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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Jiang, Yuna Kang, Jiawen Niyato, Dusit Ge, Xiaohu Xiong, Zehui Xu, Minrui Deng, Ruilong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350333336

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect. © 2023 IEEE.

关键词： Edge computing

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PCB Surface Defect Detection Based on TPBAS-YOLOv5 3

PCB Surface Defect Detection Based on TPBAS-YOLOv5

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Luo, Changyu Chen, Shichao Wang, Kunfeng Hu, Xiangyu Wang, Kaiyuan Han, Shuangshuang Sichuan University The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Electrical Engineering Chengdu China Chinese Academy of Sciences Beijing Huairou Academy of Pararllel Sensing The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Beijing University of Chemical Technology School of Information Science and Technology Beijing China Beijing Technology and Business University School of Artificial Intelligence Beijing China Sichuan University School of Electrical Engineering Chengdu China Technology Beijing School of Computer and Communication Engineering and Technology Beijing Beijing China

ISBN: (纸本)9798350318470

The use of machine vision for industrial defect detection in industrial manufacturing can effectively improve the production efficiency and production quality. Due to the complex defect background, similar defects and small defect targets of printed circuit boards(PCB), the methods of using machine vision to detect PCB defects not work well. To solve these problems, this paper proposes an algorithm model TPBAS-YOLOv5, which is based on YOLOv5 and can effectively improve the accuracy and efficiency of PCB defect detection. Firstly, to improve the accuracy of the small defect detection, a transformer module is imported into the model for obtaining the information of global variables. Secondly, the P2 detection layer is added to the neck of the model layer to obtain the more feature information of the detection target, which can also improve the detection of small targets. Thirdly, to solve the problem of missing key information of detection targets , the weighted bi-directional feature pyramid network , namely BiFPN , is imported into the new network architecture, which can realize the multi-level fusion of feature information. Furthermore, an adaptive feature fusion layer ASFF is added to the model to learn the spatial weight of feature map fusion at various scales, which can achieve better trade-off efficiency for detection *** the model fuses different parts, the size of the model has not changed almost. It can still work on the edge in the industrial field. Experimental results show that TPBAS-YOLOv5 can effectively improve recall rates to 96.1%, map@0.5 to 96.6% and map0.5:0.95 to 70.3% on open-source PCB dataset. Compared with YOLOv5, it increased by 0.9%, 1.6%, and 1.6%, respectively, and the FPS reached ***, it can realize the real-time detection of irregular defects and small target defects with high accuracy, and meet the reliability and real-time requirements of PCB defect detection in industrial scenarios. © 2023 IEEE.

关键词： Printed circuit boards

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Roadmap on Neuromorphic Photonics

arXiv

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Elastic Grid Based Analysis of Motion Field for Object-Motion Detection in Airborne Video Flows

Elastic Grid Based Analysis of Motion Field for Object-Motio...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Gergely Balazs Soos Csaba Rekeczky Faculty of Information Technology Pázmány Péter Catholic University Budapest Hungary Analogic and Neural Computing Systems Laboratory Computer and Automation Research Institute Budapest Hungary

The visual navigation system of a UAV is a complex embedded device designed to modify the path of the platform depending on objects or events detected on the ground. In the visual field of the autopilot these events could be formalized as specific space-time signatures. Processing all pixels captured by the on-board camera(s) in real time with high frame rate needs huge computational effort that is often unnecessary. An adequate computational strategy would focus on the interesting locations only as in the visual system of various species. In this article we describe an automatic focusing mechanism relying on optical flow calculation for detecting moving objects on the ground, thus efficiently separating the motion of interest from ego-motion of the platform.

关键词： Motion analysis Motion detection Object detection Event detection Unmanned aerial vehicles Navigation Cameras Image motion analysis Animals Floods

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Advances in Neural Networks – ISNN 2011 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Derong Liu Huaguang Zhang Marios Polycarpou Cesare Alippi Haibo He

ISBN: (数字)9783642211119

ISBN: (纸本)9783642211102

The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.

关键词： Computation by Abstract Devices computer Communication Networks Algorithm Analysis and Problem Complexity Discrete Mathematics in computer Science Artificial Intelligence Pattern Recognition

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Unmanned Aerial Vehicles (UAV) Heading Optimal Tracking Control Using Online Kernel-based HDP Algorithm

Unmanned Aerial Vehicles (UAV) Heading Optimal Tracking Cont...

