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Sensors, Volume 24, Issue 22 (November-2 2024) – 253 articles

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23 pages, 17187 KiB  
Article
Human Daily Breathing Monitoring via Analysis of CSI Ratio Trajectories for WiFi Link Pairs on the I/Q Plane
by Wei Zhuang, Yuhang Lu, Yixian Shen and Jian Su
Sensors 2024, 24(22), 7352; https://doi.org/10.3390/s24227352 (registering DOI) - 18 Nov 2024
Abstract
The measurement of human breathing is crucial for assessing the condition of the body. It opens up possibilities for various intelligent applications, like advanced medical monitoring and sleep analysis. Conventional approaches relying on wearable devices tend to be expensive and inconvenient for users. [...] Read more.
The measurement of human breathing is crucial for assessing the condition of the body. It opens up possibilities for various intelligent applications, like advanced medical monitoring and sleep analysis. Conventional approaches relying on wearable devices tend to be expensive and inconvenient for users. Recent research has shown that inexpensive WiFi devices commonly available in the market can be utilized effectively for non-contact breathing monitoring. WiFi-based breathing monitoring is highly sensitive to motion during the breathing process. This sensitivity arises because current methods primarily rely on extracting breathing signals from the amplitude and phase variations of WiFi Channel State Information (CSI) during breathing. However, these variations can be masked by body movements, leading to inaccurate counting of breathing cycles. To address this issue, we propose a method for extracting breathing signals based on the trajectories of two-chain CSI ratios on the I/Q plane. This method accurately monitors breathing by tracking and identifying the inflection points of the CSI ratio samples’ trajectories on the I/Q plane throughout the breathing cycle. We propose a dispersion model to label and filter out CSI ratio samples representing significant motion interference, thereby enhancing the robustness of the breathing monitoring system. Furthermore, to obtain accurate breathing waveforms, we propose a method for fitting the trajectory curve of the CSI ratio samples. Based on the fitted curve, a breathing segment extraction algorithm is introduced, enabling precise breathing monitoring. Our experimental results demonstrate that this approach achieves minimal error and significantly enhances the accuracy of WiFi-based breathing monitoring. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Wireless Sensor Networks and the Internet of Things)
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24 pages, 9386 KiB  
Article
Toward Improving Human Training by Combining Wearable Full-Body IoT Sensors and Machine Learning
by Nazia Akter, Andreea Molnar and Dimitrios Georgakopoulos
Sensors 2024, 24(22), 7351; https://doi.org/10.3390/s24227351 (registering DOI) - 18 Nov 2024
Abstract
This paper proposes DigitalUpSkilling, a novel IoT- and AI-based framework for improving and personalising the training of workers who are involved in physical-labour-intensive jobs. DigitalUpSkilling uses wearable IoT sensors to observe how individuals perform work activities. Such sensor observations are continuously processed to [...] Read more.
This paper proposes DigitalUpSkilling, a novel IoT- and AI-based framework for improving and personalising the training of workers who are involved in physical-labour-intensive jobs. DigitalUpSkilling uses wearable IoT sensors to observe how individuals perform work activities. Such sensor observations are continuously processed to synthesise an avatar-like kinematic model for each worker who is being trained, referred to as the worker’s digital twins. The framework incorporates novel work activity recognition using generative adversarial network (GAN) and machine learning (ML) models for recognising the types and sequences of work activities by analysing an individual’s kinematic model. Finally, the development of skill proficiency ML is proposed to evaluate each trainee’s proficiency in work activities and the overall task. To illustrate DigitalUpSkilling from wearable IoT-sensor-driven kinematic models to GAN-ML models for work activity recognition and skill proficiency assessment, the paper presents a comprehensive study on how specific meat processing activities in a real-world work environment can be recognised and assessed. In the study, DigitalUpSkilling achieved 99% accuracy in recognising specific work activities performed by meat workers. The study also presents an evaluation of the proficiency of workers by comparing kinematic data from trainees performing work activities. The proposed DigitalUpSkilling framework lays the foundation for next-generation digital personalised training. Full article
(This article belongs to the Special Issue Wearable and Mobile Sensors and Data Processing—2nd Edition)
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21 pages, 5749 KiB  
Article
Research on Improved GPC of Pantograph Considering Actuator Time Delay and External Disturbance
by Ying Wang, Yixuan Wang, Xiaoqiang Chen, Yuting Wang and Aiping Ma
Sensors 2024, 24(22), 7350; https://doi.org/10.3390/s24227350 (registering DOI) - 18 Nov 2024
Abstract
Active control of pantograph is an effective method to improve the current received quality in electrified railway systems. To alleviate the negative impact of time delay in pantograph actuator, a Controlled Auto-Regressive Integrated Moving Average (CARIMA) model was designed for pantograph active control. [...] Read more.
