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33rd Southern African Universities Power Engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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High-Performance Image Restoration with SpectraNet: A Lightweight CNN Architecture

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Procedia computer science 2025年 258卷 2268-2277页

作者： Nitesh Patnaik Rishi Raj Piyush Kumar Singh Shreekara Mahapatra Debashis Pani Ravi Shankar Singh Dept. of Computer Science and Engineering Kalinga Institute Of Industrial Technology Bhubaneswar 751024 India Dept. of Computer Science Central University of South Bihar Bihar 824236 India Dept. of Computer Science and Engineering Indian Institute of Technology BHU Varanasi 221005 India

Deep learning-based image restoration faces significant challenges when deployed on resource-constrained platforms due to the computational demands and large number of parameters in existing models. The high computational load and extensive memory requirements make it difficult to implement these models on devices with limited processing power and storage capacity, such as mobile phones and embedded systems. Additionally, maintaining real-time performance while ensuring high-quality image restoration is a critical challenge, as traditional deep learning models often fail to meet the stringent latency and efficiency requirements of such platforms. This paper introduces a new, lightweight convolutional neural network (CNN) architecture tailored for efficient pixelation detection and restoration. Our approach combines a pre-trained MobileNetV2 with finetuning for pixelation detection, and SpectraNet, a architecture incorporating dept.-wise separable convolutions for image restoration. The proposed architecture was trained on the HQ-50k dataset, with 70% of the data used for training and 30% for testing. It achieved a Peak Signal-to-Noise Ratio (PSNR) of 17-23, outperforming 10 state-of-the-art architectures in both efficiency and image quality. This design addresses the dual problems of high computational load and extensive memory usage, prioritizing real-time performance and resource efficiency. The proposed architecture is ideal for deployment on mobile and embedded devices since it maintains high accuracy while significantly reducing the number of trainable parameters. Our findings advance the feasibility of deploying advanced image restoration techniques in real-world, resource-constrained environments.

关键词： dept.-wise Separable Convolutions Embedded Devices Image Restoration Lightweight Real-Time Performance Resource-Constrained Platforms Deep Learning

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Advancing Environmental Sustainability: Renewable Energy Transition, Carbon Footprint Reduction, and Emission Control Innovations 22

Advancing Environmental Sustainability: Renewable Energy Tra...

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22nd International Learning and technology Conference, L and T 2025

作者： Hussein, Aziza I. Alghamdi, Roba W. Al-Amoudi, Amal Abdullah Altahawi, Omar Hisham Kabbaja, Narjisse Mabrook, Mohamed Mourad Lotfy, Mostafa Wageh Effat University Electrical & Computer Eng. Dept. Jeddah Saudi Arabia Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt Beni-Suef University Faculty of Technology and Education Dept. of Process Control Technology Beni-Suef Egypt

ISBN: (纸本)9798331532215

Combating climate change and promoting environmental sustainability necessitate a dedicated emphasis on renewable energy sources and lowering carbon footprints. Shifting from fossil fuels to renewable resources, such as solar, wind, biomass, and geothermal energy, is imperative. Equally important is enhancing energy efficiency across industries, transportation, buildings, and household appliances to reduce overall energy consumption and greenhouse gas emissions. Technological advancements, such as improved energy storage and smart grid systems, are essential for boosting the efficiency and dependability of renewable energy sources. Strategies to lower carbon footprints include transitioning to clean transportation, optimizing land use, reducing waste, recycling, promoting sustainable agriculture, and implementing carbon offset initiatives. Public education, corporate responsibility, and personal environmental awareness play vital roles in supporting carbon reduction. By combining sustainable energy practices with carbon reduction measures, we can collectively work towards curbing climate change and fostering a sustainable future. Additionally, utilized techniques to address the problem of high air pollution during vehicle cold starts. We modeled catalytic converter dynamics in MATLAB Simulink, using simplifying assumptions. One significant assumption involved treating the catalytic converter as a Continuous Stirred-Tank Reactor (CSTR), which allowed us to model its temperature uniformly, without accounting for temperature changes along its length. This approach is aimed at saving lives and reducing costs by effectively minimizing air pollution. © 2025 IEEE.

