viewed by 1836
Abstract
The need for affordable low-power devices has led MEMS-based thermal emitters to become an interesting option for optical gas sensors. Since these emitters have a low thermal mass, they can be easily modulated and combined with a lock-in amplifier for detection. In this [...] Read more.
The need for affordable low-power devices has led MEMS-based thermal emitters to become an interesting option for optical gas sensors. Since these emitters have a low thermal mass, they can be easily modulated and combined with a lock-in amplifier for detection. In this paper, we show that the signal measured by a lock-in amplifier from a thermal emitter that varies its temperature periodically can have different spectral profiles, depending on the reference signal used. These virtual emitters appear because the Fourier series expansion of the emitted radiance, as a function of time, has different coefficients for each wavelength, and this spectral signature, which is different for each harmonic, can be retrieved using a reference signal that corresponds to its frequency. In this study, the effect is first proved theoretically and then is measured experimentally. For this purpose, we performed measurements with an IR camera provided with six different spectral filters of a modulated emitter, in combination with lock-in amplification via software. Finally, we show a potential application of this effect using multiple virtual emitters to gain spectral selectivity and distinguish between two gases, CO2 and CH4. Full article
(This article belongs to the Section Optical Sensors)
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14 pages, 37957 KiB  
Article
Digital Twin-Based Integrated Monitoring System: Korean Application Cases
by Sangsu Choi, Jungyub Woo, Jun Kim and Ju Yeon Lee
Sensors 2022, 22(14), 5450; https://doi.org/10.3390/s22145450 - 21 Jul 2022
Cited by 26 | Viewed by 5727
Abstract
A digital twin is a virtual model of a process, product, or service, which is one of the key technologies in the fourth industry. The pairing of the virtual and physical world allows analysis of data and monitoring of systems to head off [...] Read more.
A digital twin is a virtual model of a process, product, or service, which is one of the key technologies in the fourth industry. The pairing of the virtual and physical world allows analysis of data and monitoring of systems to head off problems before they occur. This paper presents a digital twin architecture and a system based on an interoperable data model. It explains how to build a digital twin for the integrated control monitoring using edge devices, data analytics, and realistic 3D visualization. The system allows continuous collaboration between field engineers for data gathering, designers for modeling 3D models, and layout engineers for layout changing by generating 3D digital twin models automatically. The system helps stakeholders focus on their respective roles to build digital twins. Examples applied to the Korean automotive parts makers are also introduced in this paper. The system can be easily used by small and medium-sized enterprises (SMEs) as well as large companies. Beyond simply watching the production site with CCTV, the production site can be intuitively managed based on the digital twin. Full article
(This article belongs to the Section Sensors and Robotics)
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18 pages, 7284 KiB  
Article
Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM
by Wei Liu, Xu Liu, Yuan Hu, Jie Shi, Xinqiang Chen, Jiansen Zhao, Shengzheng Wang and Qingsong Hu
Sensors 2022, 22(14), 5449; https://doi.org/10.3390/s22145449 - 21 Jul 2022
Cited by 15 | Viewed by 2653
Abstract
Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of [...] Read more.
Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 4102 KiB  
Article
What Can We Learn from Depth Camera Sensor Noise?
by Azmi Haider and Hagit Hel-Or
Sensors 2022, 22(14), 5448; https://doi.org/10.3390/s22145448 - 21 Jul 2022
Cited by 13 | Viewed by 11325
Abstract
Although camera and sensor noise are often disregarded, assumed negligible or dealt with in the context of denoising, in this paper we show that significant information can actually be deduced from camera noise about the captured scene and the objects within it. Specifically, [...] Read more.
Although camera and sensor noise are often disregarded, assumed negligible or dealt with in the context of denoising, in this paper we show that significant information can actually be deduced from camera noise about the captured scene and the objects within it. Specifically, we deal with depth cameras and their noise patterns. We show that from sensor noise alone, the object’s depth and location in the scene can be deduced. Sensor noise can indicate the source camera type, and within a camera type the specific device used to acquire the images. Furthermore, we show that noise distribution on surfaces provides information about the light direction within the scene as well as allows to distinguish between real and masked faces. Finally, we show that the size of depth shadows (missing depth data) is a function of the object’s distance from the background, its distance from the camera and the object’s size. Hence, can be used to authenticate objects location in the scene. This paper provides tools and insights into what can be learned from depth camera sensor noise. Full article
(This article belongs to the Section Sensing and Imaging)
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29 pages, 11240 KiB  
Article
Morphological Fabrication of Equilibrium and Auditory Sensors through Electrolytic Polymerization on Hybrid Fluid Rubber (HF Rubber) for Smart Materials of Robotics
by Kunio Shimada
Sensors 2022, 22(14), 5447; https://doi.org/10.3390/s22145447 - 21 Jul 2022
Cited by 3 | Viewed by 1981
Abstract
The development of auditory sensors and systems is essential in smart materials of robotics and is placed at the strategic category of mutual communication between humans and robots. We designed prototypes of the rubber-made equilibrium and auditory sensors, mimicking hair cells in the [...] Read more.