Active control of pantograph is an effective method to improve the current received quality in electrified railway systems. To alleviate the negative impact of time delay in pantograph actuator, a Controlled Auto-Regressive Integrated Moving Average (CARIMA) model was designed for pantograph active control. In conjunction with the Euler-Bernoulli catenary model, an improved generalized predictive control (IGPC) algorithm was proposed, and its stability was analyzed. Then, the control performance was verified and discussed through testing. Subsequently, the effects of external disturbances and time delay on control performance were discussed. The results indicate that the proposed controller with a larger control gain, exhibits better performance in reducing fluctuation in contact force between pantograph and catenary, despite being affected by external disturbance and actuator time delay, it still shows significant control performance. Full article
(This article belongs to the Section Electronic Sensors)
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21 pages, 4050 KiB  
Article
Visual Localization Based on Torus-Like Surfaces
by Xuandong Liu, Lihong Luo and Bingren Shen
Sensors 2024, 24(22), 7349; https://doi.org/10.3390/s24227349 (registering DOI) - 18 Nov 2024
Abstract
Previous visual localization started from point correspondences (PCs) to estimate poses. This article takes the camera position as the entry point and finds that the camera position solution set rotates around an axis connected by two observed 3D points to form a surface [...] Read more.
Previous visual localization started from point correspondences (PCs) to estimate poses. This article takes the camera position as the entry point and finds that the camera position solution set rotates around an axis connected by two observed 3D points to form a surface called a torus-like surface (TLS). The relevant parameters of TLS are calculated based on PCs and camera intrinsic parameters. In order to reduce the number of solutions, this article uses four PCs to construct three TLSs. By utilizing four PCs, the pose determination problem is reformulated as the task of finding the optimal intersection of three surfaces. By using the step-size adaptive tracking method, the candidate set of intersections can be quickly and accurately found. Combining the feature information of intersections on TLS and the camera intrinsic parameters, the optimal position is obtained. Based on this position, the rotation matrix can be determined. In the synthetic data experiments and the dataset experiments based on image localization, it is shown that the visual localization based on TLS is more accurate than current state-of-the-art methods, which provides a new entry angle and effective ideas for visual localization. Its accuracy and practicality are fully demonstrated in the application test of augmented reality indoor navigation. Full article
(This article belongs to the Section Navigation and Positioning)
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10 pages, 8355 KiB  
Communication
Triple Spectral Line Imaging of Whole-Body Human Skin: Equipment, Image Processing, and Clinical Data
by Janis Spigulis, Uldis Rubins, Edgars Kviesis-Kipge, Inga Saknite, Ilze Oshina and Egija Vasilisina
Sensors 2024, 24(22), 7348; https://doi.org/10.3390/s24227348 (registering DOI) - 18 Nov 2024
Abstract
Multispectral imaging can provide objective quantitative data on various clinical pathologies, e.g., abnormal content of bio-substances in human skin. Performance of diagnostics increases with decreased spectral bandwidths of imaging; from this point, ultra-narrowband laser spectral line imaging is well suited for diagnostic applications. [...] Read more.
Multispectral imaging can provide objective quantitative data on various clinical pathologies, e.g., abnormal content of bio-substances in human skin. Performance of diagnostics increases with decreased spectral bandwidths of imaging; from this point, ultra-narrowband laser spectral line imaging is well suited for diagnostic applications. In this study, 40 volunteers participated in clinical validation tests of a newly developed prototype device for triple laser line whole-body skin imaging. The device comprised a vertically movable high-resolution camera coupled with a specific illumination unit—a side-emitting optical fiber spiral that emits simultaneously three RGB laser spectral lines at the wavelengths 450 nm, 520 nm, and 628 nm. The prototype’s design details, skin spectral image processing, and the obtained first clinical data are reported and discussed. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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17 pages, 8896 KiB  
Article
MST-YOLO: Small Object Detection Model for Autonomous Driving
by Mingjing Li, Xinyang Liu, Shuang Chen, Le Yang, Qingyu Du, Ziqing Han and Junshuai Wang
Sensors 2024, 24(22), 7347; https://doi.org/10.3390/s24227347 (registering DOI) - 18 Nov 2024
Abstract
Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, [...] Read more.
Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures. To address this challenge, the MST-YOLOv8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model’s ability to detect small objects, is proposed. This paper introduces mixed local channel attention (MLCA) into the C2f structure, enabling the model to pay more attention to the region of small objects. A P2 detection layer is added to the neck part of the YOLOv8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. Compared with the original YOLOv8 model, MST-YOLOv8 demonstrates a 3.43% improvement in precision (P), an 8.15% improvement in recall (R), an 8.42% increase in mAP_0.5, a reduction in missed detection rate by 18.47%, a 70.97% improvement in small object detection AP, and a 68.92% improvement in AR. Full article
(This article belongs to the Section Intelligent Sensors)
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1 pages, 143 KiB  
Correction
Correction: Kaur, M.; Menon, C. Submillimeter Sized 2D Electrothermal Optical Fiber Scanner. Sensors 2023, 23, 404
by Mandeep Kaur and Carlo Menon
Sensors 2024, 24(22), 7346; https://doi.org/10.3390/s24227346 (registering DOI) - 18 Nov 2024
Viewed by 33
Abstract
The Editorial Office and Editorial Board of Sensors are jointly issuing a resolution and removal of the Journal Notice linked to this article [...] Full article
(This article belongs to the Section Physical Sensors)
19 pages, 8885 KiB  
Article
Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning
by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan and Yuhong Li
Sensors 2024, 24(22), 7345; https://doi.org/10.3390/s24227345 (registering DOI) - 18 Nov 2024
Viewed by 124
Abstract
The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a [...] Read more.
