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ON INSTRUCTION SETS AND THEIR FORMATS

IEEE TRANSACTIONS ON computerS 1985年第3期34卷 242-254页

作者： FLYNN, MJ JOHNSON, JD WAKEFIELD, SP Computer Systems Laboratory Department of Electrical Engineering Stanford University

Central to instruction set design is the ❉ of the instruction format. We discuss some common format encoding techniques, and introduce a method of representing high-level language parse trees by means of formats that cover successively larger portions of a tree. We then introduce variations on the method that represent directed acyclic graphs as well as simple parse trees, and that encode constants in a special fashion. For a particular representation, we measure the number of times each format is executed to run a sample program to completion.

关键词： computer architecture execution architecture instruction format instruction set program representation

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ON DISTRIBUTED COMPUTATIONS WITH LIMITED RESOURCES

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IEEE TRANSACTIONS ON computerS 1987年第5期36卷 517-528页

作者： KANAKIA, HR TOBAGI, FA Computer Systems Laboratory Department of Electrical Engineering Stanford University

We consider two styles of executing a single job or an algorithm: either the job is subdivided into tasks, each of which is executed. on a separate processor, or the entire job is executed on a single processor, that has the same capacity as the sum of the processors in the earlier case. The algorithm is abstracted as consisting of a number of tasks with dependencies among them. Our model of dependencies among tasks allows sequential execution, parallel execution, synchronization, and spawning of tasks. The model assumes that the dependencies are known before the job begins, and a task in not preempted after its execution begins. With the usual assumptions such as exponential distribution of task execution times, and Poisson arrival of input data, we are able to show that the centralized execution completes the job faster than the decentralized execution only for a certain range of parameters of algorithms. We also give counterexamples that show that, contrary to popular belief, the reverse is true for some values of parameters of algorithms.

关键词： Comparison of distributed versus centralized execution of algorithms distributed algorithms performance analysis theory of distributed algorithms

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Reconfigurable Intelligent Surface Enhanced Wireless Localization: Phase Optimization for Malicious Interference Mitigation

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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

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Kernel contextual fuzzy rule model based on conditional input space partitioning driven by data reconstruction in autoencoder and randomization-based neural networks

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Knowledge-Based systems 2025年 320卷

作者： Zhang, Congcong Oh, Sung-Kwun Fu, Zunwei Pedrycz, Witold School of Mathematics and Statistics Linyi University Shandong Linyi276005 China Shandong Data Open Innovative Application Laboratory Linyi276005 China School of Automation and Electrical Engineering Linyi University Shandong Linyi276005 China School of Electrical & Electronic Engineering The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electronic Engineering Seokyeong University Seoul 02713 Korea Republic of Department of Computer Science The University of Suwon Gyeonggi-do Hwaseong-si18323 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonAB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw 00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkey

Fuzzy Rule-based Models (FRMs) have attracted significant interest in the field of machine learning owing to their modular architecture, robust design methodologies, and sound interpretability. This study introduces a novel Kernel Contextual Fuzzy Rule Model (KCFRM) designed to cope with regression tasks. A Kernel Contextual Fuzzy Clustering (KCFC) algorithm is proposed for conditional partitioning of the input space. Specifically, we incorporate the Mercer kernel into context fuzzy clustering to enhance the abilities of the model to distinguish, extract, and amplify useful features via nonlinear mapping, thereby generating more suitable information granules. Additionally, we employ an autoencoder's "encoding-decoding" mechanism to extract differences between data patterns and subsequently transform these into KCFC contexts via a conversion function, therefore leading to the creation of high-quality fuzzy sets. In the conclusion portion of fuzzy rules, conventional numerical or linear functions struggle to adequately describe the complex behavior present in local fuzzy regions. To mitigate this, we incorporate a Randomization-based Neural Network (RANN), capable of providing superior approximation capabilities and substantial computational efficiency. RANN overcomes traditional method constraints in representing complex behaviors within the fuzzy region. The inclusion of RANN results in more accurate and efficient conclusions for fuzzy rules. The uniqueness of this study lies in its holistic approach to designing fuzzy models with KCFC and RANN, improving the expressiveness of the rules and enhancing the generalization of the models. KCFRM's performance is assessed using various publicly available machine learning datasets, with experimental results underscoring its effectiveness and performance enhancements. © 2025

关键词： Fuzzy rules

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

引用

arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Constrained Optimization with Decision-Dependent Distributions

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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STARS Assisted Semi-Grant-Free NOMA Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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SPECIFICATION AND ANALYSIS OF parallel DISTRIBUTED SOFTWARE AND systems BY PETRI NETS WITH TRANSITION ENABLING FUNCTIONS

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IEEE TRANSACTIONS ON SOFTWARE engineering 1992年第3期18卷 252-261页

作者： PAPELIS, YE CASAVANT, TL Parallel Processing Laboratory Department of Electrical and Computer Engineering University of Iowa Iowa City IA 52242

This paper investigates a new approach for visually specifying parallel/distributed software using Petri Nets (PN's) extended with Transition Enabling Functions (TEF's). The approach is demonstrated to be useful in the specification of decision-making activities that control distributed computing systems. PN's are employed because of their highly visual nature that can give insight into the nature of the controller of such a system and because of their well-known analytical properties. In order to increase the expressive power of PN's, the extension of TEF's is used. The main focus of this paper is the specification and analysis of parallel/distributed software and systems. A key element of this approach is a set of rules derived to automatically transform such an extended net into a basic PN. Once the rules have been applied to transform the specification, well-known analytical methods can be used to investigate characteristic properties of the system and validate correct operation.

关键词： DISTRIBUTED SOFTWARE parallel SOFTWARE SOFTWARE SPECIFICATION parallel PETRI NETS TRANSITION ENABLING FUNCTIONS CONCURRENCY

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