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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89982 条记录，以下是641-650 订阅

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Towards Hardware-Software Self-Adaptive Acceleration of Spiking Neural Networks on reconfigurable Digital Hardware 36

Towards Hardware-Software Self-Adaptive Acceleration of Spik...

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36th IEEE international System-on-Chip conference (SOCC)

作者： Pachideh, Brian Zielke, Christian Nitzsche, Sven Becker, Juergen FZI Res Ctr Informat Technol Karlsruhe Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798350300116

Self-adaptive systems can dynamically and autonomously modify their parameters or structure to adjust their behavior in response to changes in their environment or input. The resulting flexibility and robustness can be a great advantage in various fields like robotics or industrial processes. Here, we demonstrate the feasibility of combining field-programmable gate arrays (FPGAs) and spiking neural networks (SNNs) to create self-adaptive systems capable of dynamic and autonomous modification of their structure in both software and hardware, allowing them to learn and evolve over time. We present a proof-of-concept implementation of an SNN accelerator that can adapt its network topology using dynamic partial reconfiguration and its weight parameters using online learning methods, such as Spike-Timing Dependent Plasticity (STDP) and anti-STDP. We evaluated different configurations of the accelerator in terms of resource utilization, power consumption, and runtime and tested its online learning capabilities in a classification task using the N-MNIST dataset. In our experiments, the accelerator saved 33% dynamic power by adapting to changing task conditions, and achieved a speedup factor of 5.3 with respect to the input data time resolution. Our findings suggest that combining FPGAs and SNNs can lead to the effective instantiation of SNN hardware resources to meet changing task conditions, thus paving the way for future research on self-adaptive SNNs that use diverse online learning algorithms to improve themselves both in software and hardware.

关键词： adaptive systems FPGA partial reconfiguration SNN online learning STDP neuromorphic

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Partial Discharge Source Classification Using Machine Learning algorithms 33

Partial Discharge Source Classification Using Machine Learni...

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33rd Southern African Universities Power engineering conference, SAUPEC 2025

作者： Thobejane, Lucas T. Thango, Bonginkosi A. University of Johannesburg Department of Electrical Engineering Johannesburg South Africa

ISBN: (纸本)9798331535162

This article proposes a machine-learning algorithm for the automatic classification of single-source partial discharge (PD) in power transformers. PD testing provides valuable information of the state and deterioration of the insulation systems of transformer windings and core. A PD testing setup based on the IEC 60270 international standard is used to test PD from transformers of varying size and operational age. PD is recorded at different voltage levels applied to the transformer under test. Where the majority of PD classification literature has focused on laboratory developed artificial PD models, this work uses practical power transformers as a basis for testing and collecting the PD database. The PD database collected from this testing is utilized for the training, validation and testing of the machine learning algorithm. In this article, a comparative analysis of various trained machine learning algorithms for classifying PD is performed. The results of the classification show very pleasing performance from the tested classifier algorithms, with Bilayered Neural Network achieving a 96.97% validation accuracy of and a test accuracy of 97%. © 2025 IEEE.

关键词： Adversarial machine learning

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Emerging VLSI Technologies for High performance AI and ML Applications 2

Emerging VLSI Technologies for High performance AI and ML Ap...

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

作者： Nagar, Prashray Boruah, Sashwat Bhoi, Akash Kumar Patel, Arpita Sarda, Jigar Darjij, Pallavi Charotar University of Science and Technology V T Patel Department of Electronics & Communication Engineering India Sikkim Manipal University Research Gangtok India Charotar University of Science and Technology M & v Patel Department of Electrical Engineering India Dharamsinh Desai Univeristy Department of Electronics & Communication Engineering Nadiad India

ISBN: (纸本)9798350370188

The capabilities of artificial intelligence (AI) and machine learning (ML) algorithms are constantly expanding, necessitating efficient and high-performance hardware systems. We have investigated the creation of hardware accelerators based on VLSI that are intended to effectively manage the heavy workloads of machine learning jobs, also explored low-power VLSI architectures that preserve computing capabilities while reducing power consumption to solve energy efficiency issues in AI and ML systems. To balance performance and energy utilization, power management strategies and circuit design improvements are analysed. The study emphasizes hardware-software co-design techniques, considering the integration of VLSI-based hardware accelerators with software frameworks to obtain optimal performance and flexibility, to address the complexity and scalability of AI and ML systems. We also examined the cutting-edge VLSI technologies that have the potential to support powerful AI and ML applications. The speed and effectiveness of AI and ML algorithms could be improved significantly by these technologies, which include neuromorphic computing, approximation computing, and in-memory computing. The research also discusses the difficulties in designing VLSI for AI and ML algorithms, and its possible solutions for challenges including design complexity, scalability, memory management, and data mobility. © 2024 IEEE.

