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CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face Sensors (IF 3.9) Pub Date : 2024-03-14 Yingbo Fan, Shanjun Mao, Mei Li, Zheng Wu, Jitong Kang
With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face
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Semantic Segmentation of Remote Sensing Images Depicting Environmental Hazards in High-Speed Rail Network Based on Large-Model Pre-Classification Sensors (IF 3.9) Pub Date : 2024-03-14 Qi Dong, Xiaomei Chen, Lili Jiang, Lin Wang, Jiachong Chen, Ying Zhao
With the rapid development of China’s railways, ensuring the safety of the operating environment of high-speed railways faces daunting challenges. In response to safety hazards posed by light and heavy floating objects during the operation of trains, we propose a dual-branch semantic segmentation network with the fusion of large models (SAMUnet). The encoder part of this network uses a dual-branch
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Synchronous Driving Method for Stitching Pixel Arrays Based on an Adaptive Correction Technique Sensors (IF 3.9) Pub Date : 2024-03-15 Suiyang Liu, Zhongjie Guo, Xinqi Cheng, Ruiming Xu, Ningmei Yu
With the application of stitching technology in large-pixel-array CMOS image sensors, the problem of non-synchronized output signals from pixel array bilateral driver circuits has become progressively more serious and has led to the DC perforation of bilateral driver circuits, while conventional clock tree synchronization design methodology does not apply to stitching technology. Therefore, this paper
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Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method Sensors (IF 3.9) Pub Date : 2024-03-15 Feng Xiao, Yu Yan, Xiangwei Meng, Yuxue Mao, Gang S. Chen
Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness matrix of the overall structure into the stiffness matrices of its substructures
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Dopamine Measurement Using Engineered CNT–CQD–Polymer Coatings on Pt Microelectrodes Sensors (IF 3.9) Pub Date : 2024-03-15 Mahdieh Darroudi, Kevin A. White, Matthew A. Crocker, Brian N. Kim
This study aims to develop a microelectrode array-based neural probe that can record dopamine activity with high stability and sensitivity. To mimic the high stability of the gold standard method (carbon fiber electrodes), the microfabricated platinum microelectrode is coated with carbon-based nanomaterials. Carboxyl-functionalized multi-walled carbon nanotubes (COOH-MWCNTs) and carbon quantum dots
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New Scheme of MEMS-Based LiDAR by Synchronized Dual-Laser Beams for Detection Range Enhancement Sensors (IF 3.9) Pub Date : 2024-03-15 Chien-Wei Huang, Chun-Nien Liu, Sheng-Chuan Mao, Wan-Shao Tsai, Zning-Way Pei, Charles W. Tu, Wood-Hi Cheng
A new scheme presents MEMS-based LiDAR with synchronized dual-laser beams for detection range enhancement and precise point-cloud data without using higher laser power. The novel MEMS-based LiDAR module uses the principal laser light to build point-cloud data. In addition, an auxiliary laser light amplifies the single-noise ratio to enhance the detection range. This LiDAR module exhibits the field
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Embodimetrics: A Principal Component Analysis Study of the Combined Assessment of Cardiac, Cognitive and Mobility Parameters Sensors (IF 3.9) Pub Date : 2024-03-15 Andrea Chellini, Katia Salmaso, Michele Di Domenico, Nicola Gerbi, Luigi Grillo, Marco Donati, Marco Iosa
There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac, and motor variables using principal component analysis (PCA). Additionally, we assessed
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A Crowd Movement Analysis Method Based on Radar Particle Flow Sensors (IF 3.9) Pub Date : 2024-03-15 Li Zhang, Lin Cao, Zongmin Zhao, Dongfeng Wang, Chong Fu
Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the
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Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications Sensors (IF 3.9) Pub Date : 2024-03-16 Ana Beatriz Rodrigues Costa De Mattos, Glauber Brante, Guilherme L. Moritz, Richard Demo Souza
