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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

共 36970 条记录，以下是961-970 订阅

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Enhancing Cyber-Physical Security: Integrating Virtual Fences Within Digital Twins

Enhancing Cyber-Physical Security: Integrating Virtual Fence...

引用

4th ieee Annual international conference on Cyber Security and Resilience (ieee CSR)

作者： Aghazadeh Ardebili, Ali Longo, Antonella Ficarella, Antonio Univ Salento Dept Engn Innovat Lecce Italy HSPI SpA Roma Rome Italy Univ Salento Data Lab Dept Engn Innovat Lecce Italy Univ Salento Green Engine Lab Dept Engn Innovat Lecce Italy

ISBN: (纸本)9798350375367

Virtual fencing exploits the transformation of a dynamic environment into a cyber-physical traceable system to restrict dangerous movements and create early warning zones. While most real-life applications rely on GPS systems, this article explores the use of computer vision to regulate the distribution of human crowds. The proposal includes utilizing Unmanned Aircraft systems (UAS) to create risk-based ranked early warning zones to enhance the resilience of critical infrastructures and citizen safety/security in smart cities. The proposed system conducts surveillance of labeled areas comprising infrastructure and/or populated by citizens. In the current research, a physical testbed is constructed to monitor human mobility, which simulates human behavior as a part of the cyber-physical-social dynamic environment, triggering an alert whenever a person enters a warning zone. These warning zones are categorized based on their level of risk, ranging from low to high depending on the distance from the risky area. The different warning levels can be dynamically set to leave sufficient time to react and mitigate potential hazards. The experiments to simulate the crossing of various virtual fences in the testbed demonstrate the system's high efficiency for early warning purposes. Finally, the main challenges of bringing EWZ into action are identified and discussed.

关键词： Early Warning Zone Virtual Fence Digital Twin Cyber-Physical-Social System Urban Digital Twins Public Spaces Security Civil Safety Critical Infrastructure Protection Computer Vision UAS UAV Smart City Crowd Monitoring Remote Pilot Aircraft systems (RPAS)

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Circuit Design of Memristor-based GRU and its applications in SOC Estimation

Circuit Design of Memristor-based GRU and its Applications i...

引用

ieee international conference on Consumer Electronics (ICCE)

作者： Wang, Jiayang Ji, Xiaoyue Dong, Zhekang Zhang, Zhaowei Lai, Chun Sing Qi, Donglian Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou Peoples R China Zhejiang Univ Coll Electral Engn Hangzhou Peoples R China Brunel Univ London Dept Elect & Comp Engn London England

ISBN: (纸本)9781665491303

Gated recurrent unit (GRU) is a variant of recurrent neural network (RNN), which is widely used in applications and tasks related to sequence data processing. However, traditional GRU networks based on von Neumann computing construction, have been facing challenges such as big data dimension and high real-time requirements. Meanwhile, limited internal storage resources and external storage bandwidth have jointly limited the overall performance of hardware implementation of GRU networks. Based on these, a compact scheme for the hardware circuit design of memristor-based GRU network is presented, along with the concrete circuit design of nonlinear activation and the linear matrix operation. The entire scheme is validated by the application of the Lithium ion battery state of charge (SOC) estimation. This work is expected to integrate the neuromorphic electronics and battery management systems, and further promote the development of electric vehicles in smart cities.

关键词： gated recurrent unit (GRU) hardware circuit design memristor crossbar array state of charge (SOC)

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Highly Sensitive real-time Detection of Surface Texture Changes 17

Highly Sensitive Real-Time Detection of Surface Texture Chan...

