检索结果-内蒙古大学图书馆

Telecommunications Conference (ITC-Egypt), International

作者： Maher Tarek Shady Zahran Ahmed Radi Sherif F Nafea Eyad S. Oda Alaa El-Din Sayed Hafez Control and Navigation Departement Technical Researches and Development Center Cairo Egypt College of Computing and Information technology Arab academy for Science Technology and Maritime Transport Cairo Egypt Technical Researches Center Cairo Egypt Electrical Engineering Department Suez Canal University Cairo Egypt Electrical Engineering Affiliate Instructor Alexandria University Cairo Egypt

Unmanned vehicle navigation using various combination of sensors in different fields, civilian or military, has always known to be a challenging and nontrivial task. Bayesian filter algorithms are usually being used for combining and optimally processing measurements provided by such sensors, considering the available knowledge such as the uncertainty in measurements. Since the performance of any navigation system mainly depends on the efficiency of the chosen filter algorithm associated with the utilized sensors’ quality, the choice of suitable filtering approach (according to the demand application) can be considered as a critical and essential designing point. In this paper, the performance of two different attitudes representation filtering approaches (Quaternion and Euler angles-based) is investigated, when applied in Multiplicative Extended Kalman Filter (MEKF) design. The overall performance and accuracy of both approaches were analyzed and compared during various GNSS signal outages for a self-driving cars application in straight and curved trajectory paths. Multiple in-filed experiments were conducted using a special setup that includes a MEMS-based SPATIAL Advanced Navigation INS/GNSS system where the output of both approaches was compared to the ground truth of a utilized reference navigation system. The comparison includes 1) Position, Velocity and Attitude (PVA) estimation accuracy, 2) processing time and computational complexity. Results concluded that each approach has its own merits in demerits where the Quaternion-based approach outperforms the Euler one in terms of PVA navigation solution estimation accuracy. On the other hand, a faster calculation for the Euler-based approach associated with lower computational complexity was significantly noticeable.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Smart IoT-Enabled Wildfire Monitoring and Early Detection System using Convolutional Neural Networks and Sensor Data Analysis

A Smart IoT-Enabled Wildfire Monitoring and Early Detection ...

引用

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： U.V. Anbazhagu A. Alice Blessie R. Santhana Krishnan G. Yamini J. Relin Francis Raj P. Sundaravadivel Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of English Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Wildfires pose a significant threat to ecosystems, biodiversity, and human settlements, with climate change and deforestation exacerbating the frequency and intensity. Conventional forest fire detection methods, including satellite-based monitoring and ground surveillance, suffer from limitations such as delayed detection, high false alarm rates, and dependency on manual intervention. Existing sensor-based detection systems often lack integration with intelligent decision-making frameworks, leading to inefficient fire prevention strategies. The absence of real-time processing and predictive capabilities further reduces the effectiveness of current methodologies in mitigating wildfire risks. This work presents an IoT-based deep learning approach utilizing Convolutional Neural Networks (CNNs) for real-time forest fire detection and prevention. Data is collected from satellite images, drone feeds, and IoT sensors, including temperature, humidity, gas concentration, and infrared readings. Advanced data preprocessing techniques, including image augmentation, normalization, and feature extraction, enhance the robustness of the model. A CNN-based classification model is implemented to analyze fire patterns and assess risk levels. The system is trained using sensor data and image datasets, ensuring high detection accuracy with minimal false positives. The key features of this methodology include real-time wildfire detection, automated risk assessment, and deployment on IoT edge devices for immediate decision-making. The integration of sensor-driven insights with deep learning improves early detection capabilities while reducing reliance on manual surveillance. Efficient data handling and cloud-based alert mechanisms enable proactive measures against fire outbreaks. The proposed system enhances wildfire monitoring by ensuring fast response times, improved accuracy, and scalable deployment in fire-prone regions.

关键词： Deep learning Wildfires Accuracy Prevention and mitigation Surveillance Decision making Manuals Real-time systems Satellite images Time factors Climate change Fires Detection algorithms

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Enabled Energy Trading in Microgrids for Sustainable computing Infrastructure

Blockchain-Enabled Energy Trading in Microgrids for Sustaina...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： M. L. Sworna Kokila Sunitha R P. Kalyanakumar J. Relin Francis Raj R. Santhana Krishnan S. Gopikumar Department of Computing Technologies Faculty of Engineering and Technology School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Rajarajeswari College of Engineering Bangalore India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Civil Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research investigates Blockchain-enabled energy trading within microgrids to enhance sustainable computing infrastructure. A novel algorithm, GridCoin, is proposed and evaluated against existing methodologies including Optimal Power Flow (OPF), Microgrid Energy Management System (MEMS), and Hierarchical control Algorithms (HCA). The study employs simulation metrics such as Transaction Throughput, Energy Trading Efficiency, and Transaction Latency to compare the performance of these algorithms. GridCoin demonstrates superior capabilities in optimizing energy trading operations, achieving higher transaction throughput, improved efficiency in energy utilization, and reduced transaction latency. These results highlight the potential of Blockchain technology to revolutionize energy markets by providing secure, transparent, and decentralized transaction mechanisms. Future research directions include enhancing optimization techniques, integrating renewable energy sources, addressing scalability and security challenges, conducting real-world deployments, and analyzing economic and policy implications. This study contributes to advancing sustainable computing infrastructure through innovative Blockchain-based solutions.