Millimeter-wave (mmWave) radars attain high resolution without compromising privacy while being unaffected by environmental factors such as rain, dust, and fog. This study explores the challenges of using mmWave radars for the simultaneous detection of people and small animals, a critical concern in applications like indoor wireless energy transfer systems. This work proposes innovative methodologies
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Assessing the Validity of the Ergotex IMU in Joint Angle Measurement: A Comparative Study with Optical Tracking Systems Sensors (IF 3.9) Pub Date : 2024-03-16 Jose M. Jimenez-Olmedo, Juan Tortosa-Martínez, Juan M. Cortell-Tormo, Basilio Pueo
An observational, repeated measures design was used in this study to assess the validity of the Ergotex Inertial Measurement Unit (IMU) against a 3D motion capture system for measuring trunk, hip, and shoulder angles in ten healthy adult males (38.8 ± 7.3 y, bodyweight 79.2 ± 115.9 kg, body height 179.1 ± 8.1 cm). There were minimal systematic differences between the devices, with the most significant
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Automated Seizure Detection Based on State-Space Model Identification Sensors (IF 3.9) Pub Date : 2024-03-16 Zhuo Wang, Michael R. Sperling, Dale Wyeth, Allon Guez
In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure
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The Potential Role of Wearable Inertial Sensors in Laboring Women with Walking Epidural Analgesia Sensors (IF 3.9) Pub Date : 2024-03-16 Mikhail Dziadzko, Adrien Péneaud, Lionel Bouvet, Thomas Robert, Laetitia Fradet, David Desseauve
There is a growing interest in wearable inertial sensors to monitor and analyze the movements of pregnant women. The noninvasive and discrete nature of these sensors, integrated into devices accumulating large datasets, offers a unique opportunity to study the dynamic changes in movement patterns during the rapid physical transformations induced by pregnancy. However, the final cut of the third trimester
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Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection Sensors (IF 3.9) Pub Date : 2024-03-16 Ge Shi, Wei Cheng, Xiang Gao, Fupeng Wei, Heng Zhang, Qingzheng Wang
In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching
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PixRevive: Latent Feature Diffusion Model for Compressed Video Quality Enhancement Sensors (IF 3.9) Pub Date : 2024-03-16 Weiran Wang, Minge Jing, Yibo Fan, Wei Weng
In recent years, the rapid prevalence of high-definition video in Internet of Things (IoT) systems has been directly facilitated by advances in imaging sensor technology. To adapt to limited uplink bandwidth, most media platforms opt to compress videos to bitrate streams for transmission. However, this compression often leads to significant texture loss and artifacts, which severely degrade the Quality
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A Review on Traversability Risk Assessments for Autonomous Ground Vehicles: Methods and Metrics Sensors (IF 3.9) Pub Date : 2024-03-16 Mohamed Benrabah, Charifou Orou Mousse, Elie Randriamiarintsoa, Roland Chapuis, Romuald Aufrère
Evaluating the risk associated with operations is an essential element of safe planning and an essential prerequisite in mobile robotics. This issue is very broad, with numerous definitions emerging in the recent literature adapting different application scenarios and leading to different algorithmic approaches. In this review, we will investigate how the state-of-the-art approaches define the traversability
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PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition Sensors (IF 3.9) Pub Date : 2024-03-16 Imran Ullah Khan, Jong Weon Lee
Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques
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Comparing Video Analysis to Computerized Detection of Limb Position for the Diagnosis of Movement Control during Back Squat Exercise with Overload Sensors (IF 3.9) Pub Date : 2024-03-16 André B. Peres, Andrei Sancassani, Eliane A. Castro, Tiago A. F. Almeida, Danilo A. Massini, Anderson G. Macedo, Mário C. Espada, Víctor Hernández-Beltrán, José M. Gamonales, Dalton M. Pessôa Filho