关键词： Clean energy

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Quantum Resistance Blockchain Algorithm using QHF and Novel Consensus Mechanism

Quantum Resistance Blockchain Algorithm using QHF and Novel ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Paul, Soumyadip Mazumdar, Mausumi Maitra Chakraborty, Sarit Government College of Engineering & Ceramic Technology Dept. of Information Technology Kolkata700010 India Government College of Engineering and Leather Technology Department of Computer Science and Engineering Kolkata700106 India

ISBN: (纸本)9798331529833

The emergence of quantum computing poses a significant threat to the security of traditional blockchain systems, which rely heavily on classical cryptographic algorithms. To safeguard the integrity and reliability of blockchain technology in the upcoming era, this work proposes a novel quantum-resistant blockchain architecture. This approach integrates a robust consensus mechanism, with advanced quantum cryptographic primitives, including quantum digital signatures based on a Quantum Hash Function (QHF). Incorporating a quantum hash function enhances the blockchain resistance, providing an additional layer of security against quantum attacks. By combining these elements, our method creates a blockchain system fortified against the potential vulnerabilities introduced by quantum computers. The proposed solution leverages the power of QHFs to safeguard transaction integrity, while the consensus mechanism ensures the reliability and decentralization of the network. This paper also delves into the theoretical underpinnings of quantum blockchains, exploring their design, feasibility, and potential applications, emphasizing their promising future, particularly in areas such as decentralized identity authentication. Finally, it contributes to the development of a secure and resilient blockchain ecosystem capable of thriving in the coming era. © 2025 IEEE.

关键词： Quantum computers

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Dual-Domain Feature-Guided Task Alignment for Enhanced Small Object Detection

Dual-Domain Feature-Guided Task Alignment for Enhanced Small...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Fangrui Wu, Junwei Zhang, Quan Dept. Master of Engineering University of California Los Angeles Los Angles United States Dept. Computer Science University of Liverpool Liverpool United Kingdom Dept. School of advanced technology Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798350368741

Small object detection is a critical challenge in Unmanned Aerial Vehicles (UAVs) due to the limited pixel representation of small objects and the impact of successive pooling operations, which frequently results in the disappearance of small objects within intricate backgrounds. To tackle this issue, we propose the Small Object Enhancement Pyramid (SOEP) module, which first transforms feature representations (i.e., in the spatial domain) into the frequency domain to better capture small objects typically characterized by high-frequency components. These feature representations are then fused in the spatial domain using a frequency-based attention map, enhancing small object representations by integrating information from both complementary domains. Furthermore, we introduce a Task Aligned Head (TAH) that integrates classification and localization tasks interactively, reducing the misalignment that occurs when these tasks are learned independently, particularly in the context of small objects. Experimental results on the Visdrone dataset verify that our proposed method (D2FTA) outperforms the baseline method by 12.7%, 14.19% on mAP0.5 and mAP0.5:0.95. © 2025 IEEE.

关键词： frequency domain small object detection spatial domain

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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HLSPilot: LLM-based High-Level Synthesis 24

HLSPilot: LLM-based High-Level Synthesis

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Xiong, Chenwei Liu, Cheng Li, Huawei Li, Xiaowei SKLP Institute of Computing Technology Chinese Academy of Sciences Beijing China Dept. of Computer Science University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400710773

Large language models (LLMs) have catalyzed an upsurge in automatic code generation, garnering significant attention for register transfer level (RTL) code generation. Despite the potential of RTL code generation with natural language, it remains error-prone and limited to relatively small modules because of the substantial semantic gap between natural language expressions and hardware design intent. In response to the limitations, we propose a methodology that reduces the semantic gaps by utilizing C/C++ for generating hardware designs via High-Level Synthesis (HLS) tools. Basically, we build a set of C-to-HLS optimization strategies catering to various code patterns, such as nested loops and local arrays. Then, we apply these strategies to sequential C/C++ code through in-context learning, which provides the LLMs with exemplary C/C++ to HLS prompts. With this approach, HLS designs can be generated effectively. Since LLMs still face problems in determining the optimized pragma parameters precisely, we have a design space exploration (DSE) tool integrated for pragma parameter tuning. Furthermore, we also employ profiling tools to pinpoint the performance bottlenecks within a program and selectively convert bottleneck components to HLS code for hardware acceleration. By combining the LLM-based profiling, C/C++ to HLS translation, and DSE, we have established HLSPilot—the first LLM-enabled high-level synthesis framework, which can fully automate the high-level application acceleration on hybrid CPU-FPGA architectures. According to our experiments on real-world application benchmarks, HLSPilot achieve comparable performance in general and can even outperform manually crafted counterparts, thereby underscoring the substantial promise of LLM-assisted hardware designs. © 2024 Copyright is held by the owner/author(s).

