Journal Description
Sensors
Sensors
is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB) and International Society for the Measurement of Physical Behaviour (ISMPB) are affiliated with Sensors and their members receive a discount on the article processing charges.
- Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Instruments & Instrumentation) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Sensors.
- Companion journals for Sensors include: Chips, Automation, JCP and Targets.
Impact Factor:
3.9 (2022);
5-Year Impact Factor:
4.1 (2022)
Latest Articles
Efficiency–Accuracy Trade-Off in Light Field Estimation with Cost Volume Construction and Aggregation
Sensors 2024, 24(11), 3583; https://doi.org/10.3390/s24113583 (registering DOI) - 1 Jun 2024
Abstract
The Rich spatial and angular information in light field images enables accurate depth estimation, which is a crucial aspect of environmental perception. However, the abundance of light field information also leads to high computational costs and memory pressure. Typically, selectively pruning some light
[...] Read more.
The Rich spatial and angular information in light field images enables accurate depth estimation, which is a crucial aspect of environmental perception. However, the abundance of light field information also leads to high computational costs and memory pressure. Typically, selectively pruning some light field information can significantly improve computational efficiency but at the expense of reduced depth estimation accuracy in the pruned model, especially in low-texture regions and occluded areas where angular diversity is reduced. In this study, we propose a lightweight disparity estimation model that balances speed and accuracy and enhances depth estimation accuracy in textureless regions. We combined cost matching methods based on absolute difference and correlation to construct cost volumes, improving both accuracy and robustness. Additionally, we developed a multi-scale disparity cost fusion architecture, employing 3D convolutions and a UNet-like structure to handle matching costs at different depth scales. This method effectively integrates information across scales, utilizing the UNet structure for efficient fusion and completion of cost volumes, thus yielding more precise depth maps. Extensive testing shows that our method achieves computational efficiency on par with the most efficient existing methods, yet with double the accuracy. Moreover, our approach achieves comparable accuracy to the current highest-accuracy methods but with an order of magnitude improvement in computational performance.
Full article
(This article belongs to the Special Issue Towards Optoelectronic Technology: From Basic Research to Applications)
Open AccessArticle
Development of a Predictive Model for Evaluation of the Influence of Various Parameters on the Performance of an Oscillating Water Column Device
by
Felice Sfravara, Emmanuele Barberi, Giacomo Bongiovanni, Massimiliano Chillemi and Sebastian Brusca
Sensors 2024, 24(11), 3582; https://doi.org/10.3390/s24113582 (registering DOI) - 1 Jun 2024
Abstract
Oscillating Water Column (OWC) systems harness wave energy using a partially submerged chamber with an underwater opening. The Savonius turbine, a vertical-axis wind turbine, is well-suited for this purpose due to its efficiency at low speeds and self-starting capability, making it an ideal
[...] Read more.
Oscillating Water Column (OWC) systems harness wave energy using a partially submerged chamber with an underwater opening. The Savonius turbine, a vertical-axis wind turbine, is well-suited for this purpose due to its efficiency at low speeds and self-starting capability, making it an ideal power take-off (PTO) mechanism in OWC systems. This study tested an OWC device with a Savonius turbine in an air duct to evaluate its performance under varying flow directions and loads. An innovative aspect was assessing the influence of power augmenters (PAs) positioned upstream and downstream of the turbine. The experimental setup included load cells, Pitot tubes, differential pressure sensors and rotational speed sensors. Data obtained were used to calculate pressure differentials across the turbine and torque. The primary goal of using PA is to increase the CP–λ curve area without modifying the turbine geometry, potentially enabling interventions on existing turbines without rotor dismantling. Additionally, another novelty is the implementation of a regression Machine-Learning algorithm based on decision trees to analyze the influence of various features on predicting pressure differences, thereby broadening the scope for further testing beyond physical experimentation.
