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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Jingyuan Liu Zheng Chang Geyong Min Zhu Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväaskyläa Jyväaskyläa Finland Department of Computer Science University of Exeter Exeter U.K. Electrical and Computer Engineering Department University of Houston Houston USA

ISBN: (纸本)9781665435413

Federated learning (FL) is a type of distributed machine learning in which mobile users can train data locally and send the results to the FL server to update the global model. However, the implementation of FL may be prevented by the self-fish nature of mobile users, as they need to contribute considerable data and computing resources for participating in the FL process. Therefore, it is of importance to design the incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission, we design an incentive scheme to facilitate the FL process by investigating interactions between the multi-access edge computing (MEC) server and mobile users in a MEC-based FL system. By using a two-stage Stackelberg game model, we explore the transmission power allocation of the users and reward policy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results show that our model is effective for different parameter settings and the utility of the MEC server can be increased significantly compared to the baseline.

关键词： Wireless communication Measurement Multi-access edge computing Costs Federated learning Computational modeling Simulation

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Efficient and Lightweight In-memory Computing Architecture for Hardware Security

arXiv

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arXiv 2022年

作者： Ajmi, Hala Zayer, Fakhreddine Fredj, Amira Hadj Hamdi, Belgacem Mohammad, Baker Werghi, Naoufel Dias, Jorge Electronic and Microelectronic Laboratory Faculty of science University of Monastir Tunisia The Department of Electrical and Computer Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

The paper proposes in-memory computing (IMC) solution for the design and implementation of the Advanced Encryption Standard (AES) based cryptographic algorithm. This research aims at increasing the cyber security of autonomous driverless cars or robotic autonomous vehicles. The memristor (MR) designs are proposed in order to emulate the AES algorithm phases for efficient in-memory processing. The main features of this work are the following: a memristor 4bit state element is developed and used for implementing different arithmetic operations for AES hardware prototype;A pipeline AES design for massive parallelism and compatibility targeting MR integration;An FPGA implementation of AES-IMC based architecture with MR emulator. The AES-IMC outperforms existing architectures in both higher throughput, and energy efficiency. Compared with the conventional AES hardware, AES-IMC shows ~30% power enhancement with comparable throughput. As for state-of-the-art AES based NVM engines, AES-IMC has comparable power dissipation, and ~62% increased throughput. By enabling the cost-effective real-time deployment of the AES, the IMC architecture will prevent unintended accidents with unmanned devices caused by malicious attacks, including hijacking and unauthorized robot control. © 2022, CC BY.

关键词： Data privacy

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuqi Yang Lijuan Zhang Qing Lan Qiujie Ma Huan Huang Jiaqing Miao Shiyu Mou Cheng Luo Dezhong Yao Ying Tan The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Chengdu China The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu China Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse.

关键词： Autism Visualization Sparse approximation Mental disorders Noise Predictive models Multilayer perceptrons Brain modeling Spatiotemporal phenomena Sparse matrices

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Intelligent Strategic Decision-Making for Optimized Project Management

Intelligent Strategic Decision-Making for Optimized Project ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Tariq Samarah Muder Almiani Ala Mughaid Shadi AlZu'bi Amer Al-Rahayfeh Electronic Marketing and Social Media Zarqa University Zarqa Jordan Department of MIS GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University of Science & Technology Kuwait Department of Computer Science Alzaytoonah University of Jordan Amman Jordan Faculty of information technology The World Islamic Sciences and Education University Amman Jordan

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This research explores the application of AI-driven strategies in engineering project management, focusing on optimizing decision-making in areas such as risk assessment, resource distribution, and performance tracking. The study reveals that AI-based predictive analytics enhance risk identification accuracy, allowing for proactive measures that reduce project delays. Resource optimization models effectively streamline allocations, minimizing waste and ensuring consistent resource availability. Furthermore, real-time performance monitoring provides project managers with actionable insights, improving responsiveness to emerging challenges and contributing to reliable projects. While these methods demonstrate significant advantages, the study identifies challenges related to technical expertise and data requirements, which may limit AI implementation. The findings underscore AI's role in supporting complex project decisions, suggesting that further research could expand its applicability and establish ethical guidelines for its use in decision-making frameworks.

