Sensors

22 pages, 1116 KiB

Open AccessArticle

Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation

by H. Ahmed Tahir, Walaa Alayed, Waqar ul Hassan and Thuan Dinh Do

Sensors 2024, 24(21), 7009; https://doi.org/10.3390/s24217009 (registering DOI) - 31 Oct 2024

Abstract

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN’s dynamic [...] Read more.

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN’s dynamic slicing and LLAMA_V2’s optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries. Full article

(This article belongs to the Special Issue Empowering Sensors in the Internet of Things with Tiny Machine Learning)

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17 pages, 3401 KiB

Open AccessArticle

Recognition of Impact Load on Connecting-Shaft Rotor System Based on Motor Current Signal Analysis

by Kun Zhang, Zhaojian Yang, Qingbao Bao and Jianwen Zhang

Sensors 2024, 24(21), 7008; https://doi.org/10.3390/s24217008 (registering DOI) - 31 Oct 2024

Abstract

Impact loads affect the operational performance and safety life of rolling equipment’s connecting-shaft rotor system, even causing faults and accidents. Therefore, recognizing and investigating impact loads is of great significance. Hence, a load recognition method based on motor current information is proposed in [...] Read more.

Impact loads affect the operational performance and safety life of rolling equipment’s connecting-shaft rotor system, even causing faults and accidents. Therefore, recognizing and investigating impact loads is of great significance. Hence, a load recognition method based on motor current information is proposed in this paper to recognize impact loads on the connecting-shaft rotor system. First, the fast Fourier transform is used to obtain the frequency domain information for the motor’s current response signal from the rotor system load recognition test. Consequently, the required load response information can be presented more clearly using the singular value decomposition method to remove the power frequency components in the current signal. Then, wavelet packet decomposition is performed on the signal to generate energy analysis feature vectors. A qualitative recognition of the impact load on the system is achieved by learning vector quantization neural networks; the resulting load recognition results are good. These findings indicate that using the motor current as the analysis signal can solve the problem of the difficult layout for traditional vibration sensors in rolling sites. The preprocessing and recognition method of the current response signal can recognize the impact load, confirming the applicability and feasibility of the proposed method. Full article

(This article belongs to the Section Physical Sensors)

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18 pages, 13017 KiB

Open AccessArticle

DeployFusion: A Deployable Monocular 3D Object Detection with Multi-Sensor Information Fusion in BEV for Edge Devices

by Fei Huang, Shengshu Liu, Guangqian Zhang, Bingsen Hao, Yangkai Xiang and Kun Yuan

Sensors 2024, 24(21), 7007; https://doi.org/10.3390/s24217007 (registering DOI) - 31 Oct 2024

Viewed by 12

Abstract

To address the challenges of suboptimal remote detection and significant computational burden in existing multi-sensor information fusion 3D object detection methods, a novel approach based on Bird’s-Eye View (BEV) is proposed. This method utilizes an enhanced lightweight EdgeNeXt feature extraction network, incorporating residual [...] Read more.

To address the challenges of suboptimal remote detection and significant computational burden in existing multi-sensor information fusion 3D object detection methods, a novel approach based on Bird’s-Eye View (BEV) is proposed. This method utilizes an enhanced lightweight EdgeNeXt feature extraction network, incorporating residual branches to address network degradation caused by the excessive depth of STDA encoding blocks. Meantime, deformable convolution is used to expand the receptive field and reduce computational complexity. The feature fusion module constructs a two-stage fusion network to optimize the fusion and alignment of multi-sensor features. This network aligns image features to supplement environmental information with point cloud features, thereby obtaining the final BEV features. Additionally, a Transformer decoder that emphasizes global spatial cues is employed to process the BEV feature sequence, enabling precise detection of distant small objects. Experimental results demonstrate that this method surpasses the baseline network, with improvements of 4.5% in the NuScenes detection score and 5.5% in average precision for detection objects. Finally, the model is converted and accelerated using TensorRT tools for deployment on mobile devices, achieving an inference time of 138 ms per frame on the Jetson Orin NX embedded platform, thus enabling real-time 3D object detection. Full article

(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)

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21 pages, 1265 KiB

Open AccessArticle

Leveraging Environmental Contact and Sensor Feedback for Precision in Robotic Manipulation

by Jan Šifrer and Tadej Petrič

Sensors 2024, 24(21), 7006; https://doi.org/10.3390/s24217006 (registering DOI) - 31 Oct 2024

Abstract

This paper investigates methods that leverage physical contact between a robot’s structure and its environment to enhance task performance, with a primary emphasis on improving precision. Two main approaches are examined: solving the inverse kinematics problem and employing quadratic programming, which offers computational [...] Read more.

This paper investigates methods that leverage physical contact between a robot’s structure and its environment to enhance task performance, with a primary emphasis on improving precision. Two main approaches are examined: solving the inverse kinematics problem and employing quadratic programming, which offers computational efficiency by utilizing forward kinematics. Additionally, geometrical methods are explored to simplify robot assembly and reduce the complexity of control calculations. These approaches are implemented on a physical robotic platform and evaluated in real-time applications to assess their effectiveness. Through experimental evaluation, this study aims to understand how environmental contact can be utilized to enhance performance across various conditions, offering valuable insights for practical applications in robotics. Full article

(This article belongs to the Special Issue Dynamics and Control System Design for Robot Manipulation)

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13 pages, 2185 KiB

Open AccessArticle

Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning

by Fulya Kavak, Sebnem Bora, Aylin Kantarci, Aybars Uğur, Sumru Cagaptay, Deniz Gokcay, Anıl Aysal, Burcin Pehlivanoglu and Ozgul Sagol

Sensors 2024, 24(21), 7005; https://doi.org/10.3390/s24217005 (registering DOI) - 31 Oct 2024

Viewed by 32

Abstract

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions [...] Read more.

