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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 36882 条记录，以下是181-190 订阅

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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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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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Enhanced EEGNet Optimization Using Lightweight Deep Neural Network for Detecting Human Stress Levels from Raw EEG Signals Dataset 3

Enhanced EEGNet Optimization Using Lightweight Deep Neural N...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Hana, Silabdi Hassan, Raini Faizabadi, Ahmed Rimaz Gubbi, Abdullah Bellary, Mohammed Zakir Manjula, V. International Islamic University Malaysia Kulliyyah of Information & Communication Technology Department of Computer Science Kuala Lumpur Malaysia International Islamic University Malaysia Centre for Unmanned Technologies Kuala Lumpur Malaysia Department of Electronics and Communication Engineering Mangalore India Department of Artificial Intelligence and Machine learning Mangalore India

ISBN: (纸本)9798331508456

Accurate stress detection is essential in neuroscience and healthcare since human stress levels have a substantial impact on general well-being. Although EEG-based stress detection is a popular non-invasive method, real-time applications are limited by the high processing costs of classic feature extraction techniques. This work removes the requirement for intricate feature extraction by presenting an efficient EEGNet framework for real-time stress prediction utilizing raw EEG signals. The suggested approach improves accuracy and efficiency by utilizing Z-score normalization and adjusting batch size. Compared to traditional CNNs, the lightweight EEGNet architecture's depth-wise and separable convolutions ensure computational efficiency by reducing trainable parameters by a factor of ten. The proposed method outperformed current EEGNet-based techniques in stress identification by 5% to 18%. Furthermore, the improved system efficiently handles raw EEG signals, which is suitable for real-time, resource-constrained scenarios. The results shows the effectiveness of model-centric optimization in deep learning and open the door to the creation of more effective stress detection techniques. This work provides a workable solution for real-time non-invasive stress monitoring, highlighting the significance of lightweight frameworks customized for particular applications. © 2025 ieee.

关键词： Deep neural networks

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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...

引用

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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Comprehensive Investigation of Channel Estimation Techniques in mmWave MIMO systems: Unlocking the Potential of Future Connectivity 3

Comprehensive Investigation of Channel Estimation Techniques...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Deepthi, S.R. Prasad, Durga Pinto, Rohan Department of Electronics and Communication Engineering Nitte Mangalore India St Joseph Engineering College Department of Electronics and Communication Engineering Mangaluru India Department of Advanced Communication Technology Nitte Mangalore India

ISBN: (纸本)9798331508456

Millimeter wave (mmWave) MIMO systems are critical for next-generation wireless communication, yet accurate channel estimation remains a key challenge due to high pilot overhead and dynamic channel conditions. This paper explores advanced channel estimation techniques, focusing on deep learning-based methods such as denoising autoencoders and supervised learning approaches. These techniques leverage spatial and temporal correlations to reduce pilot overhead while improving estimation accuracy. The study synthesizes existing literature on deep learning applications for channel estimation and highlights key trends, methodologies, and challenges. Furthermore, this review identifies research gaps, such as computational complexity in real-time applications and limitations in handling high-speed channel variations. By critically analyzing recent advancements and their limitations, this work provides insights into future research directions for enhancing mmWave MIMO channel estimation using hybrid deep learning models. © 2025 ieee.

关键词： Channel estimation

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

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

引用

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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Beyond the Blink: IoT's Next-Gen Drowsiness Detection System for Drivers 3

Beyond the Blink: IoT's Next-Gen Drowsiness Detection System...

引用

3rd ieee international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Routray, Sidheswar Patel, Nachiket Patel, Dhruv Srivastava, Jyoti Dash, Rudra Narayan Pandit Deendayal Energy University Dept. of Computer Science & Engg. Gandhinagar India Indrashil University Dept. of Computer Science & Engg. Mehsana India Ganpat University Dept. of Computer Engineering Mehsana India School of Electrical Engineering KIIT University Bhubaneswar India

ISBN: (纸本)9798331533205

Drowsiness during driving includes high risks for the driver, the co-passengers, and the other people on the road. Advance Driver Assistance systems (ADAS) have been proposed to reduce these risks. This work introduces a new Internet of Things (IoT) seizure detection system to improve driver safety using Raspberry Pi, which is equipped with various sensors and utilizes machine learning for real-time monitoring and analysis. Using sensor fusion technology, including monitoring yawning and eye-closing patterns, the system aims to detect early signs of driver sleepiness. Being a low-cost, versatile, and tiny device, it becomes an ideal core processor for integration into an in-car environment. Additionally, the simplicity enables seamless communication with various sensors and facilitates effective data collection. Extensive tests and simulations were conducted to evaluate the effectiveness and reliability of the proposed system for real-world driving situations. The result outcomes prove the system's accuracy and efficiency in detecting early signs of driver fatigue to enhance driver safety. The integration of Raspberry Pi and sensor fusion technology makes it economical as well as scalable approaches supporting multiple platforms. © 2025 ieee.

关键词： Advanced driver assistance systems

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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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