关键词： Optimization models Decision making Project management Real-time systems Resource management Risk management Reliability Predictive analytics Monitoring Guidelines

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Development of H-Slotted DGS Based Dual Band Antenna Using ANN for 5G Applications

Development of H-Slotted DGS Based Dual Band Antenna Using A...

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15th European Conference on Antennas and Propagation, EuCAP 2021

作者： Nakmouche, Mohammed Farouk Allam, A.M.M.A. Fawzy, Diaa E. Bing Lin, Ding Abo Sree, Mohamed Fathy Izmir University of Economics Faculty of Engineering Izmir Turkey German University in Cairo Department of Communication Engineering Cairo Egypt National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Arab Academy of Science Technology and Maritime Transportation Cairo Egypt

ISBN: (纸本)9788831299022

The proposed work conducts a new approach for modeling a dual band monopole antenna design using DGS assisted by ANN. In the aim of efficient dual band antenna design with gain and optimal impedance matching, the Artificial Neural Networks technique (ANN) is used for the development process. This work presents a modeling for H-slotted Defected Ground Structure (DGS) based dual band antenna using ANN for 5G Sub-6 GHz applications. The designed antenna operates at 3.76 GHz and 6.1 GHz. The antenna gain is 2.18 dB and 2.75 dB at both frequencies, respectively. Firstly, a simulation is performed using CST EM simulator, then the predicted results in term of return losses and frequencies are fed into the ANN model. Secondly using a hybrid algorithm based on both feed-forward back-propagation and Levenberg-Marquart (LM) learning algorithm, the optimal position of the H-Slotted DGS in terms of 5G Sub-6 GHz band is extracted. Finally, the experimental validation is conducted and compared with the simulation results, a good agreement is obtained. © 2021 EurAAP.

关键词： Finite element method

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Citizen science "Thundercloud Project" – multi-point radiation measurements of gamma-ray glows from accelerated electrons in thunderstorms 38

Citizen science "Thundercloud Project" – multi-point radiat...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Enoto, Teruaki Tsurumi, Miwa Ikkatai, Yuko Wu, Ting Wang, Daohong Shinoda, Taro Nakazawa, Kazuhiro Tsuji, Naoki Diniz, Gabriel Sousa Kataoka, Jun Kamogawa, Masashi Takagaki, Toru Miyake, Shoko Morimoto, Takeshi Nakamura, Yoshitaka Tsuchiya, Harufumi Department of Physics Graduate School of Science Kyoto University Kyoto Japan Extreme Natural Phenomena RIKEN Hakubi Research Team Cluster of Pioneering Research RIKEN Saitama Japan Graduate School of Science and Engineering Aoyama Gakuin University Kanagawa Japan Institute of Human and Social Sciences Kanazawa University Ishikawa Japan Department of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute for Space-Earth Environmental Research Nagoya University Aichi Japan Kobayashi-Maskawa Institute for the Origin of Particles and the Universe Nagoya University Aichi Japan Faculty of Science and Engineering Waseda University Tokyo Japan Global Center for Asian and Regional Research University of Shizuoka Shizuoka Japan TAC Inc. Kyoto Japan Ibaraki College Ibaraki Japan Faculty of Science and Engineering Kindai University Osaka Japan Department of Electrical Engineering Kobe City College of Technology Hyogo Japan Nuclear Science and Engineering Center Japan Atomic Energy Agency Ibaraki Japan

