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共 143 条记录，以下是131-140 订阅

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Intelligent Wheelchair Design: Integrating Gesture Recognition, Health Tracking and Fall Detection

Intelligent Wheelchair Design: Integrating Gesture Recogniti...

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international Conference on Computing Communication control and Automation (ICCUBEA)

作者： Mrunal Patil Mansi Subhedar Yash Kamble Omkar Labde Department of Electronics and Telecommunication Engineering Pillai HOC College of Engineering and Technology Rasayani Maharashtra India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Individuals facing physical disabilities encounter various challenges that significantly impact their daily lives. these disabilities may result from accidents, congenital conditions, or progressive diseases, leading to limitations in mobility, sensory perception, or dexterity. Tasks that others may take for granted, such as walking, using stairs, or even simple movements, can become arduous or impossible for those with physical disabilities. this paper presents the design and implementation of an intelligent wheelchair system that integrates gesture recognition, fall detection, and health monitoring functionalities. the system is developed to enhance the mobility and well-being of individuals with physical disabilities, providing them with greater autonomy and safety. Gesture recognition technology allows users to control the wheelchair’s movement and functions using intuitive head gestures, enabling effortless navigation and operation. Fall detection algorithms continuously monitor the user’s movements and promptly alert caregivers or emergency services in the event of a fall, ensuring timely assistance and intervention. Furthermore, the health monitoring system tracks vital signs and health parameters in real time, providing users and healthcare providers with valuable insights into their well-being. By combining these advanced technologies into a single platform, the intelligent wheelchair offers a comprehensive solution to support individuals with physical disabilities, promoting independence, safety, and improved quality of life.

关键词： Navigation Wheelchairs Gesture recognition Medical services Emergency services Real-time systems Fall detection Biomedical monitoring Monitoring Accidents

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DB-ELM: A Dynamic Optimization Algorithm for Longitudinal Inverse Modeling in Electric Vehicles

DB-ELM: A Dynamic Optimization Algorithm for Longitudinal In...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Detao Li Qipei Xu Xiaohui Lu Gaojian Cui Hongda Shi Shaosong Li School of Mechatronic Engineering Changchun University of Technology Changchun China Department of Automotive Engineering Baicheng Vocational and Technical College Baicheng China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

To improve the adaptive capabilities of longitudinal dynamic inverse modeling in electric vehicles across diverse road conditions, this paper presents an advanced machine learning inverse model based on the Dynamic Bayesian Extreme Learning Machine (DB-ELM). the proposed method takes the current vehicle speed, target acceleration, and road slope as inputs for the inverse model, and outputs the corresponding pedal opening to adapt to the adaptive adjustment of pedal opening under dynamic driving conditions. To enhance the adaptability and robustness of the model, the training process incorporates dynamic Bayesian techniques along with a multi-start strategy to ensure comprehensive and robust model performance. this approach effectively addresses challenges such as high validation errors, significant cumulative validation discrepancies, large fluctuations in regularization parameters, and unstable test errors, all while maintaining the original training speed. By ensuring robust adaptive performance, the algorithm remains highly responsive and effective under varying conditions. Validation of the proposed inverse model algorithm was conducted through the implementation of acceleration tests on a co-simulation platform, which were performed under varying road slopes.

关键词： Training Adaptation models Inverse problems Heuristic algorithms Roads Electric vehicles Robustness Bayes methods Vehicle dynamics Optimization

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An Attention Detection System Based on Gaze Estimation Using Self-supervised Learning 8th

An Attention Detection System Based on Gaze Estimation Usin...

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8th international workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Zeng, Xiang-Yu Zhang, Bo-Yang Liu, Zhen-Tao School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789819975891

With the rapid development of online education around world, it becomes difficult for teachers to evaluate the effect of learning during the online class. To solve this problem, an attention detection system based on gaze estimation using self-supervised learning is proposed. Self-supervised learning is used to train the gaze estimation network and it achieved an average gaze angle error of 6. 5∘ on the MPIIFaceGaze dataset. Cross dataset testing was performed by 5 test subjects in the RT-GENE dataset and all samples in the Columbia dataset, in which achieved average gaze angle error 13. 8∘ and 5. 5∘. It is found that the network has good cross dataset generalization performance, but it is susceptible to factors such as feature occlusion and low image clarity. then, we developed an attention detection system which has functions of unmanned detection, eye state detection, and gaze detection. Online classroom experiments were conducted to verify the effectiveness of the proposed method, in which the average accuracy of the detection of distraction is around 60%. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Adaptive Load Balancing Strategies for Reducing System Failures in Network Infrastructures

Adaptive Load Balancing Strategies for Reducing System Failu...

