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Sensors, Volume 23, Issue 7 (April-1 2023) – 419 articles

Cover Story (view full-size image): Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market MEMS sensors, the standalone PDR module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the GNSS is one of the most popular positioning systems; however, the ultra-low-cost GNSS module is susceptible to serious interference from the multipath effect. We propose a fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference model, the second phase proposes a novel multipath mitigation method to suppress the multipath error, and the third phase is to propose the joint stride lengths and heading estimations to reduce the long-time drift and noise. View this paper
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17 pages, 2806 KiB  
Article
CSMOT: Make One-Shot Multi-Object Tracking in Crowded Scenes Great Again
by Haoxiong Hou, Chao Shen, Ximing Zhang and Wei Gao
Sensors 2023, 23(7), 3782; https://doi.org/10.3390/s23073782 - 6 Apr 2023
Cited by 2 | Viewed by 2764
Abstract
The current popular one-shot multi-object tracking (MOT) algorithms are dominated by the joint detection and embedding paradigm, which have high inference speeds and accuracy, but their tracking performance is unstable in crowded scenes. Not only does the detection branch have difficulty in obtaining [...] Read more.
The current popular one-shot multi-object tracking (MOT) algorithms are dominated by the joint detection and embedding paradigm, which have high inference speeds and accuracy, but their tracking performance is unstable in crowded scenes. Not only does the detection branch have difficulty in obtaining the accurate object position, but the ambiguous appearance of features extracted by the re-identification (re-ID) branch also leads to identity switches. Focusing on the above problems, this paper proposes a more robust MOT algorithm, named CSMOT, based on FairMOT. First, on the basis of the encoder–decoder network, a coordinate attention module is designed to enhance the information interaction between channels (horizontal and vertical coordinates), which improves its object-detection abilities. Then, an angle-center loss that effectively maximizes intra-class similarity is proposed to optimize the re-ID branch, and the extracted re-ID features are made more discriminative. We further redesign the re-ID feature dimension to balance the detection and re-ID tasks. Finally, a simple and effective data association mechanism is introduced, which associates each detection instead of just the high-score detections during the tracking process. The experimental results show that our one-shot MOT algorithm achieves excellent tracking performance on multiple public datasets and can be effectively applied to crowded scenes. In particular, CSMOT decreases the number of ID switches by 11.8% and 33.8% on the MOT16 and MOT17 test datasets, respectively, compared to the baseline. Full article
(This article belongs to the Special Issue Target Detection, Tracking and Identification Using Multi-Sensor Systems)
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16 pages, 6297 KiB  
Article
Damage Analysis of Segmental Dry Joint Full-Scale Prestressed Cap Beam Based on Distributed Optical Fiber Sensing
by Duo Liu, Shengtao Li, Joan R. Casas, Xudong Chen and Yangyang Sun
Sensors 2023, 23(7), 3781; https://doi.org/10.3390/s23073781 - 6 Apr 2023
Cited by 2 | Viewed by 1835
Abstract
Distributed fiber optic sensors (DFOS) can detect structural cracks and structural deformation with high accuracy and wide measurement range. This study monitors the segmental prestressed bent cap, assembled with a large key dry joint, based on optical fiber technology, and it allows the [...] Read more.
Distributed fiber optic sensors (DFOS) can detect structural cracks and structural deformation with high accuracy and wide measurement range. This study monitors the segmental prestressed bent cap, assembled with a large key dry joint, based on optical fiber technology, and it allows the comparison of its damaging process with that of a monolithic cast in place counterpart. The obtained results, comprising cross-section strain distributions, longitudinal strain profiles, neutral axis location, crack pattern, and the damage process, show that the DFOS technology can be successfully used to analyze the complex working stress state of the segmental beam with shear key joints, both in the elastic range and at the ultimate load, and to successfully identify the changing characteristics of the stress state of the segmental capping beam model when elastic beam theory no longer applies. The DFOS data confirm that the shear key joint, as the weak point of the segmental cap beam, results in the high stress concentration area, and the damage rate is higher than that of the cast-in-place beam. The accurate monitoring by the DFOS allows for the realization that the damage occurs at the premature formation of a concentrated compression zone on the upper part of the shear key. Full article
(This article belongs to the Special Issue Research Progress and Applications of Distributed Optical Fiber Sensing)
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17 pages, 12412 KiB  
Article
Effect of Excitation Signal on Double-Coil Inductive Displacement Transducer
by Yanchao Li, Ruichuan Li, Junru Yang, Jikang Xu and Xiaodong Yu
Sensors 2023, 23(7), 3780; https://doi.org/10.3390/s23073780 - 6 Apr 2023
Cited by 3 | Viewed by 2184
Abstract
A double-coil inductive displacement transducer is a non-contact element for measuring displacement and is widely used in large power equipment systems such as construction machinery and agricultural machinery equipment. The type of coil excitation signal has an impact on the performance of the [...] Read more.
