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Quantized Average Consensus With a Plateau Escaping Strategy in Undirected Graphs

IEEE Control Systems Letters 2025年 9卷 709-714页

作者： Oliva, Gabriele Fioravanti, Camilla Makridis, Evagoras Charalambous, Themistoklis Department of Engineering University Campus Bio-Medico of Rome Roma00128 Italy University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo02150 Finland

In this letter, the average consensus problem has been considered for undirected networks under finite bit-rate communication. While other algorithms reach approximate average consensus or require global information about the network for reaching the exact average consensus, we propose a fully distributed consensus algorithm that incorporates an adaptive quantization scheme and achieves convergence to the exact average while only requiring knowledge of an upper bound of the network diameter. Using Lyapunov stability analysis, we characterize the convergence properties of the resulting nonlinear quantized system. Moreover, we provide a fully distributed strategy to escape plateaux, i.e., situations where the Lyapunov function stops descending. Simulation results justify the performance of our proposed algorithm. © 2017 IEEE.

关键词： Lyapunov functions

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Explainable Emotion Recognition in Social Networks with Transformers 24

Explainable Emotion Recognition in Social Networks with Tran...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Perikos, Isidoros Pylarinou, Chara University of Patras Computer Technology Institute and Press 'Diophantus' Computer Engineering and Informatics Department Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350376647

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences. © 2024 IEEE.

关键词： Distribution transformers

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Method of Fundamental Solutions for Doubly Periodic Potential Problems 10th

Method of Fundamental Solutions for Doubly Periodic Potentia...

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10th International Conference on Mathematics and Computing, ICMC 2024

作者： Ogata, Hidenori Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Chofu182-8585 Japan

ISBN: (纸本)9789819720651

In this paper, we propose a method of fundamental solution for two-dimensional doubly periodic problems, especially potential flow problems with a doubly periodic array of obstacles. In the proposed method, we approximate the solution, which involves doubly periodic functions, by a linear combination of the logarithmic potentials consisting of the theta functions. The method inherits the efficiency of the ordinary method of fundamental solutions and gives an approximate solution that has the same periodicity as the one of the exact solution. Numerical examples show the efficiency of the presented method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

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Joint Controller Placement and TDMA Scheduling in Software Defined Wireless Multihop Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2807-2821页

作者： Papageorgiou, Yiannis Karaliopoulos, Merkouris Choumas, Kostas Koutsopoulos, Iordanis Athens University of Economics and Business Department of Informatics Athens10434 Greece Department of Electrical & Computer Engineering University of Thessaly Volos Greece

We study TDMA-scheduled Software Defined Wireless Multihop Networks (SDWMNs), whereby the data traffic and SDN control messages share the same network links and TDMA resources. Since the topology of WMNs dynamically changes, maintaining a responsive SDN plane is essential for meeting data traffic rate requirements. Placing more SDN controllers reduces communication delays at the SDN layer and increases its responsiveness. However, it demands more TDMA resources and reduces the available ones for data traffic. We analyze this trade-off between data traffic performance and SDN layer responsiveness by delving into two distinct resource allocation mechanisms in the WMN, the SDN controller placement and TDMA scheduling. We capture their interaction into an optimization problem formulation, which aims at maximizing the SDN-responsiveness subject to data traffic rate requirements, topology conditions, and the available TDMA resources. We propose a novel heuristic for the hard-to-solve problem that leverages the network state information gathered at the SDN layer. We find that our heuristic can increase the SDN-responsiveness by 44% when varying the rate reserved for rate-elastic data traffic within 40% of what is nominally requested. The heuristic is modular in accommodating different controller placement algorithms and robust to different alternative for the SDN software implementation. © 2004-2012 IEEE.

关键词： Resource allocation

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Simulating inexact DNNs for use in Custom Hardware Architectures

Simulating inexact DNNs for use in Custom Hardware Architect...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Danopoulos, Dimitrios Zervakis, Georgios Soudris, Dimitrios National Technical University of Athens School of Electrical and Computer Engineering Athens Greece University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350318845

In response to the growing complexity of Deep Neural Network (DNN) models, the paradigm of approximate computing has emerged as a compelling approach to strike a balance between computational efficiency and model accuracy. Approximate computing involves intentionally allowing errors using inexact computations, exploiting the inherent resilience of many applications to such inaccuracies. While current state-of-the-art approaches leverage approximate multipliers for approximate DNN accelerators, there has been a notable difficulty in emulating such DNNs on common frameworks such as PyTorch, as they do not inherently support inexact arithmetic. This paper introduces a seamless PyTorch plugin, to evaluate inexact DNN models while having GPU acceleration to enable rapid emulation. Additionally, we support approximate-aware retraining and perform evaluation on several popular convolutional neural networks on Cifar10 and ImageNet datasets. © 2024 IEEE.