Incorrect limb position while lifting heavy weights might compromise athlete success during weightlifting performance, similar to the way that it increases the risk of muscle injuries during resistance exercises, regardless of the individual’s level of experience. However, practitioners might not have the necessary background knowledge for self-supervision of limb position and adjustment of the lifting
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Elastic Tactile Sensor Glove for Dexterous Teaching by Demonstration Sensors (IF 3.9) Pub Date : 2024-03-16 Philipp Ruppel, Jianwei Zhang
We present a thin and elastic tactile sensor glove for teaching dexterous manipulation tasks to robots through human demonstration. The entire glove, including the sensor cells, base layer, and electrical connections, is made from soft and stretchable silicone rubber, adapting to deformations under bending and contact while preserving human dexterity. We develop a glove design with five fingers and
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Optically Controlled Gain Modulation for Microwave Metasurface Antennas Sensors (IF 3.9) Pub Date : 2024-03-16 Charlotte Tripon-Canseliet, Cristian Della Giovampaola, Nicolas Pavy, Jean Chazelas, Stefano Maci
Over the past decade, metasurfaces (MTSs) have emerged as a highly promising platform for the development of next-generation, miniaturized, planar devices across a wide spectrum of microwave frequencies. Among their various applications, the concept of MTS-based antennas, particularly those that are based on surface wave excitation, represents a groundbreaking advancement with significant implications
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Reliability Evaluation Method for Array Antenna Considering Performance Changes Sensors (IF 3.9) Pub Date : 2024-03-16 Xinxin Huang, Sai Zhu, Guanhui Liang
The existing array antenna reliability evaluation method based on the n/k system is analyzed. As the failed T/R module’s influence on the array antenna’s performance is not considered, the reliability of the array antenna is overestimated. To improve the accuracy of the array antenna reliability evaluation, the performance changes caused by T/R failures in different locations are considered. The reliability
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The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications Sensors (IF 3.9) Pub Date : 2024-03-16 Cezar-Ion Adomnitei, Cezar-Eduard Lesanu, Adrian Done, Ang Yu, Mihai Dimian, Alexandru Lavric
LEO satellite constellations can provide a viable alternative to expand connectivity to remote, isolated geographical areas and complement existing IoT terrestrial communication infrastructures. This paper aims to improve LEO satellite communications by implementing a new phased antenna array system that can significantly improve the radio communication link’s performance. By adjusting the progressive
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Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning Sensors (IF 3.9) Pub Date : 2024-03-16 Oleksandr Melnychenko, Lukasz Scislo, Oleg Savenko, Anatoliy Sachenko, Pavlo Radiuk
In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and
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Investigation of Using Hyperspectral Vegetation Indices to Assess Brassica Downy Mildew Sensors (IF 3.9) Pub Date : 2024-03-16 Bo Liu, Marco Antonio Fernandez, Taryn Michelle Liu, Shunping Ding
Downy mildew caused by Hyaloperonospora brassicae is a severe disease in Brassica oleracea that significantly reduces crop yield and marketability. This study aims to evaluate different vegetation indices to assess different downy mildew infection levels in the Brassica variety Mildis using hyperspectral data. Artificial inoculation using H. brassicae sporangia suspension was conducted to induce different
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Enhancing View Synthesis with Depth-Guided Neural Radiance Fields and Improved Depth Completion Sensors (IF 3.9) Pub Date : 2024-03-16 Bojun Wang, Danhong Zhang, Yixin Su, Huajun Zhang