The development of auditory sensors and systems is essential in smart materials of robotics and is placed at the strategic category of mutual communication between humans and robots. We designed prototypes of the rubber-made equilibrium and auditory sensors, mimicking hair cells in the saccule and the cochlea at the vestibule of the human ear by utilizing our previously proposed technique of electrolytic polymerization on the hybrid fluid rubber (HF rubber). The fabricated artificial hair cells embedded with mimicked free nerve endings and Pacinian corpuscles, which are well-known receptors in the human skin and have already been elucidated effective in the previous study, have the intelligence of equilibrium and auditory sensing. Moreover, they have a voltage that is generated from built-in electricity caused by the ionized particles and molecules in the HF rubber due to piezoelectricity. We verified the equilibrium and auditory characteristics by measuring the changes in voltage with inclination, vibration over a wide frequency range, and sound waves. We elucidated experimentally that the intelligence has optimum morphological conditions. This work has the possibility of advancing the novel technology of state-of-the-art social robotics. Full article
(This article belongs to the Section Sensors and Robotics)
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23 pages, 1408 KiB  
Article
An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network
by Paulo Vitor de Campos Souza, Edwin Lughofer and Huoston Rodrigues Batista
Sensors 2022, 22(14), 5446; https://doi.org/10.3390/s22145446 - 21 Jul 2022
Cited by 8 | Viewed by 2061
Abstract
Evolving fuzzy neural networks have the adaptive capacity to solve complex problems by interpreting them. This is due to the fact that this type of approach provides valuable insights that facilitate understanding the behavior of the problem being analyzed, because they can extract [...] Read more.
Evolving fuzzy neural networks have the adaptive capacity to solve complex problems by interpreting them. This is due to the fact that this type of approach provides valuable insights that facilitate understanding the behavior of the problem being analyzed, because they can extract knowledge from a set of investigated data. Thus, this work proposes applying an evolving fuzzy neural network capable of solving data stream regression problems with considerable interpretability. The dataset is based on a necessary prediction of k barriers with wireless sensors to identify unauthorized persons entering a protected territory. Our method was empirically compared with state-of-the-art evolving methods, showing significantly lower RMSE values for separate test data sets and also lower accumulated mean absolute errors (MAEs) when evaluating the methods in a stream-based interleaved-predict-and-then-update procedure. In addition, the model could offer relevant information in terms of interpretable fuzzy rules, allowing an explainable evaluation of the regression problems contained in the data streams. Full article
(This article belongs to the Special Issue Reliability Analysis of Wireless Sensor Network)
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11 pages, 2650 KiB  
Article
Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis
by Seon-Chil Kim and Young-Sik Cho
Sensors 2022, 22(14), 5445; https://doi.org/10.3390/s22145445 - 21 Jul 2022
Cited by 8 | Viewed by 6347
Abstract
Urinalysis, an elementary chemical reaction-based method for analyzing color conversion factors, facilitates examination of pathological conditions in the human body. Recently, considerable urinalysis-centered research has been conducted on the analysis of urine dipstick colors using smartphone cameras; however, such methods have a drawback: [...] Read more.
Urinalysis, an elementary chemical reaction-based method for analyzing color conversion factors, facilitates examination of pathological conditions in the human body. Recently, considerable urinalysis-centered research has been conducted on the analysis of urine dipstick colors using smartphone cameras; however, such methods have a drawback: the problem of reproducibility of accuracy through quantitative analysis. In this study, to solve this problem, the function values for each concentration of a range of analysis factors were implemented in an algorithm through urine dipstick RGB semi-quantitative color analysis to enable real-time results. Herein, pH, glucose, ketones, hemoglobin, bilirubin, protein (albumin), and nitrites were selected as analysis factors, and the accuracy levels of the existing equipment and the test application were compared and evaluated using artificial urine. In the semi-quantitative analysis, the red (R), green (G), and blue (B) characteristic values were analyzed by extracting the RGB characteristic values of the analysis factors for each concentration of artificial urine and obtaining linear function values. In addition, to improve the reproducibility of detection accuracy, the measurement value of the existing test equipment was set to an absolute value; using a machine-learning technique, the confusion matrix, we attempted to stabilize test results that vary with environment. Full article
(This article belongs to the Special Issue Advanced Applications in Smartphone-Based Analysis)
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14 pages, 2637 KiB  
Article
IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing
by Muhammad Umar Nasir, Safiullah Khan, Shahid Mehmood, Muhammad Adnan Khan, Atta-ur Rahman and Seong Oun Hwang
Sensors 2022, 22(14), 5444; https://doi.org/10.3390/s22145444 - 21 Jul 2022
Cited by 31 | Viewed by 3325
Abstract
Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, [...] Read more.
Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma’s manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency. Full article
(This article belongs to the Special Issue Machine Learning for Medical Data Analysis)
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32 pages, 5816 KiB  
Review
Advanced Waveguide Based LOC Biosensors: A Minireview
by Muzafar A. Kanjwal and Amal Al Ghaferi
Sensors 2022, 22(14), 5443; https://doi.org/10.3390/s22145443 - 21 Jul 2022
Cited by 1 | Viewed by 2582
Abstract
This mini review features contemporary advances in mid-infrared (MIR) thin-film waveguide technology and on-chip photonics, promoting high-performance biosensing platforms. Supported by recent developments in MIR thin-film waveguides, it is expected that label-free assimilated MIR sensing platforms will soon supplement the current sensing technologies [...] Read more.
This mini review features contemporary advances in mid-infrared (MIR) thin-film waveguide technology and on-chip photonics, promoting high-performance biosensing platforms. Supported by recent developments in MIR thin-film waveguides, it is expected that label-free assimilated MIR sensing platforms will soon supplement the current sensing technologies for biomedical diagnostics. The state-of-the-art shows that various types of waveguide material can be utilized for waveguide spectroscopic measurements in MIR. However, there are challenges to integrating these waveguide platforms with microfluidic/Lab-on-a-Chip (LOC) devices, due to poor light–material interactions. Graphene and its analogs have found many applications in microfluidic-based LOC devices, to address to this issue. Graphene-based materials possess a high conductivity, a large surface-to-volume ratio, a smaller and tunable bandgap, and allow easier sample loading; which is essential for acquiring precise electrochemical information. This work discusses advanced waveguide materials, their advantages, and disease diagnostics with MIR thin-film based waveguides. The incorporation of graphene into waveguides improves the light–graphene interaction, and photonic devices greatly benefit from graphene’s strong field-controlled optical response. Full article
(This article belongs to the Section Biomedical Sensors)
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13 pages, 1797 KiB  
Article
I Can Step Clearly Now, the TENS Is On: Transcutaneous Electric Nerve Stimulation Decreases Sensorimotor Uncertainty during Stepping Movements
by Tyler T. Whittier, Zachary D. Weller and Brett W. Fling
Sensors 2022, 22(14), 5442; https://doi.org/10.3390/s22145442 - 21 Jul 2022
Cited by 2 | Viewed by 2227
Abstract
Transcutaneous electric nerve stimulation (TENS) is a method of electrical stimulation that elicits activity in sensory nerves and leads to improvements in the clinical metrics of mobility. However, the underlying perceptual mechanisms leading to this improvement are unknown. The aim of this study [...] Read more.
Transcutaneous electric nerve stimulation (TENS) is a method of electrical stimulation that elicits activity in sensory nerves and leads to improvements in the clinical metrics of mobility. However, the underlying perceptual mechanisms leading to this improvement are unknown. The aim of this study was to apply a Bayesian inference model to understand how TENS impacts sensorimotor uncertainty during full body stepping movements. Thirty healthy adults visited the lab on two occasions and completed a motor learning protocol in virtual reality (VR) on both visits. Participants were randomly assigned to one of three groups: TENS on first visit only (TN), TENS on second visit only (NT), or a control group where TENS was not applied on either visit (NN). Using methods of Bayesian inference, we calculated the amount of uncertainty in the participants’ center of mass (CoM) position estimates on each visit. We found that groups TN and NT decreased the amount of uncertainty in the CoM position estimates in their second visit while group NN showed no difference. The least amount of uncertainty was seen in the TN group. These results suggest that TENS reduces the amount of uncertainty in sensory information, which may be a cause for the observed benefits with TENS. Full article
(This article belongs to the Special Issue Feature Papers in Wearables 2022)
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17 pages, 831 KiB  
Article
Modulation Awareness Method for Dual-Hop Cooperative Transmissions over Frequency-Selective Channels
by Mohamed Marey and Hala Mostafa
Sensors 2022, 22(14), 5441; https://doi.org/10.3390/s22145441 - 21 Jul 2022
Cited by 2 | Viewed by 1558
Abstract
Modulation awareness and cooperative transmissions have individually received a significant amount of research in the scholarly literature. However, a limited number of works are principally concerned with the combination of the two topics, and they are restricted to frequency-flat wireless channels. In this [...] Read more.
