Previous Issue
Volume 24, June-2
 
 
sensors-logo

Journal Browser

Journal Browser

Sensors, Volume 24, Issue 13 (July-1 2024) – 240 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
15 pages, 5461 KiB  
Article
Drone-Borne Magnetic Gradiometry in Archaeological Applications
by Filippo Accomando and Giovanni Florio
Sensors 2024, 24(13), 4270; https://doi.org/10.3390/s24134270 (registering DOI) - 1 Jul 2024
Abstract
The use of magnetometers arranged in a gradiometer configuration offers a practical and widely used solution, particularly in archaeological applications where the sources of interest are generally shallow. Since magnetic anomalies due to archaeological remains often have low amplitudes, highly sensitive magnetic sensors [...] Read more.
The use of magnetometers arranged in a gradiometer configuration offers a practical and widely used solution, particularly in archaeological applications where the sources of interest are generally shallow. Since magnetic anomalies due to archaeological remains often have low amplitudes, highly sensitive magnetic sensors are kept very close to the ground to reveal buried structures. However, the deployment of Unmanned Aerial Vehicles (UAVs) is increasingly becoming a reliable and valuable tool for the acquisition of magnetic data, providing uniform coverage of large areas and access to even very steep terrain, saving time and reducing risks. However, the application of a vertical gradiometer for drone-borne measurements is still challenging due to the instability of the system drone magnetometer in flight and noise issues due to the magnetic interference of the mobile platform or related to the oscillation of the suspended sensors. We present the implementation of a magnetic vertical gradiometer UAV system and its use in an archaeological area of Southern Italy. To reduce the magnetic and electromagnetic noise caused by the aircraft, the magnetometer was suspended 3m below the drone using ropes. A Continuous Wavelet Transform analysis of data collected in controlled tests confirmed that several characteristic power spectrum peaks occur at frequencies compatible with the magnetometer oscillations. This noise was then eliminated with a properly designed low-pass filter. The resulting drone-borne vertical gradient data compare very well with ground-based magnetic measurements collected in the same area and taken as a control dataset. Full article
(This article belongs to the Special Issue Advances on UAV-Based Sensing and Imaging)
Show Figures

Figure 1

23 pages, 11691 KiB  
Article
Cost-Effective Data Acquisition Systems for Advanced Structural Health Monitoring
by Kamer Özdemir and Ahu Kömeç Mutlu
Sensors 2024, 24(13), 4269; https://doi.org/10.3390/s24134269 (registering DOI) - 30 Jun 2024
Abstract
With the growing demand for infrastructure and transportation facilities, the need for advanced structural health monitoring (SHM) systems is critical. This study introduces two innovative, cost-effective, standalone, and open-source data acquisition devices designed to enhance SHM through the latest sensing technologies. The first [...] Read more.
With the growing demand for infrastructure and transportation facilities, the need for advanced structural health monitoring (SHM) systems is critical. This study introduces two innovative, cost-effective, standalone, and open-source data acquisition devices designed to enhance SHM through the latest sensing technologies. The first device, termed CEDAS_acc, integrates the ADXL355 MEMS accelerometer with a RaspberryPi mini-computer, ideal for measuring strong ground motions and assessing structural modal properties during forced vibration tests and structural monitoring of mid-rise buildings. The second device, CEDAS_geo, incorporates the SM24 geophone sensor with a Raspberry Pi, designed for weak ground motion measurements, making it suitable for seismograph networks, seismological research, and early warning systems. Both devices function as acceleration/velocity Data Acquisition Systems (DAS) and standalone data loggers, featuring hardware components such as a single-board mini-computer, sensors, Analog-to-Digital Converters (ADCs), and micro-SD cards housed in protective casings. The CEDAS_acc includes a triaxial MEMS accelerometer with three ADCs, while the CEDAS_geo uses horizontal and vertical geophone elements with an ADC board. To validate these devices, rigorous tests were conducted. Offset Test, conducted by placing the sensor on a leveled flat surface in six orientations, demonstrating the accelerometer’s ability to provide accurate measurements using gravity as a reference; Frequency Response Test, performed at the Gebze Technical University Earthquake and Structure Laboratory (GTU-ESL), comparing the devices’ responses to the GURALP-5TDE reference sensor, with CEDAS_acc evaluated on a shaking table and CEDAS_geo’s performance assessed using ambient vibration records; and Noise Test, executed in a low-noise rural area to determine the intrinsic noise of CEDAS_geo, showing its capability to capture vibrations lower than ambient noise levels. Further field tests were conducted on a 10-story reinforced concrete building in Gaziantep, Turkey, instrumented with 8 CEDAS_acc and 1 CEDAS_geo devices. The building’s response to a magnitude 3.2 earthquake and ambient vibrations was analyzed, comparing results to the GURALP-5TDE reference sensors and demonstrating the devices’ accuracy in capturing peak accelerations and modal frequencies with minimal deviations. The study also introduced the Record Analyzer (RECANA) web application for managing data analysis on CEDAS devices, supporting various data formats, and providing tools for filtering, calibrating, and exporting data. This comprehensive study presents valuable, practical solutions for SHM, enhancing accessibility, reliability, and efficiency in structural and seismic monitoring applications and offering robust alternatives to traditional, costlier systems. Full article
(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)
Show Figures

