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共 50194 条记录，以下是91-100 订阅

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Bandwidth-Efficient Communication Modelling for Autonomous Vehicle Collaborative Perception

Bandwidth-Efficient Communication Modelling for Autonomous V...

引用

2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Jin, Dinghao Zeng, Yuan Gong, Yi Southern University of Science and Technology China Shenzhen Technology University China

ISBN: (纸本)9798331510831

Accurate perception systems are crucial for intelligent vehicle motion planning and control. Cooperative per-ception can significantly enhance the perception capabili-ties of autonomous vehicles by sharing information beyond the field of view of individual vehicles. However, most re-cent studies on cooperative perception have overlooked the bandwidth limitations of information sharing. To address this issue, this paper presents a novel multi-agent coop-erative 3D object detection framework to improve the de-tection accuracy of connected autonomous vehicles (CAVs) in bandwidth-constrained wireless communication environments. The framework includes an attention-based commu-nication mechanism to select optimal communication part-ners and a foreground-background separation strategy to prioritize key information. The effectiveness of the proposed framework is evaluated on three benchmark datasets CARLA-3D, V2XSet, and OPV2V, demonstrating the state-of-the-art performance-bandwidth trade-off. © 2025 ieee.

关键词： Bandwidth

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Development and Analysis of a High-Efficiency T-Type Inverter based 3.7 kW Wireless EV Charging applications

Development and Analysis of a High-Efficiency T-Type Inverte...

引用

2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Venugopal, R. Balaji, C. Shanmugam, Yuvaraja Srm Institute of Science and Technology Dept. of Electrical and Electronics Engineering Tamil Nadu Chennai603203 India Vignan's Foundation for Science Technology and Research Department of Eee Andhra Pradesh Vadlamudi522213 India

ISBN: (纸本)9798331529833

This paper presents the design and performance analysis of a T-type inverter optimized for wireless charging applications in electric vehicle (EV) batteries, aiming to enhance efficiency and reduce power losses. Traditional H-bridge inverter-based wireless power transfer (WPT) systems face challenges such as complex circuitry, voltage spikes, switching noise, and losses from switching, which impact overall efficiency. The proposed T-type inverter addresses these issues by improving voltage control and reducing switching losses, resulting in enhanced system efficiency. The system employs a series-series (S-S) compensation technique that provides better transmission control and performance, making it more adaptable to diverse load conditions. This compensation method, combined with optimized alignment tolerance and a high coupling coefficient, allows the system to efficiently use rectangular coils, which generate a more uniform magnetic field and minimize current loss. Designed for a 3.7 kW output at an operating frequency of 85 kHz, the T-type inverter-based WPT system was validated through MATLAB/Simulink simulations and a laboratory prototype. Results demonstrate an efficiency of 95.20% in simulation and 93.248% in hardware testing. Additionally, DC-DC efficiencies achieved are 94.78% in simulation and 96.177% in hardware. This research confirms the potential of T-type inverters in high-efficiency wireless EV charging systems. © 2025 ieee.

关键词： Inductive power transmission

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A Survey on Recognizing Lower Limb Movements from Surface Electromyography(sEMG) Signals 1

A Survey on Recognizing Lower Limb Movements from Surface El...

引用

1st International conference on AIML-applications for Engineering and technology, ICAET 2025

作者： Pandharikar, Atharva Jahagirdar, Aditi Dr. Vishwanath Karad MIT World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350355611

The paper presents a comprehensive framework for recognizing lower limb movements using surface electromyography (sEMG) signals, addressing the growing need for effective human activity recognition (HAR) systems in healthcare and rehabilitation. The study explores various machine learning and deep learning algorithms including Random Forest, Support Vector Machine (SVM), and hybrid models like CNN-LSTM, PC-GNN (Pearson Correlation-based Graph Neural Network) to classify movements based on sEMG sensors data collected from multiple muscles during distinct lower limb activities. The study focuses on important signal processing methods including wavelet denoising and time-domain feature extraction to improve classification accuracy. Challenges such as electrode displacement, muscle fatigue, and inter-subject variability are addressed. The suggested techniques for classifying sEMG signals outperform conventional methods, as shown by performance metrics. The results highlight how important precise sEMG signal analysis is for applications in prosthetic control, rehabilitation, and human-machine interaction. This research aims to advance the understanding of sEMG signal analysis and its practical implications for real-time human activity recognition. Future work will focus on improving model robustness for practical implementation. © 2025 ieee.

