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
Modeling and Optimization of an Enhanced Soft Sensor for the Fermentation Process of Pichia pastoris
Sensors 2024, 24(10), 3017; https://doi.org/10.3390/s24103017 (registering DOI) - 9 May 2024
Abstract
This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of Pichia pastoris, caused by changes in working conditions. Initially, the transfer component analysis (TCA) method is utilized
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This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of Pichia pastoris, caused by changes in working conditions. Initially, the transfer component analysis (TCA) method is utilized to minimize the differences in data distribution across various working conditions. Subsequently, a least squares support vector machine (LSSVM) model is constructed using the dataset adapted by TCA, and strategies for improving the northern goshawk optimization (INGO) algorithm are proposed to optimize the parameters of the LSSVM model. Finally, to further enhance the model’s generalization ability and prediction accuracy, considering the transfer of knowledge from multiple-source working conditions, a sub-model weighted ensemble scheme is proposed based on the maximum information coefficient (MIC) algorithm. The proposed soft sensor model is employed to predict cell and product concentrations during the fermentation process of Pichia pastoris. Simulation results indicate that the RMSE of the INGO-LSSVM model in predicting cell and product concentrations is reduced by 47.3% and 42.1%, respectively, compared to the NGO-LSSVM model. Additionally, TCA significantly enhances the model’s adaptability when working conditions change. Moreover, the soft sensor model based on TCA and the MIC-weighted ensemble method achieves a reduction of 41.6% and 31.3% in the RMSE for predicting cell and product concentrations, respectively, compared to the single-source condition transfer model TCA-INGO-LSSVM. These results demonstrate the high reliability and predictive performance of the proposed soft sensor method under varying working conditions.
Full article
(This article belongs to the Special Issue Sensors-Based Biomarker Detection and Bioinformatics Analysis)
Open AccessArticle
Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance
by
Daniel Vera-Yanez, António Pereira, Nuno Rodrigues, José Pascual Molina, Arturo S. García and Antonio Fernández-Caballero
Sensors 2024, 24(10), 3016; https://doi.org/10.3390/s24103016 - 9 May 2024
Abstract
The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated
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The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.
Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
Photothermal Radiometry Data Analysis by Using Machine Learning
by
Perry Xiao and Daqing Chen
Sensors 2024, 24(10), 3015; https://doi.org/10.3390/s24103015 - 9 May 2024
Abstract
Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new
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Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new data, without being explicitly programmed to do so. In this paper, we first review the latest development of machine learning and its applications in photothermal techniques. Next, we present our latest work on machine learning for data analysis in opto-thermal transient emission radiometry (OTTER), which is a type of photothermal technique that has been extensively used in skin hydration, skin hydration depth profiles, skin pigments, as well as topically applied substances and skin penetration measurements. We have investigated different algorithms, such as random forest regression, gradient boosting regression, support vector machine (SVM) regression, and partial least squares regression, as well as deep learning neural network regression. We first introduce the theoretical background, then illustrate its applications with experimental results.
Full article
(This article belongs to the Special Issue Photonics for Advanced Spectroscopy and Sensing)
Open AccessArticle
Multi-Drone Cooperation for Improved LiDAR-Based Mapping
by
Flavia Causa, Roberto Opromolla and Giancarmine Fasano
Sensors 2024, 24(10), 3014; https://doi.org/10.3390/s24103014 - 9 May 2024
Abstract
This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g.,
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This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case.