Figure 1

20 pages, 6837 KiB  
Article
Broad Learning System under Label Noise: A Novel Reweighting Framework with Logarithm Kernel and Mixture Autoencoder
by Jiuru Shen, Huimin Zhao and Wu Deng
Sensors 2024, 24(13), 4268; https://doi.org/10.3390/s24134268 (registering DOI) - 30 Jun 2024
Abstract
The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a [...] Read more.
The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS. Full article
Show Figures

Figure 1

14 pages, 2268 KiB  
Article
A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model
by Iqra Mariam, **aorong Xue and Kaleb Gadson
Sensors 2024, 24(13), 4267; https://doi.org/10.3390/s24134267 (registering DOI) - 30 Jun 2024
Abstract
Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet’s generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We [...] Read more.
Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet’s generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model (training loss of 0.45709 and validation loss of 0.40266), the SAM-trained RF-UNet model achieved a significant reduction in both training loss (0.094225) and validation loss (0.08053). Furthermore, compared to the non-SAM model (training accuracy of 0.90169 and validation accuracy of 0.93999), the SAM-trained model demonstrated higher training accuracy (0.96225) and validation accuracy (0.96821). Additionally, the model performed better in terms of sensitivity, specificity, AUC, and F1 score, indicating improved generalization to unseen data. Our results corroborate the notion that SAM facilitates the learning of flatter minima, thereby improving generalization, and are consistent with other research highlighting the advantages of advanced optimization methods. With wider implications for other medical imaging tasks, these results imply that SAM can successfully reduce overfitting and enhance the robustness of retinal vessel segmentation models. Prospective research avenues encompass verifying the model on vaster and more diverse datasets and investigating its practical implementation in real-world clinical situations. Full article
Show Figures

Figure 1

15 pages, 4084 KiB  
Article
Nucleic Acid Target Sensing Using a Vibrating Sharp-Tip Capillary and Digital Droplet Loop-Mediated Isothermal Amplification (ddLAMP)
by Bethany J. Fike, Kathrine Curtin and Peng Li
Sensors 2024, 24(13), 4266; https://doi.org/10.3390/s24134266 (registering DOI) - 30 Jun 2024
Abstract
Nucleic acid tests are key tools for the detection and diagnosis of many diseases. In many cases, the amplification of the nucleic acids is required to reach a detectable level. To make nucleic acid amplification tests more accessible to a point-of-care (POC) setting, [...] Read more.
Nucleic acid tests are key tools for the detection and diagnosis of many diseases. In many cases, the amplification of the nucleic acids is required to reach a detectable level. To make nucleic acid amplification tests more accessible to a point-of-care (POC) setting, isothermal amplification can be performed with a simple heating source. Although these tests are being performed in bulk reactions, the quantification is not as accurate as it would be with digital amplification. Here, we introduce the use of the vibrating sharp-tip capillary for a simple and portable system for tunable on-demand droplet generation. Because of the large range of droplet sizes possible and the tunability of the vibrating sharp-tip capillary, a high dynamic range (~2 to 6000 copies/µL) digital droplet loop-mediated isothermal amplification (ddLAMP) system has been developed. It was also noted that by changing the type of capillary on the vibrating sharp-tip capillary, the same mechanism can be used for simple and portable DNA fragmentation. With the incorporation of these elements, the present work paves the way for achieving digital nucleic acid tests in a POC setting with limited resources. Full article
(This article belongs to the Special Issue Advancements in Microfluidic Technologies and BioMEMS)
Show Figures