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international Conference on Computing Communication control and Automation (ICCUBEA)

作者： Minal Shahakar S. A. Mahajan Lalit Patil Smt. KashibaiNavale College of Engineering Savitribai Phule Pune University India Department of Information Technology PVG' College of Engg& Tech & GK Pate (Wani) IOM Savitribai Phule Pune University India Department of Information Technology Smt. KashibaiNavale College of Engineering Savitribai Phule Pune University India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

WSN are now essential components of contemporary network infrastructures, providing instantaneous data collection and environmental surveillance in diverse domains. Nevertheless, these networks encounter substantial obstacles such as congestion, system failures, and inefficient energy usage. Adaptive load balancing strategies are essential for enhancing network performance and resilience in order to tackle these issues. this study introduces a new method that utilizes density-based cluster head selection algorithms to enhance load balancing, effective data aggregation, routing, and energy management in wireless sensor networks (WSN). the proposed adaptive load balancing strategy employs an advanced clustering algorithm that chooses cluster heads based on both node density and residual energy, guaranteeing effective network organization and communication. this method employs adaptive mechanisms to accommodate fluctuations in network conditions, ensuring equitable distribution of traffic throughout the network and mitigating the potential for system failures caused by excessive load. the proposed strategy shows substantial enhancements in energy efficiency and network lifespan when compared to conventional density-based cluster head selection methods, as evidenced by experimental evaluations. the findings demonstrate that our methodology results in decreased energy usage and enhanced energy retention within the network. the results indicate that the proposed method exhibits lower energy consumption (170 units) compared to density-based cluster head selection (299 units), while also maintaining higher remaining energy levels (13,930 units) compared to the traditional approach (13,801 units). the findings emphasize the effectiveness of the adaptive load balancing strategy in prolonging the lifespan of WSN and decreasing the likelihood of system failures.

关键词： Wireless sensor networks Adaptive systems Fluctuations Surveillance Clustering algorithms Organizations Load management Routing Energy states Resilience

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An Integrated Mobile Application for Automated Detection of Plant Leaf Diseases and Pest Infestations

An Integrated Mobile Application for Automated Detection of ...

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international Conference on Inventive Systems and control (ICISC)

作者： D. Roja Ramani B. Ben Sujitha D. M. Mary Synthia Regis Prabha S.R. Sylaja Vallee Narayan C Subha Darathy I. Jeya Kumar Department of Computer Science and Engineering New Horizon College of Engineering Department of CSE Noorul Islam Centre for Higher Education Thuckalay Tamilnadu Department of EEE Noorul Islam Centre for Higher Education Thuckalay Tamilnadu Department of CSE GTAM University(Deemed to be University) Bangalore Computer Science and Engineering Arunachala college of engineering for women Computer Science and Engineering Mar Ephraem College of Engineering and Technology

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

this research study intends to develop a comprehensive mobile application poised to transform agricultural practices by enabling accurate identification of leaf, disease, and pest damage in plants. By analyzing the capabilities of modern smartphone technology, the application will leverage the device’s camera for users to capture images of affected plants. Anchored by an extensive database, featuring a diverse collection of crop and plant images, meticulously curated to showcase various symptoms induced by pests, our application ensures thorough analysis. advanced algorithms for object detection, image classification, and pattern recognition will be employed to provide precise identification and analysis of user inputs. Additionally, pattern matching algorithms will enable the detection of exact matches within existing patterns. By providing farmers and agricultural professionals with a powerful tool for prompt and reliable identification of plant issues, our application aims to significantly enhance pest management and crop preservation efforts. Our proposed method includes the utilization of 11 features calculated with the Gray Level Co-occurrence Matrix (GLCM) for precise detection, augmenting the accuracy of our system. Additionally, we integrate hybrid Convolutional Autoencoder (CAE) and Convolutional Neural Network (CNN) models such as ResNetV2 and CNNIR-OWELM, enabling precise identification of plant damage and pest infestation, thereby enhancing the effectiveness of our solution in aiding farmers and agricultural professionals.

关键词： Support vector machines Plant diseases Accuracy Plants (biology) Feature extraction Mobile applications Convolutional neural networks

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Secure Vision: Enhancing Data Security in Wireless Sensor Networks through Image Processing

Secure Vision: Enhancing Data Security in Wireless Sensor Ne...