Modulation awareness and cooperative transmissions have individually received a significant amount of research in the scholarly literature. However, a limited number of works are principally concerned with the combination of the two topics, and they are restricted to frequency-flat wireless channels. In this study, we propose a new modulation awareness method applicable to dual-hop amplify-and-forward cooperative broadcasts. The suggested method is built on the creation of theoretical representations of cross-correlation functions of the received signals. We conceptually prove that a family of modulation types generates spikes for certain cross-correlation functions, while others do not. We create a numerous layer hypothesis evaluation for the purpose of making judgments centered on this attribute. The suggested method has a number of benefits, such as the ability to operate on both frequency-flat and frequency-selective channels, as well as the absence of the necessity of channel awareness or noise power. Computer simulations analyze the performance of the proposed method, which delivers adequate awareness performance in a variety of operational scenarios. Full article
(This article belongs to the Special Issue Advances in Future Communication System)
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17 pages, 8338 KiB  
Article
Non-Destructive Testing Using Eddy Current Sensors for Defect Detection in Additively Manufactured Titanium and Stainless-Steel Parts
by Heba E. Farag, Ehsan Toyserkani and Mir Behrad Khamesee
Sensors 2022, 22(14), 5440; https://doi.org/10.3390/s22145440 - 21 Jul 2022
Cited by 25 | Viewed by 6078
Abstract
In this study, different eddy-current based probe designs (absolute and commercial reflection) are used to detect artificial defects with different sizes and at different depths in parts composed of stainless-steel (316) and titanium (TI-64) made by Laser Additive Manufacturing (LAM). The measured defect [...] Read more.
In this study, different eddy-current based probe designs (absolute and commercial reflection) are used to detect artificial defects with different sizes and at different depths in parts composed of stainless-steel (316) and titanium (TI-64) made by Laser Additive Manufacturing (LAM). The measured defect signal value using the probes is in the range of (20–200) millivolts. Both probes can detect subsurface defects on stainless-steel samples with average surface roughness of 11.6 µm and titanium samples with average surface roughness of 8.7 µm. It is found the signal reading can be improved by adding a coating layer made of thin paper to the bottom of the probes. The layer will decrease the surface roughness effect and smooth out the detected defect signal from any ripples. The smallest subsurface artificial defect size detected by both probes is an artificially made notch with 0.07 mm width and 25 mm length. In addition, both probes detected subsurface artificial blind holes in the range of 0.17 mm–0.3 mm radius. Results show that the absolute probe is more suitable to detect cracks and incomplete fusion holes, whereas the reflection probe is more suitable to detect small diameter blind holes. The setup can be used for defect detection during the additive manufacturing process once the melt pool is solidified. Full article
(This article belongs to the Special Issue Integrated Circuits and Technologies for Real-Time Sensing)
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19 pages, 3797 KiB  
Article
Satellite Network Task Deployment Method Based on SDN and ICN
by Zhiguo Liu, Xiaoqi Dong, Lin Wang, Jianxin Feng, Chengsheng Pan and Yunqi Li
Sensors 2022, 22(14), 5439; https://doi.org/10.3390/s22145439 - 21 Jul 2022
Cited by 7 | Viewed by 2365
Abstract
With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This [...] Read more.
With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This is, however, challenging due to the topological dynamics and limited resources of satellite networks. To improve the performance of edge computing in a satellite network, we propose a satellite network task deployment method based on SDN (software-defined network) and ICN (information-centric network). In this method, based on the full analysis of satellite network resources, a mission deployment model of a low-orbit satellite network is established. The genetic algorithm is then used to solve the proposed method. Experiments confirm that this method can effectively reduce the response delay of the tasks and the network traffic caused by task processing. Full article
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19 pages, 4374 KiB  
Article
Experimental Analysis of Commercial Optical Methods for Foot Measurement
by Matthias C. Jäger, Jörg Eberhardt and Douglas W. Cunningham
Sensors 2022, 22(14), 5438; https://doi.org/10.3390/s22145438 - 21 Jul 2022
Cited by 1 | Viewed by 2000
Abstract
Due to the increasing trend of online shopping, shoes are more and more often bought without being tried on. This leads to a strong increase in returns, which results in a high financial as well as ecological burden. To prevent this, feet can [...] Read more.