The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring. Full article
(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)
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13 pages, 14573 KiB  
Article
A Feature Integration Network for Multi-Channel Speech Enhancement
by Xiao Zeng, Xue Zhang and Mingjiang Wang
Sensors 2024, 24(22), 7344; https://doi.org/10.3390/s24227344 (registering DOI) - 18 Nov 2024
Viewed by 139
Abstract
Multi-channel speech enhancement has become an active area of research, demonstrating excellent performance in recovering desired speech signals from noisy environments. Recent approaches have increasingly focused on leveraging spectral information from multi-channel inputs, yielding promising results. In this study, we propose a novel [...] Read more.
Multi-channel speech enhancement has become an active area of research, demonstrating excellent performance in recovering desired speech signals from noisy environments. Recent approaches have increasingly focused on leveraging spectral information from multi-channel inputs, yielding promising results. In this study, we propose a novel feature integration network that not only captures spectral information but also refines it through shifted-window-based self-attention, enhancing the quality and precision of the feature extraction. Our network consists of blocks containing a full- and sub-band LSTM module for capturing spectral information, and a global–local attention fusion module for refining this information. The full- and sub-band LSTM module integrates both full-band and sub-band information through two LSTM layers, while the global–local attention fusion module learns global and local attention in a dual-branch architecture. To further enhance the feature integration, we fuse the outputs of these branches using a spatial attention module. The model is trained to predict the complex ratio mask (CRM), thereby improving the quality of the enhanced signal. We conducted an ablation study to assess the contribution of each module, with each showing a significant impact on performance. Additionally, our model was trained on the SPA-DNS dataset using a circular microphone array and the Libri-wham dataset with a linear microphone array, achieving competitive results compared to state-of-the-art models. Full article
(This article belongs to the Section Sensor Networks)
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12 pages, 1118 KiB  
Article
Influence of Bladder Filling on Parameters of Body Composition by Bioimpedance Electrical Analysis: Observational Study
by Asunción Ferri-Morales, Sara Ando-Lafuente, Cristina Lirio-Romero, Emanuele Marzetti and Elisabeth Bravo-Esteban
Sensors 2024, 24(22), 7343; https://doi.org/10.3390/s24227343 (registering DOI) - 18 Nov 2024
Viewed by 174
Abstract
Bioelectrical impedance analysis (BIA) is a widely used method for estimating body composition, and its accuracy may be influenced by various factors, including bladder filling. This study aims to investigate the impact of bladder filling on the accuracy of BIA measurements. An experimental [...] Read more.
Bioelectrical impedance analysis (BIA) is a widely used method for estimating body composition, and its accuracy may be influenced by various factors, including bladder filling. This study aims to investigate the impact of bladder filling on the accuracy of BIA measurements. An experimental crossover study was conducted with sedentary young adults. The influence of bladder filling on total body water (TBW), fat mass (FM), fat-free mass (FFM), and basal metabolic rate (BMR) was assessed. Participant in underwear followed an overnight fast. They were instructed to abstain from vigorous physical activity and alcohol for at least 24 h prior to the session. The results obtained from single-frequency and multi-frequency BIA devices were compared. The findings suggest that bladder filling does not affect measured impedance; however, changes in weight following bladder voiding influenced derived BIA results. Specifically, TBW, FM, and BMR values significantly reduced after voiding (p < 0.05). Furthermore, the study found poor agreement between single-frequency and multi-frequency BIA devices, indicating that they are not interchangeable. Bladder filling does affect BIA measurements, not clinically meaningful. Further research is needed to explore the implications of these findings for clinical practice and research protocols. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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18 pages, 891 KiB  
Article
Prediction of Margin of Gait Stability by Using Six-DoF Motion of Pelvis
by Tomohito Kuroda, Shogo Okamoto and Yasuhiro Akiyama
Sensors 2024, 24(22), 7342; https://doi.org/10.3390/s24227342 (registering DOI) - 18 Nov 2024
Viewed by 171
Abstract
Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during [...] Read more.
Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during walking. Although MoS is a reliable indicator, its computation typically requires specialized equipment, such as motion capture systems, limiting its application to laboratory settings. To address this limitation, we propose a method for estimating MoS using time-series data from the translational and angular velocities of a single body segment—the pelvis. By applying principal motion analysis to process the multivariate time-series data, we successfully estimated MoS. Our results demonstrate that the estimated MoS in the mediolateral direction achieved an RMSE of 0.88 cm and a correlation coefficient of 0.72 with measured values, while in the anterior-posterior direction, the RMSE was 0.73 cm with a correlation coefficient of 0.87. These values for the mediolateral direction are better than those obtained in previous studies using only the three translational velocity components of the pelvis, whereas the values for the anterior direction are comparable to previous approaches. Our findings suggest that MoS can be reliably estimated using six-axial kinematic data of the pelvis, offering a more accessible method for assessing gait stability. Full article
(This article belongs to the Special Issue Wearable Sensors for Postural Stability and Fall Risk Analyses)
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12 pages, 5540 KiB  
Article
Automatic Image Registration Provides Superior Accuracy Compared with Surface Matching in Cranial Navigation
by Henrik Frisk, Margret Jensdottir, Luisa Coronado, Markus Conrad, Susanne Hager, Lisa Arvidsson, Jiri Bartek, Jr., Gustav Burström, Victor Gabriel El-Hajj, Erik Edström, Adrian Elmi-Terander and Oscar Persson
Sensors 2024, 24(22), 7341; https://doi.org/10.3390/s24227341 (registering DOI) - 18 Nov 2024
Viewed by 91
Abstract
Objective: The precision of neuronavigation systems relies on the correct registration of the patient’s position in space and aligning it with radiological 3D imaging data. Registration is usually performed by the acquisition of anatomical landmarks or surface matching based on facial features. Another [...] Read more.
