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IEEE Globecom Workshops

作者： Helin Yang Kwok-Yan Lam Jiangtian Nie Jun Zhao Sahil Garg Liang Xiao Zehui Xiong Mohsen Guizani Strategic Centre for Research in Privacy-Preserving Technologies and Systems Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Ecole de Technologie Superieure Universite du Quebec Montreal Canada School of Informatics Xiamen University Xiamen China Pillar of Information System Technology and Design Singapore University of Technology and Design Singapore Qatar University Doha Qatar

ISBN: (纸本)9781665423915

Three-dimensional (3D) beamforming is a potential technique to enhance communication security of new generation networks such as 5G and beyond. However, it is difficult to achieve optimal beamforming due to the challenges of nonconvex optimization problem and imperfect channel state information (CSI). To tackle this problem, this paper proposes a novel deep learning-based 3D beamforming scheme, where a deep neural network (DNN) is trained to optimize the beamforming design for wireless signals in order to guard against eavesdropper under the imperfect CSI. With our approach, the system is capable of training the DNN model offline, and the trained model can then be adopted to instantaneously select the 3D secure beamforming matrix for achieving the maximum secrecy rate of the system, which is measured by the signal received by eavesdroppers outside the path of the beam. Simulation results demonstrate that the proposed solution outperforms the classical deep learning algorithm and 2D beamforming solution in terms of the secrecy rate and robust performance.

关键词： Deep learning Solid modeling Three-dimensional displays Array signal processing 5G mobile communication Simulation Wireless networks

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Time-of-arrival estimation for IR-UWB systems in dense multipath environment

Time-of-arrival estimation for IR-UWB systems in dense multi...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Yizhi Xu Edward K. S. Au Albert K. Wong Qin Wang Wireless IC System Design Center HKUST Fok Ying Tung Graduate School Nansha China Modulation and Coding Department Institute for Infocomm Research Agency for Science Technology and Research Singapore Department ofElectronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School ofInformation Engineering University of Science and Technology of Beijing Beijing China

In this paper, a novel time-of-arrival estimation algorithm is proposed for impulse radio-based UWB systems in dense multipath environment. In particular, a dedicated ranging sequence with good cross-correlation property is proposed and a threshold-based search-back unit is introduced, with the purpose of supporting high-precision ranging even at low signal-to-noise ratio. An approximate closed-form expression on the optimal threshold is derived based on the Rayleigh statistical analysis, and its accuracy and tightness are verified by simulations. Results show that the proposed ranging scheme achieves an excellent mean absolute error performance in the order of nanoseconds without introducing too much latency.

关键词： Time of arrival estimation Matched filters design engineering Signal to noise ratio Statistical analysis Ultra wideband technology Bandwidth Mathematical model Closed-form solution Analytical models

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MPCS - THE MANUFACTURING PROCESS-CONTROL system

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AT&T TECHNICAL JOURNAL 1986年第4期65卷 35-45页

作者： DUNIETZ, IS HSU, JLC MCEACHERN, MT STOCKING, JH SWARTZ, MA TROMBLY, RM The authors Irwin S. Dunietz John L.C. Hsu Michael T. McEachern James H. Stocking Mark A. Swartz andRodney M. Tromblyare responsible for design and development of the Manufacturing Process Control System. Mr. Dunietz joined AT&T in 1980. He is a member of the technical staff in the Manufacturing Information Automation department at AT&T Engineering Research Center Princeton New Jersey. He received an A.B. in mathematics from Cornell University and an M.S.E. in computer science from Princeton University. Mr. Hsu who joined AT&T in 1970 is a department head in the Manufacturing Information Automation department at the Engineering Research Center. He received an M.S. in electrical engineering from the University of Missouri. Mr. McEachern joined AT&T in 1962 and is a supervisor in the 5ESS™ Line Unit Manufacturing department at AT&T Technologies in Oklahoma City Oklahoma. He is responsible for the manufacturing process control center in Oklahoma City which provides computerized support for all circuit pack manufacturing. Mr. Stocking who joined AT&T in 1975 is a supervisor in the Manufacturing Information Automation department at the Engineering Research Center. He received a B.S. in chemical engineering from Rensselaer Polytechnic Institute and a Ph.D. in chemical engineering from the University of California Berkeley. Mr. Swartz joined AT&T in 1980 and is a member of the technical staff in the Manufacturing Information Automation department at the Engineering Research Center. He received an A.B. in computer science from Cornell University and an M.S. in computer science from Rutgers—The State University. Mr. Trombly who joined AT&T in 19 78 is an assistant manager at the AT&T Merrimack Valley Works in Massachusetts. Previously he was a supervisor at the Engineering Research Center. He holds a B.S. in computers and systems engineering and an M.S.E.E. from Rensselaer Polytechnic Institute.

