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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 36833 条记录，以下是151-160 订阅

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Design and Implementation of a real-time Telemetry System for Remote Data Acquisition in Unmanned Vehicle Application

Design and Implementation of a Real-Time Telemetry System fo...

引用

Robotics and Technologies for Industrial Automation (ROBOTHIA), ieee international conference on

作者： Muhammad 'Uzair Mu'izz Sallehuddin Norashikin M. Thamrin Muhammad Hasif Azami Zuraidah Salleh School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Microwave Research Institute (MRI) Universiti Teknologi MARA Shah Alam Selangor UiTM-MTC High Energy Material Research Laboratory (HEMREL) College of Engineering Universiti Teknologi MARA Shah Alam Selangor

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

This study addresses the challenge of real-time telemetry data acquisition for unmanned vehicle applications, where traditional Wi-Fi-based communication is inadequate. The proposed telemetry system integrates low-power LoRa technology to ensure reliable long-range data transmission. The system is designed with a compact embedded structure and utilizes multiple sensors, including a global positioning system (GPS) module, altimeter, and accelerometer, to enhance data accuracy. The research objectives focus on (1) designing a compact embedded system, (2) integrating long-range low-power communication, and (3) evaluating performance in real-world settings. Experimental results demonstrate minimal measurement errors (altitude: 0.067-0.1319 meters; location: 0.0001-0.0003 degrees), confirming the reliability and efficiency of the system. The findings highlight the system's potential for resource-constrained environments in unmanned aerial vehicles (UAVs) and robotics applications.

关键词： Meters Accuracy embedded systems Service robots LoRa real-time systems Telemetry Reliability Monitoring Global Positioning System

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A Review on the applications of Machine Learning and Deep Learning Algorithms for Image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

引用

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

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 ieee.

关键词： Adversarial machine learning

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Evaluation of Cuckoo Search MPPT Algorithm for Solar Photovoltaic Inverter applications 1

Evaluation of Cuckoo Search MPPT Algorithm for Solar Photovo...

引用

1st international conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Joddumahanthi, Vijaychandra Gopal, Yatindra Pakalapati, Janaki Kakinada, Veda Prakash Potnuru, Varshitha Rajesh, V.D.K.S.R. Lendi Institute of Engineering and Technology Department of Electrical and Electronics Engineering AP Vizianagaram India

ISBN: (纸本)9798331520540

As the demand for renewable energy sources continues to grow, optimizing the efficiency of solar photovoltaic (PV) systems becomes increasingly important. This study illustrates a solar PV system that determines maximum power point tracking (MPPT) in solar inverter applications adopting a Cuckoo Search Algorithm (CSA). The CSA provides a distinctive approach for determining the maximum power under various scenarios and is modeled after the brood parasitism of some cuckoo species. This study examines the algorithm's performance in real-time tracking of maximum power output, analyzing its responsiveness to change in irradiance and temperature. Simulation results using MATLAB demonstrate that the CSA achieves greater efficiency and faster convergence compared to traditional MPPT methods. © 2025 ieee.

关键词： Cuckoo search algorithm MPPT rectifier solar PV system step-up converter

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Quantum Dot-Based real-time Disease Detection Using embedded systems: A Novel Approach for Portable Health Monitoring 8

Quantum Dot-Based Real-Time Disease Detection Using Embedded...

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8th international conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Sarna, Soumil Sarna, Mukesh K Vellore Institute of Technology Vellore India Mahatma Gandhi Medical College and Hospital MGUMST Department of General Medicine Jaipur India

ISBN: (纸本)9798331520762

This paper presents an innovative approach to real-time disease diagnostics using a microcontroller integrated quantum dot sensor device. Quantum dots, renowned for their high sensitivity and precise fluorescence properties, are deployed to detect specific biomarkers in biological samples, offering rapid and accurate health diagnostics. This work lays the foundation for future advancements in embedded systems for biomedical applications, offering an efficient and low-cost solution to improve patient outcomes through timely intervention. © 2025 ieee.

关键词： Microcontrollers

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MERGERS: Multi-Access Edge Resource Governance for real-time SaaS systems

MERGERS: Multi-Access Edge Resource Governance for Real-Time...