关键词： Energy efficiency

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Open Architecture for Distributed and Cooperative Media algorithms 3

Open Architecture for Distributed and Cooperative Media Algo...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Bhambu, Pawan Manjunatha, R. Srivastava, Pratima Vivekananda Global University Department of Computer Science and Engineering Jaipur India School of Sciences Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Arka Jain University Department of Biotechnology Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

The Open structure for allotted and Cooperative Media algorithms (OADCMA) is an open-deliver framework imparting a plug-in platform that lets customers, without problem, develop distributed and cooperative media algorithms to utilize the computational assets of diverse heterogeneous structures. It's miles designed for operations along with video capture, transmission, and machine operations. OADCMA affords unified APIs for allotted and cooperative media algorithms, which simplifies the implementation of distributed and cooperative media algorithms. Additionally, OADCMA permits green parallel computing while averting scalability problems. OADCMA helps a widespread fashion of verbal exchange networks, including allotted finite discipline computing. It is appropriate for an extensive kind of allotted and cooperative media programs together with video surveillance and broadcasting. © 2024 IEEE.

关键词： Security systems

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Exploring Post-Quantum Cryptographic algorithms for Secure Data Transmission 3

Exploring Post-Quantum Cryptographic Algorithms for Secure D...

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3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Gulomov, Sherzod R. Khudayberganov, Temur R. Ravshanova, Maftuna X. Turdiev, Temur T. Atabayev, Sultonbek S. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dean of the Faculty of Cyber Security Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Information security Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Computer engineering Urgench Uzbekistan Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Information technology Urgench Uzbekistan Urgench Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Dept. Computer engineering Urgench Uzbekistan

ISBN: (纸本)9798331516321

The rapid advancement of quantum computing introduces a critical threat to established cryptographic systems, such as RSA and ECC, by enabling quantum attacks those classical systems cannot withstand. This evolving threat creates a necessity for post-quantum cryptographic (PQC) algorithms to ensure secure data transmission in the quantum era. PQC encompasses diverse approaches, including lattice-based, hash-based, code-based, and multivariate polynomial cryptography. Each category offers distinct advantages and challenges: lattice-based algorithms provide robust security but may require substantial computational resources, while hash-based approaches are easier to implement but limited in flexibility. Code-based algorithms deliver strong security with significant computational demand, and multivariate polynomial approaches boast efficiency, though they face scrutiny over quantum resilience. This paper evaluates these PQC algorithms in terms of their security, performance, and feasibility, identifying optimal methods to secure data transmission in a future where quantum threats are prevalent. © 2024 IEEE.

关键词： Quantum cryptography

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An Innovative Deep Learning-Based Predictive Maintenance Model for Smart Automotive System

An Innovative Deep Learning-Based Predictive Maintenance Mod...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Exploring Machine Learning algorithms for Robust Cyber Threat Detection and Classification: A Comprehensive Evaluation

Exploring Machine Learning Algorithms for Robust Cyber Threa...

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2024 international conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Ravikumar, C. Naresh, Burri Prasanna, P. Laxmi Chander, Nenavath Goud, Ediga Amarnath Prasad, P Raghavendar Chaitanya Bharathi Institute of Technology Department of Ai and Ds Hyderabad India Cvr College of Engineering Department of CSE-CS Hyderabad India Tkr College of Engineering and Technology Department of Cse Hyderabad India Hyderabad India Sreenidhi Institute of Science and Technology Department of It Hyderabad India Mallareddy Engineering College Department of It Hyderabad India

ISBN: (纸本)9798350375237

In response to the escalating frequency and complexity of cyber threats, the imperative need to enhance cybersecurity measures is evident. This study explores the potential of machine learning (ML) algorithms in advancing threat detection and classification by automating the identification of security incidents. The abstract presents a thorough assessment of various ML algorithms, including decision trees, support vector machines, and neural networks, for their efficacy in detecting and categorizing cyber threats. The evaluation encompasses a diverse dataset featuring different cyber-attack scenarios and incorporates multiple features such as network traffic patterns, system logs, and user behavior. Performance metrics, such as training accuracy and testing accuracy, are employed to assess the effectiveness of each algorithm. Furthermore, the study investigates the impact of feature selection techniques and model optimization strategies on algorithm performance. The results underscore the capability of ML algorithms to accurately identify and categorize cyber threats, providing valuable insights into their strengths and limitations. This research contributes to the field of cybersecurity by facilitating the development of practical and robust ML-based solutions, ultimately reinforcing cyber defence mechanisms against evolving threats. © 2024 IEEE.

