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X-ray Induced Electric Currents in Anodized Ta2O5: Towards a Large-Area Thin-Film Sensor Sensors (IF 3.9) Pub Date : 2024-04-16 Davide Brivio, Matt Gagne, Erica Freund, Erno Sajo, Piotr Zygmanski
Purpose: We investigated the characteristics of radiation-induced current in nano-porous pellet and thin-film anodized tantalum exposed to kVp X-ray beams. We aim at developing a large area (≫cm2) thin-film radiation sensor for medical, national security and space applications. Methods: Large area (few cm2) micro-thin Ta foils were anodized and coated with a counter electrode made of conductive polymer
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Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8 Sensors (IF 3.9) Pub Date : 2024-04-17 Yan Gao, Wei Liu, Hsiang-Chen Chui, Xiaoming Chen
In this work, an object detection method using variable convolution-improved YOLOv8 is proposed to solve the problem of low accuracy and low efficiency in detecting spanning and irregularly shaped samples. Aiming at the problems of the irregular shape of a target, the low resolution of labeling frames, dense distribution, and the ease of overlap, a deformable convolution module is added to the original
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3D DAS VSP for Coal Seam Exploration: A Case Study from Queensland, Australia Sensors (IF 3.9) Pub Date : 2024-04-17 Konstantin Tertyshnikov, Alexey Yurikov, Andrej Bona, Milovan Urosevic, Roman Pevzner
Seismic methods are extensively used in coal mining for expanding resource discoveries and definition as well as for mine monitoring. However, the use of borehole seismic methods is relatively uncommon due to the high cost of borehole seismic acquisition using conventional downhole tools. The introduction of distributed acoustic sensing (DAS), which uses optical fibres to record seismic data, has dramatically
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Study of the Prediction of Vibrations in Soft Soil Foundations Based on Field Tests Sensors (IF 3.9) Pub Date : 2024-04-17 Jiaxin Lin, Nan Zhang, Yunshi Zhang
To explore the prediction of vibrations in soft soil foundations, in light of the construction of laboratories with microvibration requirements on soft soil foundations which are subject to the limitations of urban land planning, field testing was conducted, and the soil surface vibration responses were recorded at different distances from a road under various highway traffic loads. By analyzing the
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Non-Intrusive Load Identification Based on Retrainable Siamese Network Sensors (IF 3.9) Pub Date : 2024-04-17 Lingxia Lu, Ju-Song Kang, Fanju Meng, Miao Yu
Non-intrusive load monitoring (NILM) can identify each electrical load and its operating state in a household by using the voltage and current data measured at a single point on the bus, thereby behaving as a key technology for smart grid construction and effective energy consumption. The existing NILM methods mainly focus on the identification of pre-trained loads, which can achieve high identification
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Fractional-Order Electrical Modeling of Aluminum Coated via Plasma Electro-Oxidation and Thermal Spray Methods to Optimize Radiofrequency Medical Devices Sensors (IF 3.9) Pub Date : 2024-04-17 Noelia Vaquero-Gallardo, Oliver Millán-Blasco, Herminio Martínez-García
Active medical devices rely on a source of energy that is applied to the human body for specific purposes such as electrosurgery, ultrasounds for breaking up kidney stones (lithotripsy), laser irradiation, and other medical techniques and procedures that are extensively used. These systems must provide adequate working power with a commitment not to produce side effects on patients. Therefore, the
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Enhancing Pure Inertial Navigation Accuracy through a Redundant High-Precision Accelerometer-Based Method Utilizing Neural Networks Sensors (IF 3.9) Pub Date : 2024-04-17 Qinyuan He, Huapeng Yu, Dalei Liang, Xiaozhuo Yang
The pure inertial navigation system, crucial for autonomous navigation in GPS-denied environments, faces challenges of error accumulation over time, impacting its effectiveness for prolonged missions. Traditional methods to enhance accuracy have focused on improving instrumentation and algorithms but face limitations due to complexity and costs. This study introduces a novel device-level redundant