Full article
(This article belongs to the Special Issue Real-Time Monitoring Technology for Built Infrastructure Systems)
►▼
Show Figures
Figure 1
Open AccessArticle
Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems
by
Yong Ding, Honggao Deng, Yuelei Xie, Haitao Wang and Shaoshuai Sun
Sensors 2024, 24(11), 3581; https://doi.org/10.3390/s24113581 (registering DOI) - 1 Jun 2024
Abstract
For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose
[...] Read more.
For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.
Full article
(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation: 2nd Edition)
►▼
Show Figures
Figure 1
Open AccessArticle
On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements
by
Abhishek Saini, John James Greenhall, Eric Sean Davis and Cristian Pantea
Sensors 2024, 24(11), 3580; https://doi.org/10.3390/s24113580 (registering DOI) - 1 Jun 2024
Abstract
Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural
[...] Read more.
Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.
Full article
(This article belongs to the Special Issue Ultrasound Imaging and Sensing for Nondestructive Testing)
►▼
Show Figures
Figure 1
Open AccessArticle
RSDNet: A New Multiscale Rail Surface Defect Detection Model
by
Jingyi Du, Ruibo Zhang, Rui Gao, Lei Nan and Yifan Bao
Sensors 2024, 24(11), 3579; https://doi.org/10.3390/s24113579 (registering DOI) - 1 Jun 2024
Abstract
The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm,
[...] Read more.
The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network’s attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.
Full article
(This article belongs to the Special Issue Target Tracking and Navigation for Intelligent Autonomous Unmanned Systems Application)
Open AccessArticle
Gamified Exercise with Kinect: Can Kinect-Based Virtual Reality Training Improve Physical Performance and Quality of Life in Postmenopausal Women with Osteopenia? A Randomized Controlled Trial
by
Saima Riaz, Syed Shakil Ur Rehman, Danish Hassan and Sana Hafeez
Sensors 2024, 24(11), 3577; https://doi.org/10.3390/s24113577 (registering DOI) - 1 Jun 2024
Abstract
Background: Osteopenia, caused by estrogen deficiency in postmenopausal women (PMW), lowers Bone Mineral Density (BMD) and increases bone fragility. It affects about half of older women’s social and physical health. PMW experience pain and disability, impacting their health-related Quality of Life (QoL) and
[...] Read more.
Background: Osteopenia, caused by estrogen deficiency in postmenopausal women (PMW), lowers Bone Mineral Density (BMD) and increases bone fragility. It affects about half of older women’s social and physical health. PMW experience pain and disability, impacting their health-related Quality of Life (QoL) and function. This study aimed to determine the effects of Kinect-based Virtual Reality Training (VRT) on physical performance and QoL in PMW with osteopenia. Methodology: The study was a prospective, two-arm, parallel-design, randomized controlled trial. Fifty-two participants were recruited in the trial, with 26 randomly assigned to each group. The experimental group received Kinect-based VRT thrice a week for 24 weeks, each lasting 45 min. Both groups were directed to participate in a 30-min walk outside every day. Physical performance was measured by the Time Up and Go Test (TUG), Functional Reach Test (FRT), Five Times Sit to Stand Test (FTSST), Modified Sit and Reach Test (MSRT), Dynamic Hand Grip Strength (DHGS), Non-Dynamic Hand Grip Strength (NDHGS), BORG Score and Dyspnea Index. Escala de Calidad de vida Osteoporosis (ECOS-16) questionnaire measured QoL. Both physical performance and QoL measures were assessed at baseline, after 12 weeks, and after 24 weeks. Data were analyzed on SPSS 25. Results: The mean age of the PMW participants was 58.00 ± 5.52 years. In within-group comparison, all outcome variables (TUG, FRT, FTSST, MSRT, DHGS, NDHGS, BORG Score, Dyspnea, and ECOS-16) showed significant improvements (p < 0.001) from baseline at both the 12th and 24th weeks and between baseline and the 24th week in the experimental group. In the control group, all outcome variables except FRT (12th week to 24th week) showed statistically significant improvements (p < 0.001) from baseline at both the 12th and 24th weeks and between baseline and the 24th week. In between-group comparison, the experimental group demonstrated more significant improvements in most outcome variables at all points than the control group (p < 0.001), indicating the positive additional effects of Kinect-based VRT. Conclusion: The study concludes that physical performance and QoL measures were improved in both the experimental and control groups. However, in the group comparison, these variables showed better results in the experimental group. Thus, Kinect-based VRT is an alternative and feasible intervention to improve physical performance and QoL in PMW with osteopenia. This novel approach may be widely applicable in upcoming studies, considering the increasing interest in virtual reality-based therapy for rehabilitation.