关键词： Cybersecurity

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Metric Generative Adversarial Network with Gated Attention Unit for Text-to-Image Translation

Metric Generative Adversarial Network with Gated Attention U...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Sheela, S.V. Radhika, K.R. Vishalakshi, T.N. Hegde, Vinay S. Vedha, R. Prajwal, A. Bms College of Engineering Information Science and Engineering Bengaluru India Mathematics Bms College of Engineering Bengaluru India

ISBN: (纸本)9798350360660

The text-to-image translation is a technique that involves generating visuals accurately representing the details described in the text. However, challenges remain where the produced image cannot completely capture the semantics of text description. Here, the target object in the produced image is incomplete and the texture architecture of the target object lacks richness and detail. In this research, the Metric Generative Adversarial Network (MGAN) with Gated Attention Unit (GAU) method is proposed to translate text to image. The encoder-decoder architecture is developed including the gating blocks utilizing a decoupling style phase-aware technique. This technique estimates the phase data and magnitude of clean data in parallel and effectively minimises interaction between the phase and magnitude. The convolution-augmented gated attention unit is utilized for capturing the time and frequency dependencies with less execution complexity to attain good outcomes. The MGAN with GAU obtains an IS value of 4.42 on Oxford-102 flowers dataset and an IS value of 5.16 on the CUB Bird dataset, which is higher when compared with the Recurrent Convolutional Generative Adversarial Network (RC-GAN). © 2024 IEEE.

关键词： Generative adversarial networks

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Stock Price Prediction using Bi-directional Long-Short Term Memory based Deep Neural Network

Stock Price Prediction using Bi-directional Long-Short Term ...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Sowmya, M. Al-Farouni, Mohammed Reddy Panga, Naresh Kumar Indira, R. Saranya, N. Naga Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Virtusa Corporation New York United States Panimalar Engineering College Department of Ece Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350360660

In the area of finance, forecasting of stock price is becoming very popular increasingly. Predicting the stock price of a firm is very important to increase speculators interest in investing for a company and for growth of shareholders in that stock. In addition to a unique investment portfolio model based on the projections produced, this article suggests a novel method for time-series stock price prediction. To achieve this, a Bidirectional Long-Short Term Memory-based Deep Neural Network (Bi-LSTM-DNN) is equipped with novel regression technique. Once made, the predictions are utilized to build investment portfolio, more precisely, a projected portfolio. Numerous tests have been conducted using NIFTY-50 stock data that was acquired from Indian National Stock Exchange. The outcomes verify that suggested model performs better than a number of conventional prediction models and a number of conventional portfolio optimization algorithms. Reducing investing risks and increasing rewards are possible with an accurate stock price prediction. The Root Mean Square Error (RMSE) value for the proposed deep learning network for stock prediction using Bi-LSTM-DNN is 0.81. © 2024 IEEE.

关键词： Prediction models

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Machine Learning algorithms for Cyber Attack Detection And Classification 24

Machine Learning Algorithms for Cyber Attack Detection And C...

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25th international conference on Computer systems and Technologies, CompSysTech 2024

作者： Note, Johan Mullalli, Erind Cico, Betim Department of Technical Support Abissnet Tirana Albania Department of Computer Engineering Epoka University Tirana Albania

ISBN: (纸本)9798400716843

As a result of the accelerated development and expansion of technology in the present day, a new concern has emerged: cyberattacks. This has generated significant concern across various domains globally, leading to considerable disruption in networks and presenting PC users with a multitude of challenges. Presently, a multitude of organisations are striving to combat these types of cyber-attacks through the implementation of novel detection and subsequent destruction methods. The domain of machine learning enables computers to acquire knowledge and skills without requiring explicit programming. There is an abundance of implementation strategies for this technology. This study aims to demonstrate a diverse array of algorithms utilised in the defense against various cyber-attacks. This paper will examine various classification algorithms utilised to defend against diverse cyber-attacks, as well as the methods of defense against these attacks. The implementation, accuracy, and testing time of these algorithms will vary depending on the classification of the attack. This thesis will discuss various varieties of these algorithms. © 2024 ACM.

关键词： Adversarial machine learning

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