Neural radiance fields (NeRFs) leverage a neural representation to encode scenes, obtaining photorealistic rendering of novel views. However, NeRF has notable limitations. A significant drawback is that it does not capture surface geometry and only renders the object surface colors. Furthermore, the training of NeRF is exceedingly time-consuming. We propose Depth-NeRF as a solution to these issues
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Leveraging the Sensitivity of Plants with Deep Learning to Recognize Human Emotions Sensors (IF 3.9) Pub Date : 2024-03-16 Jakob Adrian Kruse, Leon Ciechanowski, Ambre Dupuis, Ignacio Vazquez, Peter A. Gloor
Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants, which have been shown to be sensitive to human steps and sounds. A methodology to use plants as human emotion
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Ultralow-Noise Chopper Amplifier for Seafloor E-Field Measurement Sensors (IF 3.9) Pub Date : 2024-03-17 Sixuan Song, Kai Chen
The seafloor E-field signal is extremely weak and difficult to measured, even with a high signal-to-noise ratio. The preamplifier for electrodes is a key technology for ocean-bottom electromagnetic receivers. In this study, a chopper amplifier was proposed and developed to measure the seafloor E-field signal in the nanovolt to millivolt range at significantly low frequencies. It included a modulator
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Relationship between Body Posture Assessed by Dynamic Baropodometry and Dental Occlusion in Patients with and without Dental Pathology Sensors (IF 3.9) Pub Date : 2024-03-17 Isabel Carda-Navarro, Lidia Lacort-Collado, Nadia Fernández-Ehrling, Alicia Lanuza-Garcia, Javier Ferrer-Torregrosa, Clara Guinot-Barona
Body biomechanics and dental occlusion are related, but this interaction is not fully elucidated. The aim of this study was to investigate the association between body posture and occlusion in patients with and without dental pathology. A cross-sectional study was carried out with 29 patients divided into a control group and a group with pathology (malocclusions). Body posture was evaluated by dynamic
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Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm Sensors (IF 3.9) Pub Date : 2024-03-17 Maritza Albán-Escobar, Pablo Navarrete-Arroyo, Danni Rodrigo De la Cruz-Guevara, Johanna Tobar-Quevedo
This paper explores the potential benefits of integrating a brain–computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies
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A Deep Learning Model for Markerless Pose Estimation Based on Keypoint Augmentation: What Factors Influence Errors in Biomechanical Applications? Sensors (IF 3.9) Pub Date : 2024-03-17 Ana V. Ruescas-Nicolau, Enrique Medina-Ripoll, Helios de Rosario, Joaquín Sanchiz Navarro, Eduardo Parrilla, M.-Carmen Juan
In biomechanics, movement is typically recorded by tracking the trajectories of anatomical landmarks previously marked using passive instrumentation, which entails several inconveniences. To overcome these disadvantages, researchers are exploring different markerless methods, such as pose estimation networks, to capture movement with equivalent accuracy to marker-based photogrammetry. However, pose
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A Real-Time Monitoring Method for Droplet Transfer Frequency in Wire-Filled GTAW Based on Arc Sensing Sensors (IF 3.9) Pub Date : 2024-03-17 Aiting Jia, Yifang Luo, Bo Hong, Xiangwen Li, Li Yin, Mina Luo
Droplet transfer frequency is a decisive factor in welding quality and efficiency in gas tungsten arc welding (GTAW). However, there still needs to be a monitoring method for droplet transfer frequency with high precision and good real-time performance. Therefore, a real-time monitoring method for droplet transfer frequency in wire-filled GTAW using arc sensing is proposed in this paper. An arc signal
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Machine Learning in Communication Systems and Networks Sensors (IF 3.9) Pub Date : 2024-03-17 Yichuang Sun, Haeyoung Lee, Oluyomi Simpson
The landscape of communication environments is undergoing a revolutionary transformation, driven by the relentless evolution of technology and the growing demands of an interconnected world [...]