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Influence of the Degree of Fruitiness on the Quality Assessment of Virgin Olive Oils Using Electronic Nose Technology Sensors (IF 3.9) Pub Date : 2024-04-17 Javiera P. Navarro Soto, Sergio Illana Rico, Diego M. Martínez Gila, Silvia Satorres Martínez
The electronic nose is a non-invasive technology suitable for the analysis of edible oils. One of the practical applications in the olive oil industry is the classification of virgin oils based on their sensory characteristics. Notwithstanding that this technology, at this stage, cannot realistically replace the currently used methods, it is fruitful for a preliminary analysis of the oil quality. This
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Adopting Graph Neural Networks to Analyze Human–Object Interactions for Inferring Activities of Daily Living Sensors (IF 3.9) Pub Date : 2024-04-17 Peng Su, Dejiu Chen
Human Activity Recognition (HAR) refers to a field that aims to identify human activities by adopting multiple techniques. In this field, different applications, such as smart homes and assistive robots, are introduced to support individuals in their Activities of Daily Living (ADL) by analyzing data collected from various sensors. Apart from wearable sensors, the adoption of camera frames to analyze
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Comprehensive Analysis of Xiaomi Mi 8 GNSS Antenna Performance Sensors (IF 3.9) Pub Date : 2024-04-17 Mónica Zabala Haro, Ángel Martín Furones, Ana Anquela Julián, María Jesús Jiménez-Martínez
The interest in precise point positioning techniques using smartphones increased with the launch of the world’s first dual-frequency L1/L5 GNSS smartphone, Xiaomi Mi 8. The smartphone GNSS antenna is low-cost, sensitive to multipath, and limited by physical space and design. The main purpose of this work is to determine the mechanical location and antenna performance in terms of radiation pattern in
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Modular and Cost-Effective Computed Tomography Design Sensors (IF 3.9) Pub Date : 2024-04-17 André Bieberle, Rainer Hoffmann, Alexander Döß, Eckhard Schleicher, Uwe Hampel
We present a modular and cost-effective gamma ray computed tomography system for multiphase flow investigations in industrial apparatuses. It mainly comprises a 137Cs isotopic source and an in-house-assembled detector arc, with a total of 16 scintillation detectors, offering a quantum efficiency of approximately 75% and an active area of 10 × 10 mm2 each. The detectors are operated in pulse mode to
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Electrochemical Detection of Bisphenol A Based on Gold Nanoparticles/Multi-Walled Carbon Nanotubes: Applications on Glassy Carbon and Screen Printed Electrodes Sensors (IF 3.9) Pub Date : 2024-04-17 Maximina Luis-Sunga, Soledad Carinelli, Gonzalo García, José Luis González-Mora, Pedro A. Salazar-Carballo
Bisphenol A (BPA) has been classified as an endocrine-disrupting substance that may cause adverse effects on human health and the environment. The development of simple and sensitive electrochemical biosensors is crucial for the rapid and effective quantitative determination of BPA. This work presents a study on electrochemical sensors utilizing gold nanoparticle-modified multi-walled carbon nanotubes
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Error Model for the Assimilation of All-Sky FY-4A/AGRI Infrared Radiance Observations Sensors (IF 3.9) Pub Date : 2024-04-17 Dongchuan Pu, Yali Wu
The Advanced Geostationary Radiation Imager (AGRI) carried by the FengYun-4A (FY-4A) satellite enables the continuous observation of local weather. However, FY-4A/AGRI infrared satellite observations are strongly influenced by clouds, which complicates their use in all-sky data assimilation. The presence of clouds leads to increased uncertainty, and the observation-minus-background (O−B) differences
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A Sensor-Based Upper Limb Treatment in Hemiplegic Patients: Results from a Randomized Pilot Study Sensors (IF 3.9) Pub Date : 2024-04-17 Fabio Vanoglio, Laura Comini, Marta Gaiani, Gian Pietro Bonometti, Alberto Luisa, Palmira Bernocchi
In post-stroke patients, the disabling motor deficit mainly affects the upper limb. The focus of rehabilitation is improving upper limb function and reducing long-term disability. This study aims to evaluate the feasibility of using the Gloreha Aria (R-Lead), a sensor-based upper limb in-hospital rehabilitation, compared with conventional physiotherapist-led training in subacute hemiplegic patients
