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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Shan, Chan Liu, Ying Chen, Huan Huo, Dong-Yang Xu, Tian-Rong Jimei University College of Ocean Information Engineering Xiamen China Xiamen Institute of Software Technology Department of Software Technology Xiamen China Minjiang University School of Journalism and Communication Fuzhou China Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia Universiti Malaya Planning Faculty of Built Environment Department of Urban and Regional Kuala Lumpur Malaysia

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

In this paper, a new n-MOSFET layout with multi-finger Z gate is proposed to reduce the total ionizing dose (TID) effect. In addition to the proposed layout, multi-finger single gate layout is also simulated using Sentaurus TCAD in 3D. Firstly, drain current - gate voltage curves of both layouts are compared in order to verify the radiation hardening ability of the proposed layout. Secondly, we compare TID responses by extracting threshold voltages and leakage currents from both layouts. Thirdly, the finger number is investigated for both layouts because it is an important factor for multi-finger layout. The results have indicated that the finger number significantly affects the multi-finger single gate layout, whereas the proposed multi-finger Z gate layout is relatively little affected by the finger number in radiation environment. © 2022 IEEE.

关键词： Drain current

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Machine learning-based prediction model for sports injury risk in biomechanics: A case study of joint injuries in basketball at a university in Xi’an

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MCB Molecular and Cellular Biomechanics 2024年第4期21卷

作者： Min, Liang Li, Nan Bi, Peng Gao, Bo School of Computer Science Xi’an Jiaotong University City College Xi’an710018 China The Youth Innovation Team of Shaanxi Universities "Multi-modal Data Mining and Fusion" Xi’an710018 China Engineering Research Center of IoT Intelligent Sensing Interactive Platform Universities of Shaanxi Province Xi’an710018 China School of Electrical and Information Engineering Xi’an Jiaotong University City College Xi’an710018 China DHC Software Co. Ltd. Xi’an710000 China

Basketball players are prone to joint injuries due to the sport’s high intensity and physical demands. Early prediction of injury risk is crucial for implementing effective prevention strategies. Incorporating biomechanics, this study focuses on basketball players at a university in Xi’an, China, aiming to develop a machine learning-based model to predict joint injury risk using easily collectable data such as training load, fatigue levels, and previous injury history. Considering regional differences, we observed that local and northern Chinese students are generally taller, while students from southern China are typically shorter. This anthropometric variation was included in our sampling and analysis. Utilizing data from 100 basketball players, the Random Forest algorithm achieved the best predictive performance with an accuracy of 85%. Key risk factors identified include high training load, elevated subjective fatigue scores, and a history of previous joint injuries. Additionally, biomechanical data were integrated to elucidate the underlying mechanisms of joint injuries, and the cellular responses to injury were explored. The results demonstrate that even with limited data types, machine learning methods can effectively predict joint injury risk among basketball players, providing a valuable tool for injury prevention. © 2024 by author(s).

关键词： Prediction models

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Multi-resolution transformer-based point cloud completion network for intelligent manufacture

Multi-resolution transformer-based point cloud completion ne...

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Chinese Automation Congress (CAC)

作者： Yongcong Zhang Fang Fang Yahui Gan Bo Zhou College of Software Engineering Southeast University Suzhou China Key Faboratory of Measurement and Control of CSE (School of Automation Southeast University) Ministry of Education Nanjing China

Due to the limitations of sensor technology and the material (transparency) of the scanned object, the point clouds collected by 3D scanning sensor such as 3D vision sensors will have problems such as missing point clouds and uneven distribution of data points .However, the point cloud complement technique of the past frequently failed to successfully generate the local characteristics of the point cloud due to the point cloud's general structure being refactored. Therefore ,a transformer-based multi-resolution point cloud completion network is suggested. The entire network structure adopts an encoder-decoder structure. Through multi-resolution domain feature embedding, different range of neighborhood features are fused; Then, through the transformer coding structure of adaptive spatial coding, the global features are obtained. Query object adds spatial location information to enhance the absolute spatial location features; Finally, a complete reconstructed point cloud is generated through the transformer decoding structure The experimental results show that compared with PCT, the experimental effect of this paper is improved, and evidence is provided to support the revised algorithm's efficacy.