关键词： High level synthesis

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Advancing Wi-Fi Intrusion Detection Systems with Machine Learning Techniques for Enhanced Classification of Wireless Attacks

Advancing Wi-Fi Intrusion Detection Systems with Machine Lea...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Hasseeb Ahmad Bhutta Muhammad Talha Jahangir Areeb Ahmad Bhutta Computer Science Dept. National University of Sciences and Technology Islamabad Pakistan Computer Science Dept. MNS University of Engineering and Technology Multan Pakistan Computer Science Dept. Information Technology University Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

WiFi usability and scalability have contributed to an improvement in human living standards. However, this has also led to increased vulnerabilities and attack vectors within WiFi networks. Authentic users can face network failures and denial of service caused by attacks on wireless networks by some third-party. Wireless Intrusion Detection System (IDS) is needed to detect wireless attacks. Main of objective of wireless IDS is to monitor network traffic and classify if it is an attack or normal traffic. Modern anomaly based wireless intrusion detection systems use machine learning (ML) to learn from previous attacks in the dataset to learn patterns of the attack. Although this method is effective, but there is also downside to this approach. The time required to train and test these ML models are exceedingly high along with high computational costs. Big data is emerging technologies that is getting advanced day by day, due to which, network technology is increasing rapidly. For that reason, we need to discuss the issue, that is high computational costs. In our proposed solution, we used our custom oversampling technique BBOT (Balanced Boost Oversampling Technique) to rectify class imbalance, generating artificial samples for the classes, enhancing model performance. The results of the experiments show that the combination of feature selection, BBOT, and Decision Tree outperformed all other classifiers with reduced computational overhead, providing a practical and efficient solution for real-time intrusion detection in WiFi networks. When compared to XGBoost, the provided solution achieves a similar level of accuracy, but with significantly reduced training time.

关键词： Wireless communication Computational modeling Wireless networks Intrusion detection Machine learning Big Data Feature extraction Vectors Computational efficiency Wireless fidelity

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Geometrically Shaped Constellation in Short-Packet Visible Light Communications for IIoT Applications

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IEEE Internet of Things Journal 2025年第11期12卷 18220-18234页

作者： Guo, Jia-Ning Zhang, Jian Gong, Chen Li, Longguang Yang, Xu Zhou, Jing Chen, Ru-Han information engineering university Henan Province 450000 China The CAS Key Laboratory of Wireless-Optical Communications School of Information Science and Technology University of Science and Technology of China Hefei230027 China The SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China Sixty-Third Research Institute National University of Defense Technology Nanjing China Dept. Computer Science and Engineering Shaoxing University Shaoxing China

Due to the high data rates, license-free operations and inherent security, visible light communication (VLC) is highly valued in industrial Internet of Things (IIoT) applications. Short packet communication, which offers ultra-low latency and efficient resource utilization, is suitable for the low-latency and massive connectivity demand of the IIoT system. It adopts finite blocklength codewords for data transmissions. In this paper, we present a general framework of designing geometrically shaped constellations in short-packet VLC with peak and average intensity constraints for IIoT applications. By leveraging tools from large deviation theory, we first characterize the second-order asymptotics of the optimal constellation shaping region under aforementioned intensity constraints, which serves as a good performance metric for the best geometric shaping in finite blocklength. To further incorporate a sufficiently large coding gain and a nearly maximum shaping gain, we construct multidimensional constellations by the nested structure of Construction B lattices, where the constellation shaping is implemented by controlling the boundary of the embedded sublattice, i.e., a strategy called coarsely shaping and finely coding. Fast algorithms for constellation mapping and demodulation are presented as well. As an illustrative example, we present an energy-efficient 24-dimensional constellation design based on the Leech lattice, whose superiority over existing constellation designs is verified by numerical results. © 2014 IEEE.

关键词： Network security

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