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9th International Electrical engineering Congress, iEECON 2021

作者： Chompoosawat, Worrathep Boonpoonga, Akkarat Akkaraekthalin, Prayoot Bannawat, Lakkhana Lertwiriyaprapa, Titipong Electrical Engineering Department of Electrical and Computer Engineering Faculty of Engineering Bangkok Thailand Rajamangala University of Technology Rattanakosin Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand

ISBN: (纸本)9781728195841

This paper presents a development of the chipless RFID tag for the metal crack detection. A circular microstrip patch antenna (CMPA) resonator was employed as a main part of the chipless RFID tag. Slots was creased within the CMPA resonator in order generate the resonant frequencies used to indicate the bit ID of tags. The performance of the chipless RFID sensor used to detect the metal crack was evaluated via simulations. The simulation results in term of RCS spectra of the tag sensors were shown. The RCS spectra of the tag attached to the metal plate in order to detect the crack was investigated. The results shown the notch of the RCS spectra was shifted when there exists the crack on the metal. © 2021 IEEE.

关键词： Metals

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Deep Reinforcement Learning for Resource Management in Blockchain-Enabled Federated Learning Network

IEEE Networking Letters

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IEEE Networking Letters 2022年第3期4卷 137-141页

作者： Hieu, Nguyen Quang Tran, The Anh Nguyen, Cong Luong Niyato, Dusit Kim, Dong In Elmroth, Erik University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Phenikaa University Faculty of Computer Science Hanoi10000 Viet Nam Sungkyunkwan University Department of Electrical and Computer Engineering Seoul16419 Korea Republic of Umeå University Department of Computing Science Umeå901 87 Sweden

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO. © 2019 IEEE.

关键词： Neural networks

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Comparison of Rating Scale and Pairwise Comparison Methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration

Comparison of Rating Scale and Pairwise Comparison Methods f...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Qiao Wang Ziqi Wang Marc G. Carmichael Dikai Liu Chin-Teng Lin Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity *** variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

关键词： Surveys Accuracy NASA Collaboration Reliability engineering Particle measurements Task analysis

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V-VTEAM: A Compact Behavioral Model for Volatile Memristors

V-VTEAM: A Compact Behavioral Model for Volatile Memristors

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International Flexible Electronics technology Conference (IFETC)

作者： Tanay Patni Rishona Daniels Shahar Kvatinsky The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel

ISBN: (数字)9798331529468

ISBN: (纸本)9798331529475

Volatile memristors have recently gained popularity as promising devices for neuromorphic circuits, capable of mimicking the leaky function of neurons and offering advantages over capacitor-based circuits in terms of power dissipation and area. Additionally, volatile memristors are useful as selector devices and for hardware security circuits such as physical unclonable functions. To facilitate the design and simulation of circuits, a compact behavioral model is essential. This paper proposes V-VTEAM, a compact, simple, general, and flexible behavioral model for volatile memristors, inspired by the VTEAM nonvolatile memristor model and developed in MATLAB 1 1 The MATLAB code can be found in [12].. The validity of the model is demonstrated by fitting it to an ion drift/diffusion-based Ag/SiOx/C/W volatile memristor, achieving a relative root mean error square of 4.5%.

关键词： Adaptation models Neuromorphic engineering Computational modeling Circuits Fitting Memristors Mathematical models Power dissipation Integrated circuit modeling MATLAB

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Inflated 3D ConvNet for detection of sign language 4

Inflated 3D ConvNet for detection of sign language

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4th International Conference on Signal Processing and Machine Learning, CONF-SPML 2024

作者： Chandana, D. Tushara, M. Sri, A. Ramya Sakhamuri, Sridevi Abualigah, Laith Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram522502 India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman Jordan

ISBN: (纸本)9781510674721

Every human has their own kind of disabilities, we all try to live and overcome them in our life. We educate ourselves to overcome them, we invent technology to achieve our goals. Sign Language is a communication path for deaf-mute people through hand gestures and actions. Sign Language helps people who can’t speak sign language to interact with the people who can speak sign language, this deep learning paper aims to help build a communication bridge for this reason. We used Amazon Rekognition service which uses Deep CNN algorithm for the detection of static images of the signs. As most of the signs are for words they are in the form of videos. We used the I3D algorithm for the classification of videos of the signs of words. The PyTorch framework provides support for CuDNN (NVIDIA CUDA Deep Neural Network) which provides fast GPU implementations for the deep neural networks. The Experimental results has shown that the models used has displayed good results in detecting the words. © 2024 SPIE.

