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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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Joint Beamforming, Power Control, and Interference Coordination: A Reinforcement Learning Approach Replacing Rewards With Examples

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IEEE Access 2023年 11卷 88854-88868页

作者： Sheu, Jeng-Shin Huang, Cheng-Kuei Tsai, Chun-Lung National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin640002 Taiwan

In this paper, we consider the problem of multi-cell interference coordination by joint beamforming and power control. Recent efforts have explored the use of reinforcement learning (RL) methods to tackle this complex optimization problem. Typically, a decentralized multi-agent framework is adopted, wherein each base station operates as an independent RL agent. This distributed coordination has gained attention because designing a reward function that effectively captures the condition of the entire cellular network is challenging for single-agent RL models. However, the distributed approach introduces unique challenges, particularly the non-stationary of the multi-agent environment, as agents continually adapt their policies to interact with one another. The non-stationary environment necessitates information exchange among agents, as local observations of each agent are insufficient to fully capture the true state of the environment. Unfortunately, this information exchange incurs a significant overhead, thereby limiting data transmission capabilities. To address these challenges, we propose a novel single-agent RL approach that eliminates the need for information exchange and the conventional reward function. Instead, we leverage success examples to guide the learning process. Simulation results show that the proposed approach outperforms the existing multi-agent method and theoretical algorithm in terms of sum rates. Additionally, our approach ensures a uniform quality of service while maximizing the overall sum rate. © 2013 IEEE.

关键词： Beamforming

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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AsPeer: Method to Self and Peer Assessment in Large Online Design Classes

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第10期18卷 19-38页

作者： Sankaran, Hariesh K. Shanmugam, Raju Shah, Bhavesh Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Karnavati University Gujrat Gandhinagar India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

Peer and self-assessment open opportunities to scale assessments in online classrooms. This article reports our experiences of using AsPeer-peer assessment system, with two iterations of a university online class. We observed that peer grades highly correlated with staff assigned grades. It was recorded that, the peer grade of all student submissions within range of instructor grade averaged to 21.0% and that within the next 2 ranges was 49.0%. We performed three experiments to improve accuracy of peer grading. First, we observed grading bias and introduced a data driven feedback mechanism to inform peers of it. Students aided by feedback were mindful and performed grading with better accuracy. Second, we observed that the rubric lacked efficiency in translating intent to students. Simplified guiding questions improved accuracy in assessment by 89% students. Third, we encouraged peers to provide personalized qualitative feedback along with rating. We provided them with feedback snippets that addressed common issues. 64% students responded that the snippets helped them to critically look at submissions before rating © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

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CNN-Based Multi-Output and Multi-Task Regression for Supershape Reconstruction from 3D Point Clouds

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

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Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour Mathematical Engineering and Computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of Computer Science Mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

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From Traces to Packets:Realistic Deep Learning Based Multi-Tab Website Fingerprinting Attacks

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Tsinghua science and technology 2025年第2期30卷 830-850页

作者： Haoyu Yin Yingjian Liu Zhongwen Guo Yu Wang College of Computer Science and Technology Ocean University of ChinaQingdao 266100China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122USA

Recent advancements in deep learning(DL)have introduced new security challenges in the form of side-channel attacks.A prime example is the website fingerprinting attack(WFA),which targets anonymity networks like Tor,enabling attackers to unveil users’protected browsing activities from traffic *** state-of-the-art WFAs have achieved remarkable results,they often rely on unrealistic single-website *** this paper,we undertake an exhaustive exploration of multi-tab website fingerprinting attacks(MTWFAs)in more realistic *** delve into MTWFAs and introduce MTWFA-SEG,a task involving the fine-grained packet-level classification within multi-tab Tor *** employing deep learning models,we reveal their potential to threaten user privacy by discerning visited websites and browsing session *** design an improved fully convolutional model for MTWFA-SEG,which are enhanced by both network architecture advances and traffic data *** the evaluations on interlocking browsing datasets,the proposed models achieve remarkable accuracy rates of over 68.6%,71.8%,and 76.1%in closed,imbalanced open,and balanced open-world settings,***,the proposed models exhibit substantial robustness across diverse train-test *** further validate our designs in a coarse-grained task,MTWFA-MultiLabel,where they not only achieve state-of-the-art performance but also demonstrate high robustness in challenging situations.