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across various domains, including health care. There is a significant effort to use AI for the early diagnosis and detection of diseases, offering cost-effective and timely solutions to enhance patient outcomes. This study introduces a deep learning network aimed at analyzing pathology images for the accurate diagnosis of pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC). Utilizing a novel dataset comprised of cases diagnosed with PDAC and/or chronic pancreatitis, this study applies deep learning algorithms to assess the effectiveness and reliability of the diagnostic process. The dataset was enhanced through image duplication and the creation of a second dataset with varied dimensions, facilitating the training of advanced transfer learning models including InceptionV3, DenseNet, ResNet, VGG, EfficientNet, and a specially designed deep neural network. The study presents a convolutional neural network model, optimized for the rapid and accurate detection of pancreatic cancer, and conducts a comparative analysis with other models to select the most accurate algorithm for a decision support system. The results from Dataset 1 show that EfficientNetB0 achieved a high success rate of 92%. In Dataset 2, VGG16 was found to have high performance, with a success rate of 92%. On the other hand, ResNet50 achieved a remarkable success rate of 96% despite a moderate training time and showed high precision, recall, F1 score, and accuracy. These results provide valuable data to demonstrate and share the relevance of different deep learning models in pancreatic cancer diagnosis. Full article

(This article belongs to the Special Issue Digital Imaging Processing, Sensing, and Object Recognition)

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16 pages, 1294 KiB

Open AccessArticle

Near-Infrared Spectroscopy for Neonatal Sleep Classification

by Naser Hakimi, Emad Arasteh, Maren Zahn, Jörn M. Horschig, Willy N. J. M. Colier, Jeroen Dudink and Thomas Alderliesten

Sensors 2024, 24(21), 7004; https://doi.org/10.3390/s24217004 (registering DOI) - 31 Oct 2024

Viewed by 96

Abstract

Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, [...] Read more.

Sleep, notably active sleep (AS) and quiet sleep (QS), plays a pivotal role in the brain development and gradual maturation of (pre) term infants. Monitoring their sleep patterns is imperative, as it can serve as a tool in promoting neurological maturation and well-being, particularly important in preterm infants who are at an increased risk of immature brain development. An accurate classification of neonatal sleep states can contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) and heart rate (HR) serving as key components in sleep assessment systems for neonates. Recent studies have demonstrated the feasibility of extracting both RR and HR using near-infrared spectroscopy (NIRS) in neonates. This study introduces a comprehensive sleep classification approach leveraging high-frequency NIRS signals recorded at a sampling rate of 100 Hz from a cohort of nine preterm infants admitted to a neonatal intensive care unit. Eight distinct features were extracted from the raw NIRS signals, including HR, RR, motion-related parameters, and proxies for neural activity. These features served as inputs for a deep convolutional neural network (CNN) model designed for the classification of AS and QS sleep states. The performance of the proposed CNN model was evaluated using two cross-validation approaches: ten-fold cross-validation of data pooling and five-fold cross-validation, where each fold contains two independently recorded NIRS data. The accuracy, balanced accuracy, F1-score, Kappa, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic) were employed to assess the classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, and XGBoost (XGB), were conducted. Our results reveal the CNN model’s superior performance, achieving an average accuracy of 88%, a balanced accuracy of 94%, an F1-score of 91%, Kappa of 95%, and an AUC-ROC of 96% in data pooling cross-validation. Furthermore, in both cross-validation methods, RF and XGB demonstrated accuracy levels closely comparable to the CNN classifier. These findings underscore the feasibility of leveraging high-frequency NIRS data, coupled with NIRS-based HR and RR extraction, for assessing sleep states in neonates, even in an intensive care setting. The user-friendliness, portability, and reduced sensor complexity of the approach suggest its potential applications in various less-demanding settings. This research thus presents a promising avenue for advancing neonatal sleep assessment and its implications for infant health and development. Full article

(This article belongs to the Special Issue Sensors and Sensing Technologies for Neuroscience)

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38 pages, 18360 KiB

Open AccessReview

A Review of Rotational Seismology Area of Interest from a Recording and Rotational Sensors Point of View

by Anna T. Kurzych and Leszek R. Jaroszewicz

Sensors 2024, 24(21), 7003; https://doi.org/10.3390/s24217003 (registering DOI) - 31 Oct 2024

Viewed by 144

Abstract

This article reviews rotational seismology, considering different areas of interest, as well as measuring devices used for rotational events investigations. After a short theoretical description defining the fundamental parameters, the authors summarized data published in the literature in areas such as the indirect [...] Read more.

