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International Conference on Intelligent Transportation

作者： Eurico Almeida Bruno Silva Jorge Batista Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Electrical and Computers Engineering Faculty of Science and Technology University of Coimbra Coimbra Portugal

This paper presents an efficient and lightweight multi-branch deep architecture to improve vehicle reidentification (V-ReID). While most V-ReID work uses a combination of complex multi-branch architectures to extract robust and diversified embeddings towards re-identification, we advocate that simple and lightweight architectures can be designed to fulfill the Re-Idtask without compromising performance. We propose a combination of Grouped-convolution and Loss-Branch-Split strategies to design a multi-branch architecture that improve feature diversity and feature discriminability. We combine a ResNet50 global branch architecture with a BotNet self-attention branch architecture, both designed within a Loss-Branch-Split (LBS) strategy. We argue that specialized loss-branch-splitting helps to improve re-identification tasks by generating specialized re-identification features. A lightweight solution using grouped convolution is also proposed to mimic the learning of loss-splitting into multiple embeddings while significantly reducing the model size. In addition, we designed an improved solution to leverage additional metadata, such as camera ID and pose information, that uses 97% less parameters, further improving re-identification performance. In comparison to state-of-the-art (SoTA) methods, our approach outperforms competing solutions in Veri-776 by achieving 85.6% mAP and 97.7% CMC1 and obtains competitive results in Veri-Wild with 88.1% mAP and 96.3% CMC1. Overall, our work provides important insights into improving vehicle re-identification and presents a strong basis for other retrieval tasks. Our code is available at the link.

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Reactive astrocytes contribute to decreased blood-brain barrier integrity after impulsive pressure loading in vitro

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Journal of Biomechanical science and Engineering 2021年第2期16卷 1-10页

作者： Nakadate, Hiromichi Aomura, Shigeru Nishimura, Akiyoshi Department of Mechanical Engineering and Robotics Faculty of Textile Science and Technology Shinshu University 3-15-1 Tokida UedaNagano386-8567 Japan Graduate School of Biomedical Sciences Tokushima University Japan

Although it has been reported that blood brain barrier (BBB) disruption following head impact can lead to increased vascular permeability and subsequent brain injury, the influence of pressure loading on BBB dysfunction is not fully understood. In this study, we exposed in vitro BBB models to impulsive pressure to mimic changes in intracranial pressure during head impact. Barrier function was examined by measuring transendothelial electrical resistance (TEER). Four models were used: an endothelial monolayer of rat brain capillaries (E00 model), a co-culture model of endothelial cells and pericytes (EP0 model), a co-culture model of endothelial cells and astrocytes (EA0 model), and a triple co-culture model of endothelial cells, pericytes, and astrocytes (EPA model). Immediately after loading, the E00 model showed a 13% decrease in TEER, the EP0 model showed an 8% decrease, the EA0 model showed a 40% decrease, and the EPA model showed a 33% decrease. At 2 days post-loading, TEER values in the EPA model remained decreased and the expression of claudin-5 and ZO-1 was significantly decreased, whereas GFAP expression was significantly increased. In conclusion, increased endothelial paracellular permeability induced by exposure to impulsive pressure is associated with astrocyte activation and decreased tight junction protein expression in vitro. © 2021 The Japan Society of Mechanical Engineers

关键词： Endothelial cells

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A Novel F-SVM based on PSO for Gait Phase Recognition in Lower Limb Exoskeleton 18

A Novel F-SVM based on PSO for Gait Phase Recognition in Low...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lv, Pingping Zhang, Chi Yi, Feng Yuan, Ting Li, Shupei Zhang, Meitong Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9798350312201

To meet the requirements of lower limb exoskeleton robots for lightweight equipment and accurate gait phase detection under different speeds, the kinematics and dynamics information are investigated. In this paper, we propose a novel radius-margin-based supporting vector machine (SVM) model with particle swarm optimization algorithm (PSO) in feature space called PSO-F-SVM for gait phase detection. The proposed method considers both the maximization of margin and the minimization of radius and uses PSO to optimize the parameter in F-SVM, which can enhance the performance of the SVM classifier. Plantar pressure insoles with flexible and elastic sensors are designed for gait feature measurement. We conduct comparative experiments between the proposed method and other variants of SVM under four speeds. The experimental result indicates that the proposed method improves the accuracy by 2.68% compared with the standard SVM. Moreover, more than 98% accuracy can be obtained at four speeds, which demonstrates that the proposed method outperforms the other variants of SVM. © 2023 IEEE.

