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Quantized Decoders that Maximize Mutual Information for Polar Codes

China Communications 2024年第7期21卷 125-134页

作者： Zhu Hongfei Cao Zhiwei Zhao Yuping Li Dou School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China

In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized *** nested structure of polar codes ensures that the MMI quantization can be implemented stage by *** results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization ***,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.

关键词： maximize mutual information polar codes quantization successive cancellation decoding

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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery:A Systematic Review

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Big Data Mining and Analytics 2025年第2期8卷 407-429页

作者： Antonia Ivanda LjiljanaŠerić Maja Braović Department of Electronics and Computer Science Faculty of Electrical EngineeringMechanical Engineering and Naval ArchitectureUniversity of SplitSplit 21000Croatia

Remote sensing is of great importance for analyzing and studying various phenomena occurrence and development on *** is possible to extract features specific to various fields of application with the application of modern machine learning techniques,such as Convolutional Neural Networks(CNN)on MultiSpectral Images(MSI).This systematic review examines the application of 1D-,2D-,3D-,and 4D-CNNs to MSI,following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)*** review addresses three Research Questions(RQ):RQ1:“In which application domains different CNN models have been successfully applied for processing MSI data?”,RQ2:“What are the commonly utilized MSI datasets for training CNN models in the context of processing multispectral satellite imagery?”,and RQ3:“How does the degree of CNN complexity impact the performance of classification,regression or segmentation tasks for multispectral satellite imagery?”.Publications are selected from three databases,Web of science,IEEE Xplore,and *** on the obtained results,the main conclusions are:(1)The majority of studies are applied in the field of agriculture and are using Sentinel-2 satellite data;(2)Publications implementing 1D-,2D-,and 3D-CNNs mostly utilize *** 4D-CNN,there are limited number of studies,and all of them use segmentation;(3)This study shows that 2D-CNNs prevail in all application domains,but 3D-CNNs prove to be better for spatio-temporal pattern recognition,more specifically in agricultural and environmental monitoring applications.1D-CNNs are less common compared to 2D-CNNs and 3D-CNNs,but they show good performance in spectral analysis tasks.4D-CNNs are more complex and still underutilized,but they have potential for complex data *** details about metrics according to each CNN are provided in the text and supplementary files,offering a comprehensive overview of the evaluation metrics for each type of machine learning technique

关键词： remote sensing MultiSpectral Images(MSI) satellite images Convolutional Neural Networks(CNN) machine learning classification regression segmentation

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Hybrid heuristic driving training-rider optimization algorithm for QoS-aware multicast communication system in MANET

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Australian Journal of Electrical and electronics engineering 2024年第1期21卷 59-78页

作者： Sarangi, Sanjaya Kumar Nanda, Arabinda Lenka, Rasmita Behera, Prafulla Kumar Department of Computer Science and Applications Utkal University Bhubaneswar India Department of Computer Science & Engineering Krupajal Engineering College Bhubaneswar India School of electronics engineering KIIT University Bhubaneswar India

Mobile Ad hoc Network (MANET) is broadly applicable in various sectors within a short amount of time, which is connected to mobile developments. However, the communication in the MANET faces several issues like synchronization, managing power, resources, and routing management. Often, finding the optimal path in the MANET becomes complicated in the existing approaches. In this work, the latest QoS-assisted multiple-cast communication system in MANET is presented using hybridized approaches for better communication over the MANET domain. Especially, an Exploitation-assisted Driving Training-Rider Optimization (EDT-RO) model is suggested for the multicast routing in MANET. The developed EDT-RO algorithm optimizes the paths for communication in MANET, where the network considers more than two receivers. The proposed EDT-RO-based multicast routing algorithm aims to attain the objective function considering the data packet delivery rate, the throughput, the end-to-end latency, and the Quality of Service (QoS). Throughout the result analysis, the offered model shows better performance in terms of 0.2%, 0.3%, 0.5%, and 1.5% than DHOA, Jaya, DTBO, and ROA regarding mean. Simulations are conducted by comparing with other optimization algorithms used for QoS-assisted multiple casts routing to showcase and attain better routing performance of the developed models. ©, Engineers Australia.

关键词： Optimization

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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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science China(Information sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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CEEO: an innovative Coulomb-enhanced Equilibrium Optimizer for task scheduling of cloud services in IoT environments

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Computing 2025年第6期107卷 1-24页

作者： Ebrahimi Mood, Sepehr Souri, Alireza İnanç, Nihat Li, Kuan-Ching Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of computer science and information engineering Providence University Taichung43301 Taiwan

In this paper, we address the multi-objective task scheduling problem in cloud computing environments for IoT-generated tasks, focusing on minimizing makespan, load imbalance, energy consumption, and CO2 emissions. We propose a novel load imbalance metric tailored for this specific context and introduce an enhanced Equilibrium Optimizer (EO) algorithm, termed CEEO. The CEEO integrates a Coulomb operator inspired by Coulomb’s law to improve exploration and escape local optima in multimodal optimization problems. The algorithm’s performance is evaluated using standard benchmark functions from CEC2017, where it demonstrates significant improvements over existing optimization algorithms in terms of convergence speed and solution quality. In addition, the CEEO is applied to a simulated cloud computing task scheduling environment, considering varying numbers of tasks and virtual machines. The results reveal that CEEO consistently outperforms other scheduling algorithms, reducing makespan and improving load balancing, energy consumption, and CO2 emissions. Statistical analysis using the Friedman test confirms the statistical significance of these improvements. This research provides an efficient and robust solution for multi-objective task scheduling in cloud computing, offering substantial improvements in operational costs, execution time, and overall service quality. © The Author(s) 2025.