Figure 1

14 pages, 1886 KiB  
Article
THz Generation by Two-Color Plasma: Time Sha** and Ultra-Broadband Polarimetry
by Domenico Paparo, Anna Martinez, Andrea Rubano, Jonathan Houard, Ammar Hideur and Angela Vella
Sensors 2024, 24(13), 4265; https://doi.org/10.3390/s24134265 (registering DOI) - 30 Jun 2024
Abstract
The generation of terahertz radiation via laser-induced plasma from two-color femtosecond pulses in air has been extensively studied due to its broad emission spectrum and significant pulse energy. However, precise control over the temporal properties of these ultra-broadband terahertz pulses, as well as [...] Read more.
The generation of terahertz radiation via laser-induced plasma from two-color femtosecond pulses in air has been extensively studied due to its broad emission spectrum and significant pulse energy. However, precise control over the temporal properties of these ultra-broadband terahertz pulses, as well as the measurement of their polarization state, remain challenging. In this study, we review our latest findings on these topics and present additional results not previously reported in our earlier works. First, we investigate the impact of chir** on the fundamental wave and the effect of manipulating the phase difference between the fundamental wave and the second-harmonic wave on the properties of generated terahertz pulses. We demonstrate that we can tune the time shape of terahertz pulses, causing them to reverse polarity or become bipolar by carefully selecting the correct combination of chirp and phase. Additionally, we introduce a novel technique for polarization characterization, termed terahertz unipolar polarimetry, which utilizes a weak probe beam and avoids the systematic errors associated with traditional methods. This technique is effective for detecting polarization-structured terahertz beams and the longitudinal component of focused terahertz beams. Our findings contribute to the improved control and characterization of terahertz radiation, enhancing its application in fields such as nonlinear optics, spectroscopy, and microscopy. Full article
(This article belongs to the Special Issue Research Development in Terahertz and Infrared Sensing Technology)
11 pages, 2476 KiB  
Communication
An Insulin-Modified pH-Responsive Nanopipette Based on Ion Current Rectification
by Xu-Fan Wang, Yi-Fan Duan, Yue-Qian Zhu, Zi-**g Liu, Yu-Chen Wu, Tian-Hao Liu, Ling Zhang, Jian-Feng Wei and Guo-Chang Liu
Sensors 2024, 24(13), 4264; https://doi.org/10.3390/s24134264 (registering DOI) - 30 Jun 2024
Abstract
The properties of nanopipettes largely rely on the materials introduced onto their inner walls, which allow for a vast extension of their sensing capabilities. The challenge of simultaneously enhancing the sensitivity and selectivity of nanopipettes for pH sensing remains, hindering their practical applications. [...] Read more.
The properties of nanopipettes largely rely on the materials introduced onto their inner walls, which allow for a vast extension of their sensing capabilities. The challenge of simultaneously enhancing the sensitivity and selectivity of nanopipettes for pH sensing remains, hindering their practical applications. Herein, we report insulin-modified nanopipettes with excellent pH response performances, which were prepared by introducing insulin onto their inner walls via a two-step reaction involving silanization and amidation. The pH response intensity based on ion current rectification was significantly enhanced by approximately 4.29 times when utilizing insulin-modified nanopipettes compared with bare ones, demonstrating a linear response within the pH range of 2.50 to 7.80. In addition, insulin-modified nanopipettes featured good reversibility and selectivity. The modification processes were monitored using the I-V curves, and the relevant mechanisms were discussed. The effects of solution pH and insulin concentration on the modification results were investigated to achieve optimal insulin introduction. This study showed that the pH response behavior of nanopipettes can be greatly improved by introducing versatile molecules onto the inner walls, thereby contributing to the development and utilization of pH-responsive nanopipettes. Full article
(This article belongs to the Special Issue Electrochemical Nanobiosensors II)
Show Figures

Graphical abstract

14 pages, 6445 KiB  
Article
Multi-Sensor-Assisted Low-Cost Indoor Non-Visual Semantic Map Construction and Localization for Modern Vehicles
by Guangxiao Shao, Fanyu Lin, Chao Li, Wei Shao, Wennan Chai, **aorui Xu, Mingyue Zhang, Zhen Sun and Qingdang Li
Sensors 2024, 24(13), 4263; https://doi.org/10.3390/s24134263 (registering DOI) - 30 Jun 2024
Abstract
With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This [...] Read more.
With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic map** and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shop** mall scenes with different underground parking layouts to verify the proposed non-visual semantic map** and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article
Show Figures

Figure 1

21 pages, 3390 KiB  
Article
Bearing-DETR: A Lightweight Deep Learning Model for Bearing Defect Detection Based on RT-DETR
by Minggao Liu, Haifeng Wang, Luyao Du, Fangsong Ji and Ming Zhang
Sensors 2024, 24(13), 4262; https://doi.org/10.3390/s24134262 (registering DOI) - 30 Jun 2024
Abstract
Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and [...] Read more.
Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and Deformable Large Kernel Attention (D-LKA), Bearing-DETR offers significant improvements in defect detection while maintaining a lightweight framework suitable for low-resource devices. Validated on a dataset from a chemical plant, Bearing-DETR outperformed the standard RT-DETR, achieving a mean average precision (mAP) of 94.3% at IoU = 0.5 and 57.5% at IoU = 0.5–0.95. It also reduced floating-point operations (FLOPs) to 8.2 G and parameters to 3.2 M, underscoring its enhanced efficiency and reduced computational demands. These results demonstrate the potential of Bearing-DETR to transform maintenance strategies and quality control across manufacturing environments, emphasising adaptability and impact on sustainability and operational costs. Full article
(This article belongs to the Special Issue Sensors and Machine-Learning Based Signal Processing)
13 pages, 3586 KiB  
Article
Manoeuvre Target Tracking in Wireless Sensor Networks Using Convolutional Bi-Directional Long Short-Term Memory Neural Networks and Extended Kalman Filtering
by Duo Peng, Kun **e and Mingshuo Liu
Sensors 2024, 24(13), 4261; https://doi.org/10.3390/s24134261 (registering DOI) - 30 Jun 2024
Abstract
Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended [...] Read more.
Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios. Full article
(This article belongs to the Special Issue Wireless Sensor Networks: Signal Processing and Communications)
Show Figures