关键词： Deep neural networks

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Implementation of Industrial Automation Sustainable Training in Savannakhet University, Laos 8

Implementation of Industrial Automation Sustainable Training...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Georgieva, Tsvetelina Bencheva, Nina Daskalov, Plamen Yahoui, Hamed Khemmarath, Sitha Norasane, Khonesavanh Chanthanouvong, Lamnguen Leaunglith, Youlananh Keovorabouth, Vinai University of Ruse Angel Kanchev Automation and Mechatronics Department Ruse Bulgaria University of Ruse Angel Kanchev Telecommunications Department Ruse Bulgaria Automation &processing Claude Bernard University Lyon 1 Department of Mechanical Engineering Lyon France Savannakhet University Faculty of Engineering Savannakhet Laos Savannakhet University Faculty of Information Technology Technology Computer Network Dept. Savannakhet Laos Savannakhet University Faculty of Information Technology Savannakhet Laos

ISBN: (纸本)9798350396546

The paper presents part of the work fulfilled under the Asean Factori 4.0 Erasmus+ project focused on the implementation of industrial automation in the education in 6 universities from 3 countries in South-East Asia: Thailand, Laos and Cambodia. The implementation of the project tasks, related to increasing the capacity and more precisely to the training of the professors from the Asian Universities is presented. According to the planned activities, professors from the University of Ruse conduct training for teachers from two universities: Chulalongkorn University, Thailand and Savannakhet University, Laos. The phases of training included in the project activities and how many of them have already been implemented are briefly presented. The program and the tasks of the teachers practical training for building the necessary teaching capacity in Savannakhet University and implementation of sustainable training in the field of industrial automation were presented and analyzed. Conclusions and future training of the staff of Asian universities planned under the project are presented in the paper. © 2023 IEEE.

关键词： Personnel training

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Resource-efficient neural networks for embedded systems

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 2506-2556页

作者： Wolfgang Roth Günther Schindler Bernhard Klein Robert Peharz Sebastian Tschiatschek Holger Fröning Franz Pernkopf Zoubin Ghahramani Graz University of Technology Austria Laboratory of Signal Processing and Speech Communication Heidelberg University Germany Institute of Computer Engineering Graz University of Technology Austria Institute of Theoretical Computer Science University of Vienna Austria Faculty of Computer Science University of Cambridge UK

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on resource-efficient inference based on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briey discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark data sets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and prediction quality.

关键词： resource-efficient machine learning inference deep neural networks

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Metric-Based Resolvability of Quartz Structure

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computers, Materials & Continua 2022年第4期71卷 2053-2071页

作者： Muhammad Imran Ali Ahmad Muhammad Azeem Kashif Elahi Department of Mathematical Sciences United Arab Emirates UniversityAl AinUnited Arab Emirates College of Computer Science&Information Technology Jazan University JazanSaudi Arabia Department of Aerospace Engineering Faculty of EngineeringUniversiti Putra MalaysiaMalaysia Deanship of E-learning and Information Technology Jazan UniversityJazanSaudi Arabia

Silica has three major varieties of crystalline. Quartz is the main andabundant ingredient in the crust of our earth. While other varieties are formedby the heating of quartz. Silica quartz is a rich chemical structure containingenormous properties. Any chemical network or structure can be transformedinto a graph, where atoms become vertices and the bonds are converted toedges, between vertices. This makes a complex network easy to visualize towork on it. There are many concepts to work on chemical structures in termsof graph theory but the resolvability parameters of a graph are quite advanceand applicable topic. Resolvability parameters of a graph is a way to getting agraph into unique form, like each vertex or edge has a unique identification bymeans of some selected vertices, which depends on the distance of vertices andits pattern in a particular graph. We have dealt some resolvability parametersof SiO2 quartz. We computed the resolving set for quartz structure and itsvariants, wherein we proved that all the variants of resolvability parameters ofquartz structures are constant and do not depend on the order of the graph.