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international Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： B. Venkata Swamy S. Gopi Kattupalli Sudhakar Pechetti Girish M. Janaki Rani Shrikant Upadhyay Department of Chemistry B V Raju Institute of Technology Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of AI & DS Lakireddy Bali Reddy College of Engineering Mylavaram Bonam Venkata Chalamayya Institute of Technology and Science Amalapuram Batlapalem Department of ECE Dr. MGR Educational and Research Institute Chennai Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

the secure Wireless Sensor Network (WSN) architecture was designed in such a way that the tradeoffs among efficiency, scalability and security were balanced. It was consisted of several sensor nodes, cluster heads, base station as well as the control center all of them played a vital role in functioning and managing of data. this network security was provided with a layered security approach like the physical, data link, network, transport, and application layers. Data security and integrity are secured through advanced image processing algorithms, and reliable communication protocol. Regular maintenance, constant monitoring and hardening solutions through resilience and fault tolerance mechanisms added a layer of trust in securing this WSN. A comparative analysis was made using different algorithms, such as Intrusion Detection Systems (IDS) and detection accuracy is 98% and proposed model which has shown a better result in the energy consumption, data transmission latency, encryption time, detection precision as well as efficiency to insure an original data integrity.

关键词： Wireless communication Wireless sensor networks Protocols Image processing Data security Scalability Network security Maintenance Monitoring Resilience

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Generative AI Powered Forensic Device

Generative AI Powered Forensic Device

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international Conference on Computing Communication control and Automation (ICCUBEA)

作者： Vanita G. Kshirsagar Digvijay G. Bhosale Shubhangi Suryawanshi Anita Sachin Mahajan Pramod Patil Jyotsna Vilas Barpute Prashant G. Ahire Rahul A. Patil Automation and Robotics Dr. D. Y. Patil Institute of Technology Pune India Artificial Intelligence and Data Secience Dr. D. Y. Patil Institute of Technology Pune Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pune India Computer Engineering Ajeenkya D. Y. Patil School of Engineering Pune India Computer Engineering Dr. D.Y. Patil Institute of Technology Pune India Computer Science and Engineering Symbiosis Institute of Technology Pune India Computer Engineering Pimpri Chinchwad College of Engineering Pune

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Crime is a dominant concern in contemporary society. It is crucial to prevent it and deliver justice promptly. Forensic science is critical for criminal investigations and maintaining law and order. the traditional tactics utilized in this field are frequently based on manual procedures, which are both time-consuming and prone to error, rendering them ineffectual in providing prompt and dependable justice. Utilizing automation and advanced technologies for forensic science and crime scene investigation is now essential. this paper suggests a portable gadget that utilizes Internet of things (IOT), Machine Learning and Deep Learning to predict, gain understanding, and possibly recognize offenders. this gadget is designed to reconstruct fingerprints, detect weapons and recognize suspicious activities. It utilizes algorithms and technologies like autoencoders, GANs, SSD and YOLOv8. We talk about the benefits of merging these technologies into one easy-to-use device, which will offer data integration, instant data processing, and improved forensic analysis. the article offers information about automating important areas to improve investigative processes and enhance the criminal justice system's efficiency and effectiveness in the future.

关键词： Cloud computing Automation Forensics Weapons Prevention and mitigation Fingerprint recognition Internet of things Image reconstruction Testing Videos

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Single-Stage Deep Learning in Aviation: YOLOv5 for Satellite-Based Aircraft Detection

Single-Stage Deep Learning in Aviation: YOLOv5 for Satellite...

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international Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Melis Duhter Klaudia Schultheiss Hana Marie Marek Monika Lenka Starha Hilal Kuzu Department of Computer Engineering Mersin University Mersin Turkey Department of Engineering Technology University of Bremen Bremen Germany Department of Applied Mechanics Technical University of Liberec Liberec Czechia Aerospace Engineering Department Tarsus University Mersin Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Recent advancements in object detection technology have revolutionized real-time remote sensing capabilities through satellite imagery. Close-orbit satellites, unmanned aerial vehicles, and autonomous vehicles have emerged as preferred platforms for object detection, offering operational advantages across various applications. Deep learning algorithms play a pivotal role in object detection. Aircraft detection is particularly critical in civil and military aviation contexts, facilitating efficient traffic routing at airports and enhancing national defense strategies. However, satellite-based remote sensing faces challenges such as image resolution limitations, environmental influences, and complexities in distinguishing small, densely packed aircraft in datasets. To address these challenges, deep learning techniques harness neural networks optimized for parallel processing on GPUs, significantly improving object detection accuracy and efficiency. this study focuses on evaluating the YOLOv5 detection algorithm for ground aircraft detection using satellite-based aerial imaging. YOLOv5’s single-stage architecture enables real-time detection and classification of objects with enhanced accuracy and efficiency, making it ideal for applications requiring rapid analysis of large-scale aerial images. Leveraging advanced deep learning methodologies, this research assesses YOLOv5’s performance in aircraft detection, highlighting its potential in advancing aviation management, surveillance, and security practices.