A double-coil inductive displacement transducer is a non-contact element for measuring displacement and is widely used in large power equipment systems such as construction machinery and agricultural machinery equipment. The type of coil excitation signal has an impact on the performance of the transducer, but there is little research on this. Therefore, the influence of the coil excitation signal on transducer performance is investigated. The working principle and characteristics of the double-coil inductive displacement transducer are analyzed, and the circuit simulation model of the transducer is established. From the aspects of phase shift, linearity, and sensitivity, the effects of a sine signal, a triangle signal, and a pulse signal on the transducer are compared and analyzed. The results show that the average phase shift, linearity, and sensitivity of the sine signal were 11.53°, 1.61%, and 0.372 V/mm, respectively; the average phase shift, linearity and sensitivity of the triangular signal were 1.38°, 1.56%, and 0.300 V/mm, respectively; and the average phase shift, linearity, and sensitivity of the pulse signal were 0.73°, 1.95%, and 0.621 V/mm, respectively. It can be seen that the phase shift of a triangle signal and a pulse signal is smaller than that of a sine signal, which can result in better signal phase-locked processing. The linearity of the triangle signal is better than the sine signal, and the sensitivity of the pulse signal is better than that of the sine signal. Full article
(This article belongs to the Section Physical Sensors)
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13 pages, 1300 KiB  
Communication
Self Capacitance Mismatch Calibration Technique for Fully-Differential Touch Screen Panel Self Capacitance Sensing System
by Siheon Seong, Sewon Lee, Sunghyun Bae and Minjae Lee
Sensors 2023, 23(7), 3779; https://doi.org/10.3390/s23073779 - 6 Apr 2023
Viewed by 2162
Abstract
This paper presents a fully-differential touch screen panel (TSP) self-capacitance sensing (SCS) system with a self-capacitance mismatch calibration technique. Due to the self-capacitance mismatch of TSP, the analog front-end (AFE) of the receiver (RX) circuit suffers from dynamic range degradation and gain limitations, [...] Read more.
This paper presents a fully-differential touch screen panel (TSP) self-capacitance sensing (SCS) system with a self-capacitance mismatch calibration technique. Due to the self-capacitance mismatch of TSP, the analog front-end (AFE) of the receiver (RX) circuit suffers from dynamic range degradation and gain limitations, which lead to the signal-to-noise ratio (SNR) loss for the TSP SCS system. The proposed calibration introduces the difference in input resistance and the driving amplifier’s strength between the fully-differential input. Thus, the mismatch effect is efficiently relieved in terms of area and power consumption. The proposed calibration restores the SNR by 19.54 dB even under the worst self-capacitance mismatch case. Full article
(This article belongs to the Special Issue Advanced CMOS Integrated Circuit Design and Application II)
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14 pages, 3275 KiB  
Article
Validation of a Custom Interface Pressure Measurement System to Improve Fitting of Transtibial Prosthetic Check Sockets
by Lucy Armitage, Kenny Cho, Emre Sariyildiz, Angela Buller, Stephen O’Brien and Lauren Kark
Sensors 2023, 23(7), 3778; https://doi.org/10.3390/s23073778 - 6 Apr 2023
Viewed by 2274
Abstract
Achievement of fit between the residual limb and prosthetic socket during socket manufacture is a priority for clinicians and is essential for safety. Clinicians have recognised the potential benefits of having a sensor system that can provide objective socket-limb interface pressure measurements during [...] Read more.
Achievement of fit between the residual limb and prosthetic socket during socket manufacture is a priority for clinicians and is essential for safety. Clinicians have recognised the potential benefits of having a sensor system that can provide objective socket-limb interface pressure measurements during socket fitting, but the cost of existing systems makes current technology prohibitive. This study will report on the characterisation, validation and preliminary clinical implementation of a low cost, portable, wireless sensor system designed for use during socket manufacture. Characterisation and benchtop testing demonstrated acceptable accuracy, behaviour at variable temperature, and dynamic response for use in prosthetic socket applications. Our sensor system was validated with simultaneous measurement by a commercial sensor system in the sockets of three transtibial prosthesis users during a fitting session in the clinic. There were no statistically significant differences between the sensor system and the commercial sensor for a variety of functional activities. The sensor system was found to be valid in this clinical context. Future work should explore how pressure data relates to ratings of fit and comfort, and how objective pressure data might be used to assist in clinical decision making. Full article
(This article belongs to the Special Issue Challenges and Future Trends of Wearable Robotics)
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20 pages, 6643 KiB  
Article
Simplification of Deep Neural Network-Based Object Detector for Real-Time Edge Computing
by Kyoungtaek Choi, Seong Min Wi, Ho Gi Jung and Jae Kyu Suhr
Sensors 2023, 23(7), 3777; https://doi.org/10.3390/s23073777 - 6 Apr 2023
Cited by 11 | Viewed by 2656
Abstract
This paper presents a method for simplifying and quantizing a deep neural network (DNN)-based object detector to embed it into a real-time edge device. For network simplification, this paper compares five methods for applying channel pruning to a residual block because special care [...] Read more.
