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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Multi-hop emergency message dissemination through optimal cooperative forwarder in grid-based 5G-VANETs

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4461-4476页

作者： Ullah, Sami Abbas, Ghulam Waqas, Muhammad Abbas, Ziaul Haq Halim, Zahid Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Computer Science Shaheed Benazir Bhutto University Sheringal Upper Dir Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan School of Engineering Edith Cowan University PerthWA6027 Australia Department of Computer Engineering College of Information Technology University of Bahrain Zallaq32038 Bahrain Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links’ qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs’ dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs’ dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

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computers, Materials & Continua 2022年第8期72卷 2797-2810页

作者： Waleed Shahjehan Abid Ullah Syed Waqar Shah Imran Khan Nor Samsiah Sani Ki-Il Kim Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Center for Artificial Intelligence Technology Faculty of Information Science&TechnologyUniversiti KebangsaanKajang43000Malaysia Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless *** improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base ***,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power *** order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains *** sparse digital precoding problem is generated by utilizing the analog precoding ***,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)*** results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

关键词： 5G mmwave precoding massive mimo complexity

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The Application of Machine Learning Techniques to Evaluate the Impact of Transmission Line compensation on Fault Classification 4

The Application of Machine Learning Techniques to Evaluate t...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Mazibuko, N. Akindeji, K.T. Moloi, K. Department of Electrical Power Engineering South Africa Department of electronics and computer engineering South Africa Durban University of Technology Smartgrid Research Center Steve Biko Campus Durban South Africa

ISBN: (纸本)9798350353266

The use of machine learning represents a significant advancement in the field of detecting defects in transmission lines. This study looks at how machine learning methods can be used to analyze the impacts of compensation in transmission lines in fault detection and classification. The main focuses is on the misclassification percentage based on different types of faults, including Line to Line (L-L), Line to Ground (L-G), Double Line to Ground (L-L-G), and Three-phase faults (L-L-L). The study shows that introducing series capacitors in the fault loop will affects the impedance seen by the relay, and makes the fault classification task more challenges as the compensation level increases. see for line compensation alters the impedance measured by relays, resulting in a more complex fault classification technique as compensation levels increases. The first model is a two-bus system developed to test the R/X response at different types of faults. There after transmission line compensation is added at 50% and 100% compensation. Data is collected from these models and implemented on machine learning based classifier to assess the fault classification accuracy in the presence of transmission line compensation. It is concluded that transmission line compensation does affects the performance of the protection system and three phase faults are the most affected in terms of accuracy when the capacitive reactance (introduced by compensation) is present in the fault loop. © 2024 IEEE.

关键词： Adversarial machine learning

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Reinforcement Learning-Based Time-Synchronized Optimized Control for Affine Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5216-5231页

作者： Zhang, Yuxiang Liang, Xiaoling Li, Dongyu Ge, Shuzhi Sam Gao, Bingzhao Chen, Hong Lee, Tong Heng National University of Singapore Department of Electrical and Computer Engineering 117576 Singapore Beihang University School of Cyber Science and Technology Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Tongji University Clean Energy Automotive Engineering Center Shanghai201804 China

The approach of (fixed-) time-synchronized control (FTSC) aims at attaining the outcome where all the system state-variables converge to the origin simultaneously/synchronously. This type of outcome can be the highly essential performance desired in various real-world high-precision control applications. Toward this objective, this article proposes and investigates the development of a time-synchronized reinforcement learning algorithm (TSRL) applicable to a particular class of first- and second-order affine nonlinear systems. The approach developed here appropriately incorporates the norm-normalized sign function into the optimal system control design, leveraging on the special properties of this norm-normalized sign function in attaining time-synchronized stability and control. Concurrently, the actor–critic framework in reinforcement learning (RL) is invoked, and the dual quantities of system control and gradient term of the cost function are decomposed with appropriate time-synchronized control items and unknown actor/critic part and to be learned independently. By additionally employing the adaptive dynamic programming technique, the solution of the Hamilton–Jacobi–Bellman equation is iteratively approximated under this actor–critic framework. As an outcome, the proposed TSRL method optimizes the system control while attaining the notable time-synchronized convergence property. The performance and effectiveness of the proposed method are demonstrated to be effectively applicable via detailed numerical studies and on an autonomous vehicle nonlinear system motion control problem. © 2020 IEEE.

