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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18716 条记录，以下是4911-4920 订阅

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Assessing Physical Rehabilitation Exercises using Graph Convolutional Network with Self-supervised regularization 43

Assessing Physical Rehabilitation Exercises using Graph Conv...

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43rd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Du, Chen Graham, Sarah Depp, Colin Truong Nguyen Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA Univ Calif San Diego Dept Psychiat San Diego CA 92093 USA Univ Calif San Diego Sam & Rose Stein Inst Res Aging San Diego CA 92093 USA

ISBN: (纸本)9781728111797

computer-vision techniques provide a way to conduct low-cast, portable, and real-time evaluations of exercises performed as a part of physical rehabilitation. Recent data-driven methods have explored using deep learning on 3D body-landmark sequences for automatic assessment of physical rehabilitation exercises. However, existing deep learning methods using convolutional neural networks (CNN) fail to utilize the spatial connection information of the human body, which limits the accuracy of these assessments. To overcome these limitations and provide a more accurate method to assess physical rehabilitation exercises, we propose a deep learning framework using a graph convolutional network (GCN) with self-supervised regularization. The experimental results on an existing benchmark dataset validate that the proposed method achieves state-of-the-art performance with lower error than other CNN methods, and the self-supervised learning improves the prediction accuracy.

关键词： Deep learning computer vision Three-dimensional displays Supervised learning Benchmark testing Real-time systems Robustness

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Assistive control System of Appliances through Gaze Point Eye Tracker with Integrated Graphical User Interface for Bedridden Person

Assistive Control System of Appliances through Gaze Point Ey...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Chrizian EG T. Ballenas Andrew Jefferson G. Candare Glenn V. Magwili School of Electrical Electronics and Computer Engineering Mapúa University Manila City Philippines

Persons with Disabilities (PWD), specifically, the bedridden person incapable of controlling certain appliances or devices to continue with their daily life which they struggle with. The need for assistive technology arises nowadays, for which artificial intelligence has become valuable and prominent. The implementation of different pattern recognition algorithms is applied to various applications. This paper discusses using the Tobii Eye tracker algorithm to control the appliances with an Arduino with an infrared emitter and relays. In this paper, the measurement of the system response time as a variable leads the parameter of such different calculations. The sample of data was collected at varying head angles of inclination with also varying distances relative to the monitor. The P-values gathered were 0.11947, 0.287368, and 0.120036 at distances of 30 cm, 40cm, and 60 cm, respectively. From the data, it is found that at a level of significance of 0.05, which indicates the system is accurate, leading to not rejecting the null hypothesis that there is a variance in the mean of samples.

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Unleashing the Potential of Battery Algorithms for Optimal Performance in Electric Vehicles 3

Unleashing the Potential of Battery Algorithms for Optimal P...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ashwini, B. Shukla, Gaurav Singh, Jaskirat Sau, Suman Aggarwal, Prateek Pattanaik, Ashutosh Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

The increasing demand for electric-powered vehicles (EVs) has brought about a growing desire for battery algorithms that can optimize battery performance. Those algorithms can substantially affect the range, charging time, and usual efficiency of EVs, making them an essential factor in the transition to a greener transportation system. However, there is nevertheless tons of potential to be unlocked in battery algorithms to enhance EV overall performance. Contemporary battery algorithms depend upon traditional management techniques, which may be restricted in their capability to adapt to changing situations and may need to utilize the skills of modern-day battery technologies fully. To absolutely unleash the capacity of battery algorithms, new procedures such as gadget mastering and artificial intelligence may be implemented. These superior strategies can examine and adapt to actual-time facts from the battery and its environment, taking into account extra correct and green control. Moreover, integrating data from diverse sensors and communiqué technology can provide an extra comprehensive understanding of the battery device, leading to higher performance and longer battery existence. It additionally allows predictive preservation, assisting in saving you from capacity disasters and amplifying the lifespan of batteries. With the aid of constantly optimizing battery algorithms, the range, charging time and typical overall performance of EVs may be drastically stepped forward, making them a possible alternative for consumers. It will, in the long run, accelerate the transition towards a sustainable and greener transportation system. © 2025 IEEE.

关键词： Electric vehicles

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Automated Monitoring for Construction Productivity Recognition 3

Automated Monitoring for Construction Productivity Recogniti...