Figure 1

14 pages, 3726 KiB  
Article
Presenting a Multispectral Image Sensor for Quantification of Total Polyphenols in Low-Temperature Stressed Tomato Seedlings Using Hyperspectral Imaging
by Ye Seong Kang, Chan Seok Ryu and Jeong Gyun Kang
Sensors 2024, 24(13), 4260; https://doi.org/10.3390/s24134260 (registering DOI) - 30 Jun 2024
Abstract
Hyperspectral imaging was used to predict the total polyphenol content in low-temperature stressed tomato seedlings for the development of a multispectral image sensor. The spectral data with a full width at half maximum (FWHM) of 5 nm were merged to obtain FWHMs of [...] Read more.
Hyperspectral imaging was used to predict the total polyphenol content in low-temperature stressed tomato seedlings for the development of a multispectral image sensor. The spectral data with a full width at half maximum (FWHM) of 5 nm were merged to obtain FWHMs of 10 nm, 25 nm, and 50 nm using a commercialized bandpass filter. Using the permutation importance method and regression coefficients, we developed the least absolute shrinkage and selection operator (Lasso) regression models by setting the band number to ≥11, ≤10, and ≤5 for each FWHM. The regression model using 56 bands with an FWHM of 5 nm resulted in an R2 of 0.71, an RMSE of 3.99 mg/g, and an RE of 9.04%, whereas the model developed using the spectral data of only 5 bands with a FWHM of 25 nm (at 519.5 nm, 620.1 nm, 660.3 nm, 719.8 nm, and 980.3 nm) provided an R2 of 0.62, an RMSE of 4.54 mg/g, and an RE of 10.3%. These results show that a multispectral image sensor can be developed to predict the total polyphenol content of tomato seedlings subjected to low-temperature stress, paving the way for energy saving and low-temperature stress damage prevention in vegetable seedling production. Full article
(This article belongs to the Special Issue Novel Sensors for Precision Agriculture Application)
17 pages, 6700 KiB  
Article
A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning
by Peng **, Qida Yao, Wei Guo and Changrong Liao
Sensors 2024, 24(13), 4259; https://doi.org/10.3390/s24134259 (registering DOI) - 30 Jun 2024
Viewed by 110
Abstract
The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, [...] Read more.
The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system’s situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults. Full article
16 pages, 8042 KiB  
Article
Anomaly Detection and Remaining Useful Life Estimation for the Health and Usage Monitoring Systems 2023 Data Challenge
by Omri Matania, Eric Bechhoefer, David Blunt, Wenyi Wang and Jacob Bortman
Sensors 2024, 24(13), 4258; https://doi.org/10.3390/s24134258 (registering DOI) - 30 Jun 2024
Viewed by 107
Abstract
Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS [...] Read more.
Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge. Full article
(This article belongs to the Special Issue Sensors for Predictive Maintenance of Machines)
Show Figures

Figure 1

28 pages, 8390 KiB  
Article
Bridging Convolutional Neural Networks and Transformers for Efficient Crack Detection in Concrete Building Structures
by Dhirendra Prasad Yadav, Bhisham Sharma, Shivank Chauhan and Imed Ben Dhaou
Sensors 2024, 24(13), 4257; https://doi.org/10.3390/s24134257 (registering DOI) - 30 Jun 2024
Viewed by 139
Abstract
Detecting cracks in building structures is an essential practice that ensures safety, promotes longevity, and maintains the economic value of the built environment. In the past, machine learning (ML) and deep learning (DL) techniques have been used to enhance classification accuracy. However, the [...] Read more.
Detecting cracks in building structures is an essential practice that ensures safety, promotes longevity, and maintains the economic value of the built environment. In the past, machine learning (ML) and deep learning (DL) techniques have been used to enhance classification accuracy. However, the conventional CNN (convolutional neural network) methods incur high computational costs owing to their extensive number of trainable parameters and tend to extract only high-dimensional shallow features that may not comprehensively represent crack characteristics. We proposed a novel convolution and composite attention transformer network (CCTNet) model to address these issues. CCTNet enhances crack identification by processing more input pixels and combining convolution channel attention with window-based self-attention mechanisms. This dual approach aims to leverage the localized feature extraction capabilities of CNNs with the global contextual understanding afforded by self-attention mechanisms. Additionally, we applied an improved cross-attention module within CCTNet to increase the interaction and integration of features across adjacent windows. The performance of CCTNet on the Historical Building Crack2019, SDTNET2018, and proposed DS3 has a precision of 98.60%, 98.93%, and 99.33%, respectively. Furthermore, the training validation loss of the proposed model is close to zero. In addition, the AUC (area under the curve) is 0.99 and 0.98 for the Historical Building Crack2019 and SDTNET2018, respectively. CCTNet not only outperforms existing methodologies but also sets a new standard for the accurate, efficient, and reliable detection of cracks in building structures. Full article
(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)
Show Figures