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Cluster-Based Pairwise Contrastive Loss for Noise-Robust Speech Recognition Sensors (IF 3.9) Pub Date : 2024-04-17 Geon Woo Lee, Hong Kook Kim
This paper addresses a joint training approach applied to a pipeline comprising speech enhancement (SE) and automatic speech recognition (ASR) models, where an acoustic tokenizer is included in the pipeline to leverage the linguistic information from the ASR model to the SE model. The acoustic tokenizer takes the outputs of the ASR encoder and provides a pseudo-label through K-means clustering. To
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Fault Diagnosis for Reducers Based on a Digital Twin Sensors (IF 3.9) Pub Date : 2024-04-17 Weimin Liu, Bin Han, Aiyun Zheng, Zhi Zheng
A new method based on a digital twin is proposed for fault diagnosis, in order to compensate for the shortcomings of the existing methods for fault diagnosis modeling, including the single fault type, low similarity, and poor visual effect of state monitoring. First, a fault diagnosis test platform is established to analyze faults under constant and variable speed conditions. Then, the obtained data
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A Single-Longitudinal-Mode S + C Band Wavelength-Tunable Fiber Laser Sensors (IF 3.9) Pub Date : 2024-04-17 Da Liu, Yi Jiang
An external cavity wavelength-fiber ring laser (ECWTFL) based on a semiconductor optical amplifier and a combined wavelength scanning filter in the Littrow configuration is proposed and experimentally demonstrated. With the benefit of the combination of an external cavity wavelength filter and a Lyot filter, the laser achieves a single-mode narrow linewidth output with a linewidth of 1.75 kHz. The
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Comparison of Metabolic Power and Energy Cost of Submaximal and Sprint Running Efforts Using Different Methods in Elite Youth Soccer Players: A Novel Energetic Approach Sensors (IF 3.9) Pub Date : 2024-04-17 Gabriele Grassadonia, Pedro E. Alcaraz, Tomás T. Freitas
Sprinting is a decisive action in soccer that is considerably taxing from a neuromuscular and energetic perspective. This study compared different calculation methods for the metabolic power (MP) and energy cost (EC) of sprinting using global positioning system (GPS) metrics and electromyography (EMG), with the aim of identifying potential differences in performance markers. Sixteen elite U17 male
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A Review of Microsphere Super-Resolution Imaging Techniques Sensors (IF 3.9) Pub Date : 2024-04-14 Wenbo Jiang, Jingchun Wang, Yidi Yang, Yun Bu
Conventional optical microscopes are only able to resolve objects down to a size of approximately 200 nm due to optical diffraction limits. The rapid development of nanotechnology has increased the demand for greater imaging resolution, with a need to break through those diffraction limits. Among super-resolution techniques, microsphere imaging has emerged as a strong contender, offering low cost,
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Inspection Robot Navigation Based on Improved TD3 Algorithm Sensors (IF 3.9) Pub Date : 2024-04-15 Bo Huang, Jiacheng Xie, Jiawei Yan
The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient
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Optimal Design and Development of Magnetic Field Detection Sensor for AC Power Cable Sensors (IF 3.9) Pub Date : 2024-04-15 Yong Liu, Yuepeng Xin, Youcong Huang, Boxue Du, Xingwang Huang, Jingang Su
The state detection of power cables is very important to ensure the reliability of the power supply. Traditional sensors are mostly based on electric field detection. The operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. First, through the establishment of the magnetic field sensor
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An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools Sensors (IF 3.9) Pub Date : 2024-04-15 Endah Kristiani, Lu-Yan Wang, Jung-Chun Liu, Cheng-Kai Huang, Shih-Jie Wei, Chao-Tung Yang
This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using
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Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data Sensors (IF 3.9) Pub Date : 2024-04-15 Tan-Hsu Tan, Jia-Hong Tian, Alok Kumar Sharma, Shing-Hong Liu, Yung-Fa Huang
Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user’s quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler
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Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments Sensors (IF 3.9) Pub Date : 2024-04-16 Brenda S. de S. Barbosa, Hugo A. O. Cruz, Alex S. Macedo, Caio M. M. Cardoso, Filipe C. Fernandes, Leslye E. C. Eras, Jasmine P. L. de Araújo, Gervásio P. S. Calvacante, Fabrício J. B. Barros
