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共 36882 条记录，以下是361-370 订阅

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Healthcare System Using IoT and Health Monitor Website

Healthcare System Using IoT and Health Monitor Website

引用

Industrial Electronics: Developments & applications (ICIDeA), ieee international conference on-

作者： Priyanka Vijay Tandalekar Khushi Avinash Kokane Shruti Devendra Gokulpure Aditi Prashant Deshmukh Rashmi Panherkar Amruta Udapurkar Department of Electrical Engineering JSPM's Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Technological advancements and the miniaturization of sensors have prompted initiatives to implement these innovations in diverse fields, aiming to enhance the standard of living for individuals. A notable domain that has adopted this technology is the healthcare industry. Individuals requiring healthcare services often encounter high costs, a situation that is especially prevalent in developing nations. Consequently, the project aims to address a pressing healthcare issue that societies are currently facing, with the primary goal of developing a remote healthcare system. By analyzing data via a website, healthcare professionals or guardians can monitor a patient's health status from outside the hospital environment. In this initiative, we have designed a tailored Internet of Things (IoT) framework specifically aimed at healthcare applications. The project's aim was to establish a Health Analyzing System that employs locally sourced sensors, ensuring its cost-effectiveness for potential large-scale production. The suggested model gets the sensor data via the Arduino Nano microcontroller and sends the data to the cloud for processing and analysis., thereby facilitating remote access. The feedback actions derived from the analyzed data can be viewed on the website associated with the device.

关键词： Temperature sensors Temperature measurement Cloud computing Hospitals Medical services Sensor systems real-time systems Sensors Internet of Things Monitoring

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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry Sensor phenomena and characterization Sensor systems real-time systems Gas detectors Resilience

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Advancements in real-time Image Processing Using Kintex and Virtex FPGAs: Enhancing Speed, Efficiency, and Versatility

Advancements in Real-Time Image Processing Using Kintex and ...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Shishir Shrivastava Bura Vijay Kumar Rupesh Gupta Vibhor Sharma Institute of Engineering and Technology Chitkara University School of Computer Science and Artificial Intelligence SR University Warangal Telangana India University of New Haven

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Image processing is a critical component in various applications, ranging from medical imaging to autonomous systems. This research explores the utilization of high-performance Field Programmable Gate Arrays (FPGAs) such as Kintex and Virtex to enhance image processing capabilities. By leveraging the parallel processing power and reconfigurability of these FPGAs, the study achieves significant improvements in processing speed and efficiency, enabling real-time image analysis and decision-making. This advancement supports the United Nations’ Sustainable Development by enhancing smart city technologies. The implementation of Kintex and Virtex in image processing not only drives innovation but also contributes to building resilient and sustainable infrastructures, thereby promoting well-being and sustainable urban development.

关键词： Technological innovation Smart cities Autonomous systems Image processing Scalability Decision making real-time systems Distance measurement Sustainable development Field programmable gate arrays

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A Comprehensive Deep Learning Approach for real-time Detection and Classification of Large and Small Defects in Industrial applications

A Comprehensive Deep Learning Approach for Real-Time Detecti...

引用

international conference on Big Data and Smart computing (BIGCOMP)

作者： Joo In Kim Sung Hoon Kim Gi Nam Kwan Hee Yoo F M Fahmid Hossain Department of Computer Science Chungbuk National University Cheongju South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Object detection is one of the most important and continuously evolving research areas in computer vision, and as image processing technology advances, its significance is increasing across various application fields. In particular, detecting small objects is essential for identifying various defect types, but conventional defect detection methods using single AI models face limitations in achieving high accuracy across all defect types, making practical application challenging. To address this, this paper proposes a real-time method for recognizing both large and small defects in images by utilizing a deep learning approach that combines detection and classification models. The proposed structure initially integrates the Small Object Information Flow (SOIF) module into the detection model, which reduces variables related to lens conditions and enhances the accuracy of circular object detection. This helps minimize interference from complex elements in the background and image, enabling stable initial defect detection. Following this, a classification model is used to categorize specific defect types among relatively large defects. Finally, the detection model is applied once more to more precisely identify smaller defect types, creating a structure capable of comprehensively detecting defects of various sizes and shapes. This approach significantly improves overall accuracy and speed, making it a promising solution for real-world industrial settings where defects of varying sizes need to be reliably identified.