引用

international conference on VLSI Design

作者： Aakashjit Bhattacharya Arnab Sarkar Ansuman Banerjee ATDC IIT Kharagpur India ACMU ISI Kolkata India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

The Multi-access Edge computation (MEC) paradigm is being regarded as a viable alternative for handling data and computation requirements of complex real-time and safety-critical applications in upcoming IoT systems. This paper presents a resource arbitration technique called MERGERS that utilizes a central adaptive orchestrator called Resource Arbiter (RA), to dynamically allocate real-time service requests among heterogeneous MEC servers of a 5G MEC Software-as-a-Service (SaaS) provider. The objective of MERGERS is to maximize overall resource utilization of the given set of MEC servers through efficient load balancing. The approach partitions time into uniform intervals known as epochs. At the start of each epoch, RA takes into account the computational, storage, and deadline requirements of new client requests received in the previous epoch. It then uses a strict admission control policy to assign the new requests to suitable MEC edge servers, while ensuring that the hard real-time requirements of already scheduled tasks are not compromised. A newly arrived request is rejected if it's inclusion in the system can lead to deadline misses. Experimental results show MERGERS is able to achieve high task acceptance rates over a variety of simulation scenarios.

关键词： Multi-access edge computing embedded systems Corporate acquisitions Software as a service Admission control Very large scale integration Load management real-time systems Servers Resource management

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Developing Scalable and Fault Tolerant Distributed Architecture for Big Unstructured Data systems 6

Developing Scalable and Fault Tolerant Distributed Architect...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Saranya, G. Kumaran, K. Pratheep, V. Dhanush, S.R. School of Computer Science and Engineering Vellore Institue of Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the era of big data, efficiently processing and retrieving insights from unstructured data presents a critical challenge. This paper introduces a scalable leader-worker distributed data pipeline designed to handle large sets of unstructured data blobs. Leveraging data parallelism, the system distributes workload across worker nodes, ensuring high performance for embarrassingly parallel tasks with minimal inter-process communication. Fault tolerance is achieved through an optimized next-in-line leader re-election strategy, addressing cascading failures and mitigating the thundering herd effect. The use of Apache Zookeeper for service discovery ensures seamless coordination within the cluster while maintaining a loosely coupled architecture for enhanced scalability. Google Protocol Buffers are employed for secure data transmission, offering tamper resistance against eavesdropping and man-in-the-middle attacks. The system also integrates the TF-IDF algorithm for efficient information retrieval, demonstrating the ability to achieve data parallelism while preserving task uniformity across nodes. Performance evaluation highlights significant reductions in response times with increasing cluster size, validating the system’s scalability, efficiency, and robustness. The proposed architecture is well-suited for real-world applications requiring secure, fault-tolerant, and scalable solutions for unstructured data analysis. © 2025 ieee.

关键词： Big data

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Adaptive Moving Target Defense in Web applications and Networks using Factored MDP 17

Adaptive Moving Target Defense in Web Applications and Netwo...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Bose, Megha Paruchuri, Praveen Kumar, Akshat IIIT Hyderabad India Singapore Management University Singapore

ISBN: (纸本)9798331531195

Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attacker's knowledge and behavior. However, real-world scenarios are inherently more complex, with adaptive attackers and limited prior knowledge of their payoffs and intentions. This paper1 introduces a novel approach to MTD using a Markov Decision Process (MDP) model that does not rely on predefined attacker payoffs. Our framework integrates the attacker's real-time responses into the defender's MDP using a dynamic Bayesian Network. By employing a factored MDP model, we provide a comprehensive and realistic representation of the system and its dependencies. We also incorporate incremental updates to an attack response predictor as new data emerges. This ensures an adaptive and robust defense mechanism. Additionally, we consider the costs of switching configurations in MTD, integrating them into the reward structure to balance execution and defense costs. We first highlight the challenges of the problem through a theoretical negative result on regret. However, our empirical evaluations, conducted in network and web application settings, demonstrate the framework's effectiveness in environments characterized by high uncertainty and dynamically changing attack surfaces. © 2025 ieee.