This article reviews rotational seismology, considering different areas of interest, as well as measuring devices used for rotational events investigations. After a short theoretical description defining the fundamental parameters, the authors summarized data published in the literature in areas such as the indirect numerical investigation of rotational effects, rotation measured during earthquakes, teleseismic wave investigation, rotation induced by artificial explosions, and mining activity. The fundamental data on the measured rotation parameters and devices used for the recording are summarized and compared for the above areas. In the section on recording the rotational effects associated with artificial explosions and mining activities, the authors included results recorded by a rotational seismograph of their construction—FOSREM (fibre-optic system for rotational events and phenomena monitoring). FOSREM has a broad range of capabilities to measure rotation rates, from several dozen nrad/s to 10 rad/. It can be controlled remotely and operated autonomously for a long time. It is a useful tool for systematic seismological investigations in various places. The report concludes with a short discussion of the importance of rotational seismology and the great need to obtain experimental data in this field. Full article

(This article belongs to the Section Remote Sensors)

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13 pages, 1726 KiB

Open AccessArticle

Portable Detection of Copper Ion Using Personal Glucose Meter

by Bin Du, Taoying Chen, Anqi Huang, Haijun Chen and Wei Liu

Sensors 2024, 24(21), 7002; https://doi.org/10.3390/s24217002 (registering DOI) - 31 Oct 2024

Viewed by 166

Abstract

A simple and sensitive method for Cu²⁺ detection was developed using the Cu⁺-catalyzed alkyne–azide cycloaddition reaction, Fe₃O₄ magnetic nanoparticles (MNPs) as the reaction platform, and a portable blood glucose meter (PGM) as the detection method. Gold nanoparticles [...] Read more.

A simple and sensitive method for Cu²⁺ detection was developed using the Cu⁺-catalyzed alkyne–azide cycloaddition reaction, Fe₃O₄ magnetic nanoparticles (MNPs) as the reaction platform, and a portable blood glucose meter (PGM) as the detection method. Gold nanoparticles (AuNPs) were labeled with glucose oxidase (GOx) and alkyne-functionalized, terminally thiolated ssDNA (C2). In the presence of Cu²⁺ and ascorbate, the functionalized AuNPs were captured onto MNPs modified with azide-functionalized ssDNA (C1) via the Cu⁺-catalyzed alkyne–azide cycloaddition reaction. The GOx on the AuNPs’ surface oxidized glucose (Glu) into gluconic acid and H₂O₂, reducing the Glu content in the reaction solution, which was quantitatively detected by the PGM. Under optimal conditions, the PGM response of the system showed a good linear relationship with the logarithm of Cu²⁺ concentration in the range of 0.05 to 10.00 μmol/L, with a detection limit of 0.03 μmol/L (3σ). In actual tap water samples, the spiked recovery rate of Cu²⁺ was between 92.30% and 113.33%, and the relative standard deviation was between 0.14% and 0.34%, meeting the detection requirements for Cu²⁺ in real water samples. Full article

(This article belongs to the Section Biomedical Sensors)

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13 pages, 12964 KiB

Open AccessArticle

Isotopic and Geophysical Investigations of Groundwater in Laiyuan Basin, China

by Weiqiang Wang, Zilong Meng, Chenglong Wang and Jianye Gui

Sensors 2024, 24(21), 7001; https://doi.org/10.3390/s24217001 (registering DOI) - 31 Oct 2024

Viewed by 138

Abstract

Due to the complex intersection and control of multiple structural systems, the hydrogeological conditions of the Laiyuan Basin in China are complex. The depth of research on the relationship between geological structure and groundwater migration needs to be improved. The supply relationship of [...] Read more.

Due to the complex intersection and control of multiple structural systems, the hydrogeological conditions of the Laiyuan Basin in China are complex. The depth of research on the relationship between geological structure and groundwater migration needs to be improved. The supply relationship of each aquifer is still uncertain. This paper systematically conducts research on the characteristics of hydrogen and oxygen isotopes, and combines magnetotelluric impedance tensor decomposition and two-dimensional fine inversion technology to carry out fine exploration of the strata and structures in the Laiyuan Basin, as well as comprehensive characteristics of groundwater migration and replenishment. The results indicate the following: (i) The hydrogen and oxygen values all fall near the local meteoric water line, indicating that precipitation is the main groundwater recharge source. (ii) The excess deuterium decreased gradually from karst mountain to basin, and karst water and pore water experienced different flow processes. (iii) The structure characteristics of three main runoff channels are described by MT fine processing and inversion techniques. Finally, it is concluded that limestone water moved from the recharge to the discharge area, mixed with the deep dolomite water along the fault under the control of fault F2, and eventually rose to the surface of the unconsolidated sediment blocked by fault F1 to emerge into an ascending spring. Full article

(This article belongs to the Section Remote Sensors)

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13 pages, 3686 KiB

Open AccessCommunication

A Novel Robust Position Integration Optimization-Based Alignment Method for In-Flight Coarse Alignment

by Xiaoge Ning, Jixun Huang and Jianxun Li

Sensors 2024, 24(21), 7000; https://doi.org/10.3390/s24217000 (registering DOI) - 31 Oct 2024

Viewed by 140

Abstract

In-flight alignment is a critical milestone for inertial navigation system/global navigation satellite system (INS/GNSS) applications in unmanned aerial vehicles (UAVs). The traditional position integration formula for in-flight coarse alignment requires the GNSS velocity data to be valid throughout the alignment period, which greatly [...] Read more.