关键词： Point cloud compression Three-dimensional displays Manufacturing processes Vision sensors Transformers Robot sensing systems Solids

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Model Reference Adaptive Stabilizing control for Leader-following Consensus

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IEEE Transactions on Automatic control 2025年

作者： Yue, Dongdong Shi, Jiantao Shi, Ling Frasca, Paolo Baldi, Simone Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay China Univ. Grenoble Alpes Cnrs Inria Grenoble Inp GIPSALab Grenoble38000 France Southeast University School of Mathematics Nanjing210096 China

Complex networks, neuroscience, and other applications have shown examples of multi-agent adaptive systems that must follow (over possibly short times) reference dynamics that are neither Hurwitz nor neutrally stable. However, such leader-following behavior would be impossible with existing adaptive consensus methods, e.g., based on model reference adaptive control (MRAC), since the stability of the reference dynamics is required. To fill this gap, we propose a novel model reference adaptive stabilizing control (MRASC) framework for leader-following consensus of multi-agent systems with unknown and heterogeneous dynamics. Differently from several approaches in the leader-following consensus literature, the proposed framework is free of any extra distributed observer layer for the leader's signal, as the reconstruction of such signals is intrinsic in the adaptive laws. Besides, the framework does not require Hurwitz or neutral stability of the leader and generalizes existing acyclic requirements on the communication graph among the follower. Starting from any weakly connected communication digraph, the proposed method allows to derive a lower bound, useful from the network design point of view, for the minimum number of followers that should be pinned by the leader. © 2025 IEEE.

关键词： Multi agent systems

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FPGA Implementation of SIMON-128 Cryptographic Algorithm Using Artix-7

arXiv

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arXiv 2023年

作者： Ghayoula, Ridha Fattahi, Jaouhar Smida, Amor Gmati, Issam El Pricop, Emil Ziadia, Marwa Unit of Research in High Frequency Electronic Circuits and Systems University of Tunis El Manar Tunis2092 Tunisia Department of Electrical and Computer Engineering Laval University Quebec Canada Department of Computer Science and Software Engineering Laval University Quebec Canada Department of Medical Equipment Technology College of Applied Medical Sciences Majmaah University Majmaah11952 Saudi Arabia College of Engineering Al Gunfudha Umm Al Qura University Saudi Arabia Automatic Control Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania

FPGA is a hardware architecture based on a matrix of programmable and configurable logic circuits thanks to which a large number of functionalities inside the device can be modified using a hardware description language. These functionalities must often be secured especially when the context is sensitive (military, banking, medical, legal, etc.). In this paper, we put forward an efficient implementation of SIMON’s block cipher algorithm using Xilinx Vivado 2018.2. The proposed design is analyzed through simulation on Xilinx Artix-7. A prototype of our design is implemented using the xc7a35tcsg324-1 FPGA chip. Performance and results are discussed. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Scalable variational Bayesian kernel selection for sparse gaussian process regression 34

Scalable variational Bayesian kernel selection for sparse ga...

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34th AAAI Conference on Artificial Intelligence, AAAI 2020

作者： Teng, Tong Chen, Jie Zhang, Yehong Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781577358350

This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. In contrast to existing GP kernel selection algorithms that aim to select only one kernel with the highest model evidence, our VBKS algorithm considers the kernel as a random variable and learns its belief from data such that the uncertainty of the kernel can be interpreted and exploited to avoid overconfident GP predictions. To achieve this, we represent the probabilistic kernel as an additional variational variable in a variational inference (VI) framework for SGPR models where its posterior belief is learned together with that of the other variational variables (i.e., inducing variables and kernel hyperparameters). In particular, we transform the discrete kernel belief into a continuous parametric distribution via reparameterization in order to apply VI. Though it is computationally challenging to jointly optimize a large number of hyperparameters due to many kernels being evaluated simultaneously by our VBKS algorithm, we show that the variational lower bound of the log-marginal likelihood can be decomposed into an additive form such that each additive term depends only on a disjoint subset of the variational variables and can thus be optimized independently. Stochastic optimization is then used to maximize the variational lower bound by iteratively improving the variational approximation of the exact posterior belief via stochastic gradient ascent, which incurs constant time per iteration and hence scales to big data. We empirically evaluate the performance of our VBKS algorithm on synthetic and massive real-world datasets. © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Additives

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A Low-Cost Smart Office Design Framework Using Arduino

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Advances in Internet of Things 2023年第3期13卷 83-108页

作者： Ssemwogerere Rajab Nabagesera Veronica Kawalya Melese Alemsra Tsehayu Logose Masitula Wamwoyo Faruk Rahman Shiddiqur Ekwaro Dominic Mbalyowere Pasis Maritah Nambobi Mutwalibi Sayo Nakeleh Turay Mugerwa Derrick Kasagga Usama Seguya Asad Department of Computer Science & Information Technology Islamic University in Uganda Kampala Uganda College of Computing & Informatics Technology Makerere University Kampala Uganda School of Information and Software Engineering University of Electronic Science & Technology of China Chengdu China School of Computer Science Neijiang Normal University Neijiang China School of Computer Science and Engineering University of Electronic Science & Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science & Technology of China Chengdu China

The main motive of our research work is security enhancement and light energy conservation. This paper describes a study investigating the potential of a controlled office solution by integrating the Internet of Things (IoT) with wireless sensor networks (WSNs). A prototype of a smart office is developed using a global system of mobile Bluetooth and Radio Frequency Identification (RFID) technology. The user can turn on and off the fan remotely at any time. This prototype focuses on security and provides human-friendly assistance when in or out of the Office by integrating a mobile application platform. The innovative automated smart Office is designed with intelligent Security doors, lights, alarms, temperature humidifiers, and bright Liquid Crystal Display (LCD) screens for viewing. Our study has opened up virtual possibilities for producing cheap innovative frameworks in this Generation of IoT and the fifth Generation (5G) technology. Therefore, when implemented, this innovation will ease and improve human quality of life. So, this paper aims to provide a low-cost, effective Internet of the things-based automated smart Office.

