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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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Consistent application of capacitive touch infrastructure in wireless reloadable sensor networks

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AIP Conference Proceedings 2023年第1期2523卷

作者： G. Radha Devi R. Uma A. Vishnuprasath D. Brindha 1Department of Computer Science and Engineering Malla Reddy College of Engineering Hyderabad Telangana India 2Departmentof Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India 3Department of Information Technology Sri Ramakrishna Engineering Coimbatore Tamil Nadu India 4Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Wireless technology evolves exponentially, creating revolutionary automotive energy applications and advancements through wireless communication. Wireless Reloadable Sensor Networks (WRSN) typically feature one or more wireless vehicles charging the sensor nodes in most existing systems. However, because of off-road and speed restrictions in cars, certain SNs are not charging in time, impacting network life adversely. These schemes should address the energy problem to a certain degree. We suggest a new WRSN concept with a single wireless charging drone by a restricted distance of flight attached with multiple inductive indicting mats used to load the drone when it cannot hit the following halt. Our concept successfully addresses these charging challenges and overcomes drone energy capacities. Thus, an issue is formulated with wireless loading pad deployment, which is intended to use a minimal number of pads to create a practicable routing route for the drone to meet each Sensor Node (SN) in a defined WRSN. Four potential heuristics are suggested for this problem, three based on graph theory and one based on geometry. In addition, the drone has been designed to attend charging requests with the aid of pads by a new drone programming angered, the shortest multi-hop route algorithm. We look into detailed simulations of the proposed schemes. The findings show the feasibility of the suggested models in terms of wear rate, the scale of the area, and overall flight duration.

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Impact of data augmentation on identifying water bodies in satellite images

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AIP Conference Proceedings 2022年第1期2576卷

作者： Darshana A. Naik Sai Ootej Reddy Bachapally Preetham C. Nayak Rao G. Lakshman Prasanna H.E. Lakshmi 1Department Of Computer Science Engineering Ramaiah Institution Of Technology Bangalore 560054 India.

Recognizing surface water is helpful in an assortment of remote sensing applications, the applications such as assessing the accessibility of water, estimating its adjustment of time, and anticipating dry seasons and floods. However, identifying surface water with old-style techniques is certainly not an essential endeavour. Instead, distant identifying with expansive consideration and distinctive passing noticing is the most intelligent response for surface water checking. To Identify surface water efficiently, we developed a deep learning model that can process full Landsat Image inside a single shot without separating the contribution to tiles and increasing the model accuracy by applying data augmentation resulting in the model coming over a modified version of the original data in every case by using the data augmentation to the images and their identical masks.

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Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach

arXiv

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arXiv 2025年

作者： He, Changpeng Lu, Yang Chen, Wei Ai, Bo Wong, Kai-Kit Niyato, Dusit State Key Laboratory of Advanced Rail Autonomous Operation China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an "unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into a unsupervised loss function to train the two-stage GNN. The numerical results demonstrates that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately. © 2025, CC BY.

关键词： Graph neural networks

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Framework, Standards, Applications and Best practices of Responsible AI: A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gadekallu, Thippa Reddy Dev, Kapal Khowaja, Sunder Ali Wang, Weizheng Feng, Hailin Fang, Kai Pandya, Sharnil Wang, Wei College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Department of Computer Science ADAPT Centre Munster Technological University Bishopstown CorkT12 P928 Ireland School of Computing Faculty of Engineering and Computing Dublin City University Glasnevin Campus DublinD09 V209 Ireland Department of Computer Science City University of Hong Kong Hong Kong Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China

Responsible Artificial Intelligence (RAI) is a combination of ethics associated with the usage of artificial intelligence aligned with the common and standard frameworks. This survey paper extensively discusses the global and national standards, applications of RAI, current technology and ongoing projects using RAI, and possible challenges in implementing and designing RAI in the industries and projects based on AI. Currently, ethical standards and implementation of RAI are decoupled which caters each industry to follow their own standards to use AI ethically. Many global firms and government organizations are taking necessary initiatives to design a common and standard framework. Social pressure and unethical way of using AI forces the RAI design rather than implementation. © 2025, CC BY.

关键词： Ethical technology

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Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach

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IEEE Transactions on Vehicular technology 2025年

作者： He, Changpeng Lu, Yang Chen, Wei Ai, Bo Wong, Kai-Kit Niyato, Dusit State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom Yonsei University Yonsei Frontier Laboratory School of Integrated Technology Seoul03722 Korea Republic of Nanyang Technological University College of Computing and Data Science 639798 Singapore

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an "unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into an unsupervised loss function to train the two-stage GNN. The numerical results demonstrate that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately. © 1967-2012 IEEE.