It has been a long-standing question whether cosmic rays promote the triggering of lightning and how cosmic-ray air showers interact with the electric field of thunderclouds. The strong electric field in the thunderclouds accelerates electrons to the relativistic regime, of which seed electrons are thought to be supplied from cosmic-ray air shower. Such relativistic electrons emit bremsstrahlung photons in gamma rays, which have been detected by on-ground measurements called gamma-ray glows. Low-altitude winter thunderstorm in Japan provides an ideal environment for observations of gamma-ray glows. We newly launched the citizen science "Thundercloud Project" to construct a multi-point radiation mapping campaign for glows from winter thunderstorms around Kanazawa, Japan. We developed a new handy radiation monitor and shipped about 60 detectors to citizen supporters. The radiation data are stored in the microSD cards in the detectors, and a part of them is remotely sent to the web server so that researchers and supporters can watch the real-time data. In addition, an automatic alert is sent to public Twitter from the server when a glow is detected. The purpose of this project is (1) to characterize the methodological condition of electron acceleration, (2) to investigate whether accelerated relativistic electrons can enhance the chance of the initiation of lightning discharges, and (3) to find a new way of the citizen science to join in the cutting edge science in the physics field. Here we report this growing citizen science project and examples of successful gamma-ray glow observations. Our first scientific result from this citizen science project was published in Tsurumi et al., GRL 2023 [1], where we reported lightning discharges started in or near the electron acceleration site of a gamma-ray glow. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Lightning flash started near the electron acceleration region in the thundercloud 38

Lightning flash started near the electron acceleration regio...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Tsurumi, Miwa Enoto, Teruaki Ikkatai, Yuko Wu, Ting Wang, Daohong Shinoda, Taro Nakazawa, Kazuhiro Tsuji, Noki Gabriel, Diniz Kaoataoka, Jun Iwashita, Ryoji Koshikawa, Nanase Kamogawa, Masashi Takagaki, Toru Miyake, Shoko Tomioka, Dai Morimoto, Takeshi Nakamura, Yoshitaka Tsuchiya, Harufumi Department of Physics Graduate School of Science Kyoto University Kyoto Japan Graduate School of Science and Engineering Aoyama Gakuin University Kanagawa Japan Extreme Natural Phenomena RIKEN Hakubi Research Team Cluster of Pioneering Research RIKEN Saitama Japan Institute of Human and Social Sciences Kanazawa University Ishikawa Japan Department of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute for Space-Earth Environmental Research Nagoya University Aichi Japan Kobayashi-Maskawa Institute for the Origin of Particles and the Universe Nagoya University Aichi Japan Faculty of Science and Engineering Waseda University Tokyo Japan Global Center for Asian and Regional Research University of Shizuoka Shizuoka Japan TAC Inc. Kyoto Japan Ibaraki College Ibaraki Japan Faculty of Science and Engineering Kindai University Osaka Japan Department of Electrical Engineering Kobe City College of Technology Hyogo Japan Nuclear Science and Engineering Center Japan Atomic Energy Agency Ibaraki Japan

The interaction between cosmic rays and thunderclouds has attracted much attention in recent years. Cosmic rays have been considered as one possible cause of the trigger of lightning flashes: e.g., a possible correlation between the number of observed lightning and cosmic ray flux (e.g.,Themis G. Chronis, journal of Climate,2009). In thunderclouds, electric fields are thought to cause relativistic acceleration of electrons as an interaction with cosmic ray air shower and emit bremsstrahlung photons, known as gamma-ray glows. One unresolved is that such electron acceleration, and thus gamma-ray glows, may be related to initiating lightning flashes. To verify this hypothesis, we conducted winter thundercloud observations using mapping radiation measurements in Japan. At 04:06–04:08 JST on December 30, 2021, five radiation detectors recorded a gamma-ray glow, terminated with the lightning flash recorded by two lightning mapping systems (FALMA and DALMA). Our multi-location observation revealed that the gamma-ray glow region had a horizontal size of about 2 km×2.5 km. The XRAIN radar observation showed that the high reflectivity area, presumably corresponding to the electron acceleration region, reaches more than 2 km above the gamma-ray detected radiation detectors. The initiation of this lightning discharge started from 1.6 km altitude from the ground around the gamma-ray glow region [1]. This result indicates that the lightning flash began in the vicinity of the electric field region and a possible connection between the cosmic ray interaction with the electric fields in thundercloud and the triggering of the lightning flash. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Research on Fine-Grained Modeling and Optimal Control Used for Occupant-Centric Smart Lighting Systems