This paper presents a method for simplifying and quantizing a deep neural network (DNN)-based object detector to embed it into a real-time edge device. For network simplification, this paper compares five methods for applying channel pruning to a residual block because special care must be taken regarding the number of channels when summing two feature maps. Based on the comparison in terms of detection performance, parameter number, computational complexity, and processing time, this paper discovers the most satisfying method on the edge device. For network quantization, this paper compares post-training quantization (PTQ) and quantization-aware training (QAT) using two datasets with different detection difficulties. This comparison shows that both approaches are recommended in the case of the easy-to-detect dataset, but QAT is preferable in the case of the difficult-to-detect dataset. Through experiments, this paper shows that the proposed method can effectively embed the DNN-based object detector into an edge device equipped with Qualcomm’s QCS605 System-on-Chip (SoC), while achieving a real-time operation with more than 10 frames per second. Full article
(This article belongs to the Special Issue Emerging Technologies in Edge Computing and Networking)
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18 pages, 7930 KiB  
Article
Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors
by Edwin Villanueva, Soledad Espezua, George Castelar, Kyara Diaz and Erick Ingaroca
Sensors 2023, 23(7), 3776; https://doi.org/10.3390/s23073776 - 6 Apr 2023
Cited by 4 | Viewed by 3468
Abstract
A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality [...] Read more.
A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer. Full article
(This article belongs to the Section Environmental Sensing)
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18 pages, 5323 KiB  
Article
A Super-Efficient GSM Triplexer for 5G-Enabled IoT in Sustainable Smart Grid Edge Computing and the Metaverse
by Mohammad (Behdad) Jamshidi, Salah I. Yahya, Leila Nouri, Hamed Hashemi-Dezaki, Abbas Rezaei and Muhammad Akmal Chaudhary
Sensors 2023, 23(7), 3775; https://doi.org/10.3390/s23073775 - 6 Apr 2023
Cited by 20 | Viewed by 2796
Abstract
Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are [...] Read more.
Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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16 pages, 5425 KiB  
Article
Research on Medical Security System Based on Zero Trust
by Zhiqiang Wang, Xinyue Yu, Peiyang Xue, Yunhan Qu and Lei Ju
Sensors 2023, 23(7), 3774; https://doi.org/10.3390/s23073774 - 6 Apr 2023
Cited by 6 | Viewed by 2571
Abstract
With the rapid development of Internet of Things technology, cloud computing, and big data, the combination of medical systems and information technology has become increasingly close. However, the emergence of intelligent medical systems has brought a series of network security threats and hidden [...] Read more.
With the rapid development of Internet of Things technology, cloud computing, and big data, the combination of medical systems and information technology has become increasingly close. However, the emergence of intelligent medical systems has brought a series of network security threats and hidden dangers, including data leakage and remote attacks, which can directly threaten patients’ lives. To ensure the security of medical information systems and expand the application of zero trust in the medical field, we combined the medical system with the zero-trust security system to propose a zero-trust medical security system. In addition, in its dynamic access control module, based on the RBAC model and the calculation of user behavior risk value and trust, an access control model based on subject behavior evaluation under zero-trust conditions (ABEAC) was designed to improve the security of medical equipment and data. Finally, the feasibility of the system is verified through a simulation experiment. Full article
(This article belongs to the Special Issue Internet of Medical Things and Smart Healthcare)
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17 pages, 1793 KiB  
Article
Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors
by Manu Airaksinen, Sampsa Vanhatalo and Okko Räsänen
Sensors 2023, 23(7), 3773; https://doi.org/10.3390/s23073773 - 6 Apr 2023
Cited by 1 | Viewed by 1874
Abstract
Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from [...] Read more.
Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data. Full article
(This article belongs to the Special Issue Wearables and Artificial Intelligence in Health Monitoring)
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16 pages, 6674 KiB  
Article
Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion
by Zhaoyun Sun, Junzhi Zhai, Lili Pei, Wei Li and Kaiyue Zhao
Sensors 2023, 23(7), 3772; https://doi.org/10.3390/s23073772 - 6 Apr 2023
Cited by 7 | Viewed by 2487
Abstract
To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and [...] Read more.