关键词： Markov processes

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Application and Evaluation of real-time Analysis Algorithms in Dynamic Data Processing systems

Application and Evaluation of Real-Time Analysis Algorithms ...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Si Chen College of Digital Business Liaoning Vocational University of Technology Jinzhou Liaoning China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Dynamic data processing systems are key technologies in modern data-driven applications, widely used in fields such as the Internet of Things, finance and healthcare. This study explores the application and evaluation of real-time analytical algorithms for dynamic data processing. An efficient algorithmic framework combining adaptive windowing, incremental learning and distributed computing is proposed to achieve low-latency and high-accuracy decision-making capabilities. Through experimental validation in a real data streaming environment, the results show that the proposed algorithm significantly outperforms traditional algorithms with about 35% reduction in latency and 20% improvement in prediction accuracy. The research results in this paper show that the real-time analysis algorithm has a promising application in dealing with dynamic heterogeneous data streams. Meanwhile, the article also discusses the limitations and future optimisation directions of the method, which provides a reference for further research in related fields.

关键词： Performance evaluation Incremental learning Accuracy Heuristic algorithms Data processing Prediction algorithms real-time systems Low latency communication Distributed computing Streams

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The Impact of Ultra-Wideband Sensors in Industrial Automation and Robotics applications 3

The Impact of Ultra-Wideband Sensors in Industrial Automatio...

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

作者： Chaudhary, Pavan Sidhu, Amritpal Nayak, Chinmayee Das, Sidhant Kantharaj, I.K. Thapa, K.Naresh Kumar Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Artificial Intelligence and Machine Learning Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Ultra-Wideband Technology has garnered a reputation as an enabler of advancing industrial automation and robotics applications. UWB sensors emit short pulses of electromagnetic radiation to measure and can deliver highly accurate and real-time data over long distances. Compared to other sensing technologies, UWB sensors have a remarkably broad frequency spectrum and provide high data rates at low power. There are several advantages of UWB sensors implemented in industrial automation and robotics. These are done in a lab with some reference;you can take the data offline. UWB sensors can complement and be used in integration with other technologies, such as lidar or vision systems, to achieve higher performance results for a complete perception of the environment. This allows better and more efficient decision-making for autonomous systems. UWB sensors deliver highly accurate and repeatable measurements for robotic movement monitoring and control. They are also used for asset monitoring and inventory control, which finally help logistics & SCM. UWB sensors also improve workplace safety by warning about potential collisions or other hazardous conditions. © 2025 ieee.

关键词： Electromagnetic pulse

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A Comprehensive Survey on Enhancing Digital Twin Security systems with Explainable AI Techniques 3

A Comprehensive Survey on Enhancing Digital Twin Security Sy...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Lalithadevi, B. Krishnaveni, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

Digital Twin (DT) technology has emerged as a cornerstone in modern engineering, enabling real-time digital replication of physical assets for monitoring, diagnostics, and optimization. However, as DTs become increasingly integrated into critical infrastructure, their security has become a paramount concern. This paper presents a comprehensive survey on the integration of Explainable Artificial Intelligence (XAI) into Digital Twin security, highlighting the potential of XAI in enhancing the transparency, interpretability, and trustworthiness of AI - driven security mechanisms in DTs. We explore various XAI techniques, including model- agnostic approaches, interpretable neural networks, and rule-based systems, detailing their potential applications in bolstering the security of Digital Twins. Furthermore, we identify several key challenges in implementing XAI for DT security, such as the need for standardized metrics for interpretability, the trade-offs between model complexity and explainability, and the potential for adversarial attacks on explainable models. Through our analysis, we highlight the critical research gaps, including the security of Digital Twin (DT) systems and explores the integration of Explainable AI (XAI) to enhance transparency, interpretability, and trustworthiness. Outcomes include detailed XAI applications for DT security, identification of limitations, and a roadmap for future research. We discuss various XAI techniques, their applications, and the current challenges in implementing XAI for DT security. Our survey identifies key research gaps and offers a roadmap for future research in this emerging field. © 2025 ieee.

关键词： Artificial intelligence

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