The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers
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Development and Evaluation of a Hybrid Measurement System to Determine the Kinematics of the Wrist Sensors (IF 3.9) Pub Date : 2024-04-16 Jason Dellai, Martine A. Gilles, Olivier Remy, Laurent Claudon, Gilles Dietrich
Optical Motion Capture Systems (OMCSs) are considered the gold standard for kinematic measurement of human movements. However, in situations such as measuring wrist kinematics during a hairdressing activity, markers can be obscured, resulting in a loss of data. Other measurement methods based on non-optical data can be considered, such as magneto-inertial measurement units (MIMUs). Their accuracy is
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Pupil Response in Visual Tracking Tasks: The Impacts of Task Load, Familiarity, and Gaze Position Sensors (IF 3.9) Pub Date : 2024-04-16 Yun Wu, Zhongshi Zhang, Yao Zhang, Bin Zheng, Farzad Aghazadeh
Pupil size is a significant biosignal for human behavior monitoring and can reveal much underlying information. This study explored the effects of task load, task familiarity, and gaze position on pupil response during learning a visual tracking task. We hypothesized that pupil size would increase with task load, up to a certain level before decreasing, decrease with task familiarity, and increase
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An Era of Digital Healthcare—A Comprehensive Review of Sensor Technologies and Telehealth Advancements in Chronic Heart Failure Management Sensors (IF 3.9) Pub Date : 2024-04-16 Tejaswini Manavi, Haroon Zafar, Faisal Sharif
Heart failure (HF) is a multi-faceted, complex clinical syndrome characterized by significant morbidity, high mortality rate, reduced quality of life, and rapidly increasing healthcare costs. A larger proportion of these costs comprise both ambulatory and emergency department visits, as well as hospital admissions. Despite the methods used by telehealth (TH) to improve self-care and quality of life
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Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance Sensors (IF 3.9) Pub Date : 2024-04-16 Songhao Zhang, Min Cui, Peng Zhang
There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems’ overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass
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Implementation of a High-Accuracy Neural Network-Based Pupil Detection System for Real-Time and Real-World Applications Sensors (IF 3.9) Pub Date : 2024-04-16 Gabriel Bonteanu, Petronela Bonteanu, Arcadie Cracan, Radu Gabriel Bozomitu
In this paper, the implementation of a new pupil detection system based on artificial intelligence techniques suitable for real-time and real-word applications is presented. The proposed AI-based pupil detection system uses a classifier implemented with slim-type neural networks, with its classes being defined according to the possible positions of the pupil within the eye image. In order to reduce
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Choice of Piezoelectric Element over Accelerometer for an Energy-Autonomous Shoe-Based System Sensors (IF 3.9) Pub Date : 2024-04-16 Niharika Gogoi, Yuanjia Zhu, Jens Kirchner, Georg Fischer
Shoe-based wearable sensor systems are a growing research area in health monitoring, disease diagnosis, rehabilitation, and sports training. These systems—equipped with one or more sensors, either of the same or different types—capture information related to foot movement or pressure maps beneath the foot. This captured information offers an overview of the subject’s overall movement, known as the
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Flextory: Flexible Software Factory of IoT Data Consumers Sensors (IF 3.9) Pub Date : 2024-04-16 Rafael López-Gómez, Laura Panizo, María-del-Mar Gallardo
The success of the Internet of Things (IoT) has driven the development, among others, of many different software architectures for producing, processing, and analyzing heterogeneous data. In many cases, IoT applications share common features, such as the use of a platform or middleware, also known as message broker, that collects and manages data traffic between endpoints. However, in general, data
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Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach Sensors (IF 3.9) Pub Date : 2024-04-16 Mohammad Sheikhsamad, Vicenç Puig