Figure 1

34 pages, 3176 KiB  
Article
Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes
by Aymen Zayed, Nidhameddine Belhadj, Khaled Ben Khalifa, Mohamed Hedi Bedoui and Carlos Valderrama
Sensors 2024, 24(13), 4256; https://doi.org/10.3390/s24134256 (registering DOI) - 30 Jun 2024
Viewed by 143
Abstract
Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to [...] Read more.
Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an electroencephalography (EEG)-based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM), the K nearest neighbor (KNN), the Naive Bayes (NB), the Decision Tree (DT), and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake and drowsy). Segmentation into 10 s intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries. Full article
(This article belongs to the Section Biomedical Sensors)
18 pages, 6929 KiB  
Article
Stiffness-Based Evaluation of Hinge Joints in Prefabricated Assembled Multi-Girder Bridges under Operational Conditions
by Zhiqiang Shang, Fengzong Gong, Zhufeng Shao, Jose Matos, Gongfeng **n and Ye **a
Sensors 2024, 24(13), 4255; https://doi.org/10.3390/s24134255 (registering DOI) - 30 Jun 2024
Viewed by 119
Abstract
Presently, the prevailing approaches to assessing hinge joint damage predominantly rely on predefined damage indicators or updating finite element models (FEMs). However, these methods possess certain limitations. The damage indicator method requires high-quality monitoring data and demonstrates variable sensitivities of distinct indicators to [...] Read more.
Presently, the prevailing approaches to assessing hinge joint damage predominantly rely on predefined damage indicators or updating finite element models (FEMs). However, these methods possess certain limitations. The damage indicator method requires high-quality monitoring data and demonstrates variable sensitivities of distinct indicators to damage. On the other hand, the FEM approach mandates a convoluted FEM update procedure. Hinge joint damage represents a major kind of defect in prefabricated assembled multi-girder bridges (AMGBs). Therefore, effective damage detection methods are imperative to identify the damage state of hinge joints. To this end, a stiffness-based method for the performance evaluation of hinge joints of AMGBs is proposed in this paper. The proposed method estimates hinge joint stiffness by solving the characteristic equations of the multi-beam system. In addition, this study introduces a method for determining baseline joint stiffness using design data and FEM. Subsequently, a comprehensive evaluation framework for hinge joints is formulated, coupling a finite element model with the baseline stiffness, thereby introducing a damage indicator rooted in stiffness ratios. To verify the effectiveness of the proposed method, strain and displacement correlations are analyzed using actual bridge monitoring data, and articulation joint stiffness is identified. The results underscore the capability of the proposed method to accurately pinpoint the location and extent of hinge joint damage. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
Show Figures

Figure 1

17 pages, 2133 KiB  
Article
Network Slicing in 6G: A Strategic Framework for IoT in Smart Cities
by Ahmed M. Alwakeel and Abdulrahman K. Alnaim
Sensors 2024, 24(13), 4254; https://doi.org/10.3390/s24134254 (registering DOI) - 30 Jun 2024
Viewed by 132
Abstract
The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities’ IoT deployments. The [...] Read more.
The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities’ IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework’s high efficiency, evidenced by low round-trip time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework’s impressive performance, scalability, and security capabilities. Full article
(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)
Show Figures

Figure 1

23 pages, 8086 KiB  
Article
CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing
by Wei Wu, Haipeng Peng, Haotian Zhu and Derun Zhang
Sensors 2024, 24(13), 4253; https://doi.org/10.3390/s24134253 (registering DOI) - 30 Jun 2024
Viewed by 102
Abstract
With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. [...] Read more.
With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC’s capabilities in terms of security and robustness. Full article
(This article belongs to the Special Issue Compressed Sensing and Imaging Processing—2nd Edition)
13 pages, 4265 KiB  
Communication
Moho Imaging with Fiber Borehole Strainmeters Based on Ambient Noise Autocorrelation
by Guoheng Qi, Wenzhu Huang, **npeng Pan, Wentao Zhang and Guanxin Zhang
Sensors 2024, 24(13), 4252; https://doi.org/10.3390/s24134252 (registering DOI) - 30 Jun 2024
Viewed by 145
Abstract
Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based [...] Read more.
Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based on ambient noise cross-correlation, similar to conventional geophones. However, this array-dependent cross-correlation method is not suitable for fiber borehole strainmeters. Here, we developed a Moho imaging scheme for the characteristics of fiber borehole strainmeters based on ambient noise autocorrelation. S-wave reflection signals were extracted from the ambient noise through a series of processing steps, including phase autocorrelation (PAC), phase-weighted stacking (PWS), etc. Subsequently, the time-to-depth conversion crustal thickness beneath the station was calculated. We applied our scheme to continuous four-component recordings from four fiber borehole strainmeters in Lu’an, Anhui Province, China. The obtained Moho depth was consistent with the previous research results. Our work shows that this method is suitable for Moho imaging with fiber borehole strainmeters without relying on the number of stations. Full article
(This article belongs to the Special Issue Sensor Technologies for Seismic Monitoring)
Show Figures