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International Joint Conference on Neural Networks

作者： Fuxiao Tan Derong Liu Xinping Guan Bin Luo School of Computer and Information Fuyang Teachers College State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science and Technology Anhui University

ISBN: (纸本)9781479914821

UAV can work in places that are dangerous, or not easy to reach for humans. However, due to active control and operating difficulties, it is still a challenge to develop fully autonomous flight in complex environments. This paper applies a novel heuristic dynamic programming for the UAV heading optimal tracking controller design, using kernel-based heuristic dynamic programming (KHDP). Kernel-based HDP is developed by integrating kernel methods and approximately linear dependence (ALD) analysis with the critic learning of HDP algorithm. Compared with conventional HDP where neural networks are widely used and their features were manually designed, the proposed algorithm can obtain better generalization capability and learning efficiency through applying the sparse kernel machine into the critic learning process of HDP algorithm. Simulation and experimental results of UAV heading optimal tracking control problems demonstrate the effectiveness of the proposed kernel-based HDP algorithm.

关键词： Kernel Algorithm design and analysis Heuristic algorithms Equations Vectors Approximation algorithms Mathematical model

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automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

automation has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： automation machinery intelligence

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Synthesis of the Method of Operative Image Analysis based on Metadata and Methods of Searching for Embedded Images

Synthesis of the Method of Operative Image Analysis based on...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Y.A. Shichkina V.T. Tishchenko R.R. Fatkieva Saint Petersburg Electrotechnical University LETI St. Petersburg Russia Laboratory of Computer-Information Systems and Software Engineering Saint Petersburg Institute of Informatics and Automation St. Petersburg Russia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

The purpose of this work is to develop a mobile application for assessing the credibility of a digital image. To achieve this goal, an analysis of image forensics methods was conducted, as well as a comparative analysis of existing image confidence assessment resources. A method of trusting an image in a mobile app has been developed and implemented.

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What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?

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Journal of Integrative Agriculture 2020年第7期19卷 1789-1801页

作者： WANG Xiu-juan KANG Meng-zhen FAN Xing-rong YANG Li-li ZHANG Bao-gui HUANG San-wen Philippe DE REFFYE WANG Fei-yue The State Key Laboratory of Management and Control for Cormplex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China Bejjing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBejing 100190P.R.China Innovation Center for Parallel Agriculture Qingdao Academy of Intelligent IndustriesQingdao 266109P.R.China School of Computer Science and Information Engineering Chongqing Technology and Business UniversityChongqing 400067P.R.China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083P.R.China College of Land Science and Technology China Agricultural UniversityBeijing 100193P.R.China Agricultural Genomes Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhen 518124P.R.China AMAP University MontpellierCIRADCNRSINRAIRDMontpellier 34000France The School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049P.R.China

To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.

关键词： cucumber biomass production functional-structural plant model source-sink ratio fruit-setting PSO heterosis

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Implementation of a parallel SAD based wavefront sensor architecture on FPGA

Implementation of a parallel SAD based wavefront sensor arch...

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European Conference on Circuit Theory and Design, ECCTD

作者： Zoltan Kincses Zoltan Nagy Laszlo Orzo Peter Szolgay Gyorgy Mezo Department of Electrical Engineering and Information Systems University of Pannonia Veszprem Hungary Cellular Sensory and Wave Computing Laboratory Computer and Automation Hungarian Academy of Sciences Hungary Heliophysical Observatory Debrecen Hungary

Wavefront aberration caused by turbulent or rapidly changing media can considerably degrade the performance of an imaging system. Adaptive optics can dynamically compensate these wavefront distortions and so provide corrected imaging. We developed an affordable adaptive optic system which combines CMOS sensor and LCOS display technology with the FPGA devices parallel computing capabilities. High speed and accurate wavefront sensor is fundamental part of any adaptive optic system. In this paper, an efficient FPGA implementation of the Sum of Absolute Differences (SAD) algorithm is introduced which accomplish correlation based wavefront sensing. This architecture was implemented on a Spartan-3 FPGA and is capable to measure the incoming wavefront at the speed of sensor data acquisition speed.

关键词： Field programmable gate arrays Adaptive optics Optical imaging Optical sensors Sensor systems Degradation Optical distortion CMOS image sensors Liquid crystal on silicon Displays

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