SSRN

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SSRN 2024年

作者： Ning, Chenguang Shen, Dongdong Dong, Yanzhi Wang, Yingjie Duan, Peiyong School of Information Science and Engineering Shandong Normal University Jinan250014 China School of Physics and Electronic Information Yantai University Yantai264005 China School of Computer and Control Engineering Yantai University Yantai264005 China Faculty of Electronics Electrical and Control Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

In order to reduce energy consumption of the lighting system and improve the visual comfort of occupants in the illuminated area, it is essential to effectively and precisely control the illumination of the luminaires. The smart lighting systems can utilize the occupant's positional data to adjust lamps switching, leading to a significant decrease in energy consumption and an enhancement in illumination comfort. However, there are still several challenges associated with achieving precise and non-invasive multi-person positioning for smart lighting systems, and the implementation of the Occupant-Centric Smart Lighting System (OCSLS) is still pending. In this paper, using 3D reconstruction of human keypoints extracted from multi-view video, the robust positioning algorithm for multi-person is proposed. The algorithm achieves a positioning error of less than 0.3 meters by combining the constraints of epipolar geometry and the appearance characteristics of occupants for multi-view matching. Subsequently, a novel fine-grained model of the OCSLS is proposed through the integration of the Illumination Demand Matrix (IDM), which is derived from the distribution of occupant position, and the Illumination Supply Matrix Library (ISML), which is based on the characteristics of lighting area luminaires. Using the OCSLS model proposed in this study, the Occupant-Centric Control (OCC) strategy is constructed through the optimization algorithm based on the autoencoder and the K-Nearest Neighbor (KNN) algorithm. A verification platform has been created, and the experimental results indicate a decrease in the energy usage of the lighting system by over 40%. © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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A Framework of FAS-RIS Systems: Performance Analysis and Throughput Optimization

arXiv

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arXiv 2024年

作者： Yao, Junteng Lai, Xiazhi Zhi, Kangda Wu, Tuo Jin, Ming Pan, Cunhua Elkashlan, Maged Yuen, Chau Wong, Kai-Kit The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Computer Science Guangdong University of Education Guangdong Guangzhou China The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory the School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems. © 2024, CC BY.

关键词： Antennas

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Palm Bunch Grading Technique Using a Multi-Input and Multi-Label Deep Neural Network

SSRN

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SSRN 2022年

作者： Pipitsunthonsan, Pronthep Pan, Liangrui Peng, Shaoliang Khaorapapong, Thanate Nakasathien, Sutkhet Channumsin, Sittiporn Chongcheawchamnan, Mitchai Faculty of Engineering Prince of Songkla University 90110 Thailand College of Computer Science and Electronic Engineering Hunan University 410000 China Faculty of Agriculture Kasetsart University 10900 Thailand Geo-Informatics and Space Technology Development Agency Thailand

Oil palm bunches, which are the raw materials for several oil palm products, are traded to a crude palm oil (CPO) factory or a middleman. Since high oil-content palm bunches are desired to produce CPO, the factories and middlemen conventionally hire experts to choose high-grade palm bunches based on bunch colour, shape, and size. The reliability of this conventional method depends on their skill, experience, and working hours. In this paper, we propose a deep learning-based model working with three sensors (RGB camera, infrared sensor, and a load cell) for classifying a palm bunch. The classifying framework of the algorithm is a multi-input and multi-label deep convolutional neural network. Based on 1,585 datasets for 14 varieties of palm bunches collected from four trading sites, the sensors were installed, the algorithm was trained, validated, and tested with 70%, 20% and 10% of the overall datasets, respectively. The cross-entropy loss function was applied to investigate what? during the training process. Based on the results, the proposed model achieves an accuracy of 90.26%, precision of 89.86%, recall of 89.54%, and an F1-score of 89.68%. © 2022, The Authors. All rights reserved.

关键词： Grading

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