Due to the increasing trend of online shopping, shoes are more and more often bought without being tried on. This leads to a strong increase in returns, which results in a high financial as well as ecological burden. To prevent this, feet can be measured either in the store or at home by various systems to determine the exact dimensions of the foot and derive an optimal shoe size. In this paper, we want to present an overview of the methods currently available on the market for the measurement of feet. The most important commercial systems are classified according to the underlying basic technology. Subsequently, the most promising methods were implemented and tested. The results of the different methods were finally compared to find out the strengths and weaknesses of each technology. After determining the measurement accuracy of the length and width for each measurement method and also comparing the general shape of the 3D reconstruction with the GT, it can be said that the measurement using a ToF sensor is currently the most robust, the easiest and, among other methods, the most accurate method. Full article
(This article belongs to the Section Optical Sensors)
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16 pages, 1870 KiB  
Article
A Mean-Field Game Control for Large-Scale Swarm Formation Flight in Dense Environments
by Guofang Wang, Wang Yao, Xiao Zhang and Ziming Li
Sensors 2022, 22(14), 5437; https://doi.org/10.3390/s22145437 - 21 Jul 2022
Cited by 4 | Viewed by 2297
Abstract
As an important part of cyberphysical systems (CPSs), multiple aerial drone systems are widely used in various scenarios, and research scenarios are becoming increasingly complex. However, planning strategies for the formation flying of aerial swarms in dense environments typically lack the capability of [...] Read more.
As an important part of cyberphysical systems (CPSs), multiple aerial drone systems are widely used in various scenarios, and research scenarios are becoming increasingly complex. However, planning strategies for the formation flying of aerial swarms in dense environments typically lack the capability of large-scale breakthrough because the amount of communication and computation required for swarm control grows exponentially with scale. To address this deficiency, we present a mean-field game (MFG) control-based method that ensures collision-free trajectory generation for the formation flight of a large-scale swarm. In this paper, two types of differentiable mean-field terms are proposed to quantify the overall similarity distance between large-scale 3-D formations and the potential energy value of dense 3-D obstacles, respectively. We then formulate these two terms into a mean-field game control framework, which minimizes energy cost, formation similarity error, and collision penalty under the dynamical constraints, so as to achieve spatiotemporal planning for the desired trajectory. The classical task of a distributed large-scale aerial swarm system is simulated by numerical examples, and the feasibility and effectiveness of our method are verified and analyzed. The comparison with baseline methods shows the advanced nature of our method. Full article
(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems)
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12 pages, 3481 KiB  
Article
Indoor MIMO-VLC Using Angle Diversity Transmitters
by Biao Qin, Wanli Wen, Min Liu, Yanchao Zhang and Chen Chen
Sensors 2022, 22(14), 5436; https://doi.org/10.3390/s22145436 - 21 Jul 2022
Cited by 6 | Viewed by 2162
Abstract
In this paper, we, for the first time, apply angle diversity transmitters (ADTs) to enhance the performance of multiple-input multiple-output visible light communication (MIMO-VLC) systems. The ADT is designed to consist of one center light emitting diode (LED) and multiple inclined side LEDs. [...] Read more.
In this paper, we, for the first time, apply angle diversity transmitters (ADTs) to enhance the performance of multiple-input multiple-output visible light communication (MIMO-VLC) systems. The ADT is designed to consist of one center light emitting diode (LED) and multiple inclined side LEDs. We calculate the line-of-sight (LOS) channel gain of the MIMO-VLC system using ADTs and further derive the average achievable rate of the system. We show that the average achievable rate is related to both the inclination angle of the side LEDs and the spacing between two adjacent ADTs in the MIMO-VLC system. Simulations are conducted to verify that the average achievable rate of the ADT-enhanced MIMO-VLC system can be maximized by setting the optimal inclination angle of the side LEDs and the optimal spacing between adjacent ADTs. The obtained results further show that the average achievable rate of the ADT-enhanced MIMO-VLC system can be greatly improved when there are more LEDs in each ADT. Specifically, a substantial 42.9% average achievable rate improvement can be achieved by using the optimized ADT in comparison to using a conventional non-angle diversity transmitter. Full article
(This article belongs to the Collection Visible Light Communication (VLC))
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