Objective: The precision of neuronavigation systems relies on the correct registration of the patient’s position in space and aligning it with radiological 3D imaging data. Registration is usually performed by the acquisition of anatomical landmarks or surface matching based on facial features. Another possibility is automatic image registration using intraoperative imaging. This could provide better accuracy, especially in rotated or prone positions where the other methods may be difficult to perform. The aim of this study was to validate automatic image registration (AIR) using intraoperative cone-beam computed tomography (CBCT) for cranial neurosurgical procedures and compare the registration accuracy to the traditional surface matching (SM) registration method based on preoperative MRI. The preservation of navigation accuracy throughout the surgery was also investigated. Methods: Adult patients undergoing intracranial tumor surgery were enrolled after consent. A standard SM registration was performed, and reference points were acquired. An AIR was then performed, and the same reference points were acquired again. Accuracy was calculated based on the referenced and acquired coordinates of the points for each registration method. The reference points were acquired before and after draping and at the end of the procedure to assess the persistency of accuracy. Results: In total, 22 patients were included. The mean accuracy was 6.6 ± 3.1 mm for SM registration and 1.0 ± 0.3 mm for AIR. The AIR was superior to the SM registration (p < 0.0001), with a mean improvement in accuracy of 5.58 mm (3.71–7.44 mm 99% CI). The mean accuracy for the AIR registration pre-drape was 1.0 ± 0.3 mm. The corresponding accuracies post-drape and post-resection were 2.9 ± 4.6 mm and 4.1 ± 4.9 mm, respectively. Although a loss of accuracy was identified between the preoperative and end-of-procedure measurements, there was no statistically significant decline during surgery. Conclusions: AIR for cranial neuronavigation consistently delivered greater accuracy than SM and should be considered the new gold standard for patient registration in cranial neuronavigation. If intraoperative imaging is a limited resource, AIR should be prioritized in rotated or prone position procedures, where the benefits are the greatest. Full article
(This article belongs to the Special Issue Vision- and Image-Based Biomedical Diagnostics—2nd Edition)
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16 pages, 8167 KiB  
Article
Automated Structural Bolt Micro Looseness Monitoring Method Using Deep Learning
by Min Qin, Zhenbo Xie, Jing Xie, Xiaolin Yu, Zhongyuan Ma and Jinrui Wang
Sensors 2024, 24(22), 7340; https://doi.org/10.3390/s24227340 (registering DOI) - 18 Nov 2024
Viewed by 259
Abstract
The detection of bolt loosening in key components of aircraft engines faces problems such as complex and difficult-to-establish bolt loosening mechanism models, difficulty in identifying early loosening, and difficulty in extracting signal features with nonlinear and non-stationary characteristics. Therefore, the automated structural bolt [...] Read more.
The detection of bolt loosening in key components of aircraft engines faces problems such as complex and difficult-to-establish bolt loosening mechanism models, difficulty in identifying early loosening, and difficulty in extracting signal features with nonlinear and non-stationary characteristics. Therefore, the automated structural bolt micro looseness monitoring method using deep learning was proposed. Specifically, the addition of batch normalization methods enables the established Batch Normalized Stacked Autoencoders (BNSAEs) model to converge quickly and effectively, making the model easy to build and effective. Additionally, using characterization functions preprocess the original response signal not only simplifies the data structure but also ensures the integrity of features, which is beneficial for network training and reduces time costs. Finally, the effectiveness of the proposed method was verified by taking the bolted connection structures of two key components of aircraft engines, namely bolt connection structures and flange connection structures, as examples. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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18 pages, 903 KiB  
Article
Robustness of Deep-Learning-Based RF UAV Detectors
by Hilal Elyousseph and Majid Altamimi
Sensors 2024, 24(22), 7339; https://doi.org/10.3390/s24227339 (registering DOI) - 17 Nov 2024
Viewed by 417
Abstract
The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV [...] Read more.
The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV control signals. This approach is enhanced when integrated with machine-learning (ML) and deep-learning (DL) methods. Currently, this field is actively researched, with various studies proposing different ML/DL architectures competing for optimal accuracy. However, there is a notable gap regarding robustness, which refers to a UAV detector’s ability to maintain high accuracy across diverse scenarios, rather than excelling in just one specific test scenario and failing in others. This aspect is critical, as inaccuracies in UAV detection could lead to severe consequences. In this work, we introduce a new dataset specifically designed to test for robustness. Instead of the existing approach of extracting the test data from the same pool as the training data, we allowed for multiple categories of test data based on channel conditions. Utilizing existing UAV detectors, we found that although coefficient classifiers have outperformed CNNs in previous works, our findings indicate that image classifiers exhibit approximately 40% greater robustness than coefficient classifiers under low signal-to-noise ratio (SNR) conditions. Specifically, the CNN classifier demonstrated sustained accuracy in various RF channel conditions not included in the training set, whereas the coefficient classifier exhibited partial or complete failure depending on channel characteristics. Full article
(This article belongs to the Section Sensors and Robotics)
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19 pages, 5675 KiB  
Review
Research Progress on Applying Intelligent Sensors in Sports Science
by Jingjing Zhao, Yulong Yang, Leng Bo, Jiantao Qi and Yongqiang Zhu
Sensors 2024, 24(22), 7338; https://doi.org/10.3390/s24227338 (registering DOI) - 17 Nov 2024
Viewed by 186
Abstract
Smart sensors represent a significant advancement in modern sports science, and their effective use enhances the ability to monitor and analyze athlete performance in real time. The integration of these sensors has enhanced the accuracy of data collection related to physical activity, biomechanics, [...] Read more.