关键词： Micromechanical devices Renewable energy sources System performance Microgrids Green computing Throughput Blockchains Energy management systems Optimization Load flow

来源：评论

学校读者我要写书评

暂无评论

CNN-based Deep Transfer Learning Approach for Detecting Breast Cancer in Mammogram Images

CNN-based Deep Transfer Learning Approach for Detecting Brea...

引用

IEEE Conference on Systems, Process & control (ICSPC)

作者： Dilovan Asaad Zebari Habibollah Haron Dawlat Mustafa Sulaiman Yusliza Yusoff Mohamad Nurfalihin Mohd Othman Department of Computer Science College of Science Nawroz University Duhok Kurdistan Region Iraq Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Department of Information Technology Management Technical College of Administration Duhok Polytechnic University

ISBN: (纸本)9781665470995

Researchers are motivated to investigate deep learning (DL) techniques for mammogram images because of the limitations of conventional systems-based computer-aided detection (CAD) for mammography, the extreme significance of early breast cancer identification, and the high impact of false diagnosis on patients. This study develops a model for breast cancer detection from mammogram images that employ Convolutional Neural Network (CNN) based Transfer Learning (TL). The developed structure is comprised of several stages: breast region extraction is performed to extract the Region of Interest (ROI) from the background and artifacts, a Gaussian filter is employed for noise reduction, and data augmentation is carried out to increase the size of the original images for better learning of CNN. Then, a pre-trained model-based CNN using an augmented dataset is presented. Deep features are extracted from CNN and they are trained based on using TL. A mini-MIAS dataset was conducted as the testing ground for this experiment, and the best accuracy achieved was 95.71%. The developed framework performs significantly better than other current methods when compared to them.

关键词： Training Solid modeling Transfer learning Noise reduction Feature extraction Mammography Breast cancer

来源：评论

学校读者我要写书评

暂无评论

Bridging the gap: Empowering manufacturing and production small medium enterprises through industrial Internet of Things adoption model

IET Collaborative Intelligent Manufacturing

引用

IET Collaborative Intelligent Manufacturing 2025年第1期7卷

作者： Shah, Sajid Madni, Syed Hamid Hussain Hashim, Siti Zaitoon Mohd Faheem, Muhammad Shahzad, Hafiz Muhammad Faisal Faculty of Computing Universiti Teknologi Malaysia Johor Skudai Malaysia School of Computer Science University of Southampton Malaysia Johor Iskandar Puteri Malaysia School of Technology and Innovations University of Vaasa Vaasa Finland VTT Technical Research Centre of Finland Ltd. Espoo Finland Department of Computer Science University of Sargodha Sargodha Pakistan

The industrial Internet of Things (IIoT) is revolutionising manufacturing and production of small and medium enterprises (SMEs) by enhancing efficiency and product quality. While developed countries like the USA, UK, Canada, Finland, and Japan have widely adopted IIoT, developing nations such as Bangladesh, India, Pakistan, and Malaysia are still lagging. This study explores IIoT adoption in manufacturing SMEs, emphasising its potential for economic growth despite challenges like budget constraints and skill gaps in developing countries. It presents a novel model based on 17 factors from the TOEI (Technology, Organization, Environment, and Individual) framework to support decision-makers in integrating IIoT technologies. The model’s reliability and validity are confirmed through rigorous testing and a survey of three SMEs. This proposed model serves as a roadmap for SMEs, breaking down complex processes into manageable steps, and providing SMEs with a structured approach. © 2025 The Author(s). IET Collaborative Intelligent Manufacturing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Fabrication

来源：评论

学校读者我要写书评

暂无评论

Leveraging AI-Generated Content for Synthetic Electronic Health Record Generation With Deep Learning-Based Diagnosis Model

