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A Multiplatform parallel Approach for Lattice Sieving algorithms 20th

A Multiplatform Parallel Approach for Lattice Sieving Algori...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Andrzejczak, Michal Gaj, Kris Mil Univ Technol Warsaw Poland George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9783030602451;9783030602444

Lattice sieving is currently the leading class of algorithms for solving the shortest vector problem over lattices. the computational difficulty of this problem is the basis for constructing secure post-quantum public-key cryptosystems based on lattices. In this paper, we present a novel massively parallel approach for solving the shortest vector problem using lattice sieving and hardware acceleration. We combine previously reported algorithms with a proper caching strategy and develop hardware architecture. the main advantage of the proposed approach is eliminating the overhead of the data transfer between a CPU and a hardware accelerator. the authors believe that this is the first such architecture reported in the literature to date and predict to achieve up to 8 times higher throughput when compared to a multi-core high-performance CPU. Presented methods can be adapted for other sieving algorithms hard to implement in FPGAs due to the communication and memory bottleneck.

关键词： Lattice sieving Hardware acceleration Cryptography Multi-platform parallel approach parallel algorithms

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Improved parallel ZF-VBLAST Detector for MIMO System 4

Improved Parallel ZF-VBLAST Detector for MIMO System

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4th international conference on Communications, Signal processing, and their Applications, ICCSPA 2020

作者： Alfawaz, Oruba Eddin, Maha Alaa Alnajjar, Khawla A. El-Moursy, Ali A. University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9781728165356

the rapid growth of data traffic in the last decade is expected to continue in the next generation (5G) system. In order to service the high demand of data rates in 5G, complex communication architectures are needed such as multiple input multiple output (MIMO) technology. the main idea of MIMO is to increase the number of antennas in both sides (transmitter Tx and receiver Rx) that can achieve high throughput and energy efficiency. Receivers complexity and speed are some of the important requirements in MIMO systems. We focus on Vertical Bell Laboratories Layered Space Time (VBLAST) receiver. Using large number of antennas with VBLAST technique can increase the complexity of the system and the required time to recover the original signal. However, parallel computing can be used to divide the tasks of complex algorithm and distribute them among different microprocessors to run in an acceptable execution time. In this paper, we perform deep performance and time analysis to decide the opportunities of parallelization. We achieved speed up of 11.4X in the computation time of ZF-VBLAST algorithm for different sizes of MIMO systems using multi-threaded implementation. © 2021 IEEE.

关键词： MIMO systems

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A Bio-Inspired Hybrid Optimization Model for Enhancing Operating Performance and Efficiency of Electric Vehicles

A Bio-Inspired Hybrid Optimization Model for Enhancing Opera...

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Smart Grid and Green Energy (ICSGGE), international conference on

作者： Muhammad Zeshan Afzal Fushuan Wen Nimrah Saeed College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

Electric Vehicles (EVs) are critical for the advancement of sustainable transportation, however, there is still a need to optimize energy consumption, battery longevity, and system adaptability. In this study, we introduce Quantum-BioNet (QBN), a hybrid optimization framework that combines quantum computing principles and bio inspired algorithms to address these issues. For efficient exploration of a vast solution space, QBN utilizes parameterized quantum circuits; for real time responsiveness, it uses bio inspired adaptation. the model allows critical EV parameters to be dynamically optimized (i.e., energy management and regenerative braking) under varying operational conditions. Implemented in a MATLAB/Simulink simulation environment replicating real-world scenarios, QBN achieved significant performance improvements: 1) ${2 0 \%}$ decrease in energy consumption; 2) ${3 0 \%}$ increase in battery life; 3) fast adaptation within 5-6 seconds to changes in traffic density and road grade. QBN was found to have better computational efficiency (90 seconds to converge in a computer with ${6 0 \%}$ CPU and 950 MB memory) than state-of-the-art Quantum Optimization (QO) and Bio Inspired Optimization (BIO) algorithms, outperforming QO and BIO by $25 \%$ and $18 \%$, respectively. the results indicate QBN’s capability to revolutionize EV optimization with a scalable, energy efficient and cost-effective solution. QBN combines quantum parallelism and adaptive bio-inspired strategies to implement intelligent and green mobility systems that meet critical requirements of real-world EV operation and accommodate future progress.