Full article
(This article belongs to the Section Biomedical Sensors)
Open AccessArticle
Short-Term and Imminent Rainfall Prediction Model Based on ConvLSTM and SmaAT-UNet
by
Yuanyuan Liao, Shouqian Lu and Gang Yin
Sensors 2024, 24(11), 3576; https://doi.org/10.3390/s24113576 (registering DOI) - 1 Jun 2024
Abstract
Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the
[...] Read more.
Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the use of deep learning for radar image extrapolation for precipitation forecasting, in particular by developing algorithms for ConvLSTM and SmaAT-UNet. The ConvLSTM model is a fusion of a CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional LSTM models cannot accomplish. At the same time, SmaAT-UNet enhances the traditional UNet structure by incorporating the CBAM (Convolutional Block Attention Module) attention mechanism and replacing the standard convolutional layer with depthwise separable convolution. This innovative approach aims to improve the efficiency and accuracy of short-term precipitation forecasting by improving feature extraction and data processing techniques. Evaluation and analysis of experimental data show that both models exhibit good predictive ability, with the SmaAT-UNet model outperforming ConvLSTM in terms of accuracy. The results show that the performance indicators of precipitation prediction, especially detection probability (POD) and the Critical Success index (CSI), show a downward trend with the extension of the prediction time. This trend highlights the inherent challenges of maintaining predictive accuracy over longer periods of time and highlights the superior performance and resilience of the SmaAT-UNet model under these conditions. Compared with the statistical forecasting method and numerical model forecasting method, its accuracy in short-term rainfall forecasting is improved.
Full article
(This article belongs to the Section Radar Sensors)
►▼
Show Figures
Figure 1
Open AccessArticle
Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in IoT Environment
by
Saeed Bamashmos, Naveen Chilamkurti and Ahmad Salehi Shahraki
Sensors 2024, 24(11), 3575; https://doi.org/10.3390/s24113575 (registering DOI) - 1 Jun 2024
Abstract
Abstract: Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are
[...] Read more.
Abstract: Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.
Full article
(This article belongs to the Section Internet of Things)
Open AccessArticle
Transferring Learned Behaviors between Similar and Different Radios
by
Braeden P. Muller, Brennan E. Olds, Lauren J. Wong and Alan J. Michaels
Sensors 2024, 24(11), 3574; https://doi.org/10.3390/s24113574 (registering DOI) - 1 Jun 2024
Abstract
Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to
[...] Read more.
Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to increase the potential applicability of RFML algorithms, seeking to improve the portability of models for spectrum situational awareness and transmission source identification. Unlike most of the computer vision and natural language processing applications of TL, applications within the RF modality must contend with inherent hardware distortions and channel condition variations. This paper seeks to evaluate the feasibility and performance trade-offs when transferring learned behaviors from functional RFML classification algorithms, specifically those designed for automatic modulation classification (AMC) and specific emitter identification (SEI), between homogeneous radios of similar construction and quality and heterogeneous radios of different construction and quality. Results derived from both synthetic data and over-the-air experimental collection show promising performance benefits from the application of TL to the RFML algorithms of SEI and AMC.
Full article
(This article belongs to the Section State-of-the-Art Sensors Technologies)
Open AccessReview
Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot
by
Kornél Katona, Husam A. Neamah and Péter Korondi
Sensors 2024, 24(11), 3573; https://doi.org/10.3390/s24113573 (registering DOI) - 1 Jun 2024
Abstract
Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without
[...] Read more.
Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra’s algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies.