In-flight alignment is a critical milestone for inertial navigation system/global navigation satellite system (INS/GNSS) applications in unmanned aerial vehicles (UAVs). The traditional position integration formula for in-flight coarse alignment requires the GNSS velocity data to be valid throughout the alignment period, which greatly limits the engineering applicability of the method. In this paper, a new robust position integration optimization-based alignment (OBA) method for in-flight coarse alignment is presented to solve the problem of in-flight alignment under a prolonged ineffective GNSS. In this methodology, to achieve a higher alignment accuracy in case the GNSS is not effective throughout the alignment period, the integration of GNSS velocity into the local-level navigation frame is replaced by the GNSS position in the Earth-centered, Earth-fixed frame, which avoids the need for complete GNSS velocity data. The simulation and flight test results show that the new robust position integration method proposed in this paper achieves higher stability and robustness than the conventional position integration OBA method and can achieve an alignment accuracy of 0.2° even when the GNSS is partially time-invalidated. Thus, this greatly extends the application of the OBA method for in-flight alignment. Full article

(This article belongs to the Special Issue Recent Advances in UAVs’ GN&C (Guidance, Navigation, and Control) Technologies)

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10 pages, 3759 KiB

Open AccessCommunication

From Fiber Layout to the Sensor: Preparation Methods as Key Factors for High-Quality Coupled-Core-Fiber Sensors

by F. Lindner, J. Bierlich, M. Alonso-Murias, D. Maldonado-Hurtado, J. A. Flores-Bravo, S. Sales, J. Villatoro and K. Wondraczek

Sensors 2024, 24(21), 6999; https://doi.org/10.3390/s24216999 (registering DOI) - 30 Oct 2024

Viewed by 234

Abstract

During recent years, the optical-fiber-based simultaneous sensing of strain and temperature has attracted increased interest for different applications, e.g., in medicine, architecture, and aerospace. Specialized fiber layouts further enlarge the field of applications at much lower costs and with easier handling. Today, the [...] Read more.

During recent years, the optical-fiber-based simultaneous sensing of strain and temperature has attracted increased interest for different applications, e.g., in medicine, architecture, and aerospace. Specialized fiber layouts further enlarge the field of applications at much lower costs and with easier handling. Today, the performance of many sensors fabricated from conventional fibers suffers from cross-sensitivity (temperature and strain) and relatively high interrogation costs. In contrast, customized fiber architectures would make it possible to circumvent such sensor drawbacks. Here, we report on the development of a high-quality coupled-core fiber and its performance for sensors—from the initial fiber layout via elaboration of the preform and fiber up to the sensor evaluation. A compact, high-speed, and cost-effective interrogation unit using such a specialized coupled-core fiber has been designed to monitor reflectivity changes while even being able to distinguish the direction of the force or impact. Several fiber core material techniques and approaches were investigated, which made it possible to obtain a sufficient volume of material for the required fiber core number and a specialized fiber core geometry in terms of core distances and radial refractive index profile, whilst handling the non-symmetrical fiber architectures of such modeled, complex structures and balancing resources and efforts. Full article

(This article belongs to the Special Issue Advanced Optics and Photonics Technologies for Sensing Applications)

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16 pages, 6171 KiB

Open AccessArticle

VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network

by Hu Zhang, Yujia Liao, Chang Zhu, Wei Meng, Quan Liu and Sheng Q. Xie

Sensors 2024, 24(21), 6998; https://doi.org/10.3390/s24216998 (registering DOI) - 30 Oct 2024

Viewed by 232

Abstract

Traditional rehabilitation training for stroke patients with ankle joint issues typically relies on the expertise of physicians. However, when confronted with complex challenges, such as online decision-making or assessing rehabilitation progress, even seasoned experts may not anticipate all potential hurdles. A novel approach [...] Read more.

Traditional rehabilitation training for stroke patients with ankle joint issues typically relies on the expertise of physicians. However, when confronted with complex challenges, such as online decision-making or assessing rehabilitation progress, even seasoned experts may not anticipate all potential hurdles. A novel approach is necessary—one that effectively addresses these complexities without solely leaning on expert experience. Previous studies have introduced a rehabilitation assessment method based on fuzzy neural networks. This paper proposes a novel approach, which is a VR-aided ankle rehabilitation decision-making model based on a convolutional gated recurrent neural network. This model takes various inputs, including ankle dorsiflexion range of motion, angular velocity, jerk, and motion performance scores, gathered from wearable motion inertial sensors during virtual reality rehabilitation. To overcome the challenge of limited data, data augmentation techniques are employed. This allows for the simulation of five stages of rehabilitation based on the Brunnstrom staging scale, providing tailored control parameters for virtual training scenarios suited to patients at different stages of recovery. Experiments comparing the classification performance of convolutional neural networks and long short-term memory networks were conducted. The results were compelling: the optimized convolutional gated recurrent neural network outperformed both alternatives, boasting an average accuracy of 99.16% and a Macro-F1 score of 0.9786. Importantly, it demonstrated a strong correlation (correlation coefficient r > 0.9) with the assessments made by clinical rehabilitation experts, showing its effectiveness in real-world applications. Full article

(This article belongs to the Special Issue Sensing and Signal Processing Technologies for Outpatient Monitoring and Rehabilitation)

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20 pages, 3317 KiB

Open AccessArticle

Two-Dimensional Scattering Center Estimation for Radar Target Recognition Based on Multiple High-Resolution Range Profiles

by Kang-In Lee, Jin-Hyeok Kim and Young-Seek Chung

Sensors 2024, 24(21), 6997; https://doi.org/10.3390/s24216997 (registering DOI) - 30 Oct 2024

Viewed by 218

Abstract

A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers [...] Read more.