To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm. Full article
(This article belongs to the Section Intelligent Sensors)
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11 pages, 3060 KiB  
Article
General Purpose Transistor Characterized as Dosimetry Sensor of Proton Beams
by J. A. Moreno-Pérez, I. Ruiz-García, P. Martín-Holgado, A. Romero-Maestre, M. Anguiano, R. Vila and M. A. Carvajal
Sensors 2023, 23(7), 3771; https://doi.org/10.3390/s23073771 - 6 Apr 2023
Viewed by 1577
Abstract
A commercial pMOS transistor (MOSFET), 3N163 from Vishay (USA), has been characterized as a low-energy proton beam dosimeter. The top of the samples’ housing has been removed to guarantee that protons reached the sensitive area, that is, the silicon die. Irradiations took place [...] Read more.
A commercial pMOS transistor (MOSFET), 3N163 from Vishay (USA), has been characterized as a low-energy proton beam dosimeter. The top of the samples’ housing has been removed to guarantee that protons reached the sensitive area, that is, the silicon die. Irradiations took place at the National Accelerator Centre (Seville, Spain). During irradiations, the transistors were biased to improve the sensitivity, and the silicon temperature was monitored activating the parasitic diode of the MOSFET. Bias voltages of 0, 1, 5, and 10 V were applied to four sets of three transistors, obtaining an averaged sensitivity that was linearly dependent on this voltage. In addition, the short-fading effect was studied, and the uncertainty of this effect was obtained. The bias voltage that provided an acceptable sensitivity, (11.4 ± 0.9) mV/Gy, minimizing the uncertainty due to the fading effect (−0.09 ± 0.11) Gy was 1 V for a total absorbed dose of 40 Gy. Therefore, this off-the-shelf electronic device presents promising characteristics as a dosimeter sensor for proton beams. Full article
(This article belongs to the Special Issue Radiation Sensors and Detectors: Materials, Principles and Applications)
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24 pages, 6060 KiB  
Article
Method of Failure Diagnostics to Linear Rolling Guides in Handling Machines
by Radka Jírová, Lubomír Pešík, Lucia Žuľová and Robert Grega
Sensors 2023, 23(7), 3770; https://doi.org/10.3390/s23073770 - 6 Apr 2023
Cited by 4 | Viewed by 1629
Abstract
Linear rolling guides, used in production machines for the realisation of linear motion, demand in industrial practice early damage identification to prevent production outages and losses. Therefore, the article aims for early damage diagnostics that use the principle of a load-free diagnostic part [...] Read more.
Linear rolling guides, used in production machines for the realisation of linear motion, demand in industrial practice early damage identification to prevent production outages and losses. Therefore, the article aims for early damage diagnostics that use the principle of a load-free diagnostic part integrated into the carriage of the linear rolling guide. This principle was employed for developing an innovative method of damage identification to a guiding profile or rolling elements. The proposed innovative method is based on analysing vibration acceleration measured on the diagnostic part in the context of carriage position. In addition, a unique connection of an acceleration sensor to the diagnostic part through a mechanical component with defined parameters of stiffness and mass was designed. The innovative method was verified by laboratory testing on a designed functional sample of the diagnostic system. The computed reliability of the proposed diagnostic method reached 98%. Full article
(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)
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25 pages, 8825 KiB  
Article
Deep Learning Based Vehicle Detection on Real and Synthetic Aerial Images: Training Data Composition and Statistical Influence Analysis
by Michael Krump and Peter Stütz
Sensors 2023, 23(7), 3769; https://doi.org/10.3390/s23073769 - 6 Apr 2023
Cited by 4 | Viewed by 2513
Abstract
The performance of deep learning based algorithms is significantly influenced by the quantity and quality of the available training and test datasets. Since data acquisition is complex and expensive, especially in the field of airborne sensor data evaluation, the use of virtual simulation [...] Read more.
The performance of deep learning based algorithms is significantly influenced by the quantity and quality of the available training and test datasets. Since data acquisition is complex and expensive, especially in the field of airborne sensor data evaluation, the use of virtual simulation environments for generating synthetic data are increasingly sought. In this article, the complete process chain is evaluated regarding the use of synthetic data based on vehicle detection. Among other things, content-equivalent real and synthetic aerial images are used in the process. This includes, in the first step, the learning of models with different training data configurations and the evaluation of the resulting detection performance. Subsequently, a statistical evaluation procedure based on a classification chain with image descriptors as features is used to identify important influencing factors in this respect. The resulting findings are finally incorporated into the synthetic training data generation and in the last step, it is investigated to what extent an increase of the detection performance is possible. The overall objective of the experiments is to derive design guidelines for the generation and use of synthetic data. Full article
(This article belongs to the Special Issue Sensors for Aerial Unmanned Systems 2021-2023)
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15 pages, 460 KiB  
Article
Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry
by Miguel Rivas Pellicer, Mohamed Yoosha Tungekar and Silvia Carpitella
Sensors 2023, 23(7), 3768; https://doi.org/10.3390/s23073768 - 6 Apr 2023
Cited by 5 | Viewed by 2490
Abstract
Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor [...] Read more.
Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor placement problem in the food industry, having been applied to wine bottling line equipment at a real Italian winery. The approach helps decision-makers when discriminating within a set of alternatives based on multiple criteria. By evaluating the interconnections within the different equipment, the ideal locations of sensors are suggested, with the goal of improving the process’s performance. The results indicated that the system of electric pumps, corker, conveyor, and capper had the most influence on the other equipment which are then recommended for sensor control. Monitoring this equipment will result in the early discovery of failures, potentially also involving other dependant equipment, contributing to enhance the level of performance for the whole bottling line. Full article
(This article belongs to the Special Issue Sensors for Food Supply Chain)
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19 pages, 2356 KiB  
Article
Flood-Related Multimedia Benchmark Evaluation: Challenges, Results and a Novel GNN Approach
by Thomas Papadimos, Stelios Andreadis, Ilias Gialampoukidis, Stefanos Vrochidis and Ioannis Kompatsiaris
Sensors 2023, 23(7), 3767; https://doi.org/10.3390/s23073767 - 6 Apr 2023
Cited by 4 | Viewed by 1974
Abstract
This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but [...] Read more.
This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve performance. The authors present lessons learned from the Flood-related multimedia task in MediaEval2020, provide a dataset for reproducibility, and propose a new multimodal fusion method that uses Graph Neural Networks to combine image, text, and time information. Their method outperforms state-of-the-art approaches and can handle low-sample labelled data. Full article
(This article belongs to the Special Issue Social Media Sensing: Methodologies and Applications)
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15 pages, 9317 KiB  
Article
Neustrelitz Total Electron Content Model for Galileo Performance: A Position Domain Analysis
by Ciro Gioia, Antonio Angrisano and Salvatore Gaglione
Sensors 2023, 23(7), 3766; https://doi.org/10.3390/s23073766 - 6 Apr 2023
Viewed by 1798
Abstract
Ionospheric error is one of the largest errors affecting global navigation satellite system (GNSS) users in open-sky conditions. This error can be mitigated using different approaches including dual-frequency measurements and corrections from augmentation systems. Although the adoption of multi-frequency devices has increased in [...] Read more.
Ionospheric error is one of the largest errors affecting global navigation satellite system (GNSS) users in open-sky conditions. This error can be mitigated using different approaches including dual-frequency measurements and corrections from augmentation systems. Although the adoption of multi-frequency devices has increased in recent years, most GNSS devices are still single-frequency standalone receivers. For these devices, the most used approach to correct ionospheric delays is to rely on a model. Recently, the empirical model Neustrelitz Total Electron Content Model for Galileo (NTCM-G) has been proposed as an alternative to Klobuchar and NeQuick-G (currently adopted by GPS and Galileo, respectively). While the latter outperforms the Klobuchar model, it requires a significantly higher computational load, which can limit its exploitation in some market segments. NTCM-G has a performance close to that of NeQuick-G and it shares with Klobuchar the limited computation load; the adoption of this model is emerging as a trade-off between performance and complexity. The performance of the three algorithms is assessed in the position domain using data for different geomagnetic locations and different solar activities and their execution time is also analysed. From the test results, it has emerged that in low- and medium-solar-activity conditions, NTCM-G provides slightly better performance, while NeQuick-G has better performance with intense solar activity. The NTCM-G computational load is significantly lower with respect to that of NeQuick-G and is comparable with that of Klobuchar. Full article
(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)
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18 pages, 7182 KiB  
Article
Laser-Visible Face Image Translation and Recognition Based on CycleGAN and Spectral Normalization
by Mingyu Qin, Youchen Fan, Huichao Guo and Laixian Zhang
Sensors 2023, 23(7), 3765; https://doi.org/10.3390/s23073765 - 6 Apr 2023
Viewed by 1901
Abstract
The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, [...] Read more.