关键词： Support vector machines

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Internet of Things (IoT) Based Continuous Growth Rate Monitoring System of Plant Stem 3

Internet of Things (IoT) Based Continuous Growth Rate Monito...

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3rd IEEE Conference on VLSI Device, Circuit and System, VLSI DCS 2022

作者： Nayak, Shikha Das, Subir Chakraborty, Badal Chakraborty, Tanmoy Roy, Kishor Meghnad Saha Institute of Technology Computer Science Engineering Kolkata India Indian Institute of Engineering Science & Technology School of Mechatronics and Robotics Shibpur India Bidhan Chandra Krishi Viswavidyalaya Faculty of Agricultural Engineering Kalyani India Techno Main Salt Lake Computer Science Engineering Kolkata India Techno India Group Kolkata India

ISBN: (纸本)9781665438018

In the field of precision agriculture, plant growth rate monitoring is an important issue to study the influence of soil, weather, or agronomic practices on its growing condition. A plant's growth rate can be evaluated by measuring the radial/ circumference growth of its body parts like stem, trunk, branch, and fruit. In this paper, the internet of things (IoT) based plant's stem growth rate monitoring system has been developed. Here, the most commonly used PC (Personal Computer) optical mouse is used as a sensor for monitoring the radial growth of the sunflower stem. To sense the radial growth of the stem, a paper-based strap is used across the stem. A computer mouse is used for the measurement of strap movement which is occurred due to the radial growth of the stem. Eventually, the mouse sensor output has been calibrated using Wi-Fi enabled processing unit and transmits the same data to the server for real-time monitoring. To store the growth rate information, an open-source IoT platform namely 'ThingSpeak' has been used. In presence of the ambient condition, the performance of the developed system has been tested for a long duration of time over a sunflower stem and provided satisfactory results. © 2022 IEEE.

关键词： Internet of things

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Brain Region Knowledge Based Dual-Stream Transformer for EEG Emotion Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Yichuan Xu, Geng-Xin Liang, Haotian Wang, Yishan Wan, Feng Li, Ye Southern University of Science and Technology Shenzhen518055 China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Institute of Advanced Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering The Faculty of Science and Technology The Centre for Cognitive and Brain Sciences The Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau China

In consumer electronics, affective computing allows devices to recognize and respond to the emotional states of individuals, thereby enhancing personalization and satisfaction. Electroencephalogram (EEG)-based emotion recognition methods have garnered attention for their capability to provide real-time, objective insights into emotional and cognitive states. Existing research mainly focuses on extracting temporal and spectral features from EEG signals for emotion recognition. However, electrode shifts may occur across different experiments during data acquisition, resulting in models with limited robustness and constrained classification performance. To address this challenge, this paper introduces a Brain Region Knowledge based Dual-Stream network (BRKDSnet) for emotion recognition. It employs a dual-stream architecture to effectively fuse the temporal, spectral, and spatial features of EEG signals. By incorporating prior brain region partition knowledge, it efficiently aggregates channel features into distinct brain region features. Such a brain region based partition mitigates noise issues caused by electrode shifts during user wearing in consumer electronic applications, and leading to learn more robust feature representations. The experiments conducted on the widely used emotion recognition datasets SEED and SEED-IV shows that BRKDSnet achieves state-of-the-art results, with accuracies of 97.81% (improved by 0.64%-3.57% compared to existing works) and 90.76% (improved by 3.13%-11.39%) respectively. Finally, through analysis of the visualization results, it is found that EEG channels located in the temporal lobe contributed the most to emotion recognition classification, which also provides the potential for future applications in wearable devices. © 2025 IEEE.

关键词： Electroencephalography

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Enabling Reachability Across Multiple Domains Without Controller Synchronization in SDN

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Computers, Materials & Continua 2021年第10期69卷 945-965页

作者： Nauman Khan Rosli Bin Salleh Ihsan Ali Zahid Khan Noman Mazhar Roobaea Alroobaea Fahad Almansour Usman Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaMalaysia Department of Computer Science and IT University of MalakandPakistan Robotics and Internet-of-Things Lab Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts RassQassim UniversityBuraydah51452Saudi Arabia

Software-defined networking(SDN)makes network agile and flexible due to its programmable *** extensive network has multiple domains in SDN for the scalability and performance of the ***,the inter-domain link is also crucial for the stability of the entire network on the data plane *** than one inter-domain connection enhances the scalability of the data plane ***,it faces a reachability problem with the principal root,which causes forwarding loops and packet drops in the network,thereby degrading network *** proposed solution is a multiple controller architecture;however,this approach increases the complexity and affects network ***,in this study,we propose a framework that avoids forwarding loops and packet drops without the synchronization of multiple-domain controllers in the network using an avoid loop with test packet ***,we collect the link status for improved routing and load balancing for the upcoming flow across inter-domain links to prevent congestion and increase throughput in real *** proposed methodology can significantly reduce the controller workload against multiple controller architecture,minimize flow setup latency,and improve throughput.