引用

17th ieee international conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Yarmilko, Andrii Rozlomii, Inna Mysiura, Yuliia Bohdan Khmelnytsky National University of Cherkasy Department of Automated Systems Software Cherkasy Ukraine Cherkasy State Technological University Department of Information Security and Computer Engineering Cherkasy Ukraine Bohdan Khmelnytsky National University of Cherkasy Department of Information Technologies Cherkasy Ukraine

ISBN: (纸本)9798331520564

real-time surface analysis is becoming increasingly important in various fields, including industry, medicine, and transportation. This article examines the problem of remote monitoring of surface texture in real time. The authors review the methods and techniques used for this task, focusing on high-precision technologies such as computer vision, laser scanning, and image processing techniques. The paper examines the importance of this real-time approach to support accurate and rapid decisions in industrial processes and medical diagnostics. The authors propose a method for detecting texture artifacts with machine learning and analyze the performance and quality of possible variations of its application. Based on the discussion, the authors draw conclusions regarding the effectiveness and prospects of the proposed approach in real-time remote monitoring of surface textures. They emphasize the importance of further research in this direction and the use of innovative technologies to improve the accuracy and speed of the monitoring process. This article will be a valuable source of information for scientists, engineers and professionals in the field of industrial control and automation. © 2024 ieee.

关键词： Laser applications

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Evolution of Wearable Healthcare Technology: Opportunities, Challenges and applications 4

Evolution of Wearable Healthcare Technology: Opportunities, ...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Bokde, Rohit Dongare, Pragati Datta Meghe College of Pharmacy Datta Megha Institute of Higher Education and Research Wardha India

ISBN: (纸本)9798331523923

The rapid development and increasing popularity of wearable technologies have drawn substantial attention from firms seeking to expand into new markets and innovate in the healthcare industry. As electronic devices embedded in clothing and accessories, wearable technologies have farreaching applications, with healthcare being a very promising area. Wearable technologies allow real-time monitoring of health through intelligent sensors that send personal health information to connected devices, enabling timely medical interventions. As wearable health technologies move from personal convenience to vital community health devices, their uptake has accelerated among consumers and companies alike. This research discusses the market prospects of wearable health devices. The research considers the innovation of smartwatches and smart medical devices, investigating their technological advancements, market expansion, and prospects for the future. As consumers increasingly seek products that improve quality of life, provide real-time health information, and enable preventive care, the wearable healthcare device market offers substantial opportunities for healthcare providers and manufacturers. © 2025 ieee.

关键词： Electronic health record

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Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals

Tiny Machine Learning for Damage Classification in Concrete ...

引用

ieee international Instrumentation and Measurement Technology conference (I2MTC) - Rising Above Covid-19

作者： Adin, Veysi Zhang, Yuxuan Oelmann, Bengt Bader, Sebastian Mid Sweden Univ Dept Elect Design Sundsvall Sweden

ISBN: (纸本)9781665453837

Acoustic emission (AE) is a widely used nondestructive test method in structural health monitoring applications to identify the damage type in the material. Usually, the analysis of the AE signal is done by using traditional parameter-based methods. Recently, machine learning methods showed promising results for the analysis of AE signals. However, these machine learning models are complex, slow, and consume significant amounts of energy. To address these limitations and to explore the trade-off between model complexity and the classification accuracy, this paper presents a lightweight artificial neural network model to classify damage types in concrete material using raw acoustic emission signals. The model consists of one hidden layer with four neurons and is trained on a public acoustic emission signal dataset. The created model is deployed to several microcontrollers and the performance of the model is evaluated and compared with a state-of-the-art machine learning model. The model achieves 98.4% accuracy on the test data with only 4019 parameters. In terms of evaluation metrics, the proposed tiny machine learning model outperforms previously proposed models 10 to 1000 times. The proposed model thus enables machine learning in real-time structural health monitoring applications.

关键词： TinyML damage classification acoustic emission structural-health-monitoring embedded systems IoT machine learning

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 ieee.

关键词： Predictive maintenance

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Leveraging Machine Learning for Network Redundancy Optimization, Cost Reduction, and Latency Improvement in IoT systems

Leveraging Machine Learning for Network Redundancy Optimizat...