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CoFe2O4 on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode Sensors (IF 3.9) Pub Date : 2024-03-17 Jong Hun Kim, Yeong Uk Choi, Jong Hoon Jung, Jae-Hun Kim
In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFe2O4 (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200
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Detection of a Submillimeter Notch-Type Defect at Multiple Orientations by a Lamb Wave A0 Mode at 550 kHz for Long-Range Structural Health Monitoring Applications Sensors (IF 3.9) Pub Date : 2024-03-17 Lorenzo Capineri, Lorenzo Taddei, Eugenio Marino Merlo
The early detection of small cracks in large metal structures is a crucial requirement for the implementation of a structural health monitoring (SHM) system with a low transducers density. This work tackles the challenging problem of the early detection of submillimeter notch-type defects with a semielliptical shape and a groove at a constant width of 100 µm and 3 mm depth in a 4.1 mm thick aluminum
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Ultrasensitive Silicon Photonic Refractive Index Sensor Based on Hybrid Double Slot Subwavelength Grating Microring Resonator Sensors (IF 3.9) Pub Date : 2024-03-17 Kaiwei Lu, Beiju Huang, Xiaoqing Lv, Zan Zhang, Zhengtai Ma
Silicon photonic-based refractive index sensors are of great value in the detection of gases, biological and chemical substances. Among them, microring resonators are the most promising due to their compact size and narrow Lorentzian-shaped spectrum. The electric field in a subwavelength grating waveguide (SWG) is essentially confined in the low-refractive index dielectric, favoring enhanced analyte-photon
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On the Search for Potentially Anomalous Traces of Cosmic Ray Particles in Images Acquired by Cmos Detectors for a Continuous Stream of Emerging Observational Data Sensors (IF 3.9) Pub Date : 2024-03-13 Marcin Piekarczyk, Tomasz Hachaj
In this paper we propose the method for detecting potential anomalous cosmic ray particle tracks in big data image dataset acquired by Complementary Metal-Oxide-Semiconductors (CMOS). Those sensors are part of scientific infrastructure of Cosmic Ray Extremely Distributed Observatory (CREDO). The use of Incremental PCA (Principal Components Analysis) allowed approximation of loadings which might be
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IMU Auto-Calibration Based on Quaternion Kalman Filter to Identify Movements of Dairy Cows Sensors (IF 3.9) Pub Date : 2024-03-13 Carlos Muñoz-Poblete, Cristian González-Aguirre, Robert H. Bishop, David Cancino-Baier
This work is focused on developing a self-calibration algorithm for an orientation estimation of cattle movements based on a quaternion Kalman filter. The accelerometer signals in the earth’s frame provide more information to confirm that the cow is performing a jump to mount another cow. To obtain the measurements in the earth’s frame, we propose a self-calibration method based on a strapdown inertial
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Optimal Design of Sparse Matrix Phased Array Using Simulated Annealing for Volumetric Ultrasonic Imaging with Total Focusing Method Sensors (IF 3.9) Pub Date : 2024-03-14 Dmitry Olegovich Dolmatov, Vadim Yurevich Zhvyrblya
The total focusing method (TFM) is often considered to be the ‘gold standard’ for ultrasonic imaging in the field of nondestructive testing. The use of matrix phased arrays as probes allows for high-resolution volumetric TFM imaging. Conventional TFM imaging involves the use of full matrix capture (FMC) for ultrasonic signals acquisition, but in the case of a matrix phased array, this approach is associated
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Variability between Different Hand-Held Dynamometers for Measuring Muscle Strength Sensors (IF 3.9) Pub Date : 2024-03-14 William Du, Kayla M. D. Cornett, Gabrielle A. Donlevy, Joshua Burns, Marnee J. McKay
Muscle strength is routinely measured in patients with neuromuscular disorders by hand-held dynamometry incorporating a wireless load cell to evaluate disease severity and therapeutic efficacy, with magnitude of effect often based on normative reference values. While several hand-held dynamometers exist, their interchangeability is unknown which limits the utility of normative data. We investigated
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OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones Sensors (IF 3.9) Pub Date : 2024-03-14 Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min (Jerry) Park
For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments.