关键词： Multiple domain packet drop forwarding loop load balancing SDN multiple controller

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A Multi-User Multi-Robot Collaboration through Augmented Reality

A Multi-User Multi-Robot Collaboration through Augmented Rea...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： João G. Martins Gabriel M. Costa Marcelo R. Petry Paulo Costa António Paulo Moreira CRIIS – Centre for Robotics in Industry and Intelligent Systems INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Current industrial environments have multiple robots working alongside humans, thus providing an operator the ability to perceive the robot’s workspace correctly and to anticipate its intentions and movements through the visualization of the robot’s digital twin is of utmost importance for safe and productive human-robot collaboration scenarios. Much has been studied regarding single human-single robot collaborative scenarios, but few address multi-user multi-robot scenarios. To this end, this paper presents a multi-robot multi-operator architecture, where the users’ awareness is enhanced through an augmented reality head-mounted display. A multi-robot, multi-user collaborative scenario is presented in a laboratory environment with two industrial robots. Besides being able to interact with both robots in the system, each user becomes more aware of the robot’s workspace and its pre-defined trajectories. Furthermore, it presents how fiducial markers can help to establish the relation between the different coordinate frames.

关键词： Visualization Service robots Robot kinematics Scalability Collaboration User interfaces Trajectory

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Parameterization of Circuit Breakers for HVDC Transmission Line Between Egypt and the Kingdom of Saudi Arabia Toward Restriking Voltage

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IEEE Access 2024年 12卷 18259-18269页

作者： Eliyan, Tamer Elsisi, Mahmoud Alghaythi, Mamdouh L. Alshammari, Meshari S. Wadie, Fady Benha University Faculty of Engineering at Shoubra Department of Electrical Engineering Cairo 11629 Egypt Higher Institute of Engineering at El-Shorouk City Alshorouk Academy Department of Electrical Power and Machines Engineering Cairo 11837 Egypt National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung 807618 Taiwan Jouf University College of Engineering Department of Electrical Engineering Sakaka 72388 Saudi Arabia Egyptian Russian University Faculty of Engineering Mechatronics and Robotics Engineering Department Badr City 11829 Egypt

The high voltage direct current (HVDC) transmission lines represent the prospective way for long-distance transmission between countries, remote areas, and offshore wind farms to decrease power loss. However, the HVDC protection systems have many challenges against any system issue, such as maintenance and short circuits. Thus, the vital role played by high voltage direct current circuit breaker CB has made it the center of attention in HVDC protection systems. The main challenge in HVDC CB is the lack of naturally exiting current zero that allows the breaker to extinguish the arc during the opening process. Thus, a commutation L-C circuit is required to inject an oscillating current and enforce a zero-crossing. Nevertheless, the L-C branch is affected directly by the arcing time, the transient recovery voltage (TRV), and the rate of rise of recovery voltage (RRRV). This paper investigates the parametric uncertainties of L-C and SF6 mechanical interrupters, including cooling power and arc time constant upon TRV and RRRV, based on Mayr's black-box model. Furthermore, a part of 3000 MVA, 500 kV HVDC transmission line between Egypt and the Kingdom of Saudi Arabia is used as a testing system employing ATP/EMTP software package to demonstrate the effect of CB's parameters variations. The results indicate that the capacitance represents the major parameter having a direct impact on TRV and RRRV. In essence, the proper parameterization of the CB is highly required in the design process of HVDC CB to enhance the decision-making and ensure that the L-C is capable of reducing arcing time and TRV simultaneously. © 2013 IEEE.

关键词： arcing time black-box arc model circuit breakers HVDC protection systems RRRV Transmission lines TRV

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Histohdr-Net: Histogram Equalization for Single LDR to HDR Image Translation

Histohdr-Net: Histogram Equalization for Single LDR to HDR I...

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IEEE International Conference on Image Processing

作者： Hrishav Bakul Barua Ganesh Krishnasamy KokSheik Wong Abhinav Dhall Kalin Stefanov School of Information Technology Monash University Malaysia Malaysia Robotics and Autonomous Systems Lab TCS Research Kolkata India College of Science and Engineering Flinders University Australia Faculty of Information Technology Monash University Australia

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are overor under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.

关键词： Histograms Visualization Costs Image color analysis Shape Imaging Data integration

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