Full article
(This article belongs to the Special Issue Multi-Sensor Fusion for Positioning and Navigation of Aerospace Vehicles)
Open AccessArticle
1 V Tunable High-Quality Universal Filter Using Multiple-Input Operational Transconductance Amplifiers
by
Montree Kumngern, Fabian Khateb, Tomasz Kulej and Boonying Knobnob
Sensors 2024, 24(10), 3013; https://doi.org/10.3390/s24103013 - 9 May 2024
Abstract
This paper presents a new multiple-input single-output voltage-mode universal filter employing four multiple-input operational transconductance amplifiers (MI-OTAs) and three grounded capacitors suitable for low-voltage low-frequency applications. The quality factor (Q) of the filter functions can be tuned by both the capacitance
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This paper presents a new multiple-input single-output voltage-mode universal filter employing four multiple-input operational transconductance amplifiers (MI-OTAs) and three grounded capacitors suitable for low-voltage low-frequency applications. The quality factor (Q) of the filter functions can be tuned by both the capacitance ratio and the transconductance ratio. The multiple inputs of the OTA are realized using the bulk-driven multiple-input MOS transistor technique. The MI-OTA-based filter can also offer many filtering functions without additional circuitry requirements, such as an inverting amplifier to generate an inverted input signal. The proposed filter can simultaneously realize low-pass, high-pass, band-pass, band-stop, and all-pass responses, covering both non-inverting and inverting transfer functions in a single topology. The natural frequency and the quality factors of all the filtering functions can be controlled independently. The natural frequency can also be electronically controlled by tuning the transconductances of the OTAs. The proposed filter uses a 1 V supply voltage, consumes 120 μW of power for a 5 μA setting current, offers 40 dB of dynamic range and has a third intermodulation distortion of −43.6 dB. The performances of the proposed circuit were simulated using a 0.18 μm TSMC CMOS process in the Cadence Virtuoso System Design Platform to confirm the performance of the topology.
Full article
(This article belongs to the Section Electronic Sensors)
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Open AccessArticle
In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
by
Günter Vermeeren, Leen Verloock, Sam Aerts, Luc Martens and Wout Joseph
Sensors 2024, 24(10), 3012; https://doi.org/10.3390/s24103012 - 9 May 2024
Abstract
In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near
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In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%.
Full article
(This article belongs to the Section Communications)
Open AccessArticle
A Statistical and AI Analysis of the Frequency Spectrum in the Measurement of the Center of Pressure Track in the Seated Position in Healthy Subjects and Subjects with Low Back Pain CoP-T
by
Jan Jens Koltermann, Philipp Floessel, Franziska Hammerschmidt and Alexander C. Disch
Sensors 2024, 24(10), 3011; https://doi.org/10.3390/s24103011 - 9 May 2024
Abstract
Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and
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Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and consideration of the results in the spectrum in conjunction with an AI-supported evaluation could represent an alternative diagnostic method quantifying neuromuscular control. In a prospective cross-sectional study, 188 subjects aged between 18 and 60 years were recruited and divided into two groups: “LowBackPain” vs. “Healthy”. Subsequently, measurements of postural control in a seated position were carried out for 60 s using a modified balance board. A spectrum per trail was calculated using the measured CoP tracks in the range from 0.01 to 10 Hz. Various algorithms for data classification and prediction of these classes were tested for the parameter combination with the highest proven static influence on the parameter pain. The best results were found in a frequency spectrum of 0.001 Hz and greater than 1 Hz. After transforming the track from the time domain to the image domain for representation as power density, the influence of pain was highly significant (effect size 0.9). The link between pain and gender (p = 0.015) and pain and height (p = 0.012) also demonstrated significant results. The assessment of postural control in a seated position allows differentiation between “LowBackPain” and “Healthy” subjects. Using the AI algorithm of neural networks, the data set can be correctly differentiated into “LowBackPain” and “Healthy” with a probability of 81%.
Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
Open AccessArticle
Discrete-Time Adaptive Control for Three-Phase PWM Rectifier
by
Bo Hou, Jiayan Qi and Huan Li
Sensors 2024, 24(10), 3010; https://doi.org/10.3390/s24103010 - 9 May 2024
Abstract
This paper proposes a dual-loop discrete-time adaptive control (DDAC) method for three-phase PWM rectifiers, which considers inductance-parameter-mismatched and DC load disturbances. A discrete-time model of the three-phase PWM rectifier is established using the forward Euler discretization method, and a dual-loop discrete-time feedback linearization
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This paper proposes a dual-loop discrete-time adaptive control (DDAC) method for three-phase PWM rectifiers, which considers inductance-parameter-mismatched and DC load disturbances. A discrete-time model of the three-phase PWM rectifier is established using the forward Euler discretization method, and a dual-loop discrete-time feedback linearization control (DDFLC) is given. Based on the DDFLC, the DDAC is designed. Firstly, an adaptive inductance disturbance observer (AIDO) based on the gradient descent method is proposed in the current control loop. The AIDO is used to estimate lump disturbances caused by mismatched inductance parameters and then compensate for these disturbances in the current controller, ensuring its strong robustness to inductance parameters. Secondly, a load parameter adaptive law (LPAL) based on the discrete-time Lyapunov theory is proposed for the voltage control loop. The LPAL estimates the DC load parameter in real time and subsequently adjusts it in the voltage controller, achieving DC load adaptability. Finally, simulation and experimental results show that the DDAC exhibits better steady and dynamic performances, less current harmonic content than the DDFLC and the dual-loop discrete-time PI control (DDPIC), and a stronger robustness to inductance parameters and DC load disturbances.