Smart sensors represent a significant advancement in modern sports science, and their effective use enhances the ability to monitor and analyze athlete performance in real time. The integration of these sensors has enhanced the accuracy of data collection related to physical activity, biomechanics, and physiological responses, thus providing valuable insights for performance optimization, injury prevention, and rehabilitation. This paper provides an overview of the research progress in the application of smart sensors in the field of sports science; highlights the current advances, challenges, and future directions in the deployment of smart sensor technologies; and anticipates their transformative impact on sports science and athlete development. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Sports Science)
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15 pages, 1463 KiB  
Article
Integration of FTIR Spectroscopy, Volatile Compound Profiling, and Chemometric Techniques for Advanced Geographical and Varietal Analysis of Moroccan Eucalyptus Essential Oils
by Aimen El Orche, Abdennacer El Mrabet, Amal Ait Haj Said, Soumaya Mousannif, Omar Elhamdaoui, Siddique Akber Ansari, Hamad M. Alkahtani, Shoeb Anwar Ansari, Ibrahim Sbai El Otmani and Mustapha Bouatia
Sensors 2024, 24(22), 7337; https://doi.org/10.3390/s24227337 (registering DOI) - 17 Nov 2024
Viewed by 256
Abstract
Eucalyptus essential oil is widely valued for its therapeutic properties and extensive commercial applications, with its chemical composition significantly influenced by species variety, geographical origin, and environmental conditions. This study aims to develop a reliable method for identifying the geographical origin and variety [...] Read more.
Eucalyptus essential oil is widely valued for its therapeutic properties and extensive commercial applications, with its chemical composition significantly influenced by species variety, geographical origin, and environmental conditions. This study aims to develop a reliable method for identifying the geographical origin and variety of eucalyptus oil samples through the application of advanced analytical techniques combined with chemometric methods. Essential oils from Eucalyptus globulus and Eucalyptus camaldulensis were analyzed using Gas Chromatography–Flame Ionization Detection (GC–FID) and Fourier Transform Infrared (FTIR) Spectroscopy. Chemometric analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (O2PLS-DA) and Hierarchical Cluster Analysis (HCA), were utilized to classify the oils based on their volatile compound profiles. Notably, O2PLS-DA was applied directly to the raw FTIR data without additional spectral processing, showcasing its robustness in handling unprocessed data. For geographical origin determination, the GC–FID model achieved a Correct Classification Rate (CCR) of 100%, with 100% specificity and 100% sensitivity for both calibration and validation sets. FTIR spectroscopy achieved a CCR of 100%, specificity of 100%, and sensitivity of 100% for the calibration set, while the validation set yielded a CCR of 95.83%, specificity of 99.02%, and sensitivity of 94.44%. In contrast, the analysis based on species variety demonstrated 100% accuracy across all metrics CCR, specificity, and sensitivity—for both calibration and validation using both techniques. These findings underscore the effectiveness of volatile and infrared spectroscopy profiling for quality control and authentication, providing robust tools for ensuring the consistency and reliability of eucalyptus essential oils in various industrial and therapeutic applications. Full article
(This article belongs to the Special Issue Optical Spectroscopy for Sensing, Monitoring and Analysis: 2nd Edition)
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26 pages, 1244 KiB  
Article
Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks
by Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed and Maazen Alsabaan
Sensors 2024, 24(22), 7336; https://doi.org/10.3390/s24227336 (registering DOI) - 17 Nov 2024
Viewed by 266
Abstract
LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based [...] Read more.
LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model’s effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications. Full article
(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)
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22 pages, 6483 KiB  
Article
Demand Response Strategy Considering Industrial Loads and Energy Storage with High Proportion of Wind-Power Integration
by Chongyi Tian, Julin Li, Chunyu Wang, Longlong Lin and Yi Yan
Sensors 2024, 24(22), 7335; https://doi.org/10.3390/s24227335 (registering DOI) - 17 Nov 2024
Viewed by 221
Abstract
To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources [...] Read more.