Full article
(This article belongs to the Special Issue Mobile Robots: Navigation, Control and Sensing)
Open AccessReview
Assistive Systems for Visually Impaired Persons: Challenges and Opportunities for Navigation Assistance
by
Gabriel Iluebe Okolo, Turke Althobaiti and Naeem Ramzan
Sensors 2024, 24(11), 3572; https://doi.org/10.3390/s24113572 (registering DOI) - 1 Jun 2024
Abstract
The inability to see makes moving around very difficult for visually impaired persons. Due to their limited movement, they also struggle to protect themselves against moving and non-moving objects. Given the substantial rise in the population of those with vision impairments in recent
[...] Read more.
The inability to see makes moving around very difficult for visually impaired persons. Due to their limited movement, they also struggle to protect themselves against moving and non-moving objects. Given the substantial rise in the population of those with vision impairments in recent years, there has been an increasing amount of research devoted to the development of assistive technologies. This review paper highlights the state-of-the-art assistive technology, tools, and systems for improving the daily lives of visually impaired people. Multi-modal mobility assistance solutions are also evaluated for both indoor and outdoor environments. Lastly, an analysis of several approaches is also provided, along with recommendations for the future.
Full article
(This article belongs to the Special Issue Smart Assistive Technologies for Special Individuals: Enhancing Accessibility and Independence)
Open AccessReview
Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques
by
Metehan Gelgi, Yueting Guan, Sanjay Arunachala, Maddi Samba Siva Rao and Nicola Dragoni
Sensors 2024, 24(11), 3571; https://doi.org/10.3390/s24113571 (registering DOI) - 1 Jun 2024
Abstract
Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size,
[...] Read more.
Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field.
Full article
(This article belongs to the Special Issue Network Security and IoT Security)
Open AccessArticle
Assessing Trail Running Biomechanics: A Comparative Analysis of the Reliability of StrydTM and GARMINRP Wearable Devices
by
César Berzosa, Cristina Comeras-Chueca, Pablo Jesus Bascuas, Héctor Gutiérrez and Ana Vanessa Bataller-Cervero
Sensors 2024, 24(11), 3570; https://doi.org/10.3390/s24113570 (registering DOI) - 1 Jun 2024
Abstract
This study investigated biomechanical assessments in trail running, comparing two wearable devices—Stryd Power Meter and GARMINRP. With the growing popularity of trail running and the complexities of varied terrains, there is a heightened interest in understanding metabolic pathways, biomechanics, and performance
[...] Read more.
This study investigated biomechanical assessments in trail running, comparing two wearable devices—Stryd Power Meter and GARMINRP. With the growing popularity of trail running and the complexities of varied terrains, there is a heightened interest in understanding metabolic pathways, biomechanics, and performance factors. The research aimed to assess the inter- and intra-device agreement for biomechanics under ecological conditions, focusing on power, speed, cadence, vertical oscillation, and contact time. The participants engaged in trail running sessions while wearing two Stryd and two Garmin devices. The intra-device reliability demonstrated high consistency for both GARMINRP and StrydTM, with strong correlations and minimal variability. However, distinctions emerged in inter-device agreement, particularly in power and contact time uphill, and vertical oscillation downhill, suggesting potential variations between GARMINRP and StrydTM measurements for specific running metrics. The study underscores that caution should be taken in interpreting device data, highlighting the importance of measuring with the same device, considering contextual and individual factors, and acknowledging the limited research under real-world trail conditions. While the small sample size and participant variations were limitations, the strength of this study lies in conducting this investigation under ecological conditions, significantly contributing to the field of biomechanical measurements in trail running.
Full article
(This article belongs to the Section Wearables)
►▼
Show Figures
Figure 1
Open AccessArticle
Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates
by
Jacek Górka, Wojciech Jamrozik, Bernard Wyględacz, Marta Kiel-Jamrozik and Batalha Gilmar Ferreira
Sensors 2024, 24(11), 3569; https://doi.org/10.3390/s24113569 (registering DOI) - 1 Jun 2024
Abstract
Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes
[...] Read more.
Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes the use of a virtual sensor, which, based on temperature distributions observed on the joint surface during the welding process, allows for the determination of hardness distribution across the cross-section of a joint. Welding trials were conducted with temperature recording, hardness measurements were taken, and then, neural networks with different hyperparameters were tested and evaluated. As a basis for developing a virtual sensor, LSTM networks were utilized, which can be applied to time series prediction, as in the analyzed case of hardness value sequences across the cross-section of a welded joint. Through the analysis of the obtained results, it was determined that the developed virtual sensor can be applied to predict global temperature changes in the weld area, in terms of both its value and geometry changes, with the mean average error being less than 20 HV (mean for model ~35 HV). However, in its current form, predicting local hardness disturbances resulting from process instabilities and defects is not feasible.
Full article
(This article belongs to the Special Issue Research Development in Terahertz and Infrared Sensing Technology)
►▼
Show Figures
Figure 1
Open AccessArticle
Reliability of Dynamic Shoulder Strength Test Battery Using Multi-Joint Isokinetic Device
by
Gustavo García-Buendía, Ángela Rodríguez-Perea, Ignacio Chirosa-Ríos, Luis Javier Chirosa-Ríos and Darío Martínez-García
Sensors 2024, 24(11), 3568; https://doi.org/10.3390/s24113568 (registering DOI) - 1 Jun 2024
Abstract
This study aimed to determine the absolute and relative reliability of concentric and eccentric flexion, extension, horizontal abduction, and adduction movements of the shoulder using a functional electromechanical dynamometer (FEMD). Forty-three active male university students (23.51 ± 4.72 years) were examined for concentric
[...] Read more.
This study aimed to determine the absolute and relative reliability of concentric and eccentric flexion, extension, horizontal abduction, and adduction movements of the shoulder using a functional electromechanical dynamometer (FEMD). Forty-three active male university students (23.51 ± 4.72 years) were examined for concentric and eccentric strength of shoulder flexion, extension, horizontal abduction, and horizontal adduction with an isokinetic test at 0.80 m·s−1. Relative reliability was determined by intraclass correlation coefficients (ICCs) with 95% confidence intervals. Absolute reliability was quantified by the standard error of measurement (SEM) and coefficient of variation (CV). Reliability was very high to extremely high for all movements on concentric and eccentric strength measurements (ICC: 0.76–0.94, SEM: 0.63–6.57%, CV: 9.40–19.63%). The results of this study provide compelling evidence for the absolute and relative reliability of concentric and eccentric flexion, extension, horizontal abduction, and horizontal adduction shoulder isokinetic strength tests in asymptomatic adults. The mean concentric force was the most reliable strength value for all tests.
Full article
(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)
►▼
Show Figures
Figure 1
Open AccessArticle
Visual and Quantitative Evaluation of Low-Concentration Bismuth in Dual-Contrast Imaging of Iodine and Bismuth Using Clinical Photon-Counting CT
by
Afrouz Ataei, Vasantha Vasan, Todd C. Soesbe, Cecelia C. Brewington, Zhongxing Zhou, Lifeng Yu, Kristina A. Hallam and Liqiang Ren
Sensors 2024, 24(11), 3567; https://doi.org/10.3390/s24113567 (registering DOI) - 1 Jun 2024
Abstract
Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human
[...] Read more.
Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human subjects. This investigation sought to assess the feasibility of visually differentiating and precisely quantifying low-concentration bismuth using clinical dual-source photon-counting CT (PCCT) in a scenario involving both iodinated and bismuth-based contrast materials. Four bismuth samples (0.6, 1.3, 2.5, and 5.1 mg/mL) were prepared using Pepto-Bismol, alongside three iodine rods (1, 2, and 5 mg/mL), inserted into multi-energy CT phantoms with three different sizes, and scanned on a PCCT system at three tube potentials (120, 140, and Sn140 kV). A generic image-based three-material decomposition method generated iodine and bismuth maps, with mean mass concentrations and noise levels measured. The root-mean-square errors for iodine and bismuth determined the optimal tube potential. The tube potential of 140 kV demonstrated optimal quantification performance when both iodine and bismuth were considered. Distinct differentiation of iodine rods with all three concentrations and bismuth samples with mass concentrations ≥ 1.3 mg/mL was observed across all phantom sizes at the optimal kV setting.