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(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Fatigue Analysis of a Jacket-Supported Offshore Wind Turbine at Block Island Wind Farm
by
Nasim Partovi-Mehr, John DeFrancisci, Mohsen Minaeijavid, Babak Moaveni, Daniel Kuchma, Christopher D. P. Baxter, Eric M. Hines and Aaron S. Bradshaw
Sensors 2024, 24(10), 3009; https://doi.org/10.3390/s24103009 - 9 May 2024
Abstract
Offshore wind-turbine (OWT) support structures are subjected to cyclic dynamic loads with variations in loadings from wind and waves as well as the rotation of blades throughout their lifetime. The magnitude and extent of the cyclic loading can create a fatigue limit state
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Offshore wind-turbine (OWT) support structures are subjected to cyclic dynamic loads with variations in loadings from wind and waves as well as the rotation of blades throughout their lifetime. The magnitude and extent of the cyclic loading can create a fatigue limit state controlling the design of support structures. In this paper, the remaining fatigue life of the support structure for a GE Haliade 6 MW fixed-bottom jacket offshore wind turbine within the Block Island Wind Farm (BIWF) is assessed. The fatigue damage to the tower and the jacket support structure using stress time histories at instrumented and non-instrumented locations are processed. Two validated finite-element models are utilized for assessing the stress cycles. The modal expansion method and a simplified approach using static calculations of the responses are employed to estimate the stress at the non-instrumented locations—known as virtual sensors. It is found that the hotspots at the base of the tower have longer service lives than the jacket. The fatigue damage to the jacket leg joints is less than 20% and 40% of its fatigue capacity during the 25-year design lifetime of the BIWF OWT, using the modal expansion method and the simplified static approach, respectively.
Full article
(This article belongs to the Special Issue Smart Sensors for Machine Condition Monitoring and Fault Diagnosis)
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Open AccessArticle
A Portable Readout System for Biomarker Detection with Aptamer-Modified CMOS ISFET Array
by
Dmitriy Ryazantsev, Mark Shustinskiy, Andrey Sheshil, Alexey Titov, Vitaliy Grudtsov, Valerii Vechorko, Irakli Kitiashvili, Kirill Puchnin, Alexander Kuznetsov and Natalia Komarova
Sensors 2024, 24(10), 3008; https://doi.org/10.3390/s24103008 - 9 May 2024
Abstract
Biosensors based on ion-sensitive field effect transistors (ISFETs) combined with aptamers offer a promising and convenient solution for point-of-care testing applications due to the ability for fast and label-free detection of a wide range of biomarkers. Mobile and easy-to-use readout devices for the
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Biosensors based on ion-sensitive field effect transistors (ISFETs) combined with aptamers offer a promising and convenient solution for point-of-care testing applications due to the ability for fast and label-free detection of a wide range of biomarkers. Mobile and easy-to-use readout devices for the ISFET aptasensors would contribute to further development of the field. In this paper, the development of a portable PC-controlled device for detecting aptamer-target interactions using ISFETs is described. The device assembly allows selective modification of individual ISFETs with different oligonucleotides. Ta2O5-gated ISFET structures were optimized to minimize trapped charge and capacitive attenuation. Integrated CMOS readout circuits with linear transfer function were used to minimize the distortion of the original ISFET signal. An external analog signal digitizer with constant voltage and superimposed high-frequency sine wave reference voltage capabilities was designed to increase sensitivity when reading ISFET signals. The device performance was demonstrated with the aptamer-driven detection of troponin I in both reference voltage setting modes. The sine wave reference voltage measurement method reduced the level of drift over time and enabled a lowering of the minimum detectable analyte concentration. In this mode (constant voltage 2.4 V and 10 kHz 0.1Vp-p), the device allowed the detection of troponin I with a limit of detection of 3.27 ng/mL. Discrimination of acute myocardial infarction was demonstrated with the developed device. The ISFET device provides a platform for the multiplexed detection of different biomarkers in point-of-care testing.