关键词： Deep learning Analytical models Accuracy Shape Computational modeling Object detection real-time systems Stability analysis Reliability Defect detection

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Accurate Localization Based on Non-Visual Sensor Data for Digital Twin Model

Accurate Localization Based on Non-Visual Sensor Data for Di...

引用

ieee international conference on Artificial Intelligence and Virtual reality (AIVR)

作者： ABM Tariqul Islam Jyothi Pudota Rahul Tomar Digital Twin Technology GmbH Cologne Germany

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

In urban digital twins, precise localization of entities (e.g., vehicles) is essential but challenging due to limitations in traditional GPS methods and Odometry drift. This work presents a Systematic Monte Carlo Localization (SMCL) approach that integrates GPS and Odometry data to enhance localization accuracy. SMCL employs systematic resampling to efficiently maintain particle diversity, outperforming traditional Monte Carlo Localization (MCL) with naive resampling in both GPS-available and GPS-scarce scenarios. Experimental results show that SMCL improves localization accuracy and computational efficiency, providing a scalable, real-time solution for city-scale digital twin VR applications using existing sensors.

关键词： Location awareness Systematics Monte Carlo methods Accuracy Estimation Sensor systems real-time systems Digital twins Odometry Global Positioning System

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Emotion-Based Music Recommendation System Using Deep Learning

Emotion-Based Music Recommendation System Using Deep Learnin...

引用

Industrial Electronics: Developments & applications (ICIDeA), ieee international conference on-

作者： Ankush Kumar Singh Durbadal Mandal Satish Kumar Donakonda Venkat Department of Electronics and Communication Engineering National Institute of Technology Durgapur Durgapur India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Emotion-aware music recommendation systems have emerged as a promising approach to enhancing user experiences by tailoring music suggestions to emotional states. This paper presents a novel system that integrates facial emotion recognition and music recommendation, leveraging state-of-the-art deep learning and natural language processing techniques. Emotion recognition is achieved using fine-tuned ResNet50V2 and VGG16 models, which classify facial expressions with improved accuracy. For music recommendation, the system employs BERT to analyze song lyrics and metadata, enabling dy-namic alignment between user emotions and musical attributes such as tempo, mood, and lyrical themes. Experimental results demonstrate an emotion recognition accuracy and high user satisfaction in music recommendations. The system offers real-time contextual adaptation, ensuring seamless and personalized music experiences. Future directions include multimodal emotion detection and incorporating user contexts like location and activity for further personalization. This work highlights the potential of emotion-aware systems in revolutionizing music recommendation technologies,

关键词： Deep learning Emotion recognition Accuracy Mood Music Metadata real-time systems Natural language processing Physiology Recommender systems

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Research on the Fusion of Autonomous Navigation Algorithms and Environmental Perception in Intelligent Ship Technology

Research on the Fusion of Autonomous Navigation Algorithms a...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Zhijie Zhang Yonggang Zong Ocean Department Binzhou Polytechnic Binzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The research focus of this paper is on the organic combination of autonomous navigation algorithm and environmental perception technology, with the aim of enhancing the autonomous navigation capability of intelligent ships in complex marine environments. Autonomous navigation relies on the close coordination of multiple perception systems and algorithms, among which the simultaneous localization and mapping (SLAM) algorithm is particularly critical. The SLAM algorithm generates high-precision environmental maps in real time and provides accurate ship positioning by fusing multi-sensor information. This paper proposes and designs an autonomous navigation system that integrates environmental perception and SLAM technology. Its system architecture includes sensor data processing, path planning and control decision modules. The effectiveness of the proposed algorithm is verified through a series of model simulations, and experimental evaluation is carried out in multiple test scenarios. The simulation results show that compared with traditional navigation methods, the algorithm significantly improves the navigation accuracy and safety of ships in complex marine environments, and the positioning error rate is reduced by more than 15%. The research results of this paper provide a solid theoretical basis and technical support for the future research and development of autonomous navigation systems for intelligent ships.