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Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks Sensors (IF 3.9) Pub Date : 2024-03-14 Xiaodan Bi, Lian Zhao
With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing
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Detecting Forged Audio Files Using “Mixed Paste” Command: A Deep Learning Approach Based on Korean Phonemic Features Sensors (IF 3.9) Pub Date : 2024-03-14 Yeongmin Son, Jae Wan Park
The ubiquity of smartphones today enables the widespread utilization of voice recording for diverse purposes. Consequently, the submission of voice recordings as digital evidence in legal proceedings has notably increased, alongside a rise in allegations of recording file forgery. This trend highlights the growing significance of audio file authentication. This study aims to develop a deep learning
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Multimodal Mobile Robotic Dataset for a Typical Mediterranean Greenhouse: The GREENBOT Dataset Sensors (IF 3.9) Pub Date : 2024-03-14 Fernando Cañadas-Aránega, Jose Luis Blanco-Claraco, Jose Carlos Moreno, Francisco Rodriguez-Diaz
This paper presents an innovative dataset designed explicitly for challenging agricultural environments, such as greenhouses, where precise location is crucial, but GNNS accuracy may be compromised by construction elements and the crop. The dataset was collected using a mobile platform equipped with a set of sensors typically used in mobile robots as it was moved through all the corridors of a typical
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Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion Sensors (IF 3.9) Pub Date : 2024-03-14 Zhanxin Ma, Xiyu Zheng, Hejun Liang, Ping Luo
The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based
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Enhancing Query Formulation for Universal Image Segmentation Sensors (IF 3.9) Pub Date : 2024-03-14 Yipeng Qu, Joohee Kim
Recent advancements in image segmentation have been notably driven by Vision Transformers. These transformer-based models offer one versatile network structure capable of handling a variety of segmentation tasks. Despite their effectiveness, the pursuit of enhanced capabilities often leads to more intricate architectures and greater computational demands. OneFormer has responded to these challenges
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A Vascular Feature Detection and Matching Method Based on Dual-Branch Fusion and Structure Enhancement Sensors (IF 3.9) Pub Date : 2024-03-15 Kaiyang Xu, Haibin Wu, Yuji Iwahori, Xiaoyu Yu, Zeyu Hu, Aili Wang
How to obtain internal cavity features and perform image matching is a great challenge for laparoscopic 3D reconstruction. This paper proposes a method for detecting and associating vascular features based on dual-branch weighted fusion vascular structure enhancement. Our proposed method is divided into three stages, including analyzing various types of minimally invasive surgery (MIS) images and designing
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Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection Sensors (IF 3.9) Pub Date : 2024-03-15 Zhongbin Li, Meng Fan, Jinhua Tao, Benben Xu
Greenhouse gas satellites can provide consistently global CO2 data which are important inputs for the top-down inverse estimation of CO2 emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides
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Guided Lamb Wave Array Time-Delay-Based MUSIC Algorithm for Impact Imaging Sensors (IF 3.9) Pub Date : 2024-03-15 Fei Zheng, Shenfang Yuan
Composite materials, valued in aerospace for their stiffness, strength and lightness, require impact monitoring for structural health, especially against low-velocity impacts. The MUSIC algorithm, known for efficient directional scanning and easy sensor deployment, is gaining prominence in this area. However, in practical engineering applications, the broadband characteristics of impact response signals
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Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG) Sensors (IF 3.9) Pub Date : 2024-03-15 Vessela Krasteva, Ivo Iliev, Serafim Tabakov
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an
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Principal Component Analysis Enhanced with Bootstrapped Confidence Interval for the Classification of Parkinsonian Patients Using Gaussian Mixture Model and Gait Initiation Parameters Sensors (IF 3.9) Pub Date : 2024-03-15 Florent Loete, Arnaud Simonet, Paul Fourcade, Eric Yiou, Arnaud Delafontaine
Parkinson’s disease is one of the major neurodegenerative diseases that affects the postural stability of patients, especially during gait initiation. There is actually an increasing demand for the development of new non-pharmacological tools that can easily classify healthy/affected patients as well as the degree of evolution of the disease. The experimental characterization of gait initiation (GI)
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Analytical Analysis of Factors Affecting the Accuracy of a Dual-Heat Flux Core Body Temperature Sensor Sensors (IF 3.9) Pub Date : 2024-03-15 Jakub Żmigrodzki, Szymon Cygan, Jan Łusakowski, Patryk Lamprecht