Figure 1

22 pages, 1681 KiB  
Article
Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy
by Ziyi Tang, **nhao Hou, **nheng Huang, **n Wang and Jifeng Zou
Sensors 2024, 24(13), 4251; https://doi.org/10.3390/s24134251 (registering DOI) - 30 Jun 2024
Viewed by 144
Abstract
Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, [...] Read more.
Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy. The proposed scheme first uses Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing, and then feature extraction is performed using the Residual Network (ResNet) integrated with the SimAM module. This is combined with the proposed adaptive weighting strategy based on Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) domain adaptation algorithms, which minimizes the distribution differences between the source and target domains more effectively, thus enhancing domain adaptability. The proposed method is validated on two datasets, and experimental results show that it improves the accuracy of bearing fault diagnosis. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)
16 pages, 1463 KiB  
Article
Effect of Sampling Rate, Filtering, and Torque Onset Detection on Quadriceps Rate of Torque Development and Torque Steadiness
by McKenzie S. White, Megan C. Graham, Tereza Janatova, Gregory S. Hawk, Katherine L. Thompson and Brian Noehren
Sensors 2024, 24(13), 4250; https://doi.org/10.3390/s24134250 (registering DOI) - 30 Jun 2024
Viewed by 171
Abstract
Quadriceps rate of torque development (RTD) and torque steadiness are valuable metrics for assessing explosive strength and the ability to control force over a sustained period of time, which can inform clinical assessments of knee function. Despite their widespread use, there is a [...] Read more.
Quadriceps rate of torque development (RTD) and torque steadiness are valuable metrics for assessing explosive strength and the ability to control force over a sustained period of time, which can inform clinical assessments of knee function. Despite their widespread use, there is a significant gap in standardized methodology for measuring these metrics, which limits their utility in comparing outcomes across different studies and populations. To address these gaps, we evaluated the influence of sampling rates, signal filtering, and torque onset detection on RTD and torque steadiness. Twenty-seven participants with a history of a primary anterior cruciate ligament reconstruction (N = 27 (11 male/16 female), age = 23 ± 8 years, body mass index = 26 ± 4 kg/m2) and thirty-two control participants (N = 32 (13 male/19 female), age = 23 ± 7 years, body mass index = 23 ± 3 kg/m2) underwent isometric quadriceps strength testing, with data collected at 2222 Hz on an isokinetic dynamometer. The torque–time signal was downsampled to approximately 100 and 1000 Hz and processed using a low-pass, zero-lag Butterworth filter with a range of cutoff frequencies spanning 10–200 Hz. The thresholds used to detect torque onset were defined as 0.1 Nm, 1 Nm, and 5 Nm. RTD between 0 and 100 ms, 0 and 200 ms, and 40–160 ms was computed, as well as absolute and relative torque steadiness. Relative differences were computed by comparing all outcomes to the “gold standard” values computed, with a sampling rate of 2222 Hz, a cutoff frequency in the low-pass filter of 150 Hz, and torque onset of 1 Nm, and compared utilizing linear mixed models. While all combinations of signal collection and processing parameters reached statistical significance (p < 0.05), these differences were consistent between injured and control limbs. Additionally, clinically relevant differences (+/−10%) were primarily observed through torque onset detection methods and primarily affected RTD between 0 and 100 ms. Although measurements of RTD and torque steadiness were generally robust against diverse signal collection and processing parameters, the selection of torque onset should be carefully considered, especially in early RTD assessments that have shorter time epochs. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
Show Figures

Figure 1

16 pages, 4124 KiB  
Article
IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy
by Olivier Djakou Nekui, Wei Wang, Cheng Liu, Zhixia Wang and Bei Ding
Sensors 2024, 24(13), 4249; https://doi.org/10.3390/s24134249 (registering DOI) - 29 Jun 2024
Viewed by 346
Abstract
Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign [...] Read more.
Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical dam** is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical dam**. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method. Full article
(This article belongs to the Section Wearables)
Show Figures