To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources in the power system are analyzed, and a demand response scheduling framework based on energy storage systems and industrial loads is established. Building on this foundation, a multi-scenario stochastic programming approach is employed to develop a day-ahead and intra-day multi-time-scale optimization scheduling model, aimed at maximizing economic benefits. In response to the challenges of wind-power fluctuations with high temporal resolution, a strategy for smoothing intra-day wind-power variability is further proposed. Finally, simulations are conducted to derive optimal demand response strategies for different stages. As verified by the comparison of different scheduling strategies, the demand response strategy proposed in this paper can reduce wind curtailment when there is sufficient wind power and reduce load shedding when there is insufficient wind power, which effectively reduces the system operation cost. Full article
(This article belongs to the Special Issue Smart Sensors, Smart Grid and Energy Management)
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11 pages, 240 KiB  
Protocol
Knee4Life: Empowering Knee Recovery After Total Knee Replacement Through Digital Health Protocol
by Maedeh Mansoubi, Phaedra Leveridge, Matthew Smith, Amelia Fox, Garry Massey, Sarah E. Lamb, David J. Keene, Paul Newell, Elizabeth Jacobs, Nicholas S. Kalson, Athia Haron and Helen Dawes
Sensors 2024, 24(22), 7334; https://doi.org/10.3390/s24227334 (registering DOI) - 17 Nov 2024
Viewed by 237
Abstract
Pain and knee stiffness are common problems following total knee replacement surgery, with 10–20% of patients reporting dissatisfaction following their procedure. A remote assessment of knee stiffness could improve outcomes through continuous monitoring, facilitating timely intervention. Using machine learning algorithms, computer vision can [...] Read more.
Pain and knee stiffness are common problems following total knee replacement surgery, with 10–20% of patients reporting dissatisfaction following their procedure. A remote assessment of knee stiffness could improve outcomes through continuous monitoring, facilitating timely intervention. Using machine learning algorithms, computer vision can extract joint angles from video footage, offering a method to monitor knee range of motion in patients’ homes. This study outlines a protocol to provide proof of concept and validate a computer vision-based approach for measuring knee range of motion in individuals who have undergone total knee replacement. The study also explores the feasibility of integrating this technology into clinical practice, enhancing post-operative care. The study is divided into three components: carrying out focus groups, validating the computer vision-based software, and home testing. The focus groups will involve five people who underwent total knee replacement and ten healthcare professionals or carers who will discuss the deployment of the software in clinical settings. For the validation phase, 60 participants, including 30 patients who underwent total knee replacement surgery five to nine weeks prior and 30 healthy controls, will be recruited. The participants will perform five tasks, including the sit-to-stand test, where knee range of motion will be measured using computer vision-based markerless motion capture software, marker-based motion capture, and physiotherapy assessments. The accuracy and reliability of the software will be evaluated against these established methods. Participants will perform the sit-to-stand task at home. This will allow for a comparison between home-recorded and lab-based data. The findings from this study have the potential to significantly enhance the monitoring of knee stiffness following total knee replacement. By providing accurate, remote measurements and enabling the early detection of issues, this technology could facilitate timely referrals to non-surgical treatments, ultimately reducing the need for costly and invasive procedures to improve knee range of motion. Full article
(This article belongs to the Section Biomedical Sensors)
16 pages, 2816 KiB  
Article
High-Precision Two-Dimensional Angular Sensor Based on Talbot Effect
by Liuxing Song, Xiaoyong Wang, Jinping He, Guoliang Tian and Kailun Zhao
Sensors 2024, 24(22), 7333; https://doi.org/10.3390/s24227333 (registering DOI) - 17 Nov 2024
Viewed by 244
Abstract
The precision of two-dimensional angular sensing is crucial for applications such as navigation, robotics, and optical alignment. Conventional methods often struggle to balance precision, dynamic range, and affordability. We introduce a novel method leveraging the Talbot effect, enhanced by 3D printing technology, to [...] Read more.
The precision of two-dimensional angular sensing is crucial for applications such as navigation, robotics, and optical alignment. Conventional methods often struggle to balance precision, dynamic range, and affordability. We introduce a novel method leveraging the Talbot effect, enhanced by 3D printing technology, to fabricate a grating prototype for high-precision angular measurements. The method detects amplitude grating displacement at the self-imaging position and employs a frequency filtering algorithm for improved accuracy. Rigorous validation through simulations and physical experiments demonstrates that our method achieves a detection resolution of 0.4 arcseconds and a dynamic range exceeding 1400 arcseconds. This research presents a cost-effective, high-precision angular detection solution with potential for broad application in precision mechanical assembly, optical alignment, and other relevant domains. Full article
(This article belongs to the Section Optical Sensors)
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8 pages, 24773 KiB  
Communication
A Comparison Between Single-Stage and Two-Stage 3D Tracking Algorithms for Greenhouse Robotics
by David Rapado-Rincon, Akshay K. Burusa, Eldert J. van Henten and Gert Kootstra
Sensors 2024, 24(22), 7332; https://doi.org/10.3390/s24227332 (registering DOI) - 17 Nov 2024
Viewed by 279
Abstract
With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but [...] Read more.