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3rd international Sustainability and Resilience conference: Climate Change, ISRC 2021

作者： Alzubi, Khalid Mhmoud Alaloul, Wesam Salah Salaheen, Marsail Al Qureshi, Abdul Hannan Musarat, Muhammad Ali Baarimah, Abdullah O. Universiti Teknologi Petronas Department of Civil and Environmental Engineering Perak Malaysia Universiti Teknologi Petronas Dept. of Civil and Envr. Engg Perak Malaysia

ISBN: (纸本)9781665416320

Comparing to other sectors, the construction sector suffers from low productivity, and it is not improving over time due to the unique nature of construction projects. It is believed that construction productivity cannot be improved without efficient monitoring and measuring, and this is crucial for project success. There are many limitations for the traditional construction productivity monitoring practices like time and cost consuming and error-prone. Although a lot of studies have been implemented to eliminate these limitations, a gap still exists in the automated monitoring of construction productivity. This study proposes an automated monitoring model for indoor productivity recognition in construction projects. The model will provide an instant evaluation of the project productivity which will enhance the optimum utilization of the project resources. The proposed model will be developed by first generating a baseline for the activities state which will be represented as baseline state model. Then the as-built model will be generated. Preliminary experimentation was performed on selected images where the number of tiles and bricks was obtained. The experimentation was performed using Open-Source computer vision Library (OpenCV). Preliminary results depict that by using the proposed model the automated monitoring of productivity is achievable. Although, there is a need of dedicated efforts for improvement and development of technique for more effective and efficient results. © 2021 IEEE.

关键词： computer vision

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PSO Pretrained ADP Based control Scheme for Energy Storage System

PSO Pretrained ADP Based Control Scheme for Energy Storage S...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： P. Kavitha Leela Santi Parige D. Sateesh Kumar P. Gowrishankar Ramya Maranan Vinoth K Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Mathematics Bhavan's Vivekananda College of Science Humanities and Commerce Secunderabad Telangana India Department of Engineering Mathematics College of Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronic Engineering Kongu Engineering College Perundurai Tamil Nadu India Department of Research and Innovation Saveetha School of Engineering Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

The ADP-based controller is optimized using the PSO method so that it can respond appropriately to the changing dynamics of the system. The PSO-pre trained ADP-based control system is evaluated against the “Proportional-Integral (PI)” and “Fuzzy Logic control (FLC)” strategies in terms of performance. The simulation results show that the PSO-pretrained ADP-based control scheme has the best tracking error, settling time, and steady-state error compared to the other control schemes. In terms of real-world applications, the suggested control approach may help save costs, increase efficiency, and facilitate the incorporation of renewable energy sources into existing power grids. Additional work might investigate the possibility of integrating PSO with other optimization methods to create more sophisticated and efficient control strategies for ESSs.

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Smart Surveillance Object Detection System

Smart Surveillance Object Detection System

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Gaurav Chaudhary Shreya Pathak Vikas Choudhary Love Chauhan Abhishek N Tripathi School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

This paper presents an integrated Internet of Things based approach to smart surveillance and urban parking management systems, emphasizing robust object detection and precise parking occupancy monitoring. Our solution adopts cost-effective hardware like the ESP32 CAM module, leveraging its built-in camera and powerful processor for real-time image processing. For surveillance applications, we implement a custom object detection model built with OpenCV, a renowned Python library. This model, finely-tuned for efficient person detection, runs directly on the ESP32 CAM, eliminating the need for external processing units. Additionally, the ESP32 CAM’s Wi-Fi connectivity enables seamless integration with existing networks, simplifying deployment. This integrated approach, combining the cost-effectiveness of ESP32 CAM modules with the image processing capabilities of OpenCV, offers a comprehensive solution for smart surveillance and urban parking management. By utilizing readily available hardware and open-source software, the system promotes widespread adoption and affordability, making it a valuable tool for creating smarter and more sustainable cities.

关键词： computer vision Surveillance Object detection Real-time systems Libraries Hardware Internet of Things

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Design of an Online Monitoring System for Urban Power Optical Cables Based on Distributed Fiber Optic Sensing

Design of an Online Monitoring System for Urban Power Optica...