This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs
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RCEAU-Net: Cascade Multi-Scale Convolution and Attention-Mechanism-Based Network for Laser Beam Target Image Segmentation with Complex Background in Coal Mine Sensors (IF 3.9) Pub Date : 2024-04-16 Wenjuan Yang, Yanqun Wang, Xuhui Zhang, Le Zhu, Zhiteng Ren, Yang Ji, Long Li, Yanbin Xie
Accurate and reliable pose estimation of boom-type roadheaders is the key to the forming quality of the tunneling face in coal mines, which is of great importance to improve tunneling efficiency and ensure the safety of coal mine production. The multi-laser-beam target-based visual localization method is an effective way to realize accurate and reliable pose estimation of a roadheader body. However
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Anomaly Detection of Permanent Magnet Synchronous Motor Based on Improved DWT-CNN Multi-Current Fusion Sensors (IF 3.9) Pub Date : 2024-04-16 Minqi Tang, Lihua Liang, Haitao Zheng, Junjun Chen, Dongdong Chen
The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with
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A Joint Scheduling Scheme for WiFi Access TSN Sensors (IF 3.9) Pub Date : 2024-04-16 Zhong Li, Jianfeng Yang, Chengcheng Guo, Jinsheng Xiao, Tao Tao, Chengwang Li
In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive
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Data, Signal and Image Processing and Applications in Sensors II Sensors (IF 3.9) Pub Date : 2024-04-16 Manuel J. C. S. Reis
A vast and ever-growing amount of data in various domains and modalities is readily available, being the rapid advance of sensor technology one of its main contributor. [...]
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Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control Sensors (IF 3.9) Pub Date : 2024-04-16 Qian Fang, Pengjun Mao
Aerial manipulators expand the application scenarios of manipulators into the air. To complete various operations, the contact force between the aerial manipulator and the target must be precisely controlled. In this study, we first established the mathematical models of the multirotor and the manipulator separately. Their mutual influence is regarded as each other’s disturbance, and the overall linkage
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Relationship between Height and Exposure in Multispectral Vegetation Index Response and Product Characteristics in a Traditional Olive Orchard Sensors (IF 3.9) Pub Date : 2024-04-16 Carolina Perna, Andrea Pagliai, Riccardo Lisci, Rafael Pinhero Amantea, Marco Vieri, Daniele Sarri, Piernicola Masella
The present research had two aims. The first was to evaluate the effect of height and exposure on the vegetative response of olive canopies’ vertical axis studied through a multispectral sensor and on the qualitative and quantitative product characteristics. The second was to examine the relationship between multispectral data and productive characteristics. Six olive plants were sampled, and their
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Time-Frequency Aliased Signal Identification Based on Multimodal Feature Fusion Sensors (IF 3.9) Pub Date : 2024-04-16 Hailong Zhang, Lichun Li, Hongyi Pan, Weinian Li, Siyao Tian
The identification of multi-source signals with time-frequency aliasing is a complex problem in wideband signal reception. The traditional method of first separation and identification especially fails due to the significant separation error under underdetermined conditions when the degree of time-frequency aliasing is high. The single-mode recognition method does not need to be separated first. However
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Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System Sensors (IF 3.9) Pub Date : 2024-04-16 Fan Ye, Guangpeng Jia, Yukun Wang, Xiaobo Chen, Juntong Xi
In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we
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LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors Sensors (IF 3.9) Pub Date : 2024-04-15 Peng Lei, Fanglan Ma, Changsheng Zhu, Tianyu Li
Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with
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Mind the Exit Pupil Gap: Revisiting the Intrinsics of a Standard Plenoptic Camera Sensors (IF 3.9) Pub Date : 2024-04-15 Tim Michels, Daniel Mäckelmann, Reinhard Koch
Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of the scene. Numerous methods with this goal have been developed based on thin lens models for the plenoptic camera’s main lens and microlenses. Our work addresses the often-overlooked role of the main lens exit pupil in these