Non-invasive core body temperature (CBT) measurements using temperature and heat-flux have become popular in health, sports, work safety, and general well-being applications. This research aimed to evaluate two commonly used sensor designs: those that combine heat flux and temperature sensors, and those with four temperature sensors. We used analytical methods, particularly uncertainty analysis calculus
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6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges Sensors (IF 3.9) Pub Date : 2024-03-15 Robin Chataut, Mary Nankya, Robert Akl
In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient
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EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN Sensors (IF 3.9) Pub Date : 2024-03-15 Aniqa Arif, Yihe Wang, Rui Yin, Xiang Zhang, Ahmed Helmy
Analysis of brain signals is essential to the study of mental states and various neurological conditions. The two most prevalent noninvasive signals for measuring brain activities are electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number of channels, provides
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The Feasibility of Semi-Continuous and Multi-Frequency Thoracic Bioimpedance Measurements by a Wearable Device during Fluid Changes in Hemodialysis Patients Sensors (IF 3.9) Pub Date : 2024-03-15 Melanie K. Schoutteten, Lucas Lindeboom, Hélène De Cannière, Zoë Pieters, Liesbeth Bruckers, Astrid D. H. Brys, Patrick van der Heijden, Bart De Moor, Jacques Peeters, Chris Van Hoof, Willemijn Groenendaal, Jeroen P. Kooman, Pieter M. Vandervoort
Repeated single-point measurements of thoracic bioimpedance at a single (low) frequency are strongly related to fluid changes during hemodialysis. Extension to semi-continuous measurements may provide longitudinal details in the time pattern of the bioimpedance signal, and multi-frequency measurements may add in-depth information on the distribution between intra- and extracellular fluid. This study
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Design of Network-on-Chip-Based Restricted Coulomb Energy Neural Network Accelerator on FPGA Device Sensors (IF 3.9) Pub Date : 2024-03-15 Soongyu Kang, Seongjoo Lee, Yunho Jung
Sensor applications in internet of things (IoT) systems, coupled with artificial intelligence (AI) technology, are becoming an increasingly significant part of modern life. For low-latency AI computation in IoT systems, there is a growing preference for edge-based computing over cloud-based alternatives. The restricted coulomb energy neural network (RCE-NN) is a machine learning algorithm well-suited
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Exploring the Neural Correlates of Flow Experience with Multifaceted Tasks and a Single-Channel Prefrontal EEG Recording Sensors (IF 3.9) Pub Date : 2024-03-15 Yuqi Hang, Buyanzaya Unenbat, Shiyun Tang, Fei Wang, Bingxin Lin, Dan Zhang
Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to using a single type of task, and the exploration of its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifaceted flow-induction
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Iterative Pulse–Echo Tomography for Ultrasonic Image Correction Sensors (IF 3.9) Pub Date : 2024-03-15 Yuchen Zengqiu, Wentao Wu, Ling Xiao, Erlei Zhou, Zheng Cao, Jiadong Hua, Yue Wang
Acoustic aberration, caused by the uneven distribution of tissue speed-of-sound (SoS), significantly reduces the quality of ultrasound imaging. An important approach to mitigate this issue is imaging correction based on local SoS estimation. Computed ultrasound tomography in echo mode (CUTE) is an SoS estimation method that utilizes phase-shift information from ultrasound pulse–echo signals, offering
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DataMesh+: A Blockchain-Powered Peer-to-Peer Data Exchange Model for Self-Sovereign Data Marketplaces Sensors (IF 3.9) Pub Date : 2024-03-15 Mpyana Mwamba Merlec, Hoh Peter In
In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust
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Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed Sensors (IF 3.9) Pub Date : 2024-03-15 Minh Long Hoang, Guido Matrella, Paolo Ciampolini
This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users’ heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, the smart bed is equipped with a 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board and transmitted to a PC for recording.
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In Situ Surface Defect Detection in Polymer Tube Extrusion: AI-Based Real-Time Monitoring Approach Sensors (IF 3.9) Pub Date : 2024-03-10 Chun Muk Jo, Woong Ki Jang, Young Ho Seo, Byeong Hee Kim
While striving to optimize overall efficiency, smart manufacturing systems face various problems presented by the aging workforce in modern society. The proportion of aging workers is rapidly increasing worldwide, and visual perception, which plays a key role in quality control, is significantly susceptible to the impact of aging. Thus it is necessary to understand these changes and implement state-of-the-art
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Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model Sensors (IF 3.9) Pub Date : 2024-03-12 Eduardo Rodríguez, Judit García-Ferrero, María Sánchez-Aparicio, José M. Iglesias, Albert Oliver-Serra, M. Jesús Santos, Paula de Andrés-Anaya, J. Manuel Cascón, Gustavo Montero García, Alejandro Medina, Susana Lagüela, M. Isabel Asensio, Rafael Montenegro Armas
The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the