关键词： Decision trees

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H-Shaped Multi-Analyte PCF-Integrated SPR Sensor for Multi-Industry Fluid Analysis

H-Shaped Multi-Analyte PCF-Integrated SPR Sensor for Multi-I...

引用

2025 ieee International conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Sultana Mim, Most Fahmida Ahmed, Tanvir Electrical and Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331512965

This research conducts a numerical investigation of a multi-analyte H-patterned photonic crystal fiber (PCF) sensor utilizing surface plasmon resonance (SPR) technology developed to recognize industrial fluids in various medical and chemical applications efficiently. The sensor incorporates two fluid channels, simultaneously detecting multiple analytes. It uses a gold-titanium dioxidecoated plasmonic layer, providing high sensitivity, improved electric field confinement, and chemical stability. The sensor is optimized for refractive index ranges of 1.34-1.38 in channel-I and 1.39-1.43 in channel-II, both with high accuracy. This study aims to develop a platform for real-time analyte monitoring. The Finite Element Method is applied for arithmetical assessment after refining the sensor's architectural parameters. The sensor demonstrates a peak wavelength sensitivityof 4,000 nm/RIU in channel-I and 28,000 nm/RIU in channel-II. Additionally, it achieves maximum amplitude sensitivitiesof 410.64 RIU-1 and 1561.55 RIU-1 in the respective channels. This corresponds to a resolution of 2.5 × 10-6 RIU for channel-I and 3.57 × 10-7 RIU for channel-II. This sensor is designed to detect unknown substances with RIs between 1.34 and 1.43, making it valuable for biological fluid diagnosis, food quality control, environmental monitoring, and chemical analysis. © 2025 ieee.

关键词： Photonic crystal fibers

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Garbage Object Detection Based on Improved YOLOv7

Garbage Object Detection Based on Improved YOLOv7

引用

2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Li, Shangzhou Wu, Ruobing Wei, YingMei College of Information Science and Technology Tibet University China School of Economics and Management Zhongyuan University of Technology China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Garbage target detection plays a crucial role in environmental protection and urban management. With the continuous development of computer vision technology, deep learning methods have become one of the main tools for garbage target detection. YOLOv7 is an advanced object detection model known for its high speed and accuracy, but it still faces challenges in garbage target detection. In order to enhance the performance of YOLOv7[1] in garbage target detection, this study proposes an approach based on improved ***, we introduce the GAMAttention[2] attention module to enhance the model's ability to detect small targets. Secondly, we incorporate the Decoupled Head[3] to improve the model's robustness to targets in complex backgrounds. Additionally, we optimize the model's loss function to further enhance detection accuracy. The proposed garbage target detection method based on improved YOLOv7 has made significant progress in improving detection performance, providing more effective tools and technical support for environmental protection and urban management. This approach is expected to be widely promoted in practical applications, addressing challenges and issues in the field of garbage target detection. © 2025 SPIE.

关键词： Attention Module Loss Deep Learning Function Optimization Garbage Detection YOLOv7

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Ensuring Sustainable Safety in Fine-Grained Android Permission Model: Decidability through Finite Attribute Range Constraints 22

Ensuring Sustainable Safety in Fine-Grained Android Permissi...

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22nd International Learning and technology conference, L and T 2025

作者： Nauman, Mohammad Khan, Sohail Effat College of Engineering Effat University Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

When Android was first introduced, it permitted unrestricted access to all installed applications, posing significant security risks. In one of our previous works, we proposed, implemented, and demonstrated a comprehensive model capable of restricting access to sensitive permissions based on granular constraints. This model was partially adopted by Android and is now a standard feature in all modern Android-based systems. However, the flexibility of this model introduces a critical issue: the safety of the model. A model's safety determines whether the policies enforced by the model achieve the desired security goals. In this paper, we investigate the safety and undecidability problems associated with the model and demonstrate that, in its full form, the model is undecidable. To ensure creation and maintenance of sustainable security policies at both the individual and enterprise level, we propose the addition of a Finite Attribute Range Constraint, which renders the model both decidable and safe. © 2025 ieee.

关键词： Android Decidability Safety Analysis Sustainable Policies Usage control

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Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks

Few-shot Structure-Informed Machinery Part Segmentation with...