关键词： Energy consumption Scalability Computational modeling Transportation parallel processing Real-time systems Energy efficiency Batteries Computational efficiency Optimization

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Artificial Intelligence in Smart Mobile Communication Systems: A Review

Artificial Intelligence in Smart Mobile Communication System...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Adebajo-Akinpelu Victor O Moses Oluwafemi Onibonoje Ayodeji Olalekan Salau Department of Electrical/Electronics and Computer Engineering Afe Babalola Univerisity Ado-Ekiti Ado-Ekiti Nigeria Dept of Electrical/Electronics & Computer Engineering Afe Babalola University Ado Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Communication and information service systems are undergoing rapid advancements driven by the evolution of telecommunications and computer technology. the convergence of these fields has led to significant growth in digital networks, which has spurred extensive research and exploration. However, optimizing the use of these networks with minimal additional costs—while integrating features such as video conferencing, email services, and remote database access— remains a substantial technological challenge. this study addresses the limitations of current mobile communication networks by exploring potential enhancements through the application of smart computing solutions, particularly artificial intelligence (AI). the research methodology involves a comprehensive review of existing mobile communication technologies and their integration with AI. this includes examining advancements in AI and machine learning (ML) as applied to mobile networks, with a focus on improving performance, flexibility, and automation. the results of the review indicate that AI and ML have significant potential to enhance network operations. By leveraging vast data generated within 5G systems, these technologies can optimize resource utilization, improve service quality, and support diverse emerging applications. Despite these advancements, challenges such as reliability, speed trade-offs, complexity, privacy, and security persist. the findings highlight the need for further research in AI, including deep neural network acceleration, parallel computing, cloud computing, and distributed deep learning systems. Such research is crucial for enabling mobile communication networks to achieve higher throughput and ultra-low latency. Conclusively, while AI-enabled communication systems offer promising solutions to many challenges, simplifying AI implementation is essential for practical deployment. this will ensure that future communication networks can effectively meet the growing demands and evolving

关键词： Deep learning Privacy Reviews 5G mobile communication parallel processing throughput Complexity theory Security Resource management Artificial intelligence

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Underdetermined 2D DOA Estimation Based on Bi-directional parallel Sparse Linear Array with Reduced Mutual Coupling 2021

Underdetermined 2D DOA Estimation Based on Bi-directional Pa...

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5th international conference on Digital Signal processing, ICDSP 2021

作者： Wang, Geng Chen, Changxiao He, Yi Feng, Mingyue Jiang, Ying Li, Zheng Air Force Early Warning Academy and University of Chicago China Naval Rearch Academy China Unit 94654 of PLA China

ISBN: (纸本)9781450389365

Compared with uniform arrays, sparse arrays can achieve same estimation accuracy and degrees of freedom (DOFs) with less sensors and reduced mutual coupling, so a novel parallel sparse linear array to achieve undetermined two-dimensional (2-D) DOA estimation is proposed. To improve the estimation accuracy of sparsity-based DOA estimator, this paper introduce Low-rank matrix model for precise covariance matrix reconstruction and propose covariance vector sparse representation(CVSR) for 1-D DOA estimation. Moreover, TLS-CVSR based on proposed 1-D DOA estimator CVSR for 2-D DOA estimation of parallel sparse linear array is proposed. By implementing simulation experiments, it is demonstrated that TLS-CVSR have superior performance in terms of underdetermined 2-D DOA estimation and high estimation accuracy. © 2021 ACM.

关键词： Degrees of freedom (mechanics)

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A Comparative Study of Machine Learning Models for Identifying Nigerian Languages from Audio Clips

A Comparative Study of Machine Learning Models for Identifyi...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Oluwadunsin Emmanuel Fajemila Ayodeji Olalekan Salau Pipelolu Samuel Olayiwola Department of Electronics and Electrical Engineering Obafemi Awolowo University Osun State Nigeria Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Language is a vital aspect of human culture and communication, with over 7,000 languages spoken worldwide. Despite this diversity, many languages, particularly those spoken in Africa, lack extensive research and support for speech processing technologies. this study focuses on identifying five Nigerian languages, which are Yoruba, Nigerian Pidgin, Nigerian English, Hausa, and Igbo using machine learning techniques. We utilized Mel-Frequency Cepstral Coefficients (MFCCs) to extract acoustic features from audio clips and trained several models, including 1-Dimensional Convolutional Neural Network (1D CNN), 2-Dimensional Convolutional Neural Network (2D CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), Random Forest, and Support Vector Machine (SVM), to evaluate their effectiveness in language identification. the dataset comprised audio from OpenSLR, Common Voice, local recordings, and NWU, totaling 37.8 hours across all languages. Results indicated that the LSTM model achieved the highest accuracy (0.830) and precision (0.856), while the DNN model showed the best F1 score (0.797) and recall (0.852). Neural network-based models consistently outperformed traditional machine learning approaches. We observed varying levels of classification difficulty among the languages, with some presenting more challenges than others. the dataset's uneven distribution across languages posed additional complexities in model training and evaluation. this research underscores the importance of diverse data sources and advanced neural network architectures in improving language classification for underrepresented African languages.