The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, it becomes important to convert laser images into visible images and then identify them. For image translation, we propose a laser-visible face image translation model combined with spectral normalization (SN-CycleGAN). We add spectral normalization layers to the discriminator to solve the problem of low image translation quality caused by the difficulty of training the generative adversarial network. The content reconstruction loss function based on the Y channel is added to reduce the error mapping. The face generated by the improved model on the self-built laser-visible face image dataset has better visual quality, which reduces the error mapping and basically retains the structural features of the target compared with other models. The FID value of evaluation index is 36.845, which is 16.902, 13.781, 10.056, 57.722, 62.598 and 0.761 lower than the CycleGAN, Pix2Pix, UNIT, UGATIT, StarGAN and DCLGAN models, respectively. For the face recognition of translated images, we propose a laser-visible face recognition model based on feature retention. The shallow feature maps with identity information are directly connected to the decoder to solve the problem of identity information loss in network transmission. The domain loss function based on triplet loss is added to constrain the style between domains. We use pre-trained FaceNet to recognize generated visible face images and obtain the recognition accuracy of Rank-1. The recognition accuracy of the images generated by the improved model reaches 76.9%, which is greatly improved compared with the above models and 19.2% higher than that of laser face recognition. Full article
(This article belongs to the Special Issue Sensing Technologies and Applications in Infrared and Visible Imaging)
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3 pages, 164 KiB  
Editorial
Sensing and Processing for Infrared Vision: Methods and Applications
by Saed Moradi
Sensors 2023, 23(7), 3764; https://doi.org/10.3390/s23073764 - 6 Apr 2023
Viewed by 1334
Abstract
Dear readers and fellow researchers, [...] Full article
(This article belongs to the Special Issue Sensing and Processing for Infrared Vision: Methods and Applications)
24 pages, 9678 KiB  
Article
A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
by Yeison Nolberto Cardona-Álvarez, Andrés Marino Álvarez-Meza, David Augusto Cárdenas-Peña, Germán Albeiro Castaño-Duque and German Castellanos-Dominguez
Sensors 2023, 23(7), 3763; https://doi.org/10.3390/s23073763 - 6 Apr 2023
Cited by 8 | Viewed by 5076
Abstract
An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the [...] Read more.
An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing. Full article
(This article belongs to the Special Issue Real-Life Wearable EEG-Based BCI: Open Challenges)
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35 pages, 610 KiB  
Review
A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
by Dong Han, Beni Mulyana, Vladimir Stankovic and Samuel Cheng
Sensors 2023, 23(7), 3762; https://doi.org/10.3390/s23073762 - 5 Apr 2023
Cited by 59 | Viewed by 24432
Abstract
Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin [...] Read more.
Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject. Full article
(This article belongs to the Special Issue Advances in Intelligent Robotics Systems Based Machine Learning)
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18 pages, 5802 KiB  
Article
Defect Detection for Metal Shaft Surfaces Based on an Improved YOLOv5 Algorithm and Transfer Learning
by Bi Li and Quanjie Gao
Sensors 2023, 23(7), 3761; https://doi.org/10.3390/s23073761 - 5 Apr 2023
Cited by 5 | Viewed by 3111
Abstract
To address the problem of low efficiency for manual detection in the defect detection field for metal shafts, we propose a deep learning defect detection method based on the improved YOLOv5 algorithm. First, we add a Convolutional Block Attention Module (CBAM) mechanism layer [...] Read more.
To address the problem of low efficiency for manual detection in the defect detection field for metal shafts, we propose a deep learning defect detection method based on the improved YOLOv5 algorithm. First, we add a Convolutional Block Attention Module (CBAM) mechanism layer to the last layer of the backbone network to improve the feature extraction capability. Second, the neck network introduces the Bi-directional Feature Pyramid Network (BiFPN) module to replace the original Path-Aggregation Network (PAN) structure and enhance the multi-scale feature fusion. Finally, we use transfer learning to pre-train the model and improve the generalization ability of the model. The experimental results show that the method achieves an average accuracy of 93.6% mAP and a detection speed of 16.7 FPS for defect detection on the dataset, which can identify metal shaft surface defects quickly and accurately, and is of reference significance for practical industrial applications. Full article
(This article belongs to the Section Physical Sensors)
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15 pages, 3855 KiB  
Article
MOSFE-Capacitor Silicon Carbide-Based Hydrogen Gas Sensors
by Artur Litvinov, Maya Etrekova, Boris Podlepetsky, Nikolay Samotaev, Konstantin Oblov, Alexey Afanasyev and Vladimir Ilyin
Sensors 2023, 23(7), 3760; https://doi.org/10.3390/s23073760 - 5 Apr 2023
Cited by 5 | Viewed by 2113
Abstract
The features of the wide band gap SiC semiconductor use in the capacitive MOSFE sensors’ structure in terms of the hydrogen gas sensitivity effect, the response speed, and the measuring signals’ optimal parameters are studied. Sensors in a high-temperature ceramic housing with the [...] Read more.