引用

2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Schwingshackl, Michael Oberweger, Fabio F. Murschitz, Markus Ait Austrian Institute of Technology Center for Vision and Control Austria

ISBN: (纸本)9798331510831

This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates the foundation models CLIPSeg and Segment Anything Model (SAM) with the interest point detector SuperPoint and a graph convolutional network (GCN) to accurately segment machinery parts. By providing 1 to 25 annotated samples, our model, evaluated on a purely synthetic dataset depicting a truck-mounted loading crane, achieves effective segmentation across various levels of detail. Training times are kept under five minutes on consumer GPUs. The model demonstrates robust generalization to real data, achieving a qualitative synthetic-to-real generalization with a J&F score of 92.2 on real data using 10 synthetic support samples. When benchmarked on the DAVIS 2017 dataset, it achieves a J&F score of 71.5 in semi-supervised video segmentation with three support samples. This method's fast training times and effective generalization to real data make it a valuable tool for autonomous systems interacting with machinery and infrastructure, and illustrate the potential of combined and orchestrated foundation models for few-shot segmentation tasks. © 2025 ieee.

关键词： Semantic Segmentation

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Research on Medical Efficiency of Beijing-Tianjin-Hebei Based on SBM Model

Research on Medical Efficiency of Beijing-Tianjin-Hebei Base...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Bai, Yanxin Dong, Yaru Ding, Haozhe Zhao, Chengyu Yang, Zixi Liu, Shuo Zhang, Xinpeng Song, Weiyou School of Economics and Management Hebei University of Technology 300131 China School of Artificial Intelligence and Data Science Hebei University of Technology 300131 China School of Mathematics and Statistics Hebei University of Economics and Trade 050064 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Based on the data of Beijing-Tianjin-Hebei region from 2011 to 2019,this paper measures the medical efficiency of Beijing-Tianjin-Hebei region in recent ten years by using the super efficiency SBM model, and uses the ML index decomposition method to conduct a dynamic analysis of medical efficiency, and further discusses the calculation results through convergence analysis, The medical efficiency in Beijing-Tianjin-Hebei region was studied. The study found that the overall medical efficiency in Beijing-Tianjin-Hebei region showed an increasing trend, but the increase was not large, and there was greater room for progress;The convergence of ML index changes is also high, indicating the overall development of Beijing-Tianjin-Hebei region. © 2025 SPIE.

关键词： Medical research Data modeling Analytical research Data acquisition Medical equipment Diseases and disorders Systems modeling Biomedical applications Education and training Energy efficiency

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ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Motor Imagery Recognition

ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Mo...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Zhao, Zheng Xue, Haixiao Huang, Yuwen Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the brain's topological characteristics and the correlations between EEG channels, resulting in suboptimal decoding. To address this, we propose an adaptive spatio-temporal graph neural network (ASTGNN), which constructs a brain graph topology and adaptively learns the topological adjacency matrix, exploring both common and individual electrode connections. The spatial features are extracted using a graph convolutional network, while a gated position-aware self-attention mechanism captures movement information and global dependencies, enhancing temporal feature extraction. Experiments show that ASTGNN significantly improves recognition, achieving an average accuracy of 87.36%. © 2025 SPIE.

关键词： Electroencephalography

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Real-Time Drone System for Detecting, Tracking, and Following of a Mobile Robot in ROS/Gazebo

Real-Time Drone System for Detecting, Tracking, and Followin...

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2025 International conference on control, Automation, and Instrumentation, IC2AI 2025

作者： Salman, Ali Dika, Mohammad Diab, Hassan Owayjan, Michel Achkar, Roger American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Nantes University Saint-Nazaire France

ISBN: (纸本)9798331529604

Surveillance drones equipped with video transmission capabilities play a crucial role in modern security systems, with the integration of OpenCV for object detection marking a significant advancement. This study evaluates the detecting accuracy by varying the distance between the drone and the target object, addressing gaps in current research through a detailed practical insight. This research explores key aspects of drone-based video surveillance, including object detecting, tracking and real-time following, aiming to enhance understanding and methodology in Computer Vision (CV) applications with Unmanned Aerial Vehicles (UAVs). The Simulation of a surveillance drone system in Gazebo simulator within the ROS2 framework. The drone is programmed to dynamically follow and track a mobile robot, enabling systematic analysis of the object detection algorithm's performance. The findings contribute to advancing the reliability and effectiveness of drone-based video surveillance systems for future innovations in security and monitoring applications. © 2025 ieee.

关键词： Drones

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