引用

ieee international conference on Electro Information Technology (EIT)

作者： Sharma, Manvendra Gupta, Karan Patel, Hitesh Laxmichand Amazon Seattle WA 98170 USA SunPower Corp Austin TX USA

ISBN: (纸本)9798350330656;9798350330649

This paper introduces a novel approach for optimizing Internet of Things (IoT) network performance by employing machine learning techniques to predict network types based on measurable properties of network traffic. While traditional studies have primarily focused on either reducing latency or enhancing network redundancy, our work integrates these aspects by dynamically predicting network conditions that facilitate the selection of optimal communication pathways. This methodology not only significantly reduces latency and improves redundancy but also enhances the overall efficiency and reliability of IoT networks. By analyzing a synthesized dataset reflective of real-world IoT environments, our model achieves high accuracy in network-type prediction, demonstrating its potential for real-time network management applications. Furthermore, the study delves into error analysis and feature sensitivity, providing insights into the reliability of network predictions and their implications for network design and operation. This paper aims to serve as a cornerstone for future explorations into optimizing IoT networks, setting the stage for more resilient, efficient, and responsive IoT ecosystems.

关键词： Networking IoT Predictive Analytics Random Forest

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Deep Learning applications in Plant Disease Detection: A Comparative Study 2

Deep Learning Applications in Plant Disease Detection: A Com...

引用

2nd international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Aryan, Apurv Bilgaiyan, Saurabh Bhardwaj, Muskaan Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370188

Plant diseases cause significant crop damage, leading to economic losses for farmers and food shortages. Traditional disease detection methods are often inefficient, expensive, and inaccurate. Recently, there has been growing interest in deep learning, particularly Convolutional Neural Networks (CNNs) with PyTorch, to create automated plant disease detection systems. Our system, trained on 38,858 images encompassing 15 plant species and 6 diseases, achieved 84% accuracy on validation set and 83% on test set. This demonstrates the power of using deep learning in plant disease detection. This project underscores CNNs' effectiveness in this domain, opening avenues for future research. Whereas YOLO is something which is a real-time object detection algorithm, increasing efficient detection by simultaneously recognizing and localizing objects within a single neural network, distinguishing it from two-stage detection algorithms. © 2024 ieee.

关键词： Convolutional neural networks

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TPM based Secure Boot in embedded systems 3

TPM based Secure Boot in Embedded Systems

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3rd international conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Promila Jyothi, T. Jain, Shilpa Cdot Delhi India Cdot Bangalore India

ISBN: (纸本)9798350300710

Today embedded systems are becoming more and more sophisticated and are being used in a wide range of technologies across an array of industries. Therefore its security has become a major concern. Security is always an important and crucial task required to keep all threats or attacks away from the system. It is an extensive and constantly evolving domain. However, there are still some unexplored security aspects that may need our attention and one of them is hardware-based security. Hardware-based security enhances security by using a combination of trusted hardware and software components. In this paper, we introduce a detailed secure boot procedure based on the Trusted Platform Module (TPM) for embedded systems, which aims to achieve hardware security. Without TPM based signature verification, the system cannot boot up, thus preventing the code from getting modified and ensuring that the system always boots up in a well-defined manner. We have tested our approach on different types of embedded systems to provide better results about boot time variations and have also discussed about system updates when the secure boot feature is enabled. © 2023 ieee.

关键词： Hardware security

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Secure Key Generation Schemes for Cloud Data Storage

Secure Key Generation Schemes for Cloud Data Storage

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2023 ieee international conference on Quantum Technologies, Communications, computing, Hardware and embedded systems Security, iQ-CCHESS 2023

作者： Vamshi, A. Eswari, R. Pragadeesh, S. National Institute of Technology Trichy Department of Computer Applications Tamil Nadu Tiruchirappalli India Bannari Amman Institute of Technology Department of Information Technology Tamil Nadu Erode India

ISBN: (纸本)9798350314946

The secure generation of cryptographic keys holds significant importance in safeguarding the confidentiality, authenticity, and integrity of data stored in the cloud. Traditionally, Key Generation Centers have been trusted with the task of generating partial private keys or private keys to the user, thereby requiring a user to trust a centralized third party. The primary objective focuses on identifying effective methods to enhance the security of key generation processes within existing systems. Additionally, the research takes a deeper look into the possible alternative approaches that eliminate the need for a centralized key generation authority. This study makes a significant contribution to the field of secure key generation by thoroughly examining security advancements within conventional systems. Moreover, it highlights the feasibility of trustless key generation, empowering organizations and individuals to implement more secure key generation mechanisms while reducing their reliance on centralized authorities. © 2023 ieee.

关键词： Public key cryptography

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