The features of the wide band gap SiC semiconductor use in the capacitive MOSFE sensors’ structure in terms of the hydrogen gas sensitivity effect, the response speed, and the measuring signals’ optimal parameters are studied. Sensors in a high-temperature ceramic housing with the Me/Ta2O5/SiCn+/4H-SiC structures and two types of gas-sensitive electrodes were made: Palladium and Platinum. The effectiveness of using Platinum as an alternative to Palladium in the MOSFE-Capacitor (MOSFEC) gas sensors’ high-temperature design is evaluated. It is shown that, compared with Silicon, the use of Silicon Carbide increases the response rate, while maintaining the sensors’ high hydrogen sensitivity. The operating temperature and test signal frequency influence for measuring the sensor’s capacitance on the sensitivity to H2 have been studied. Full article
(This article belongs to the Special Issue Advanced Field-Effect Sensors: Volume II)
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23 pages, 3767 KiB  
Article
Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package
by Huibin Zhu, Zhangming He, Yaqi Xiao, Jiongqi Wang and Haiyin Zhou
Sensors 2023, 23(7), 3759; https://doi.org/10.3390/s23073759 - 5 Apr 2023
Cited by 3 | Viewed by 1909
Abstract
The singular value decomposition package (SVDP) is often used for signal decomposition and feature extraction. At present, the general SVDP has insufficient feature extraction ability due to the two-row structure of the Hankel matrix, which leads to mode mixing. In this paper, an [...] Read more.
The singular value decomposition package (SVDP) is often used for signal decomposition and feature extraction. At present, the general SVDP has insufficient feature extraction ability due to the two-row structure of the Hankel matrix, which leads to mode mixing. In this paper, an improved singular value decomposition packet (ISVDP) algorithm is proposed: the feature extraction ability is improved by changing the structure of the Hankel matrix, and similar signal sub-components are selected by similarity to avoid having the same frequency component signals being decomposed into different sub-signals. In this paper, the effectiveness of ISVDP is illustrated by a set of simulation signals, and it is utilized in fault diagnosis of bearing data. The results show that ISVDP can effectively suppress the model-mixing phenomenon and can extract the fault features in bearing vibration signals more accurately. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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11 pages, 18136 KiB  
Technical Note
High-Resolution Monitoring of Scour Using a Novel Fiber-Optic Distributed Temperature Sensing Device: A Proof-of-Concept Laboratory Study
by Rebecca Hatley, Mahmoud Shehata, Chadi Sayde and Celso Castro-Bolinaga
Sensors 2023, 23(7), 3758; https://doi.org/10.3390/s23073758 - 5 Apr 2023
Cited by 3 | Viewed by 1842
Abstract
Scour events can severely change the characteristics of streams and impose detrimental hazards on any structures built on them. The development of robust and accurate devices to monitor scour is therefore essential for studying and developing mitigation strategies for these adverse consequences. This [...] Read more.
Scour events can severely change the characteristics of streams and impose detrimental hazards on any structures built on them. The development of robust and accurate devices to monitor scour is therefore essential for studying and developing mitigation strategies for these adverse consequences. This technical note introduces a novel scour-monitoring device that utilizes new advances in the fiber-optic distributed temperature sensing (FO-DTS) technology. The novel FO-DTS scour-monitoring device utilizes the differential thermal responses of sediment, water, and air media to a heating event to accurately identify the locations of the interfaces between them. The performance of the device was tested in a laboratory flume under flow conditions with water velocities ranging from 0 m/s to 0.16 m/s. In addition, the effect of the measurement duration on the device’s measurement accuracy was also investigated. The FO-DTS scour-monitoring device managed to detect the sediment–water and water–air interfaces with average absolute errors of 1.60 cm and 0.63 cm, respectively. A measurement duration of fewer than 238 s was sufficient to obtain stable measurements of the locations of the sediment–water and water–air interfaces for all the tested flow conditions. Full article
(This article belongs to the Section Optical Sensors)
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15 pages, 3171 KiB  
Article
Transforming Industrial Manipulators via Kinesthetic Guidance for Automated Inspection of Complex Geometries
by Charalampos Loukas, Momchil Vasilev, Rastislav Zimmerman, Randika K. W. Vithanage, Ehsan Mohseni, Charles N. MacLeod, David Lines, Stephen Gareth Pierce, Stewart Williams, Jialuo Ding, Kenneth Burnham, Jim Sibson, Tom O’Hare and Michael R. Grosser
Sensors 2023, 23(7), 3757; https://doi.org/10.3390/s23073757 - 5 Apr 2023
Viewed by 2410
Abstract
The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic [...] Read more.