Figure 1

22 pages, 4588 KiB  
Review
Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
by Muhammad Shoaib Khan, Andrea Petroni and Mauro Biagi
Sensors 2024, 24(13), 4248; https://doi.org/10.3390/s24134248 (registering DOI) - 29 Jun 2024
Viewed by 271
Abstract
Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal [...] Read more.
Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation. Full article
(This article belongs to the Special Issue Feature Review Papers in the 'Sensor Networks' Section 2024)
14 pages, 2301 KiB  
Article
In Situ Preparation of Metallic Copper Nanosheets/Carbon Paper Sensitive Electrodes for Low-Potential Electrochemical Detection of Nitrite
by **ng Zhao, Guangfeng Zhou, Sitao Qin, **gwen Zhang, Guanda Wang, Jie Gao, Hui Suo and Chun Zhao
Sensors 2024, 24(13), 4247; https://doi.org/10.3390/s24134247 (registering DOI) - 29 Jun 2024
Viewed by 269
Abstract
In the realm of electrochemical nitrite detection, the potent oxidizing nature of nitrite typically necessitates operation at high detection potentials. However, this study introduces a novel approach to address this challenge by develo** a highly sensitive electrochemical sensor with a low reduction detection [...] Read more.
In the realm of electrochemical nitrite detection, the potent oxidizing nature of nitrite typically necessitates operation at high detection potentials. However, this study introduces a novel approach to address this challenge by develo** a highly sensitive electrochemical sensor with a low reduction detection potential. Specifically, a copper metal nanosheet/carbon paper sensitive electrode (Cu/CP) was fabricated using a one-step electrodeposition method, leveraging the catalytic reduction properties of copper’s high occupancy d-orbital. The Cu/CP sensor exhibited remarkable performance in nitrite detection, featuring a low detection potential of −0.05 V vs. Hg/HgO, a wide linear range of 10~1000 μM, an impressive detection limit of 0.079 μM (S/N = 3), and a high sensitivity of 2140 μA mM−1cm−2. These findings underscore the efficacy of electrochemical nitrite detection through catalytic reduction as a means to reduce the operational voltage of the sensor. By showcasing the successful implementation of this strategy, this work sets a valuable precedent for the advancement of electrochemical low-potential nitrite detection methodologies. Full article
36 pages, 8555 KiB  
Article
Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions
by Serhii Vladov, Lukasz Scislo, Valerii Sokurenko, Oleksandr Muzychuk, Victoria Vysotska, Serhii Osadchy and Anatoliy Sachenko
Sensors 2024, 24(13), 4246; https://doi.org/10.3390/s24134246 (registering DOI) - 29 Jun 2024
Viewed by 246
Abstract
The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network [...] Read more.
The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies. Full article
Show Figures

Figure 1

10 pages, 1876 KiB  
Article
Displacement Assay in a Polythiophene Sensor System Based on Supramacromolecuar Disassembly-Caused Emission Quenching
by Tsukuru Minamiki, Ryosuke Esaka and Ryoji Kurita
Sensors 2024, 24(13), 4245; https://doi.org/10.3390/s24134245 (registering DOI) - 29 Jun 2024
Viewed by 188
Abstract
Exploring new methodologies for simple and on-demand methods of manipulating the emission and sensing ability of fluorescence sensor devices with solid-state emission molecular systems is important for realizing on-site sensing platforms. In this regard, although conjugated polymers (CPs) are some of the best [...] Read more.
Exploring new methodologies for simple and on-demand methods of manipulating the emission and sensing ability of fluorescence sensor devices with solid-state emission molecular systems is important for realizing on-site sensing platforms. In this regard, although conjugated polymers (CPs) are some of the best candidates for preparing molecular sensor devices owing to their luminescent and molecular recognition properties, the development of CP-based sensor devices is still in its early stages. In this study, we herein propose a novel strategy for preparing a chemical stimuli-responsive solid-state emission system based on supramacromolecular assembly-induced emission enhancement (SmAIEE). The system was spontaneously developed by mixing only the component polymers (i.e., polythiophene and a transient cross-linking polymer). The proposed strategy can be applied to the facile preparation of molecular sensor devices. The analyte-induced fluorescent response of polythiophene originated from the dynamic displacement of the transient cross-linker in the polythiophene ensemble and the generation of the polythiophene–analyte complex. Our successful demonstration of the spontaneous preparation of the fluorescence sensor system by mixing two component polymers could lead to the development of on-site molecular analyzers including the determination of multiple analytes. Full article
(This article belongs to the Special Issue Technology Trends in Fluorescence Detection Based on Biosensor)
Show Figures

Figure 1

19 pages, 4678 KiB  
Article
Rotation Error Prediction of CNC Spindle Based on Short-Time Fourier Transform of Vibration Sensor Signals and Improved Weighted Residual Network
by Lin Song and Jianying Tan
Sensors 2024, 24(13), 4244; https://doi.org/10.3390/s24134244 (registering DOI) - 29 Jun 2024
Viewed by 189
Abstract
The spindle rotation error of computer numerical control (CNC) equipment directly reflects the machining quality of the workpiece and is a key indicator reflecting the performance and reliability of CNC equipment. Existing rotation error prediction methods do not consider the importance of different [...] Read more.
The spindle rotation error of computer numerical control (CNC) equipment directly reflects the machining quality of the workpiece and is a key indicator reflecting the performance and reliability of CNC equipment. Existing rotation error prediction methods do not consider the importance of different sensor data. This study developed an adaptive weighted deep residual network (ResNet) for predicting spindle rotation errors, thereby establishing accurate map** between easily obtainable vibration information and difficult-to-obtain rotation errors. Firstly, multi-sensor data are collected by a vibration sensor, and Short-time Fourier Transform (STFT) is adopted to extract the feature information in the original data. Then, an adaptive feature recalibration unit with residual connection is constructed based on the attention weighting operation. By stacking multiple residual blocks and attention weighting units, the data of different channels are adaptively weighted to highlight important information and suppress redundancy information. The weight visualization results indicate that the adaptive weighted ResNet (AWResNet) can learn a set of weights for channel recalibration. The comparison results indicate that AWResNet has higher prediction accuracy than other deep learning models and can be used for spindle rotation error prediction. Full article
Show Figures