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Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression Sensors (IF 3.9) Pub Date : 2024-04-15 Xiaomin Wen, Shengliang Fang, Youchen Fan
The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the
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Brake Fluid Condition Monitoring by a Fiber Optic Sensor Using Silica Nanomaterials as Sensing Components Sensors (IF 3.9) Pub Date : 2024-04-15 Mayza Ibrahim, Stanislav Petrík
In the automotive industry, there has been considerable focus on developing various sensors for engine oil monitoring. However, when it comes to monitoring the condition of brake fluid, which is crucial for ensuring safety, there has been a lack of a secure online method for this monitoring. This study addresses this gap by developing a hybrid silica nanofiber mat, or an aerogel integrated with an
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Design of a Self-Measuring Device Based on Bioelectrical Impedance Analysis for Regular Monitoring of Rheumatoid Arthritis Sensors (IF 3.9) Pub Date : 2024-04-15 JuYoung Jeong, Yun Soo Park, Eunchae Lee, SeoYoun Choi, Dokshin Lim, Jiho Kim
Rheumatoid arthritis (RA) is a chronic disease, in which permanent joint deformation is largely preventable with the timely introduction of appropriate treatment strategies. However, there is no consensus for patients with RA to monitor their progress and communicate it to the rheumatologist till the condition progresses to remission. In response to this unmet need, we proposed the design of a self-measuring
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Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs Sensors (IF 3.9) Pub Date : 2024-04-15 Karin Takahashi, Hitoshi Ueno
Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasive and non-restrictive monitoring of vital signs, including the heart rate and respiration, to detect changes in the health status of several elderly individuals
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Sensor Head Temperature Distribution Reconstruction of High-Precision Gravitational Reference Sensors with Machine Learning Sensors (IF 3.9) Pub Date : 2024-04-15 Zongchao Duan, Feilong Ren, Li-E Qiang, Keqi Qi, Haoyue Zhang
Temperature fluctuations affect the performance of high-precision gravitational reference sensors. Due to the limited space and the complex interrelations among sensors, it is not feasible to directly measure the temperatures of sensor heads using temperature sensors. Hence, a high-accuracy interpolation method is essential for reconstructing the surface temperature of sensor heads. In this study,
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On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications Sensors (IF 3.9) Pub Date : 2024-04-15 Maximiliano Barbosa, Leandro Pralon, Antonio L. L. Ramos, José Antonio Apolinário
The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited
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Shape Sensing of Cantilever Column Using Hybrid Frenet–Serret Homogeneous Transformation Matrix Method Sensors (IF 3.9) Pub Date : 2024-04-15 Peng Zhang, Duanshu Li, Ran An, Patil Devendra
The Frenet–Serret (FS) framework stands as a pivotal tool in shape sensing for various infrastructures. However, this tool suffers from accumulative errors, particularly at inflection points where the normal vector undergoes sign changes. To minimize the error, the traditional FS framework is modified by incorporating the homogeneous matrix transformation (HMT) method for segments containing inflection
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Detecting Leadership Opportunities in Group Discussions Using Off-the-Shelf VR Headsets Sensors (IF 3.9) Pub Date : 2024-04-15 Chenghao Gu, Jiadong Chen, Jiayi Zhang, Tianyuan Yang, Zhankun Liu, Shin’ichi Konomi
The absence of some forms of non-verbal communication in virtual reality (VR) can make VR-based group discussions difficult even when a leader is assigned to each group to facilitate discussions. In this paper, we discuss if the sensor data from off-the-shelf VR devices can be used to detect opportunities for facilitating engaging discussions and support leaders in VR-based group discussions. To this
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Editorial for the Special Issue “Sensing Brain Activity Using EEG and Machine Learning” Sensors (IF 3.9) Pub Date : 2024-04-15 Peter Rogelj
Sensing brain activity to reveal, analyze and recognize brain activity patterns has [...]