The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilizing traditional programming approaches demand specialized robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz, utilizing an external off-the-shelf force–torque (FT) sensor. The proposed work is demonstrated on a 500 mm2 near-net-shaped Wire–Arc Additive Manufacturing (WAAM) complex component with embedded defects by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting in the uniform coupling of ultrasound inspection. The utilized method proves superior in performance and speed, accelerating the programming time using online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources, and achieving flexible production. Full article
(This article belongs to the Special Issue New Advances in Robotically Enabled Sensing)
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14 pages, 25531 KiB  
Article
Development of a Target-to-Sensor Mode Multispectral Imaging Device for High-Throughput and High-Precision Touch-Based Leaf-Scale Soybean Phenotyping
by Xuan Li, Ziling Chen, Xing Wei, Tianzhang Zhao and Jian Jin
Sensors 2023, 23(7), 3756; https://doi.org/10.3390/s23073756 - 5 Apr 2023
Cited by 1 | Viewed by 2258
Abstract
Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, [...] Read more.
Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239). Full article
(This article belongs to the Special Issue Proximal Sensing in Precision Agriculture)
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22 pages, 9290 KiB  
Article
Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity
by Dominik Łuczak, Stefan Brock and Krzysztof Siembab
Sensors 2023, 23(7), 3755; https://doi.org/10.3390/s23073755 - 5 Apr 2023
Cited by 12 | Viewed by 3018
Abstract
The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present [...] Read more.
The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first fault diagnosis MQTT client uses only frequency data for evaluation. The authors’ enhancement to standard fast Fourier transform (FFT) was their usage of sliding discrete Fourier transform (rSDFT, mSDFT, gSDFT, and oSDFT) which allows recursively updating the spectrum based on a new sample in the time domain and previous results in the frequency domain. This approach allows to reduce the computational cost. The second approach of the MQTT client for fault diagnosis uses short-time Fourier transform (STFT) to transform IMU 6 DOF sensor data into six spectrograms that are combined into an RGB image. All three-axis accelerometer and three-axis gyroscope data are used to obtain a time-frequency RGB image. The diagnosis of the machine is performed by a trained convolutional neural network suitable for RGB image recognition. Prediction result is returned as a JSON object with predicted state and probability of each state. For HTTP, the fault diagnosis result is sent in response, and for MQTT, it is send to prediction topic. Both protocols and both proposed approaches are suitable for fault diagnosis based on the mechanical vibration of the rotary machine and were tested in demonstration. Full article
(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0)
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15 pages, 1280 KiB  
Article
Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method
by Xinkai Xia, Tao Li, Shengbo Sang, Yongqiang Cheng, Huanzhou Ma, Qiang Zhang and Kun Yang
Sensors 2023, 23(7), 3754; https://doi.org/10.3390/s23073754 - 5 Apr 2023
Cited by 16 | Viewed by 6312
Abstract
In medical and surgical scenarios, the trajectory planning of a collaborative robot arm is a difficult problem. The artificial potential field (APF) algorithm is a classic method for robot trajectory planning, which has the characteristics of good real-time performance and low computing consumption. [...] Read more.
In medical and surgical scenarios, the trajectory planning of a collaborative robot arm is a difficult problem. The artificial potential field (APF) algorithm is a classic method for robot trajectory planning, which has the characteristics of good real-time performance and low computing consumption. There are many variants of the APF algorithm, among which the most widely used variants is the velocity potential field (VPF) algorithm. However, the traditional VPF algorithm has inherent defects and problems, such as easily falling into local minimum, being unable to reach the target, poor dynamic obstacle avoidance ability, and safety and efficiency problems. Therefore, this work presents the improved velocity potential field (IVPF) algorithm, which considers direction factors, obstacle velocity factor, and tangential velocity. When encountering dynamic obstacles, the IVPF algorithm can avoid obstacles better to ensure the safety of both the human and robot arm. The IVPF algorithm also does not easily fall into a local problem when encountering different obstacles. The experiments informed the RRT* algorithm, VPF algorithm, and IVPF algorithm for comparison. Compared with the informed RRT* and VPF algorithm, the result of experiments indicate that the performances of the IVPF algorithm have significant improvements when dealing with different obstacles. The main aim of this paper is to provide a safe and efficient path planning algorithm for the robot arm in the medical field. The proposed algorithm can ensure the safety of both the human and the robot arm when the medical and surgical robot arm is working, and enables the robot arm to cope with emergencies and perform tasks better. The application of the proposed algorithm could make the collaborative robots work in a flexible and safe condition, which could open up new opportunities for the future development of medical and surgical scenarios. Full article
(This article belongs to the Special Issue Advanced Technologies in Medical and Surgical Robotics)
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3 pages, 167 KiB  
Editorial
Biometric Technologies Based on Optical Coherence Tomography
by Tomasz Marciniak
Sensors 2023, 23(7), 3753; https://doi.org/10.3390/s23073753 - 5 Apr 2023
Cited by 3 | Viewed by 1312
Abstract
Optical coherence tomography (OCT) is one of the newest and most important optical non-invasive methods for the investigation and testing of various materials (e [...] Full article
(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))
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