A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers can be successfully located at the intersection points of the lines passing through the multiple HRRP points. This geometry-based algorithm can significantly reduce the computational complexity while ensuring the ability to estimate the two-dimensional target scattering centers. The computational complexity is formulated and compared to that of the conventional methods based on the synthetic aperture radar (SAR) images and HRRP sequences. In order to verify the performance of the proposed algorithm, the numerical and experimental results for three different types of aircraft were compared to those from SAR images. At the end of this article, the estimated radar scattering centers are used as the target features for the conventional classifier machine to confirm its target classification performance. Full article

(This article belongs to the Special Issue Radar Target Detection, Imaging and Recognition)

13 pages, 805 KiB

Open AccessArticle

Influence of Training Load on Muscle Contractile Properties in Semi-Professional Female Soccer Players Across a Competitive Microcycle: A Pilot Study

by Ezequiel Rey, María Lois-Abal, Alexis Padrón-Cabo, Miguel Lorenzo-Martínez and Pablo B. Costa

Sensors 2024, 24(21), 6996; https://doi.org/10.3390/s24216996 (registering DOI) - 30 Oct 2024

Viewed by 226

Abstract

This study aimed to evaluate changes in muscle contractile properties during a training microcycle in semi-professional female football players and explore their relationship with training load variables. Nineteen players (age: 23.9 ± 3.9 years; body mass: 60.6 ± 6.9 kg; height: 164.5 ± [...] Read more.

This study aimed to evaluate changes in muscle contractile properties during a training microcycle in semi-professional female football players and explore their relationship with training load variables. Nineteen players (age: 23.9 ± 3.9 years; body mass: 60.6 ± 6.9 kg; height: 164.5 ± 6.7 cm) underwent myotonometric assessments of the biceps femoris (BF) and rectus femoris (RF) before and after the following training sessions: MD1 (i.e., 1 day after the match), MD3, MD4, and MD5. Training loads were quantified for each session, revealing significant variations, with MD4 exhibiting the highest values for high-speed running distance, number of sprints, and accelerations. Notably, MD3 showed the highest perceived exertion (RPE), while MD5 recorded the lowest total distance run. Myotonometric assessments indicated significant differences in stiffness of the RF in MD3 and BF in MD5, as well as RF tone in MD5. The findings underscore a notable relationship between training load and myotometric variables, particularly in muscle stiffness and tone. These results emphasize the need for further research to clarify how training loads affect muscle properties in female athletes. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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15 pages, 3374 KiB

Open AccessArticle

A Safe Fiber-Optic-Sensor-Assisted Industrial Microwave-Heating System

by Kivilcim Yüksel, Oguz Deniz Merdin, Damien Kinet, Murat Merdin, Corentin Guyot and Christophe Caucheteur

Sensors 2024, 24(21), 6995; https://doi.org/10.3390/s24216995 (registering DOI) - 30 Oct 2024

Viewed by 177

Abstract

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In [...] Read more.

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In this work, a fiber-optic-sensor-assisted monitoring system is presented to tackle the pressing challenges associated with uneven heating and fire hazards in industrial microwave systems. The core innovation lies in the development of a sophisticated fiber-optic 2D temperature distribution sensor and a dedicated fire detector, both designed to significantly mitigate risks and optimize the heating process. Experimental results set the stage for future innovations that could transform the landscape of industrial heating technologies toward better process quality. Full article

(This article belongs to the Section Optical Sensors)

17 pages, 6344 KiB

Open AccessArticle

Design of a Novel SiP Integrated RF Front-End Module Based on SOI Switch and SAW Filter

by Xuanhe Wei, Youming Miao, Xiao Jin, Tian Hong Loh and Gui Liu

Sensors 2024, 24(21), 6994; https://doi.org/10.3390/s24216994 (registering DOI) - 30 Oct 2024

Viewed by 188

Abstract

This paper proposes a novel System-in-Package (SiP) integrated architecture that incorporates Silicon-On-Insulator (SOI) switches and Surface Acoustic Wave (SAW) filters within the chip, aiming to fulfill the demands for miniaturization and multi-functionality for application in emerging wireless technologies. The proposed architecture not only [...] Read more.

This paper proposes a novel System-in-Package (SiP) integrated architecture that incorporates Silicon-On-Insulator (SOI) switches and Surface Acoustic Wave (SAW) filters within the chip, aiming to fulfill the demands for miniaturization and multi-functionality for application in emerging wireless technologies. The proposed architecture not only reduces the integration complexity but also considers the architectural design of the integrated module, impedance matching techniques, and signal integrity for carrier aggregation (CA) technology realization. The feasibility of employing SOI switches and SAW filters based on SiP design has been validated through the trial production of a Sub-3GHz radio frequency (RF) front-end diversity receiver module. The resulting RF front-end module demonstrates exceptionally high packaging density and enhanced communication reliability, rendering it suitable for diverse applications in miniaturized RF systems. Full article

(This article belongs to the Special Issue Advanced Technologies and Solutions for Modern Communication and Sensing Systems)

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13 pages, 690 KiB

Open AccessArticle

Auto Aligning, Error-Compensated Broadband Collimated Transmission Spectroscopy

by Karsten Pink, Alwin Kienle and Florian Foschum

Sensors 2024, 24(21), 6993; https://doi.org/10.3390/s24216993 (registering DOI) - 30 Oct 2024

Viewed by 135

Abstract

Broadband spectral measurements of the ballistic transmission of scattering samples are challenging. The presented work shows an approach that includes a broadband system and an automated adjustment unit for compensation of angular distortions caused by non-plane-parallel samples. The limits of the system in [...] Read more.