关键词： Scheduling algorithms

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A Survey on Logic-Locking Characteristics and Attacks

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 1073-1087页

作者： Subbiah, Karthik Chinnathevar, Sujatha Electronics and Communication Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India Computer Science and Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India

Integrated circuits (ICs) are ubiquitous and a crucial component of electronic systems, from satellites and military hardware to consumer devices and cell phones. The computing system’s foundation of trust is the IC. Most semiconductor businesses are shifting to fabless manufacturing and outsourcing to foundries worldwide as integrated circuit feature sizes continue to decrease. That puts the design business at risk for several things, such as unauthorized overproduction, resale on the black market, and illegal copying brought on by intellectual property theft. Logic locks offer one solution in which the chip’s actual functioning is "locked" with a key only known to the inventor. The design will only function as intended if specific keys are pressed. Unlocking overproduced chips should be impossible for supply chain attackers since designers open them after manufacturing them. Logical locks against the risk of overproduction are the main subject of this research. We examine current locking systems, define features based on crucial processing, and identify commonalities and discrepancies between the employed attacker models. This research paper is intended to assist scientists, IP distributors, and SoC developers in rapidly investigating and comprehending the most recent technologies that should be considered and analyzed for additional research on logic-locking techniques. © The Institution of Engineers (India) 2024.

关键词： Locks (fasteners)

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： COVID-19

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Enhancing epigraphy: a deep learning approach to recognize and analyze Tamil ancient inscriptions

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Neural Computing and Applications 2024年第31期36卷 19839-19861页

作者： Bhuvaneswari, S. Kathiravan, K. Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India

This research paper presents a novel algorithmic approach for character recognition and contextual analysis of temple inscriptions, specifically focusing on Tamil ancient script. The methodology combines advanced preprocessing techniques, deep learning models, and contextual analysis to address the challenges posed by noisy images, script variations, and historical context understanding. We compiled a dataset of 100 high-resolution images of temple inscriptions from various regions and periods. The preprocessing phase involves Noise Reduction, Contrast Enhancement, Orientation Correction, and Adaptive Binarization algorithms to enhance the quality of the inscription images. The character recognition stage employs Convolutional Neural Networks with Transfer Learning, further enhanced by the Multi-head Attention mechanism in Vision Transformers (ViT). The character segmentation algorithm used was the Stroke Width Transform. Transfer Learning was incorporated to adapt the pre-trained ViT model to our specific task. This approach significantly improves the model’s ability to recognize characters from diverse scripts and languages. The results demonstrate the effectiveness of the proposed methodology. The character recognition accuracy metrics include a precision of 97.25%, a recall of 95.05%, and an F1-score of 95.17%. Additionally, the model achieved a recognition rate of 98.92% for key terms related to historical events, deities, and rulers. It also demonstrated a 94% recognition rate for context-specific phrases and a 95% recognition rate for historical dates. Contextual analysis results indicate that the model successfully identifies specific terms, phrases, and historical references, contributing to a deeper understanding of the inscriptions. The model's ability to recognize characters from multiple scripts underscores its adaptability to diverse inscriptions. In conclusion, this research provides a comprehensive and efficient solution for character recognition and

关键词： Image segmentation

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A Highly-Parallel and Scalable Hardware Accelerator for the NTest Othello Game Engine

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IEEE Transactions on Parallel and Distributed Systems 2025年第8期36卷 1620-1633页

作者： Popa, Stefan Petric, Vlad Ivanovici, Mihai Transilvania University of Brasov Electronics and Computers Department Faculty of Electrical Engineering and Computer Science Romania

Othello is a two-player combinatorial game with 1E+28 legal positions and 1E+58 game tree complexity. We propose a HIghly PArallel, Scalable and configurable hardware accelerator for evaluating the middle and endgame Othello positions. We base HIPAS on NTest - a leading software Othello engine that uses the minimax algorithm with a quality pattern-based evaluation function, alpha-beta pruning, and heuristic mobility sorting. We describe its architecture and Field Programmable Gate Array implementation, measure its performance, and compare it with prior solutions. HIPAS achieves the highest quality evaluation, the highest performance with speed-ups up to several hundreds, and the best energy efficiency. The main novelty is the algorithm implementation as a circular pipeline and a Finite State Machine with pseudo-parallel processing. Although Othello was recently claimed to be weakly solved, the game remains unsolved in a stronger sense. A weak solution only shows how to force a draw. It does not guarantee a win if the opponent makes a mistake. HIPAS can validate the weak solution faster and more efficiently. A multi-threaded NTest software component evaluating the beginning and part of the middle game, combined with one or more instances of HIPAS for handling the remainder can provide a stronger solution. © 1990-2012 IEEE.

关键词： Middleware

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AI-Enhanced Lung Cancer Prediction: A Hybrid Model's Precision Triumph

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IEEE Journal of Biomedical and Health Informatics 2024年 PP卷 1-14页

作者： Kesiku, Cyrille Yetuyetu Garcia-Zapirain, Begonya University of Deusto Department of Computer Science Electronics and TelecommunicationsEngineering Faculty Bilbao Spain

Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5 %, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine. Authors

关键词： Lung cancer

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