关键词： Quartz polycyclic aromatic hydrocarbon related structure metric dimension metric or distance-based resolvability parameters

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Real-Time Detection of UAV Small Target's Infrared Imaging via OSTD-YOLOv8

Real-Time Detection of UAV Small Target's Infrared Imaging v...

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2024 IEEE URUCON, URUCON 2024

作者： Dahri, Fida Hussain Laghari, Asif Ali Dahri, Nisar Ahmed Abro, Ghulam E Mustafa Estrela, Vania V. De Oliveira, G.G. Iano, Y. Chuma, Euclides L. Southeast University School of Computer Science and Engineering Nanjing China Shenyang Normal University Software College Shenyang China University Teknologi Malaysia Faculty of Educational Sciences and Technology Johor Bahru Malaysia King Fahd University of Petroleum and Minerals Interdisciplinary Research Centre for Aviation and Space Exploration Dhahran Saudi Arabia Department of Telecommunications Niteroi Brazil School of Electrical and Computer Engineering Campinas Brazil School of Technology Limeira Brazil

ISBN: (纸本)9798350355383

Unmanned aerial vehicles (UAVs) have boosted modern living. Tiny, frail high-density targets, low resolution, complicated backgrounds, noise, and poor real-time exposure performance have augmented due to UAV firms. Realtime recognition in high-altitude (HA) infrared thermal images is intricate. Our fresh OSTD-YOLOv8 is a multi-class target recognition tactic to tackle these issues and spot and classify objects on the HIT-UAV dataset. YOLOv8 is an effective advanced object detection model. Our OSTD-YOLOv8 model detects small objects in HA thermal images with 90% precision, 91.5% recall, 90.5% F1-score, 89.1% AP, and 98% mAP. Our unique approach delivers effective object recognition in difficult thermal imaging conditions. Consequently, this distinctive UAV-focused HA, thermal, target detection scheme can be used. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Performance Analysis of Delay-Phase Precoding for THz–Massive MIMO System 7th

Performance Analysis of Delay-Phase Precoding for THz–Massi...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Sur, Samarendra Nath Tran, Huu Q. Kandar, Debdatta Nandi, Sukumar Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Information Technology North-Eastern Hill University Shillong India Department of Computer Science Indian Institute of Technology Guwahati Guwahati India

ISBN: (纸本)9789819753369

The emergence of Terahertz (THz) communication holds promise for enabling high-speed wireless data transmission, particularly in dense urban environments where spectrum congestion is a pressing concern. It is also regarded as a pivotal technology for the evolution of 6G networks, primarily due to its capability to offer ultra-wide bandwidth. However, harnessing the potential of THz frequencies requires innovative solutions to overcome the inherent challenges posed by channel characteristics and hardware constraints. The phenomenon of beam squint presents a formidable challenge for traditional wideband hybrid beamformers when applied to ultra-massive multiple input multiple output (UM-MIMO) systems operating in the THz band. This challenge is particularly pronounced due to the consequential beam gain loss, hindering the optimal exploitation of the vast available bandwidth in this frequency range. To address this issue, a cutting-edge solution has emerged in the form of delay-phase precoding (DPP), aimed at efficiently mitigating the adverse effects of beam squint. Through extensive simulations and analysis, the effectiveness of the DPP technique in enhancing the overall performance of mMIMO–THz systems is demonstrated, providing valuable insights for future THz communication system designs. In this study, the evaluation focuses on the spectral and energy efficiency of the system as performance metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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