关键词： Deep learning YOLO Bridges Accuracy Atmospheric modeling Military aircraft Real-time systems Air traffic control Aircraft Remote sensing

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JAR-Desktop Assistant

JAR-Desktop Assistant

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international Conference on Computing Communication control and Automation (ICCUBEA)

作者： Dhairya Hindoriya Prajwal Mandavgane Jash Bhowmik Rohan Nair Devaansh Tiwari Partvina Talele Department of Computer Engineering & Technology Dr Vishwanath Karad MIT WPU Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In this paper, present JAR, a sophisticated desktop assistant meticulously crafted to redefine task execution by seamlessly blending voice and text inputs. JAR represents a paradigm shift in desktop interaction, empowering users with swift and intuitive control over their computing environment. By deftly managing application launches, file organization, and system adjustments based on user directives, JARVIS eliminates the drudgery of manual navigation, heralding a new era of productivity *** to JAR’s prowess are its advanced processing algorithms, meticulously engineered to accurately interpret and execute user commands. this technological prowess enables JAR to engage in natural and intuitive interactions, effortlessly understanding and fulfilling user requests with precision. Moreover, JAR boasts a customizable interface and a suite of accessibility features, ensuring a tailored experience that caters to diverse user preferences and needs. At the heart of JAR lies its commitment to efficiency. through efficient task management, JAR streamlines workflows, liberating users from mundane tasks and enabling them to focus on more meaningful endeavors. Whether it’s organizing files, scheduling tasks, or fine-tuning system settings, JARVIS excels in optimizing user productivity while conserving valuable time and *** rigorous evaluation and user feedback, substantiate JAR’s effectiveness in enhancing desktop productivity and facilitating seamless computing experiences. By presenting JAR as a versatile and user-centric solution tailored to modern computing needs, our paper contributes to the advancement of desktop assistant technology, paving the way for a future where human-computer interaction is intuitive, efficient, and empowering.

关键词： Productivity Human computer interaction Navigation Processor scheduling Manuals Organizations Machine learning Scheduling Libraries Optimization

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An Improved RT-DETR Model for Detection of Tobacco Impurities

An Improved RT-DETR Model for Detection of Tobacco Impuritie...

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international Conference on Vision, Image and Signal Processing (ICVISP)

作者： Chengjin Tao Zhongxin Ma Lijun Yun Zaiqing Chen Bin Li Cheng Shen Yunnan Provincial Tobacco Company Kunming China Yunnan Normal University Engineering Research Center of Computer Vision and Intelligent Control Technology Kunming China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Tobacco, as a crop of significant economic importance, plays a pivotal role in global agriculture and industry. However, during the processing of tobacco products, the presence of tobacco impurities (TIs) poses a major challenge, as it substantially diminishes product quality. Traditional methods for detecting TIs are often inefficient and limited in scope, underscoring the need for precise and reliable detection techniques. this study addresses these challenges by introducing an improved Real-Time Detection Transformer (RT-DETR) model. By integrating the ShuffleNet V2 architecture and a novel Multi-Branch-Edge-Information-Select (MBEIS) block, the model enhances feature extraction and fusion capabilities, enabling efficient detection even in complex scenarios. Furthermore, a standardized imaging environment, consisting of controlled lighting and a black box setup, was employed to ensure consistent data collection, and the dataset was augmented using advanced data enhancement techniques. Experimental evaluations demonstrate that the proposed model achieves a precision of 92.7%, a recall of 93.5 %, and a mean average precision (mAP) of 87.9%, outperforming mainstream detection algorithms. In addition, the reduction in model parameters and computational complexity ensures superior real-time performance, making it both lightweight and effective for industrial applications. the results highlight the model's strong potential as a robust solution for detection of TIs, providing a significant technological advancement for the tobacco industry. this research not only addresses critical quality assurance challenges but also offers a valuable reference for applying advanced detection technologies in other industrial domains.

关键词： Accuracy Impurities Computational modeling Computer architecture Signal processing Transformers Real-time systems Product design Quality assessment Sorting

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