关键词： Support vector machines Training Accuracy Confusion matrices Transfer learning Feature extraction Multilingual Convolutional neural networks Speech processing Long short term memory

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Using Asynchronous Programming in C# - Problems, Practical Tips and Scenarios: A Short Review

Using Asynchronous Programming in C# - Problems, Practical T...

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Daniel Damyanov Zlatko Varbanov Dept. of Information Technologies University of Veliko Tarnovo Veliko Tarnovo Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Asynchronous programming in C# plays a crucial role in enhancing the performance and responsiveness of modern applications, especially in contexts involving I/O operations, network communications, and database interactions. However, despite these improvements, developers often encounter various challenges when working with asynchronous code, such as improper task management, error handling difficulties, performance issues, and synchronization concerns. this paper addresses some of the most common problems in asynchronous development, including poor task management, error propagation, and potential performance bottlenecks. It also provides practical tips for effectively applying asynchronous patterns in everyday development, drawing on real-world industry scenarios where asynchronous programming offers significant advantages in terms of scalability and application stability. Special attention is given to preventing common mistakes, such as deadlocks and task overproduction (task explosion), along with best practices for improving code readability and maintainability. By thoroughly examining these issues and offering solutions, this paper aims to help developers not only understand the fundamentals of asynchronous programming but also apply advanced techniques for optimizing performance and security. Additionally, the article explores current challenges and future opportunities in the development of asynchronous systems, with a focus on how they can contribute effectively in multithreaded and high-load environments.

关键词： Performance evaluation Codes Reviews Scalability Operating systems Instruction sets Programming parallel processing System recovery Software

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Lightweight Bimodal Rail Surface Defect Detection Method: parallel-YOLOv4-Tiny 21

Lightweight Bimodal Rail Surface Defect Detection Method: Pa...

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Proceedings of the 2021 5th international conference on Video and Image processing

作者： Jiajun Zheng Qiang Han Le Wang Shengchun Wang Zhiyong Zhang Luping Wang Liang Wang School of Electronics and Communication Engineering Sun Yat-Sen University China Infrastructure Inspection Research Institute China Academy of Railway Science Corporation Limited China

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18th international conference on algorithms and architectures for parallel processing, ICA3PP 2018

18th International Conference on Algorithms and Architecture...

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18th international conference on algorithms and architectures for parallel processing, ICA3PP 2018

ISBN: (纸本)9783030050535

the proceedings contain 191 papers. the special focus in this conference is on algorithms and architectures for parallel processing. the topics include: Incentivizing multimedia data acquisition for machine learning system;Toward performance prediction for multi-BSP programs in ML;exploiting the table of energy and power leverages;a semantic web based intelligent IoT model;Accelerating CNNs using optimized scheduling strategy;data analysis of blended learning in python programming;APs deployment optimization for indoor fingerprint positioning with adaptive particle swarm algorithm;deployment optimization of indoor positioning signal sources with fireworks algorithm;a study of sleep stages threshold based on multiscale fuzzy entropy;qoS-driven service matching algorithm based on user requirements;Blind estimation algorithm over fast-fading multipath OFDM channels;facial shape and expression transfer via non-rigid image deformation;p-schedule: Erasure coding schedule strategy in big data storage system;Answer aggregation of crowdsourcing employing an improved EM-based approach;a parallel fast fourier transform algorithm for large-scale signal data using apache spark in cloud;task offloading in edge-clouds with budget constraint;motion trajectory sequence-based map matching assisted indoor autonomous mobile robot positioning;towards the independent spanning trees in the line graphs of interconnection networks;POEM: Pricing longer for edge computing in the device cloud;mobility analysis and response for software-defined internet of things;Research on overload classification method for bus images based on image processing and SVM;DStore: A distributed cloud storage system based on smart contracts and blockchain;towards an efficient and real-time scheduling platform for mobile charging vehicles;Streaming ETL in polystore era.

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Elevated Learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance

Elevated Learning based Secured Phishing Website Identificat...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： G. Ramkumar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Accuracy Phishing Scalability Transfer learning Robustness Natural language processing Real-time systems Pattern recognition Security Protection

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