With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the objects detected by the robot over multiple viewpoints. Multi-object tracking (MOT) algorithms can be categorized between two-stage and single-stage methods. Two-stage methods tend to be simpler to adapt and implement to custom applications, while single-stage methods present a more complex end-to-end tracking method that can yield better results in occluded situations at the cost of more training data. The potential advantages of single-stage methods over two-stage methods depend on the complexity of the sequence of viewpoints that a robot needs to process. In this work, we compare a 3D two-stage MOT algorithm, 3D-SORT, against a 3D single-stage MOT algorithm, MOT-DETR, in three different types of sequences with varying levels of complexity. The sequences represent simpler and more complex motions that a robot arm can perform in a tomato greenhouse. Our experiments in a tomato greenhouse show that the single-stage algorithm consistently yields better tracking accuracy, especially in the more challenging sequences where objects are fully occluded or non-visible during several viewpoints. Full article
(This article belongs to the Section Sensors and Robotics)
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27 pages, 2578 KiB  
Article
A Novel Approach for Kalman Filter Tuning for Direct and Indirect Inertial Navigation System/Global Navigation Satellite System Integration
by Adalberto J. A. Tavares Jr. and Neusa M. F. Oliveira
Sensors 2024, 24(22), 7331; https://doi.org/10.3390/s24227331 (registering DOI) - 16 Nov 2024
Viewed by 493
Abstract
This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, [...] Read more.
This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria’s commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios. Full article
(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)
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16 pages, 4235 KiB  
Article
Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment
by Aleš Procházka, Daniel Martynek, Marie Vitujová, Daniela Janáková, Hana Charvátová and Oldřich Vyšata
Sensors 2024, 24(22), 7330; https://doi.org/10.3390/s24227330 (registering DOI) - 16 Nov 2024
Viewed by 430
Abstract
Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, [...] Read more.
Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients’ physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes. Full article
(This article belongs to the Special Issue Robust Motion Recognition Based on Sensor Technology)
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12 pages, 3108 KiB  
Article
A Microfluidic-Based Sensing Platform for Rapid Quality Control on Target Cells from Bioreactors
by Alessia Foscarini, Fabio Romano, Valeria Garzarelli, Antonio Turco, Alessandro Paolo Bramanti, Iolena Tarantini, Francesco Ferrara, Paolo Visconti, Giuseppe Gigli and Maria Serena Chiriacò
Sensors 2024, 24(22), 7329; https://doi.org/10.3390/s24227329 (registering DOI) - 16 Nov 2024
Viewed by 334
Abstract
We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In [...] Read more.
We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 107 to 108 needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here. Full article
(This article belongs to the Section Biosensors)
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15 pages, 879 KiB  
Article
Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications
by Zhifang Xing, Yunhui Qin, Changhao Du, Wenzhang Wang and Zhongshan Zhang
Sensors 2024, 24(22), 7328; https://doi.org/10.3390/s24227328 (registering DOI) - 16 Nov 2024
Viewed by 259
Abstract
Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate [...] Read more.
Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively. Full article
(This article belongs to the Special Issue Novel Signal Processing Techniques for Wireless Communications)
16 pages, 8192 KiB  
Perspective
Embedding AI-Enabled Data Infrastructures for Sustainability in Agri-Food: Soft-Fruit and Brewery Use Case Perspectives
by Milan Markovic, Andy Li, Tewodros Alemu Ayall, Nicholas J. Watson, Alexander L. Bowler, Mel Woods, Peter Edwards, Rachael Ramsey, Matthew Beddows, Matthias Kuhnert and Georgios Leontidis
Sensors 2024, 24(22), 7327; https://doi.org/10.3390/s24227327 (registering DOI) - 16 Nov 2024
Viewed by 352
Abstract
The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including changes in how food is produced and delivered to customers, new technologies, data and physical infrastructures, and algorithmic advancements. [...] Read more.
The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including changes in how food is produced and delivered to customers, new technologies, data and physical infrastructures, and algorithmic advancements. In this paper, we explore the opportunities and challenges of deploying AI-based data infrastructures for sustainability in the agri-food sector by focusing on two case studies: soft-fruit production and brewery operations. We investigate the potential benefits of incorporating Internet of Things (IoT) sensors and AI technologies for improving the use of resources, reducing carbon footprints, and enhancing decision-making. We identify user engagement with new technologies as a key challenge, together with issues in data quality arising from environmental volatility, difficulties in generalising models, including those designed for carbon calculators, and socio-technical barriers to adoption. We highlight and advocate for user engagement, more granular availability of sensor, production, and emissions data, and more transparent carbon footprint calculations. Our proposed future directions include semantic data integration to enhance interoperability, the generation of synthetic data to overcome the lack of real-world farm data, and multi-objective optimisation systems to model the competing interests between yield and sustainability goals. In general, we argue that AI is not a silver bullet for net zero challenges in the agri-food industry, but at the same time, AI solutions, when appropriately designed and deployed, can be a useful tool when operating in synergy with other approaches. Full article
(This article belongs to the Special Issue Application of Sensors Technologies in Agricultural Engineering)
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19 pages, 642 KiB  
Article
Multi-Intelligent Reflecting Surfaces and Artificial Noise-Assisted Cell-Free Massive MIMO Against Simultaneous Jamming and Eavesdropping
by Huazhi Hu, Wei Xie, Kui Xu, Xiaochen Xia, Na Li and Huaiwu Wu
Sensors 2024, 24(22), 7326; https://doi.org/10.3390/s24227326 (registering DOI) - 16 Nov 2024
Viewed by 357
Abstract
In a cell-free massive multiple-input multiple-output (MIMO) system without cells, it is assumed that there are smart jammers and disrupters (SJDs) that attempt to interfere with and eavesdrop on the downlink communications of legitimate users. A secure transmission scheme based on multiple intelligent [...] Read more.