关键词： Intelligent Office Automation System Wireless Sensor Networks Internet of Things Ultrasonic Radio Frequency Identification Arduino

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Analyzing facial temporal patterns for face anti-spoofing 9

Analyzing facial temporal patterns for face anti-spoofing

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Xia, Jingtian Song, Siyang Tang, Yan Shen, Linlin College of Computer Science and Software Engineering Shenzhen University China School of Computer Science The University of Nottingham United Kingdom

ISBN: (纸本)9781450387835

Face anti-spoofing is crucial as face recognition systems are widely challenged by the print attack and replay attack. Since facial temporal patterns of these attacks and real face are naturally different, this paper proposes two temporal modelling approaches to face anti-spoofing tasks. Firstly, we propose to analyze the temporal patterns of mid-level facial attributes in spectral domain, aiming to find the unique frequency patterns of real face and each attack, respectively. Then, we propose to directly model dynamics from the given data, by employing the dynamic image algorithm to generate low-level spatiotemporal representations of videos. In particular, we extract deep features from both global and local face parts, i.e. eyes, nose and mouth, and then fuse them for face spoofing detection. Then, a Convolutional Neural Networks (CNN) - Long Short-Term Memory (LSTM) units (CNN-LSTM) architecture is introduced to learn the high-level spatiotemporal features from dynamic facial images. The proposed approaches were evaluated on two benchmark databases. The results suggest the effectiveness of the second approaches, i.e. as low as 1.85% Equal Error Rate (EER) on CASIA-FASD and 0.00% Average Classification Error Rate (ACER) on REPLAY-ATTACK have been achieved. © 2020 ACM.

关键词： Long short-term memory

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HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation

HabiCrowd: A High Performance Simulator for Crowd-Aware Visu...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： An Vuong Toan Nguyen Minh Nhat Vu Baoru Huang H.T.T Binh Thieu Vo Anh Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Austria Austrian Institute of Technology (AIT) Austria Imperial College London UK Hanoi University of Science and Technology Vietnam Faculty of Mathematics and Statistics TDTU Vietnam Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Visual navigation, a foundational aspect of Embodied AI (E-AI) and robotics has been extensively studied in the past few years. While many 3D simulators have been introduced for the visual navigation tasks, scarcely works have combined human dynamics, creating the gap between simulation and real-world applications. Furthermore, current 3D simulators incorporating human dynamics have several limitations, particularly in terms of computational efficiency, which is a promise of modern simulators. To overcome these issues, we introduce HabiCrowd, the new standard benchmark for crowd-aware visual navigation that includes a crowd dynamics model with diverse human settings into photorealistic environments. Empirical evaluations demonstrate that our proposed human dynamics model achieves state-of-the-art performance in collision avoidance while exhibiting superior computational efficiency compared to its counterparts. We leverage HabiCrowd to conduct several comprehensive studies on crowd-aware visual navigation tasks and human-robot interactions. The source code and data can be found at https://***/.

关键词： Visualization Three-dimensional displays Navigation Computational modeling Source coding Human-robot interaction Computational efficiency Collision avoidance Standards Intelligent robots

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Unsupervised Community Discovery Algorithm via Reconstructed Graph Neural NEtwork

Unsupervised Community Discovery Algorithm via Reconstructed...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jinghong Wang Hui Wang Jiateng Yang Xizhao Wang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security (Hebei Normal University) Shijiazhuang China College of Artificial Intelligence and Big Data Hebei Polytechnic Institute Shijiazhuang China College of Computer Science and Software Engineering ShenZhen University Shenzhen China

Community discovery can help discover potential community structures in the network, which is a fundamental and important issue in network science. Graph neural network-based algorithms are receiving increasing attention. However, there is relatively little work on community discovery directly using graph neural networks in reconstruction design. This paper presents an Unsupervised Community Discovery Algorithm based on reconstructed graph neural network (UCDA) to resolve the above issues. To be specific, this paper proposes a framework that learns the representation based on network structure and attribute information. By designing input network features, encoders, decoders, this algorithm uses an unsupervised loss function to train neural networks to extract communities in an integrated way. These designs integrate higher-order modularity information with network features and use variational graph auto-encoders to train data end-to-end under the graph neural network framework. Based on a series of experiments with a wide range of datasets and advanced algorithms, UCDA has achieved superior performance and shows strong competitiveness. Finally, the last section reports the results analysis, t-SNE visualization and hyperparameter analysis, which depicts stable performance and powerful network modularity ability.

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