Full article
(This article belongs to the Special Issue Micro/Nano Biosensors and Devices)
Open AccessArticle
Flexible Self-Powered Low-Decibel Voice Recognition Mask
by
Jianing Li, Yating Shi, Jianfeng Chen, Qiaoling Huang, Meidan Ye and Wenxi Guo
Sensors 2024, 24(10), 3007; https://doi.org/10.3390/s24103007 - 9 May 2024
Abstract
In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this
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In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts.
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(This article belongs to the Special Issue Advances in Flexible Self-Powered Electronics Sensors)
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PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network
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Wai-Lun Lo, Henry Shu-Hung Chung, Richard Tai-Chiu Hsung, Hong Fu and Tak-Wai Shen
Sensors 2024, 24(10), 3006; https://doi.org/10.3390/s24103006 - 9 May 2024
Abstract
Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently,
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Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I–V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I–V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles’ population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I–V dataset and an I–V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.
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(This article belongs to the Special Issue Sensing Technology in Artificial Intelligence and Intelligent Control Systems)
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Open AccessArticle
Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry
by
Justin Gilmore and Mona Nasseri
Sensors 2024, 24(10), 3005; https://doi.org/10.3390/s24103005 - 9 May 2024
Abstract
Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion
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Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05).
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(This article belongs to the Section Wearables)
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Implementation of a Long Short-Term Memory Neural Network-Based Algorithm for Dynamic Obstacle Avoidance
by
Esmeralda Mulás-Tejeda, Alfonso Gómez-Espinosa, Jesús Arturo Escobedo Cabello, Jose Antonio Cantoral-Ceballos and Alejandra Molina-Leal
Sensors 2024, 24(10), 3004; https://doi.org/10.3390/s24103004 - 9 May 2024
Abstract
Autonomous mobile robots are essential to the industry, and human–robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a
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Autonomous mobile robots are essential to the industry, and human–robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a long short-term memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack motion capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.
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(This article belongs to the Section Navigation and Positioning)
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Open AccessArticle
Electric Susceptibility at Partial Coverage of a Circular One-Side Access Capacitive Sensor with Rigid Polyurethane Foams
by
Ilze Beverte
Sensors 2024, 24(10), 3003; https://doi.org/10.3390/s24103003 - 9 May 2024
Abstract
The capability of dielectric measurements was significantly increased with the development of capacitive one-side access physical sensors. Complete samples give no opportunity to study electric susceptibility at a partial coverage of the one-side access sensor’s active area; therefore, partial samples are proposed. The
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The capability of dielectric measurements was significantly increased with the development of capacitive one-side access physical sensors. Complete samples give no opportunity to study electric susceptibility at a partial coverage of the one-side access sensor’s active area; therefore, partial samples are proposed. The electric susceptibility at the partial coverage of a circular one-side access sensor with cylinders and shells is investigated for polyurethane materials. The implementation of the relative partial susceptibility permitted us to transform the calculated susceptibility data to a common scale of 0.0–1.0 and to outline the main trends for PU materials. The partial susceptibility, relative partial susceptibility, and change rate of relative partial susceptibility exhibited dependence on the coverage coefficient of the sensor’s active area. The overall character of the curves for the change rate of the relative partial susceptibility, characterised by slopes of lines and the ratio of the change rate in the centre and near the gap, corresponds with the character of the surface charge density distribution curves, calculated from mathematical models. The elaborated methods may be useful in the design and optimization of capacitive OSA sensors of other configurations of electrodes, independent of the particular technical solution.
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(This article belongs to the Special Issue Sensors in 2024)
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Open AccessArticle
Research on Bearing Surface Scratch Detection Based on Improved YOLOV5
by
Huakun Jia, Huimin Zhou, Zhehao Chen, Rongke Gao, Yang Lu and Liandong Yu
Sensors 2024, 24(10), 3002; https://doi.org/10.3390/s24103002 - 9 May 2024
Abstract
Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection
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Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection of bearing surface scratches. An improved YOLOV5 network, named YOLOV5-CDG, is proposed for detecting bearing surface defects using scratch images as targets. The YOLOV5-CDG model is based on the YOLOV5 network model with the addition of a Coordinate Attention (CA) mechanism module, fusion of Deformable Convolutional Networks (DCNs), and a combination with the GhostNet lightweight network. To achieve bearing surface scratch detection, a machine vision-based bearing surface scratch sensor system is established, and a self-made bearing surface scratch dataset is produced as the basis. The scratch detection final Average Precision (AP) value is 97%, which is 3.4% higher than that of YOLOV5. Additionally, the model has an accuracy of 99.46% for detecting defective and qualified products. The average detection time per image is 263.4 ms on the CPU device and 12.2 ms on the GPU device, demonstrating excellent performance in terms of both speed and accuracy. Furthermore, this study analyzes and compares the detection results of various models, demonstrating that the proposed method satisfies the requirements for detecting scratches on bearing surfaces in industrial settings.