关键词： Simultaneous localization and mapping Accuracy Navigation Heuristic algorithms Simulation systems architecture real-time systems Safety Marine vehicles Autonomous robots

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Intelligent Power Consumption Measurement and Control System Based on C/S and Euclidean Distance

Intelligent Power Consumption Measurement and Control System...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Yuanzheng Xiao Huawei Hong Feifei Chen Xiaorui Qian Ming Xu Huang Feng State Grid Fujian Marketing Service Center (Metering Center and Integrated Capital Center) Fuzhou Fujian China State Grid Info-telecom Great Power Science and Technology Co. Ltd. Fuzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

With the rapid development of smart grids, intelligent power measurement and control systems have become a key technology for achieving efficient energy management and energy conservation and emission reduction. Traditional measurement and control systems often have problems such as low data processing efficiency and poor real-time performance, which makes it difficult to meet the needs of modern power grids for precise control and intelligent management. In response to the above problems, this paper aims to explore an intelligent electricity measurement and control system based on C/S (Client/Server) architecture and Euclidean distance algorithm to improve data processing efficiency and system real-time performance. This paper elaborates on the application of C/S algorithm and Euclidean distance in intelligent power measurement and control system, including system architecture design, data processing flow and algorithm implementation. Then, through system performance testing and experimental results analysis, the effectiveness and reliability of the proposed system are verified. Finally, the research content, experimental results and shortcomings of this paper are summarized, and directions for future improvement are proposed. The experimental results show that the intelligent electricity measurement and control system based on C/S and Euclidean distance can significantly improve data processing efficiency and system real-time performance. The highest speed of data processing in the system is 200, reducing energy consumption and operating costs, providing strong support for the intelligent management of smart grids.

关键词： Power measurement Power demand Scalability systems architecture Euclidean distance Control systems Data processing real-time systems Smart grids Testing

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Design of Multifunctional Integrated real-time Traffic Supervision System

Design of Multifunctional Integrated Real-Time Traffic Super...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Boyang Liu Chang'an University Xi'an China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This study focuses on the intelligence of urban traffic management, aiming to solve the increasingly severe urban traffic congestion and safety problems by integrating computer vision, deep learning technology, and Internet of Things technology. Based on the Faster R-CNN algorithm, a multifunctional integrated real-time traffic monitoring system was developed to achieve core functions such as traffic flow monitoring, pedestrian and non motorized vehicle detection, illegal behavior recognition, traffic sign recognition, and traffic accident detection. The system uses Raspberry Pi 4B as the main control chip, transmits data through Bluetooth module, and utilizes FREETROS operating system to achieve coordinated operation of tasks. The experimental results show that the system can recognize traffic targets with high accuracy (mAP50 is 56.3 %) and issue timely alarm information when a traffic accident is detected, demonstrating the effectiveness and reliability of the system in improving traffic management efficiency and ensuring traffic safety. This study provides new solutions and technical support for building a traffic management system for smart cities.

关键词： Target recognition Smart cities Vehicle detection real-time systems Power electronics Safety Reliability Monitoring Traffic congestion Accidents

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A Deep Learning Framework for Comparative Analysis in Flood Detection and Area Estimation

A Deep Learning Framework for Comparative Analysis in Flood ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Shiva Teja Pecheti Ashwaj U Uthej K Murali K Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This research presents an innovative deep learning framework designed to be utilized for area estimation and flood detection. The most suitable architecture for real-time applications is determined using this framework by a thorough comparison of advanced architectures, including U-Net, LeNet, ResNet, and VGG16. A crucial initial phase is to classify images as flooded or non-flooded using an effective Multilayer Perceptron (MLP) classifier. Users can see areas damaged by flooding and get quick assessments with the help of a simple Streamlit interface suited to foster immediate engagement. More significantly, the results show that LeNet is the most reliable model all things considered, often estimating flooded areas with the highest accuracy. Additionally, the framework delivers essential data for rapid disaster response by estimating the intensity of floods through accurate pixel analysis. The study offers helpful techniques to improve planning and response strategies for floods in an era of growing environmental uncertainty, as well as to giving important new insights into the use of deep learning in the management of floods.

关键词： Deep learning Technological innovation Accuracy Uncertainty Estimation Streaming media real-time systems Floods Reliability Testing

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