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Resource-Efficient Multicast URLLC Service in 5G Systems Sensors (IF 3.9) Pub Date : 2024-04-15 Artem Krasilov, Irina Lebedeva, Ruslan Yusupov, Evgeny Khorov
Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC
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Measurement and Analysis of 4G/5G Mobile Signal Coverage in a Heavy Industry Environment Sensors (IF 3.9) Pub Date : 2024-04-15 Ladislav Polak, Jan Kufa, Roman Sotner, Tomas Fryza
In the evolving landscape of Industry 4.0, the integration of advanced wireless technologies into manufacturing processes holds the promise of unprecedented connectivity and efficiency. In particular, the data transmission in a heavy industry environment needs stable connectivity with mobile operators. This paper deals with the performance study of 4G and 5G mobile signal coverage within a complex
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Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon Sensors (IF 3.9) Pub Date : 2024-04-15 Benjamin Steven Vien, Thomas Kuen, Louis Raymond Francis Rose, Wing Kong Chiu
This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water’s Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other
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A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors Sensors (IF 3.9) Pub Date : 2024-04-15 Anas Dawod, Dimitrios Georgakopoulos, Prem Prakash Jayaraman, Ampalavanapillai Nirmalathas
The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort
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Experimental Study on SPR Array Sensing Chip Integrated with Microvalves Sensors (IF 3.9) Pub Date : 2024-04-15 Wanwan Chen, Peng Wang, Bin Li
This paper discusses a microfluidic system designed for surface plasmon resonance (SPR) sensing, incorporating integrated microvalves. This system is built from a layered structure of polydimethylsiloxane (PDMS) and polymethylmethacrylate (PMMA). The functionality of the microvalves is verified through a conductance method involving electrodes positioned at the microfluidic channels’ inlets and outlets
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Harmonic FMCW Radar System: Passive Tag Detection and Precise Ranging Estimation Sensors (IF 3.9) Pub Date : 2024-04-15 Ahmed El-Awamry, Feng Zheng, Thomas Kaiser, Maher Khaliel
This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4–2.5GHz frequency range for the forward channel and 4.8–5.0GHz for the backward channel, this study delves into the various challenges faced during the system’s realization. These
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A Concrete Core Void Imaging Approach and Parameter Analysis of Concrete-Filled Steel Tube Members Using Travel Time Tomography: Multi-Physics Simulations and Experimental Studies Sensors (IF 3.9) Pub Date : 2024-04-13 Wenting Zheng, Bin Xu, Zongjun Xia, Jiang Wang, Jingliang Liu, Yudi Yao, Yifei Wang
Concrete-filled steel tube (CFST) members have been widely used in civil engineering due to their advanced mechanical properties. However, internal defects such as the concrete core voids and interface debonding in CFST structures are likely to weaken their load-carrying capacity and stiffness, which affects the safety and serviceability. Visualizing the inner defects of the concrete cores in CFST
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Wind-Induced Vibration Monitoring of High-Mast Illumination Poles Using Wireless Smart Sensors Sensors (IF 3.9) Pub Date : 2024-04-14 Mona Shaheen, Jian Li, Caroline Bennett, William Collins
This paper describes the use of wireless smart sensors for examining the underlying mechanism for the wind-induced vibration of high-mast illumination pole (HMIP) structures. HMIPs are tall, slender structures with low inherent damping. Video recordings of multiple HMIPs showed considerable vibrations of these HMIPs under wind loading in the state of Kansas. The HMIPs experienced cyclic large-amplitude
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Assessment of Triboelectric Nanogenerators for Electric Field Energy Harvesting Sensors (IF 3.9) Pub Date : 2024-04-14 Oswaldo Menéndez, Juan Villacrés, Alvaro Prado, Juan P. Vásconez, Fernando Auat-Cheein
Electric-field energy harvesters (EFEHs) have emerged as a promising technology for harnessing the electric field surrounding energized environments. Current research indicates that EFEHs are closely associated with Tribo-Electric Nano-Generators (TENGs). However, the performance of TENGs in energized environments remains unclear. This work aims to evaluate the performance of TENGs in electric-field