关键词： website fingerprinting multi-tab website fingerprinting anonymity network deep learning side-channel attack

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Multi-Objective Offloading Optimization in MEC and Vehicular-Fog Systems: A Distributed-TD3 Approach

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IEEE Transactions on Intelligent Transportation Systems 2024年第11期25卷 16897-16909页

作者： Wakgra, Frezer Guteta Kar, Binayak Tadele, Seifu Birhanu Shen, Shan-Hsiang Khan, Asif Uddin The Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan The School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India

The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offloading techniques, we can transfer computationally intensive tasks from resource-constrained devices to those with sufficient capacity, for accelerating tasks and extending device battery life. In this research, we consider offloading within a two-tier MEC and VF architecture, involving offloading from MEC to MEC and from MEC to VF. The primary objective is to minimize the average system cost, considering both latency and energy consumption. To achieve this goal, we formulate a multi-objective optimization problem aimed at minimizing latency and energy while considering given resource constraints. To facilitate decision-making for nearly optimal computational offloading, we design an equivalent reinforcement learning environment that accurately represents the network architecture and the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3) approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate that our strategy achieves faster convergence and higher efficiency compared to other benchmark solutions. © 2024 IEEE.

关键词： Quality of service

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Magnification Independent Breast Cancer Analysis Using Vision Transformer

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Multimedia Tools and Applications 2025年第5期84卷 2029-2057页

作者： Tariq, Shehroz Raza, Rehan Sargano, Allah Bux Habib, Zulfiqar Department of Computer Science COMSATS University Islamabad Lahore Campus Pakistan Department of Computer Science National University of Modern Languages Islamabad Faisalabad Campus Pakistan School of Information Technology Murdoch University Perth Australia

Breast Cancer (BC) remains a critical global health challenge, accounting for millions of lives annually. Most breast cancer detection techniques are designed for binary classification. However, further classifying these types into their subtypes remains challenging due to cell coherence, variations in colour distribution, and intraclass similarity. Additionally, these techniques are limited to specific magnification levels of input images. As a result, the need to develop multiclass classification techniques independent of magnification levels has become increasingly demanding. This research introduces an innovative approach based on an enhanced vision transformer (ViT) for BC analysis that seamlessly handles binary and multiclass classification tasks in a magnification-independent manner. The proposed framework incorporates convolutional layers into the ViT model to simultaneously enhance its capability to capture local and global features. This innovative adjustment also empowers the model to capture complex patterns and subtle features by overcoming the issue of different magnification levels. Extensive experiments are conducted on the benchmark BreaKHis dataset to validate the effectiveness of the proposed framework. It achieves an accuracy of 89.43% for binary classification (benign vs. malignant) and 57.94% and 74.71% for multiclass benign and multiclass malignant subclass classification, respectively. The results confirm that the proposed framework surpasses the performance of the original ViT model and other state-of-the-art techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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QoE-Based Assignment of EVs to Charging Stations in Metropolitan Environments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Aljaidi, Mohammad Aslam, Nauman Kaiwartya, Omprakash Chen, Xiaomin Sadiq, Ali Safaa Kumar, Sushil Alsarhan, Ayoub Department of Computer Science Zarqa University Zarqa Jordan Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne U.K Department of Computer Science Nottingham Trent University U.K Jawaharlal Nehru University New Delhi India Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan

With the recent advances in battery technology enabling fast charging, public Charging Stations (CSs) are becoming a viable choice for Electric Vehicles (EVs). However, the distribution of EVs relies on strategic assignment of EVs to CSs. EVs drivers' Quality of Experience (QoE) is an significant impact factor that should be considered to find the optimal assignment of EVs to CSs. In this context, a novel framework to find the optimal assignment of EVs to CSs has been proposed based on optimization of QoE. Our proposed approach considers the travel time of EVs towards CSs taking into account the distance between EVs and CSs, the impact of congestion level on the roads resulted from the Internal Combustion Engine Vehicles (ICEVs) and EVs, queuing time at the CSs, and the time required to fully charge the EVs battery when connected to any charging slot at a CSs. The adjacency between the different zones in a city environment is also considered in order to minimize the potential number of CSs for each EVs. Specifically, the assignment problem is formulated as Mixed Integer Nonlinear Programming (MINLP), and a heuristic solution is developed using the Genetic Algorithm (GA) technique. The performance evaluation in realistic metropolitan environment attests the benefits of the proposed CSs assignment framework considering range of charging metrics. IEEE

关键词： Genetic algorithms

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