Full article
(This article belongs to the Special Issue Recent Advances in X-ray Sensing and Imaging)
►▼
Show Figures
Figure 1
Open AccessArticle
Defect Analysis in a Long-Wave Infrared HgCdTe Auger-Suppressed Photodiode
by
Małgorzata Kopytko, Kinga Majkowycz, Krzysztof Murawski, Jan Sobieski, Waldemar Gawron and Piotr Martyniuk
Sensors 2024, 24(11), 3566; https://doi.org/10.3390/s24113566 (registering DOI) - 1 Jun 2024
Abstract
Deep defects in the long-wave infrared (LWIR) HgCdTe heterostructure photodiode were measured via deep-level transient spectroscopy (DLTS) and photoluminescence (PL). The n+-P+-π-N+ photodiode structure was grown by following the metal–organic chemical vapor deposition (MOCVD) technique on a GaAs
[...] Read more.
Deep defects in the long-wave infrared (LWIR) HgCdTe heterostructure photodiode were measured via deep-level transient spectroscopy (DLTS) and photoluminescence (PL). The n+-P+-π-N+ photodiode structure was grown by following the metal–organic chemical vapor deposition (MOCVD) technique on a GaAs substrate. DLTS has revealed two defects: one electron trap with an activation energy value of 252 meV below the conduction band edge, located in the low n-type-doped transient layer at the π-N+ interface, and a second hole trap with an activation energy value of 89 meV above the valence band edge, located in the π absorber. The latter was interpreted as an isolated point defect, most probably associated with mercury vacancies (VHg). Numerical calculations applied to the experimental data showed that this VHg hole trap is the main cause of increased dark currents in the LWIR photodiode. The determined specific parameters of this trap were the capture cross-section for the holes of σp = 10−16–4 × 10−15 cm2 and the trap concentration of NT = 3–4 × 1014 cm−3. PL measurements confirmed that the trap lies approximately 83–89 meV above the valence band edge and its location.
Full article
(This article belongs to the Topic Modeling, Fabrication, and Characterization of Semiconductor Materials and Devices)
►▼
Show Figures
Figure 1
Open AccessArticle
Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching
by
Aliyu Abubakar and Christos Zachariades
Sensors 2024, 24(11), 3565; https://doi.org/10.3390/s24113565 (registering DOI) - 31 May 2024
Abstract
This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The
[...] Read more.
This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.
Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)
Open AccessArticle
Advanced Machine Learning Techniques for Corrosion Rate Estimation and Prediction in Industrial Cooling Water Pipelines
by
Desiree Ruiz, Abraham Casas, Cesar Adolfo Escobar, Alejandro Perez and Veronica Gonzalez
Sensors 2024, 24(11), 3564; https://doi.org/10.3390/s24113564 (registering DOI) - 31 May 2024
Abstract
This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit,
[...] Read more.
This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset.
Full article
(This article belongs to the Section Industrial Sensors)
Open AccessArticle
Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines
by
Ivan Malashin, Vadim Tynchenko, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Nikolay V. Krysko, Nikita A. Shchipakov, Denis M. Kozlov, Andrey G. Kusyy, Dmitry Martysyuk and Andrey Galinovsky
Sensors 2024, 24(11), 3563; https://doi.org/10.3390/s24113563 (registering DOI) - 31 May 2024
Abstract
The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, a dataset comprising 576,000 images of pipelines with and without pitting corrosion was curated. A custom-designed and optimized convolutional neural network (CNN) was employed for binary
[...] Read more.