In a cell-free massive multiple-input multiple-output (MIMO) system without cells, it is assumed that there are smart jammers and disrupters (SJDs) that attempt to interfere with and eavesdrop on the downlink communications of legitimate users. A secure transmission scheme based on multiple intelligent reflecting surfaces (IRSs) and artificial noise (AN) is proposed. First, an access point (AP) selection strategy based on user location information is designed, which aims to determine the set of APs serving users. Then, a joint optimization framework based on the block coordinate descent (BCD) algorithm is constructed, and a non-convex optimization solution based on the univariate function optimization and semi-definite relaxation (SDR) is proposed with the aim of maximising the minimum achievable secrecy rate for users. By solving the univariate function maximisation problem, the multi-variable coupled non-convex problem is transformed into a solvable convex problem, obtaining the optimal AP beamforming, AN matrix and IRS phase shift matrix. Specifically, in a single-user scenario, the scheme of multiple intelligent reflecting surfaces combined with artificial noise can improve the user’s achievable secrecy rate by about 11% compared to the existing method (single intelligent reflective surface combined with artificial noise) and about 2% compared to the scheme assisted by multiple intelligent reflecting surfaces without artificial noise assistance. Full article
(This article belongs to the Section Physical Sensors)
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11 pages, 2825 KiB  
Article
Synthesis of 2MP-CuNPs Fluorescent Probes and Their Application in Tetracycline Detection
by Qiaoya Dou, Zulpiye Hasanjan and Hongyan Zhang
Sensors 2024, 24(22), 7325; https://doi.org/10.3390/s24227325 (registering DOI) - 16 Nov 2024
Viewed by 197
Abstract
A fluorescent probe composed of 2-mercaptopyridine–copper nanoparticles (2MP-CuNPs) was synthesized through a hydrothermal method utilizing CuCl2 and 2-mercaptopyridine (2MP). The experimental results indicate that the 2MP-CuNPs probe exhibited an excellent fluorescence emission peak at 525 nm with an excitation wavelength of 200 [...] Read more.
A fluorescent probe composed of 2-mercaptopyridine–copper nanoparticles (2MP-CuNPs) was synthesized through a hydrothermal method utilizing CuCl2 and 2-mercaptopyridine (2MP). The experimental results indicate that the 2MP-CuNPs probe exhibited an excellent fluorescence emission peak at 525 nm with an excitation wavelength of 200 nm. Furthermore, this emission peak was accompanied by a substantial Stokes shift of 325 nm, which effectively minimized the overlap between the excitation and emission spectra, thereby enhancing detection sensitivity. In tetracycline (TC) detection, the dimethylamino group on TC undergoes protonation in acidic conditions, resulting in a H+ ion. Consequently, the nitrogen atom within the pyridine moiety of the 2MP-CuNPs probe forms a coordination complex with H+ via multi-toothed n-bonding interactions, leading to a significant reduction in fluorescence intensity at 525 nm. Based on this mechanism, a quantitative detection method for TC was successfully established with a linear range spanning from 0.1 to 240 µM and an impressive detection limit of 120 nM. Furthermore, during actual sample analyses involving milk and chicken feed, this analysis method based on the 2MP-CuNPs probe achieved absolute recovery rates ranging from 94% to 98%, underscoring its considerable potential for practical applications. Full article
(This article belongs to the Special Issue Nanotechnology Applications in Sensors Development)
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22 pages, 2740 KiB  
Article
Unsupervised Canine Emotion Recognition Using Momentum Contrast
by Aarya Bhave, Alina Hafner, Anushka Bhave and Peter A. Gloor
Sensors 2024, 24(22), 7324; https://doi.org/10.3390/s24227324 (registering DOI) - 16 Nov 2024
Viewed by 225
Abstract
We describe a system for identifying dog emotions based on dogs’ facial expressions and body posture. Towards that goal, we built a dataset with 2184 images of ten popular dog breeds, grouped into seven similarly sized primal mammalian emotion categories defined by neuroscientist [...] Read more.
We describe a system for identifying dog emotions based on dogs’ facial expressions and body posture. Towards that goal, we built a dataset with 2184 images of ten popular dog breeds, grouped into seven similarly sized primal mammalian emotion categories defined by neuroscientist and psychobiologist Jaak Panksepp as ‘Exploring’, ‘Sadness’, ‘Playing’, ‘Rage’, ‘Fear’, ‘Affectionate’ and ‘Lust’. We modified the contrastive learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) to train it on our original dataset and achieved an accuracy of 43.2% and a baseline of 14%. We also trained this model on a second publicly available dataset that resulted in an accuracy of 48.46% but had a baseline of 25%. We compared our unsupervised approach with a supervised model based on a ResNet50 architecture. This model, when tested on our dataset with the seven Panksepp labels, resulted in an accuracy of 74.32% Full article
(This article belongs to the Special Issue Integrated Sensor Systems for Multi-modal Emotion Recognition)
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20 pages, 3171 KiB  
Article
Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints
by Maoning Ge, Kento Ohtani, Ming Ding, Yingjie Niu, Yuxiao Zhang and Kazuya Takeda
Sensors 2024, 24(22), 7323; https://doi.org/10.3390/s24227323 (registering DOI) - 16 Nov 2024
Viewed by 254
Abstract
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, [...] Read more.
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE5 of 1.26 and minFDE5 of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics. Full article
(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)
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