引用

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Abdel-Khalek, Sayed Algarni, Abeer D. Amoudi, Ghada Alkhalaf, Salem Alhomayani, Fahad Mohammed Kathiresan, Shankar Department of Mathematics and Statistics College of Science Taif University Taif Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer College of Science and Arts in Ar Rass Qassim University Ar Rass Saudi Arabia College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Consumer electronics have transformed the way we interact with technology, improving convenience and connectivity in day-to-day lives. In the healthcare sector, recent technologies have resulted in enhanced diagnosis, treatment, and patient care. Wearables, artificial intelligence-based data analytics, and telemedicine transform the way of monitoring and managing health, fostering a proactive approach to well-being. The popularity of ChatGPT is proven great potential for AI-generated content (AIGC) that has formed a major impact on the artificial intelligence (AI) community and accelerates the reconsidering of the prospects of general AI. The AIGC is also exposed as a considerable scope to impulse healthcare electronics (HE). Although generative AI has achieved popularity like the formation of images, it could be employed for producing synthetic tabular information. The production of synthetic electronic health records (EHR) undertakes to increase the utilization of machine learning (ML) methods that commonly function with massive quantities of data. ML will identify non-intuitive classifier patterns that permit a new integration of patient feature predictive ability. Currently, deep learning (DL) techniques are effectively utilized in EHR data from medical domains. DL methods excellently captured the significant and beneficial features and patterns from the comprehensive medical information in EHR data. This study presents AI-generated content for Synthetic Electronic Health Record Generation with a Deep Learning-based Diagnosis (SEHRG-DLD) Model. The focus of the SEHRG-DLD technique is to initially generate the synthetic EHR data and then analyze the medical data for disease diagnosis using the DL model. The SEHRG-DLD technique comprises a two-stage process: synthetic data generation and disease diagnosis. At the initial stage, the SEHRG-DLD technique uses the ChatGPT tool to generate synthetic EHR data. Then, the SEHRG-DLD technique undergoes the disease diagnosi

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Telemedicine System Using Light Fidelity and PIC16F877A Microcontroller

Design and Implementation of Telemedicine System Using Light...

引用

International Conference on Information science and Technology (ICIST)

作者： M.R. Ezilarasan Man-Fai Leung Xiangguang Dai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Instituteof Science and Technology Chennai India Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications; however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light Communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted.

关键词： Wireless communication Radio frequency Wireless sensor networks Lighting Light emitting diodes Light fidelity Liquid crystal displays Communication system security Monitoring Visible light communication

来源：评论

学校读者我要写书评

暂无评论

Robust Voltage Regulation for DC Microgrids via Passivity-Based Sliding Mode control

Robust Voltage Regulation for DC Microgrids via Passivity-Ba...

引用

International Workshop on Variable Structure Systems, VSS

作者： Edoardo Vacchini Michele Cucuzzella Pablo Borja Antonella Ferrara Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen (ENTEG) Faculty of Science and Engineering University of Groningen Groningen The Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth U.K.

ISBN: (数字)9798350353686

ISBN: (纸本)9798350353693

This paper proposes a decentralized sliding mode control approach to the voltage regulation problem in a DC microgrid consisting of distributed generation units interconnected with each other through resistive-inductive power lines and supplying unknown nonlinear loads. In particular, the port-Hamiltonian structure of the system suggests the design of a suitable sliding manifold such that the system on this manifold exhibits desired passivity properties. This approach simplifies the control design and relaxes some restrictive assumptions required by other controllers proposed in the literature, while offering satisfactory performance.

关键词： Manifolds Variable structure systems Power transmission lines Microgrids Power system stability Stability analysis Robustness Reduced order systems Voltage control Sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for geoscience:Progress,challenges,and perspectives

引用

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

来源：评论

学校读者我要写书评

暂无评论

Testing user Attitudes in IoT-load interface Based Healthcare attention management system

Testing user Attitudes in IoT-load interface Based Healthcar...

引用

Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Ramanuj Tewari Zaid Alsalami H. A. Alsailawi P. Kirubanantham Dharmesh Dhabalia Osama Nazim Saadoun Zahraa N. Abdulhussain Ali Saad Alwan School of Management Graphic Era Hill University Bhimtal Uttarakhand India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Chemistry and Biochemistry College of Medicine University of Kerbala Kerbala Iraq Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Information Technology Vishwakarma Institute of Information Technology Pune India Law Department Al-kunooze University College Basra Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Business Administration Department Kut University College Al kut wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The study aims to analyze consumer perception toward adopting Internet of Things (IoT) technology in healthcare. In contrast to earlier studies that focused on the technical aspects, the proposed integrated paradigm measures how user trust is affected by the existence of security and privacy concerns, and knowledge of the Internet of Things. The proposed architecture presents a holistic system for health data management that is secure and efficient in modern health systems, designed and developed using four layers: hospital, remote IoT integrated medical device, distributed medical record, and AI-based smart contract. The system is supposed to enhance security of data and efficiency in its management with integration of the blockchain technology and the AI-based intelligence. The hospital layer is similar to the database that stores the data of the patient, and the distributed medical record layer makes the storage of the data and the access from it secure through blockchain. The data transfer of the health parameters of the patient through the IoT-enabled medical node can be done using the blockchain. The Al-based smart contract layer brings in the intelligence of the system for automatic decision-making and proactive breach detection. Evaluation was made on the system’s performance under different conditions, such as transaction processing time, average delay, where it demonstrated scalability, reliability, and robustness in the management of health data and hence sounds quite promising for this domain of modern healthcare.

关键词： Privacy Medical devices Hospitals Scalability Smart contracts Robustness Blockchains

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：