关键词： Synchronization

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Hierarchical Transfer Multi-task Learning Approach for Scene Classification 27th

Hierarchical Transfer Multi-task Learning Approach for Scen...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Khoshkangini, Reza Tajgardan, Mohsen Jamali, Mahtab Ljungqvist, Martin Georg Mihailescu, Radu-Casian Davidsson, Paul Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Axis Communications AB Lund Sweden

ISBN: (纸本)9783031781063

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Dynamic control and manipulation of near-fields using direct feedback

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Light(Science & Applications) 2024年第12期13卷 3151-3157页

作者： Jacob Kher-Aldeen Kobi Cohen Stav Lotan Kobi Frischwasser Bergin Gjonaj Shai Tsesses Guy Bartal The Andrew&Erna Viterbi Faculty of Electrical&Computer Engineering Technion—Israel Institute of TechnologyHaifa 3200003Israel Department of Physical Engineering Polytechnic University of Tirana—Faculty of Physical&Math EngineeringTirana 1000Albania Faculty of Medical Sciences Albanian UniversityDurrës StreetTirana 1000Albania Department of Physics MIT—Harvard Center for Ultracold Atoms and Research Laboratory of ElectronicsMassachusetts Institute of TechnologyCambridgeMAUSA

Shaping and controlling electromagnetic fields at the nanoscale is vital for advancing efficient and compact devices used in optical communications,sensing and metrology,as well as for the exploration of fundamental properties of light-matter interaction and optical ***-time feedback for active control over light can provide a significant advantage in these endeavors,compensating for ever-changing experimental conditions and inherent or accumulated device *** nearfield microscopy,being slow in essence,cannot provide such a real-time feedback that was thus far possible only by scattering-based ***,we present active control over nanophotonic near-fields with direct feedback facilitated by real-time near-field *** use far-field wavefront shaping to control nanophotonic patterns in surface waves,demonstrating translation and splitting of near-field focal spots at nanometer-scale precision,active toggling of different near-field angular momenta and correction of patterns damaged by structural defects using feedback enabled by the real-time *** ability to simultaneously shape and observe nanophotonic fields can significantly impact various applications such as nanoscale optical manipulation,optical addressing of integrated quantum emitters and near-field adaptive optics.

关键词： scattering metrology feedback

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Highly efficient ternary organic solar cells with excellent open-circuit voltage and fill factor via precisely tuning molecular stacking and morphology

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Science China Chemistry 2024年第3期67卷 963-972页

作者： Dinghong Sun Zhenyu Chen Jianfeng Zhang Wei Song Jingyu Shi Jintao Zhu Yuanyuan Meng Fei Jin Shuncheng Yang Ziyi Ge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Faculty of Material Science and Chemical Engineering Ningbo UniversityNingbo 315211China Zhejiang Provincial Engineering Research Center of Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology&EngineeringChinese Academy of SciencesNingbo 315201China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049China Department of Chemical and Environmental Engineering University of Nottingham NingboNingbo 315100China

The micro-morphology and molecular stacking play a key role in determining the charge transport process and nonradiative energy loss, thus impacting the performances of organic solar cells(OSCs). To address this issue, a non-fullerene acceptor PhC6-IC-F with alkylbenzene side-chain, possessing optimized molecular stacking, complementary absorption spectra and forming a cascade energy level alignment in the PM6:BTP-eC9 blend, is introduced as guest acceptor to improve efficiency of ternary OSCs. The bulky phenyl in the side-chain can regulate crystallinity and optimizing phase separation between receptors in ternary blend films, resulting in the optimal phase separations in the ternary films. As a result, high efficiencies of 18.33% as photovoltaic layer are obtained for PhC6-IC-F-based ternary devices with excellent fill factor(FF) of 78.92%. Impressively, the ternary system produces a significantly improved open circuit voltage(V_(oc)) of 0.857 V compared with the binary device,contributing to the reduced density of trap states and suppressed non-radiative recombination result in lower energy loss. This work demonstrates an effective approach for adjusting the aggregation, molecular packing and fine phase separation morphology to increase V_(oc) and FF, paving the way toward high-efficiency OSCs.

关键词： alkylbenzene side-chain morphology regulation ternary blends organic solar cells

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Multi-Scale Fusion and Decomposition Network for Single Image Deraining

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IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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