Broadband spectral measurements of the ballistic transmission of scattering samples are challenging. The presented work shows an approach that includes a broadband system and an automated adjustment unit for compensation of angular distortions caused by non-plane-parallel samples. The limits of the system in terms of optimal transmission and detected forward scattering influenced by the scattering phase function are investigated. We built and validated a setup that measures the collimated transmission signal in a spectral range from 300 nm to 2150 nm. The system was validated using polystyrene spheres and Mie calculations. The limits of the system in terms of optimal transmission and detected forward scattering were researched. The optimal working parameters of the system, analyzed by simulations using the Monte Carlo method, show that the transmission should be larger than 10% and less than 90% to allow for a reliable measurement with acceptable errors caused by noise and systematic errors of the system. The optimal transmission range is between 25% and 50%. We show that the phase function is important when considering the accuracy of the measurement. For strongly forward-scattering samples, errors of up to 80% can be observed, even for a very small numerical aperture of

6.6 \cdot 10^{- 4}

, as used in our experimental system. We also show that errors increase with optical thickness as the ballistic transmission decreases and the multiscattered fraction increases. In addition, errors caused by multiple reflections in the sample layer were analyzed and also classified as relevant for classical absorption spectroscopy. Full article

(This article belongs to the Special Issue Advances in Optical Sensing, Instrumentation and Systems: 2nd Edition)

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22 pages, 4342 KiB

Open AccessArticle

A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios

by Alessandro Orro, Gian Angelo Geminiani, Francesco Sicurello, Marcello Modica, Francesco Pegreffi, Luca Neri, Antonio Augello and Matteo Botteghi

Sensors 2024, 24(21), 6992; https://doi.org/10.3390/s24216992 (registering DOI) - 30 Oct 2024

Viewed by 195

Abstract

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in [...] Read more.

Wearable devices have a significant impact on society, and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using Internet of Things solutions can be a crucial tool for emergency management, reducing risks in rescue operations and ensuring the safety of workers. The massive amounts of data, high network traffic, and computational demands of a typical monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities. This study presents a Cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable device. The system monitors vital signs such as electrocardiograms and breathing patterns during work sessions, and it is able to manage real-time alarm events to a personnel management center. In this study, 900 paramedics and emergency workers were monitored using wearable devices over a period of 12 months. Data from these devices were collected, processed via Cloud infrastructure, and analyzed to assess the system’s reliability and scalability. The results showed a significant improvement in worker safety and operational efficiency. This study demonstrates the potential of Cloud-based systems and Internet of Things devices in enhancing emergency response efforts. Full article

(This article belongs to the Special Issue Body Sensor Networks and Wearables for Health Monitoring)

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13 pages, 4560 KiB

Open AccessArticle

A Step Forward for Smart Clothes: Printed Fabric-Based Hybrid Electronics for Wearable Health Monitoring

by Huating Tu, Zhenglin Li, Zihao Chen, Yang Gao and Fuzhen Xuan

Sensors 2024, 24(21), 6991; https://doi.org/10.3390/s24216991 (registering DOI) - 30 Oct 2024

Viewed by 238

Abstract

Smart clothes equipped with flexible sensing systems provide a comfortable means to track health status in real time. Although these sensors are flexible and small, the core signal-processing units still rely on a conventional printed circuit board (PCB), making current health-monitoring devices bulky [...] Read more.

Smart clothes equipped with flexible sensing systems provide a comfortable means to track health status in real time. Although these sensors are flexible and small, the core signal-processing units still rely on a conventional printed circuit board (PCB), making current health-monitoring devices bulky and inconvenient to wear. In this study, a printed fabric-based hybrid circuit was designed and prepared—with a series of characteristics, such as surface/sectional morphology, electrical properties, and stability—to study its reliability. Furthermore, to verify the function of the fabric-based circuit, simulations and measurements of the circuit, as well as the collection and processing of a normal adult’s electrophysiological signals, were conducted. Under 10,000 stretching and bending cycles with a certain elongation and bending angle, the resistance remained 0.27 Ω/cm and 0.64 Ω/cm, respectively, demonstrating excellent conductivity and reliability. Additionally, the results of the simulation and experiment showed that the circuit can successfully amplify weak electrocardiogram (ECG) signals with a magnification of 1600 times with environmental filtering and 50 Hz of industrial frequency interference. This technology can monitor human electrophysiological signals, such as ECGs, electromyograms (EMGs), and joint motion, providing valuable practical guidance for the unobtrusive monitoring of smart clothes. Full article

(This article belongs to the Special Issue New Magnetic and Electronic Materials, Thin Film Technology in Electronic Applications)

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18 pages, 1248 KiB

Open AccessArticle

Lineshape of Amplitude-Modulated Stimulated Raman Spectra

by Marco Lamperti, Lucile Rutkowski, Guglielmo Vesco, Luca Moretti, Davide Gatti, Giulio Cerullo, Dario Polli and Marco Marangoni

Sensors 2024, 24(21), 6990; https://doi.org/10.3390/s24216990 (registering DOI) - 30 Oct 2024

Viewed by 122

Abstract

The amplitude modulation of a pump field and the phase-sensitive detection of a pump-induced intensity change of a probe field encompass a common practice in nonlinear spectroscopies to enhance the detection sensitivity. A drawback of this approach arises when the modulation frequency is [...] Read more.