The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, a dataset comprising 576,000 images of pipelines with and without pitting corrosion was curated. A custom-designed and optimized convolutional neural network (CNN) was employed for binary classification, distinguishing between corroded and non-corroded images. This CNN architecture, despite having relatively few parameters compared to existing CNN classifiers, achieved a notably high classification accuracy of 98.44%. The proposed CNN outperformed many contemporary classifiers in its efficacy. By leveraging deep learning, this approach effectively eliminates the need for manual inspection of pipelines for pitting corrosion, thus streamlining what was previously a time-consuming and cost-ineffective process.
Full article
(This article belongs to the Special Issue AI-Driven Sensing for Image Processing and Recognition)
Journal Menu
► ▼ Journal Menu-
- Sensors Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal Browser-
arrow_forward_ios
Forthcoming issue
arrow_forward_ios Current issue - Vol. 24 (2024)
- Vol. 23 (2023)
- Vol. 22 (2022)
- Vol. 21 (2021)
- Vol. 20 (2020)
- Vol. 19 (2019)
- Vol. 18 (2018)
- Vol. 17 (2017)
- Vol. 16 (2016)
- Vol. 15 (2015)
- Vol. 14 (2014)
- Vol. 13 (2013)
- Vol. 12 (2012)
- Vol. 11 (2011)
- Vol. 10 (2010)
- Vol. 9 (2009)
- Vol. 8 (2008)
- Vol. 7 (2007)
- Vol. 6 (2006)
- Vol. 5 (2005)
- Vol. 4 (2004)
- Vol. 3 (2003)
- Vol. 2 (2002)
- Vol. 1 (2001)
Highly Accessed Articles
Latest Books
E-Mail Alert
News
31 May 2024
Meet Us at the 2024 Opto-Electronics and Communications Conference, 30 June–4 July 2024, Melbourne, Australia
Meet Us at the 2024 Opto-Electronics and Communications Conference, 30 June–4 July 2024, Melbourne, Australia
27 May 2024
Meet Us at the 45th Canadian Symposium on Remote Sensing, 10–13 June 2024, Halifax, Canada
Meet Us at the 45th Canadian Symposium on Remote Sensing, 10–13 June 2024, Halifax, Canada
Topics
Topic in
Applied Sciences, Electricity, Electronics, Energies, Sensors
Power System Protection
Topic Editors: Seyed Morteza Alizadeh, Akhtar KalamDeadline: 20 June 2024
Topic in
Applied Sciences, Energies, Machines, Sensors, Vehicles
Vehicle Dynamics and Control
Topic Editors: Peter Gaspar, Junnian WangDeadline: 30 June 2024
Topic in
Acoustics, Environments, Remote Sensing, Sensors, Vehicles
Environmental Noise Prediction, Measurement and Control
Topic Editors: Bowen Hou, Jinhan MoDeadline: 20 July 2024
Topic in
Foods, Materials, Polymers, Sensors, Applied Sciences
Scientific Advances in STEM: Synergies to Achieve Success, 3rd Volume
Topic Editors: Yadir Torres Hernández, Ana María Beltrán Custodio, Manuel Félix ÁngelDeadline: 31 July 2024
Conferences
Special Issues
Special Issue in
Sensors
Thin Film Materials and Nanostructure Devices for Sensing Applications
Guest Editors: Hugo Aguas, Matteo TonezzerDeadline: 1 June 2024
Special Issue in
Sensors
Novel Sensors and Algorithms for Outdoor Mobile Robot
Guest Editors: Levente Tamás, Andras MajdikDeadline: 20 June 2024
Special Issue in
Sensors
Deep Learning Methods for Human Activity Recognition and Emotion Detection
Guest Editor: Mario Munoz-OrganeroDeadline: 30 June 2024
Special Issue in
Sensors
Detection and Measurement of Radioactive Noble Gases
Guest Editor: Dobromir PressyanovDeadline: 20 July 2024
Topical Collections
Topical Collection in
Sensors
Robotic and Sensor Technologies in Environmental Exploration and Monitoring
Collection Editors: Jacopo Aguzzi, Corrado Costa, Sergio Stefanni, Valerio Funari
Topical Collection in
Sensors
Microfluidic Sensors
Collection Editors: Sabina Merlo, Klaus Stefan Drese