The central challenge of all manufacturing is making products to the right standards and delivering them at the right time. AT&T is upgrading its corporate and factory resource planning systems to improve control of day-to-day manufacturing. The Manufacturing Process Control system (MPCS), developed at the AT&T engineering Research Center (ERC), provides this support. MPCS connects the shop floor with production scheduling, accounting, product data archive, and engineering support systems.

关键词： PRODUCTION CONTROL

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Automatic Textual Explanations of Concept Lattices

arXiv

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arXiv 2023年

作者： Hirth, Johannes Horn, Viktoria Stumme, Gerd Hanika, Tom Knowledge & Data Engineering Group University of Kassel Germany Partizipative IT-Gestaltung University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany School of Library and Information Science Humboldt-Universität zu Berlin Berlin Germany Institute of Computer Science University of Hildesheim Germany

Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard *** Codes 06A15 03G10 68T30 68T27 Copyright © 2023, The Authors. All rights reserved.

关键词： Formal concept analysis

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Correction to: A new approach to snow avalanche rescue using UAV pictures based on convolutional neural networks

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Journal of Real-Time Image Processing 2023年第5期20卷 1-2页

作者： Zhang, Suyu Gavrilovskaya, Nadezhda Al Said, Nidal Afandi, Waleed Saeed College of Artificial Intelligence Wenzhou Polytechnic Wenzhou China Department of Computer-Aided Design Systems and Engineering Calculations Moscow Timiryazev Agricultural Academy Russian State Agrarian University Moscow Russian Federation College of Mass Communication Ajman University Ajman United Arab Emirates Department of Information System King Abdulaziz University Jeddah Kingdom of Saudi Arabia

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Decentralized Optimal Control for Linear Stochastic systems with Control Signals subject to Unknown Noises

Decentralized Optimal Control for Linear Stochastic Systems ...

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International Conference on Control and Automation (ICCA)

作者： Zhaorong Zhang Juanjuan Xu Minyue Fu Xun Li School of Computer Science and Technology Shandong University Qingdao China School of Control Science and Engineering Shandong University Jinan China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Applied Mathematics the Hong Kong Polytechnic University Hong Kong SAR China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Decentralized strategies have been extensively applied to LQ optimal control problems, whereas, stochastic systems with unknown random parameters have not been comprehensively studied. In this paper, we consider a class of stochastic systems with a decentralized configuration consisting of multiple controllers which have access to Gaussian noises with unknown statistical information. The stabilizing and optimal control strategies are acquired by designing a novel stochastic approximation algorithm recursively evaluating the zero points of certain matrix equations, which is confirmed to be equivalent with solving the corresponding Riccati equations. The proof of convergence and boundness of the proposed algorithm is presented.

关键词： Q-learning Automation Stochastic systems Gaussian noise Riccati equations Optimal control Approximation algorithms

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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

arXiv

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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Accelerating GPGPU architecture simulation 13

Accelerating GPGPU architecture simulation

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Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems

作者： Zhibin Yu Lieven Eeckhout Nilanjan Goswami Tao Li Lizy John Hai Jin Chengzhong Xu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China ELIS Department Ghent University Ghent Belgium Intelligent Design of Efficient Architectures Lab University of Florida Gainesville USA Department of Electrical and Computer Engineering University of Texas at Austin Austin USA Service Computing Technologies and System Lab/Cluster and Grid Computing Lab HUST Wuhan China Department of Electrical and Computer Engineering Wayne State University Detroit USA

ISBN: (纸本)9781450319003

Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.

关键词： micro-architecture simulation performance general purpose graphics processing unit (GPGPU)

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Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

Mixture of Experts for Intelligent Networks: A Large Languag...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

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Preliminary Application of Machine-Learning Techniques for Thermal-Electrical Parameter Optimization in 3-D IC

Preliminary Application of Machine-Learning Techniques for T...

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IEEE International Symposium on Electromagnetic Compatibility

作者： Sung Joo Park Huan Yu Madhavan Swaminathan Center for Co-Design of Chip Package System (C3PS) Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia 30332

ISBN: (纸本)9781509014439

Three-dimensional (3-D) integration technique, a promising integration technique, can increase system density but at the cost of increased thermal and power density, leading to thermal-related problems. design of three-dimensional integrated circuits and systems requires considerations of temperature and gradients observed across the die, because temperature gradients can vary the delay of clock paths. As we need to analyze a large number of parameters for thermal-electrical design, optimization of those parameters becomes important for achieving efficiency and accuracy. Machine learning methods have been applied in the past for artificial intelligence, data analysis, and for general optimization problems. In this paper we propose the application of machine learning methods for parameter optimization in 3-D systems.

关键词： Bayesian optimization. Introduction 3-D IC TSV (Throung Silicon Via) Temperature gradient Thermal-induced skew Machine learning

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