The amplitude modulation of a pump field and the phase-sensitive detection of a pump-induced intensity change of a probe field encompass a common practice in nonlinear spectroscopies to enhance the detection sensitivity. A drawback of this approach arises when the modulation frequency is comparable to the width of the spectral feature of interest, since the presence of sidebands in the amplitude-modulated pump field provides distortion to the observed spectral lineshape. This represents a problem when accurate measurements of spectral lineshapes and line positions are pursued, as recently happened in our group with the metrology of the Q(1) line in the 1-0 band of molecular hydrogen. The measurement was performed with a Stimulated Raman Scattering spectrometer that was calibrated, for the first time, against an optical frequency comb. In this work, we develop an analytical tool for nonlinear Stimulated Raman spectroscopies that allows us to precisely quantify spectral distortions arising from high-frequency amplitude modulation in one of the interacting fields. Once they are known, spectral distortions can be deconvolved from the measured spectra to retrieve unbiased data. The application of this tool to the measured spectra is discussed. Full article

(This article belongs to the Section Optical Sensors)

13 pages, 2934 KiB

Open AccessArticle

Velocity Estimation of Passive Target Based on Sparse Bayesian Learning Cross-Spectrum

by Xionghui Li, Guolong Liang, Tongsheng Shen and Zailei Luo

Sensors 2024, 24(21), 6989; https://doi.org/10.3390/s24216989 (registering DOI) - 30 Oct 2024

Viewed by 178

Abstract

To solve the poor performance or even failure of the cross-spectrum (CS) method in hydroacoustic weak-target passive velocimetry, a sparse Bayesian learning cross-spectrum method (SBL-CS), combining phase compensation with sparse Bayesian learning (SBL) is proposed in this paper. Firstly, the cross-correlation sound intensity [...] Read more.

To solve the poor performance or even failure of the cross-spectrum (CS) method in hydroacoustic weak-target passive velocimetry, a sparse Bayesian learning cross-spectrum method (SBL-CS), combining phase compensation with sparse Bayesian learning (SBL) is proposed in this paper. Firstly, the cross-correlation sound intensity is taken as the observation quantity and compensates for each frequency point of the cross-spectrum, which enables the alignment of cross-spectrum results at different frequencies. Then, the inter-correlation sound intensity of all frequencies is fused in the iterative estimation of the target velocity, verifying the proposed method’s ability to suppress the background noise when performing multi-frequency processing. The simulation results show that the proposed method is still effective in estimating the target velocity when the CS method fails and that the performance of the proposed method is better than the CS method with a decrease in SNR. As verified using the SWellEx-96 sea trial dataset, the RMSE of the proposed method for surface vessel speed measurement is 0.3545 m/s, which is 46.1% less than the traditional CS method, proving the feasibility and effectiveness of the proposed SBL-CS method for the estimation of the radial speed of a passive target. Full article

(This article belongs to the Section Physical Sensors)

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20 pages, 8326 KiB

Open AccessArticle

Modeling and Evaluation of Penetration Process Based on 3D Mechanical Simulation

by Xiaohan Chen, Huiying Gong, Bin Yang, Zengshuo Wang, Yaowei Liu, Lu Zhou, Xin Zhao and Mingzhu Sun

Sensors 2024, 24(21), 6988; https://doi.org/10.3390/s24216988 (registering DOI) - 30 Oct 2024

Viewed by 186

Abstract

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to [...] Read more.

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process. Full article

(This article belongs to the Section Biosensors)

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18 pages, 7715 KiB

Open AccessFeature PaperArticle

Research on Microscale Vehicle Logo Detection Based on Real-Time DEtection TRansformer (RT-DETR)

by Meiting Jin and Junxing Zhang

Sensors 2024, 24(21), 6987; https://doi.org/10.3390/s24216987 (registering DOI) - 30 Oct 2024

Viewed by 187

Abstract

Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 [...] Read more.

Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 × 32 pixels. In real-world scenarios, the actual pixel size of vehicle logos is significantly smaller, making it challenging to achieve precise recognition in complex environments. To address this issue, we propose a microscale vehicle logo dataset (VLD-Micro) that improves the detection of distant vehicle logos. Building upon the RT-DETR algorithm, we propose a lightweight vehicle logo detection algorithm for long-range vehicle logos. Our approach enhances both the backbone and the neck network. The backbone employs ResNet-34, combined with Squeeze-and-Excitation Networks (SENetV2) and Context Guided (CG) Blocks, to improve shallow feature extraction and global information capture. The neck network employs a Slim-Neck architecture, incorporating an ADown module to replace traditional downsampling convolutions. Experimental results on the VLD-Micro dataset show that, compared to the original model, our approach reduces the number of parameters by approximately 37.6%, increases the average accuracy (mAP@50:95) by 1.5%, and decreases FLOPS by 36.7%. Our lightweight network significantly improves real-time detection performance while maintaining high accuracy in vehicle logo detection. Full article

(This article belongs to the Special Issue Integration of Sensor Technologies and Artificial Intelligence Strategies for Autonomous Vehicles and Intelligent Transportation Systems)

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24 pages, 5816 KiB

Open AccessArticle

Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments

by Mangali Sravanthi, Sravan Kumar Gunturi, Mangali Chinna Chinnaiah, Siew-Kei Lam, G. Divya Vani, Mudasar Basha, Narambhatla Janardhan, Dodde Hari Krishna and Sanjay Dubey

Sensors 2024, 24(21), 6986; https://doi.org/10.3390/s24216986 (registering DOI) - 30 Oct 2024

Viewed by 193

Abstract

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation [...] Read more.

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot’s intention to serve based on the human’s location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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15 pages, 4974 KiB

Open AccessArticle

High-Precision and Lightweight Model for Rapid Safety Helmet Detection

by Xuejun Jia, Xiaoxiong Zhou, Chunyi Su, Zhihan Shi, Xiaodong Lv, Chao Lu and Guangming Zhang

Sensors 2024, 24(21), 6985; https://doi.org/10.3390/s24216985 (registering DOI) - 30 Oct 2024

Viewed by 190

Abstract

This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the [...] Read more.