Figure 1

15 pages, 5193 KiB  
Article
Wavelet Transforms Significantly Sparsify and Compress Tactile Interactions
by Ariel Slepyan, Michael Zakariaie, Trac Tran and Nitish Thakor
Sensors 2024, 24(13), 4243; https://doi.org/10.3390/s24134243 (registering DOI) - 29 Jun 2024
Viewed by 174
Abstract
As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. [...] Read more.
As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object gras** dataset (548 sensor tactile glove, gras** 26 objects). In addition, we investigated which dimension wavelet transform—1D, 2D, or 3D—would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics. Full article
Show Figures

Figure 1

16 pages, 19009 KiB  
Communication
A Region-Monitoring-Type Slitless Imaging Spectrometer
by Rui Ouyang, Duo Wang, Longxu **, Tianjiao Fu, Zhenzhang Zhao and **ngxiang Zhang
Sensors 2024, 24(13), 4242; https://doi.org/10.3390/s24134242 (registering DOI) - 29 Jun 2024
Viewed by 139
Abstract
In modern scientific practice, it is necessary to consistently observe predetermined zones, with the expectation of detecting and identifying emerging targets or events inside such areas. This research presents an innovative imaging spectrometer system for the continuous monitoring of specific areas. This study [...] Read more.
In modern scientific practice, it is necessary to consistently observe predetermined zones, with the expectation of detecting and identifying emerging targets or events inside such areas. This research presents an innovative imaging spectrometer system for the continuous monitoring of specific areas. This study begins by providing detailed information on the features and optical structure of the constructed instrument. This is then followed by simulations using optical design tools. The device has an F-number of 5, a focal length of 100 mm, a field of view of 3°×7°, and a wavelength range spanning from 400 nm to 600 nm. The optical path diagram demonstrates that the system’s dispersion and imaging pictures can be distinguished, hence fulfilling the system’s specifications. Furthermore, the utilization of a Modulation Transfer Function (MTF) graph has substantiated that the image quality indeed satisfies the specified criteria. To evaluate the instrument’s performance in the spectrum observation of fixed regions, a region-monitoring-type slitless imaging spectrometer was built. The equipment has the capability to identify a specific region and rapidly capture the spectra of objects or events that are present inside that region. The spectral data were collected effectively by the implementation of image processing techniques on the captured photos. The correlation coefficient between these data and the reference data was 0.9226, showing that the device successfully measured the target’s spectrum. Therefore, the instrument that was created successfully demonstrated its ability to capture images of the observed areas and collect spectral data from the targets located within those regions. Full article
(This article belongs to the Section Optical Sensors)
17 pages, 1598 KiB  
Article
Cautious Gait during Navigational Tasks in People with Hemiparesis: An Observational Study
by Albane Le Roy, Fabien Dubois, Nicolas Roche, Helena Brunel and Céline Bonnyaud
Sensors 2024, 24(13), 4241; https://doi.org/10.3390/s24134241 (registering DOI) - 29 Jun 2024
Viewed by 139
Abstract
Locomotor and balance disorders are major limitations for subjects with hemiparesis. The Timed Up and Go (TUG) test is a complex navigational task involving oriented walking and obstacle circumvention. We hypothesized that subjects with hemiparesis adopt a cautious gait during complex locomotor tasks. [...] Read more.
Locomotor and balance disorders are major limitations for subjects with hemiparesis. The Timed Up and Go (TUG) test is a complex navigational task involving oriented walking and obstacle circumvention. We hypothesized that subjects with hemiparesis adopt a cautious gait during complex locomotor tasks. The primary aim was to compare spatio-temporal gait parameters, indicators of cautious gait, between the locomotor subtasks of the TUG (Go, Turn, Return) and a Straight-line walk in people with hemiparesis. Our secondary aim was to analyze the relationships between TUG performance and balance measures, compare spatio-temporal gait parameters between fallers and non-fallers, and identify the biomechanical determinants of TUG performance. Biomechanical parameters during the TUG and Straight-line walk were analyzed using a motion capture system. A repeated measures ANOVA and two stepwise ascending multiple regressions (with performance variables and biomechanical variables) were conducted. Gait speed, step length, and % single support phase (SSP) of the 29 participants were reduced during Turn compared to Go and Return and the Straight-line walk, and step width and % double support phase were increased. TUG performance was related to several balance measures. Turn performance (R2 = 63%) and Turn trajectory deviation followed by % SSP on the paretic side and the vertical center of mass velocity during Go (R2 = 71%) determined TUG performance time. People with hemiparesis adopt a cautious gait during complex navigation at the expense of performance. Full article
Show Figures

Figure 1

Previous Issue
Back to TopTop