关键词： Graph neural networks

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Robust Security Energy Efficiency Optimization for RIS-Aided Cell-Free Networks with Multiple Eavesdroppers

arXiv

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arXiv 2022年

作者： Hao, Wanming Li, Junjie Sun, Gangcan Huang, Chongwen Zeng, Ming Dobre, Octavia A. Yuen, Chau The School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical Engineering and Computer Engineering Universite Laval QCG1V 0A6 Canada The Faculty of Engineering and Applied Science Memorial University St. JohnsNLA1B 3X5 Canada The Singapore University of Technology and Design 487372 Singapore

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIS)aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min secure EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RISs under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained convex-convex procedure (CCCP) and semi-definite programming (SDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves' links, and investigate the robust SEE beamforming optimization problem by bringing the outage probability constraints. Based on this, we first transform the uncertain outage probability constraints into the certain ones by the bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the S-procedure, successive convex approximation, CCCP and SDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes. © 2022, CC BY.

关键词： Beamforming

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Event-Driven Learning for Spiking Neural Networks

arXiv

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arXiv 2024年

作者： Wei, Wenjie Zhang, Malu Zhang, Jilin Belatreche, Ammar Wu, Jibin Xu, Zijing Qiu, Xuerui Chen, Hong Yang, Yang Li, Haizhou The University of Electronic Science and Technology of China Chengdu610054 China The School of Integrated Circuits Tsinghua University Beijing100084 China The Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom The Department of Computing The Hong Kong Polytechnic University 11 Yuk Choi Road KLN Hung Hom Hong Kong China

Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on neuromorphic hardware. However, it remains an open challenge how to effectively benefit from the sparse event-driven property of SNNs to minimize backpropagation learning costs. In this paper, we conduct a comprehensive examination of the existing event-driven learning algorithms, reveal their limitations, and propose novel solutions to overcome them. Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms. These proposed algorithms leverage precise neuronal spike timing and membrane potential, respectively, for effective learning. The two methods are extensively evaluated on static and neuromorphic datasets to confirm their superior performance. They outperform existing event-driven counterparts by up to 2.51% for STD-ED and 6.79% for MPD-ED on the CIFAR-100 dataset. In addition, we theoretically and experimentally validate the energy efficiency of our methods on neuromorphic hardware. On-chip learning experiments achieved a remarkable 30-fold reduction in energy consumption over time-step-based surrogate gradient methods. The demonstrated efficiency and efficacy of the proposed event-driven learning methods emphasize their potential to significantly advance the fields of neuromorphic computing, offering promising avenues for energy-efficiency applications. © 2024, CC BY.

关键词： Energy efficiency

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Employee performance assessment system based on smart method (case study: Banda Aceh Military Court I-01)

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AIP Conference Proceedings 2023年第1期2484卷

作者： Zalfie Ardian Arifah Che Alhadi Gusti Mas Panji 1Department of Information System Faculty of Computer Science Universitas Ubudiyah Indonesia Indonesia 2Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia 3Department of Informatics Engineering Faculty of Computer Science Universitas Ubudiyah Indonesia Indonesia

Employee performance assessment is the evaluation of the employee's performances recorded at the end of every month. This approach is currently used at the Military Court Office I-01 Banda Aceh. The main aim of this assessment is to measure employee's performances in achieving their targets. The results will be used as the measuring key, where an employee is only eligible to receive the allowance, if the targets are successfully fulfilled. At this moment, the assessment form has to be filled in manually by each employee, and they will be later assessed manually by the assessors. This manual process undeniably has many disadvantages, especially in terms of accuracy and efficiency. To solve this problem, the Employee Performance information System was designed using the SMART (Simple Multi Attribute Technique). The assessments were based on eight indicators, i.e; performance, attendance, the proportion of performance targets, behavioral scores, number of tasks, services, overtime, and monthly rewards earned. The weights for each variable were assessed, and keyed into the system. The values for each variable were compared, and results were generated. In SMART, the weighting is measured as a scale between 0 and 10, thus it is easier to make calculations and comparison. The obtained results (SI-TUKIN) indicated that the system is capable of evaluating and analysing the employee's performances, quickly and effectively.

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A blockchain based virtual machine detection system app market

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AIP Conference Proceedings 2023年第1期2523卷

作者： Karthikeyan Palaniappan V. Anantha Krishna M. Ramasubramanian T. K. S. Rathish Babu 1Department of Centre for System Design Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India 2Department of Computer Science and Engineering Sridevi Women's Engineering College Hyderabad Telangana India

Smartphone malware's rapid development suggests malicious programme developers moving from standard PC systems to smart phone computers. technology experts are now also developing new methods of anti-malware for adequate security. For the identification of smart phone dangerous applications, this study proposes a Blockchain-based malware mitigation framework (B2MDF) (app stores). For the ultimate judgement, the system combines two internal and external proprietary block chains to establish a private dual blockchain and a blockchain alliance. Blocks produced from both static and dynamically driven function removers are stored in the private internal blockchain stores, whilst blocks for current models are detected in the external blockchain stores. B2MDF also exchanges function blocks with third parties, which allows providers of anti-malware to offer more exact *** rating permissions, we employed the idea of information value. Furthermore, because blockchain has great dependability and cheap cost, we developed a consensus-based blockchain framework for generating more concrete predictions. The top 45 permissions, rated by information Value, are used to obtain this accuracy.

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