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(This article belongs to the Special Issue Fault Diagnosis Platform Based on the IoT and Intelligent Computing)
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Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks
by
Amjad Alam, Purav Shah, Ramona Trestian, Kamran Ali and Glenford Mapp
Sensors 2024, 24(10), 3001; https://doi.org/10.3390/s24103001 - 9 May 2024
Abstract
With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned
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With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency.
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(This article belongs to the Special Issue Sustainable Intelligent and Connected Transportation)
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Open AccessArticle
A Brain-Controlled and User-Centered Intelligent Wheelchair: A Feasibility Study
by
Xun Zhang, Jiaxing Li, Ruijie Zhang and Tao Liu
Sensors 2024, 24(10), 3000; https://doi.org/10.3390/s24103000 - 9 May 2024
Abstract
Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past,
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Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain–machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.
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(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis)
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Printed Thick Film Resistance Temperature Detector for Real-Time Tube Furnace Temperature Monitoring
by
Zhenyin Hai, Zhixuan Su, Kaibo Zhu, Yue Pan and Suying Luo
Sensors 2024, 24(10), 2999; https://doi.org/10.3390/s24102999 - 9 May 2024
Abstract
Accurately acquiring crucial data on tube furnaces and real-time temperature monitoring of different temperature zones is vital for material synthesis technology in production. However, it is difficult to achieve real-time monitoring of the temperature field of tube furnaces with existing technology. Here, we
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Accurately acquiring crucial data on tube furnaces and real-time temperature monitoring of different temperature zones is vital for material synthesis technology in production. However, it is difficult to achieve real-time monitoring of the temperature field of tube furnaces with existing technology. Here, we proposed a method to fabricate silver (Ag) resistance temperature detectors (RTDs) based on a blade-coating process directly on the surface of a quartz ring, which enables precise positioning and real-time temperature monitoring of tube furnaces within 100–600 °C range. The Ag RTDs exhibited outstanding electrical properties, featuring a temperature coefficient of resistance (TCR) of 2854 ppm/°C, an accuracy of 1.8% FS (full scale), and a resistance drift rate of 0.05%/h over 6 h at 600 °C. These features ensured accurate and stable temperature measurement at high temperatures. For demonstration purposes, an array comprising four Ag RTDs was installed in a tube furnace. The measured average temperature gradient in the central region of the tube furnace was 5.7 °C/mm. Furthermore, successful real-time monitoring of temperature during the alloy sintering process revealed approximately a 20-fold difference in resistivity for silver-palladium alloys sintered at various positions within the tubular furnace. The proposed strategy offers a promising approach for real-time temperature monitoring of tube furnaces.
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(This article belongs to the Special Issue Sensors for Real-Time Condition Monitoring and Fault Diagnosis)
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Deep Learning-Enhanced Sampling-Based Path Planning for LTL Mission Specifications
by
Changmin Baek and Kyunghoon Cho
Sensors 2024, 24(10), 2998; https://doi.org/10.3390/s24102998 - 9 May 2024
Abstract
The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution
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The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution of this paper is the presentation of a refined approach to sampling-based path planning algorithms that aligns with the specified mission objectives. This enhancement is achieved through a multi-layered framework approach, enabling a simplified discrete abstraction without relying on mesh decomposition. This abstraction is especially beneficial in complex or high-dimensional environments where mesh decomposition is challenging. The discrete abstraction effectively guides the sampling process, influencing the selection of vertices for extension and target points for steering in each iteration. To further improve efficiency, the algorithm incorporates a deep learning-based extension, utilizing training data to accurately model the optimal trajectory distribution between two points. The effectiveness of the proposed method is demonstrated through simulated tests, which highlight its ability to identify low-cost trajectories that meet specific mission criteria. Comparative analyses also confirm the superiority of the proposed method compared to existing methods.
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(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
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