引用

Electronic Information Engineering and computer Science (EIECS), 2021 international conference on

作者： Guangwei Hou Yan Li Zhenhao Zhang Hang Zhou Yang Xiao Power Dispatch and Control Center Power Dispatch Control Center State Grid Jilin Electric Power Co. LTD. Tonghua Power Supply Company Tonghua China Management Department Jilin Kodi Information Technology Co. LTD China

In recent years, the occurrence of fiber optic cable damage due to external breakage and other factors has become increasingly common. However, traditional fiber optic line monitoring equipment often utilizes a monitoring mode that only monitors backup fiber optic lines. This mode fails to meet the online detection requirements of fiber optic cables when backup lines are switched to working lines. This article presents the design of an online monitoring system for urban power fiber optic transmission lines, utilizing distributed fiber optic sensing technology. The system is divided into four main components: a power supply unit, an optical monitoring unit, an embedded control unit, and a data transmission unit. The monitoring equipment is controlled remotely by the main computer. It has the capability to monitor four power optical fibers simultaneously and offers two monitoring modes that can be easily switched for each monitoring line. The online monitoring system has been applied to a 2563-kilometer 10kV optical cable power communication line of a certain company in the State Grid. The system has chosen to conduct online monitoring of 2563 kilometers of power communication optical cables and 10kV optical cables operated and maintained by a specific company in the State Grid. This monitoring system achieves mode switching of power optical fiber lines and monitoring line switching. The mechanical optical switch has a switching capacity of over 3 × 107 times and an insertion loss of less than 0.8dB. This enables quick fault detection and improves the timeliness of fault monitoring.

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vision to Voice: An Advanced Blind Assistance System Integrating YOLOv3 and OCR Technologies for Enhanced Mobility

Vision to Voice: An Advanced Blind Assistance System Integra...

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Disruptive Technologies (ICDT), international conference on

作者： Gunjan Purushottam Kela Mokshada Girdhar Daga Richa Khandelwal Electronics And Computer Science Department Shri Ramdeobaba College Of Engineering And Management Nagpur India Department of Electronics Engineering Ramdeobaba University Nagpur India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The rise in need for assistive technologies has led to the development of AI-empowered solutions for better accessibility for visually impaired persons. The proposed system is the use of a real-time object and text detection application using a combination of deep learning and speech synthesis techniques to enhance mobility and independence. The system utilizes Tesseract OCR for text recognition and pyttsx3 for TTS for better interfacing with the environment. YOLOv3-based object detection towards improving environmental awareness involves real-time identification of objects, with confidence thresholding and NMS serving to enhance robustness of the overall detection by curbing false positives. Further, the distance estimation module helps in mobility as it provides information on how far one is situated from an object. This architecture is a hardware-independent, lowlatency, and highly scalable solution compared to conventional assistive systems; thus, such systems are more harmonized for practical applications. Experimental findings exhibited its very high accuracy, confident standing across situations, and enhanced user experience rendering it with huge potential for granting enhanced independence and safety for the visually challenged user base.

关键词： YOLO Deep learning Text recognition Optical character recognition Text detection Assistive technologies Real-time systems User experience Text to speech Safety

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Brain Tumour Categorization Deploying Deep Learning

Brain Tumour Categorization Deploying Deep Learning

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Vinod Kumar Kuchivaripalli Ravi Kumar Vamsi Department of Electronics and Communication Engineering Saveetha Engineering College Chennai

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In contemporary healthcare, brain tumour diagnosis is essential because it facilitates early intervention, individualized treatment planning, symptom control, scientific advancements, and better patient outcomes. In the on-going battle against brain tumours, early identification and precise diagnosis are critical to improving the prognosis and quality of life for those impacted. Brain tumours are a serious hazard in India, where there are an estimated 40,000 new cases reported annually and healthcare issues are as varied as the country’s population. Early detection methods are essential in the battle against brain tumours. The accuracy rates of different CNN (Convolutional Neural Network) architectures are Inceptionv3 is 93%, 90% is reported by Googlenet and 85% by Alexnet. Inceptionv3 provides less loss in comparison to the loss performance.

关键词： Deep learning Renewable energy sources Accuracy Scalability Medical services computer architecture Brain modeling Computational efficiency Convolutional neural networks Tumors

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Multi-viewpoint Rendering Optimization of Indoor Scene Based on Binocular vision 3rd

Multi-viewpoint Rendering Optimization of Indoor Scene Based...

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3rd EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021

作者： Jing, He School of Road Bridge and Architecture Chongqing Vocational College of Transportation Chongqing402247 China

ISBN: (纸本)9783030825645

The traditional multi-viewpoint rendering method for indoor scenes has poor lighting and shadow effects, resulting in too dark or bright indoor scene multi-viewpoint rendering. Therefore, an optimization method for indoor scene multi-viewpoint rendering based on binocular vision is proposed. This research plans light and shadow effects based on the visual relationship between point light sources and indoor scenes;sets rendering points based on binocular vision;and renders indoor scenes with multiple viewpoint angles. The simulation experiment results show that compared with the traditional rendering method, the light and shadow effect of the studied method is excellent, and the rendered indoor scene is suitable for light and dark. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Binocular vision

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