This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the modified penalty-decay intersection over union (MPDIoU) loss function to achieve more stable and precise bounding box regression. Furthermore, considering the original YOLOv5s model’s large parameter count, we adopt a lightweight design using the MobileNetV3 architecture and replace the original squeeze-and-excitation (SE) attention mechanism with CBAM, significantly reducing computational complexity. These improvements reduce the model’s parameters from 15.7 GFLOPs to 5.7 GFLOPs while increasing the mean average precision (mAP) from 82.34% to 91.56%, demonstrating its superior performance and potential value in practical industrial applications. Full article

(This article belongs to the Special Issue Novel Sensors and Sensing Technology Used for Empowering High-End Equipment Structure)

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17 pages, 532 KiB

Open AccessArticle

Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning

by Yuze Zhang, Geming Xia, Chaodong Yu, Hongcheng Li and Hongfeng Li

Sensors 2024, 24(21), 6984; https://doi.org/10.3390/s24216984 (registering DOI) - 30 Oct 2024

Viewed by 199

Abstract

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network [...] Read more.

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model’s capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities. Full article

(This article belongs to the Section Internet of Things)

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26 pages, 3176 KiB

Open AccessArticle

Exploring the Influence of Tropical Cyclones on Regional Air Quality Using Multimodal Deep Learning Techniques

by Muhammad Waqar Younis, Saritha, Bhavya Kallapu, Rama Moorthy Hejamadi, Jeny Jijo, Raghunandan Kemmannu Ramesh , Muhammad Aslam and Syeda Fizzah Jilani

Sensors 2024, 24(21), 6983; https://doi.org/10.3390/s24216983 (registering DOI) - 30 Oct 2024

Viewed by 214

Abstract

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index [...] Read more.

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones’ features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an

R^{2}

of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. Full article

(This article belongs to the Special Issue Sensors and Extreme Environments)

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19 pages, 2630 KiB

Open AccessArticle

Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8

by Goodnews Michael, Essa Q. Shahra, Shadi Basurra, Wenyan Wu and Waheb A. Jabbar

Sensors 2024, 24(21), 6982; https://doi.org/10.3390/s24216982 (registering DOI) - 30 Oct 2024

Viewed by 151

Abstract

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, [...] Read more.

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis. Full article

(This article belongs to the Special Issue Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging Sensors)

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15 pages, 14611 KiB

Open AccessArticle

Radiofrequency Enhancer to Recover Signal Dropouts in 7 Tesla Diffusion MRI

by Varun Subramaniam, Andrew Frankini, Ameen Al Qadi, Mackenzie T. Herb, Gaurav Verma, Bradley N. Delman, Priti Balchandani and Akbar Alipour

Sensors 2024, 24(21), 6981; https://doi.org/10.3390/s24216981 (registering DOI) - 30 Oct 2024

Viewed by 172

Abstract

Diffusion magnetic resonance imaging (dMRI) allows for a non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or [...] Read more.

Diffusion magnetic resonance imaging (dMRI) allows for a non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost the signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, wavelength effects at 7T cause reduced transmit magnetic field efficiency in the human brain, mainly in the posterior fossa, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. In this study, we demonstrate the feasibility and effectiveness of the RF enhancer in improving in vivo dMRI at 7T. The electromagnetic simulation results demonstrated a 2.1-fold increase in transmit efficiency with the use of the RF enhancer. The experimental results similarly showed a 1.9-fold improvement in transmit efficiency and a 1.4-fold increase in normalized SNR. These improvements effectively mitigated signal dropouts in regions with inherently lower SNR, such as the cerebellum, resulting in a better depiction of principal fiber orientations and an enhanced visualization of extended tracts. Full article

(This article belongs to the Special Issue Sensors in Magnetic Resonance Imaging)

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17 pages, 4078 KiB

Open AccessArticle

Research on Gating Fusion Algorithm for Power Grid Survey Data Based on Enhanced Mamba Spatial Neighborhood Relationship

by Aiyuan Zhang, Jinguo Lv, Yu Geng, Xiaolei Wang and Xianhu Li

Sensors 2024, 24(21), 6980; https://doi.org/10.3390/s24216980 (registering DOI) - 30 Oct 2024

Viewed by 177

Abstract

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This [...] Read more.

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This study introduces a fusion model specifically tailored for power grid surveying that significantly enhances the representation of spatial–spectral features in remote sensing images. The model comprises three main modules: a TransforRS-Mamba module that integrates the sequence processing capabilities of the Mamba model with the attention mechanism of the Transformer to effectively merge spatial and spectral features; an improved spatial proximity-aware attention mechanism (SPPAM) that utilizes a spatial constraint matrix to greatly enhance the recognition of complex object relationships; and an optimized spatial proximity-constrained gated fusion module (SPCGF) that integrates spatial proximity constraints with residual connections to boost the recognition accuracy of key object features. To validate the effectiveness of the proposed method, extensive comparative and ablation experiments were conducted on GF-2 satellite images and the QuickBird (QB) dataset. Both qualitative and quantitative analyses indicate that our method outperforms 11 existing methods in terms of fusion effectiveness, particularly in reducing spectral distortion and spatial detail loss. However, the model’s generalization performance across different data sources and environmental conditions has yet to be evaluated. Future research will explore the integration of various satellite datasets and assess the model’s performance in diverse environmental contexts. Full article

(This article belongs to the Section Electronic Sensors)

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