关键词： Deep neural networks

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Requirement Classification using Deep Learning and Nature-Inspired Optimization Technique

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 117-146页

作者： Sonawane, Sonal Puthran, Shubha Department of Computer Engineering Mukesh Patel School of Technology Management & Engineering SVKM’s NMIMS Mumbai400056 India

Recent advancements in requirements engineering (RE) methods have increasingly leveraged machine learning (ML) algorithms to address challenging RE issues such as the identification and classification of software requirements in requirement documents. Classifying software requirements accurately and efficiently is crucial for the success of the project. Functional and nonfunctional requirements define the core attributes and constraints of a system. When requirements are documented in natural language, they often introduce uncertainties such as ambiguity, inconsistency, or poor readability making the process challenging. Further, manual extraction of requirements is tideous and error prone as it requires precise interpretation, thereby mitigating the risks of miscommunication and errors in the development *** address these challenges, it is vital to employ Natural Language Processing (NLP)techniques to enhance the clarity of requirements. This paper proposes a method that combines deep learning models with NLP techniques, supplemented by Flower Pollination Optimizer (FPO) algorithm, to automate the classification of requirements. The methodology leverages NLP to extract significant features for training a Convolutional Neural Network (CNN) model. CNN model is enhanced using the FPO algorithm to ensure better convergence. The performance of the proposed CNN-FPO framework is evaluated using an industry SmartNet dataset, with results indicating that the accuracy of the classification can reach up to 98% as compared to other traditional machine learning(ML) *** integration of NLP with an optimized CNN model creates a robust framework for requirement classification, addressing the challenges posed by natural language documentation in requirements engineering. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Development of Shuttle-Tram Tracking System using IoT and Comparing of Map localization using Google APIs and Custom Map 13

Development of Shuttle-Tram Tracking System using IoT and Co...

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13th International Electrical engineering Congress, iEECON 2025

作者： Thothong, Thirasit Anuwongpinit, Thanavit Sarikprueck, Piampoom Ta, Kuson Wilasuwan, Thanapat Ponthong, Suebsakul School of Engineering Department of Computer Engineering Bangkok Thailand School of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

This paper presents the development of an Internet of Things (IoT)-based Transit Tracking System. At the Faculty of engineering, King Mongkut's Institute of Technology Ladkrabang, a public transportation system has been developed to facilitate transit around the campus using shuttle cars. Shuttle stations are placed along the routes, and cars run every 10-15 minutes. However, the problem of users not knowing the precise location and arrival time of each shuttle arose. To solve this, a transit tracking system was developed using IoT technology. IoT devices were installed on each vehicle to monitor their real-time locations. The data is collected via GPS and transmitted over the 4G network to a server, then stored in a database and displayed on a website in real-time using React. To reduce costs, a custom map was developed using the Leaflet library as an alternative to the Google Maps API. Testing within the Faculty of engineering showed that the designed IoT system could transmit data successfully, with a performance of a custom map close to that of the Google Maps API, while also significantly reducing costs. © 2025 IEEE.

关键词： Intermodal transportation

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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Science China(Information Sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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A user-adaptive privacy-preserving authentication of IoMT using zero knowledge proofs with ECC

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Multimedia Tools and Applications 2025年 1-32页

作者： Misra, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Pranveer Singh Institute of Technology Utter Pradesh Kanpur India

The Internet of Things (IoT) has developed into a crucial component for meeting the connection needs of the current smart healthcare systems. The Internet of Medical Things (IoMT) consists of medical devices that are connected to patients in order to sense their medical parameters and exchange that information with healthcare professionals so they can deliver remote healthcare services. The need to protect the patient’s private information from being misused makes IoMT security an urgent issue that merits further study. Elliptic Curve cryptography (ECC), a popular substitute for traditional public key encryption like RSA, has several advantages. ECC is a prime contender for implementation on constrained devices with confined memory and speed, as well as wireless communication protocols that use low power like IoMT environment. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the IoMT that is based on elliptic curve encryption based zero-knowledge proof (ZKP) protocols. The proposed mechanism completes in order of milliseconds up to 300 ms for 160 bits. The research will include the comparison between computational delays in traditional protocols and ZKP-ECC protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP-ECC. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Authentication Protocol

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