检索结果-内蒙古大学图书馆

MemMIMO: A Simulation Framework for Memristor-Based Massive MIMO Acceleration

IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Xu, Jiawei Zheng, Yi Stathis, Dimitrios Wang, Ruijia Shen, Ruisi Zheng, Li-Rong Zou, Zhuo Hemani, Ahmed University of Macau State-Key Laboratory of Analog and Mixed-Signal VLSI Institute of Microelectronics Faculty of Science and Technology ECE Department China Fudan University State Key Laboratory of Integrated Chips and Systems School of Information Science and Technology Shanghai China KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

Memristor-based crossbar architectures have proven highly effective for matrix vector multiplication (MVM) operations, making them a promising solution for accelerating the MVMs widely used in precoding algorithms for multiple input multiple output (MIMO) wireless communication systems. However, real-world implementation of memristor-based computing systems face challenges due to commonly observed non-idealities in both the devices themselves and the circuits they’re built into. To facilitate a rapid design flow and investigate the impact of non-idealities, an integrated open-source simulation framework MemMIMO is developed. The simulation framework estimates the accuracy and hardware performance of the computing system, offering a variety of flexible design options. MemMIMO integrates a behavioral model of the mix-signal architecture with a digital front-end. There are three major building blocks in MemMIMO: the device fitting block, the mapping block, and the performance estimation block. These blocks work together to map the complex MVMs in precoding algorithms for MIMO systems to crossbar-based architectures that incorporate memristor models characterized by physical device behavior. Using two typical use cases targeting six-generation (6G) massive MIMO communication as case studies, MemMIMO is used to model different memristor devices, explore the impact of non-idealities on system accuracy, and benchmark circuit-level performance metrics including area, speed, and power. © 1982-2012 IEEE.

关键词： System-on-chip

来源：评论

学校读者我要写书评

暂无评论

MirageNet: improving the network performance of image understanding with very low FLOPs

引用

Expert systems with Applications 2025年 286卷

作者： Fu, Yinghua Liu, Peiyong Lu, Haiyang Gupta, Rohit Zhang, Dawei School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Nanoengineered Systems Laboratory UCL Mechanical Engineering University College London LondonWC1E 7JE United Kingdom Wellcome/EPSRC Centre for Interventional and Surgical Sciences University College London LondonW1W 7TS United Kingdom

In order to realize lightweight CNN architectures, an array of methods including model pruning, distillation, and inventive convolutional designs have been developed and employed. However, the computational complexity to approximate 20M FLOPs often leads to significant performance degradation. In this paper, this study presents the innovative Mirage module, designed to produce an expanded array of feature maps through cost-effective processes. By harnessing the power of low-rank matrix factorization and group convolution, this module is capable of generating numerous feature maps, thereby comprehensively exposing the fundamental data and its attributes. MirageNet achieves high accuracy within the range of 18M-21M FLOPs by enhancing inter-group nonlinearity and feature activation and improving the DY-Shift-Max activation function. The incorporation of the Squeeze-and-Excitation (SE) module further optimizes MirageNet's architecture. Experimental results reveal that MirageNet outperforms existing lightweight models, achieving a top-1 accuracy of 63.2 % on ImageNet with 20.7M FLOPs, surpassing MobileNetV3 by 9.2 %. © 2025 Elsevier Ltd

关键词： Matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Employing transactional memory and helper threads to speedup Dijkstra'S algorithm

Employing transactional memory and helper threads to speedup...

引用

38th International Conference on Parallel Processing, ICPP-2009

作者： Nikas, Konstantinos Anastopoulos, Nikos Goumas, Georgios Koziris, Nectarios School of Electrical and Computer Engineering Computing Systems Laboratory Greece

ISBN: (纸本)9780769538020

In this paper we work on the parallelization of the inherently serial Dijkstra's algorithm on modern multicore platforms. Dijkstra's algorithm is a greedy algorithm that computes Single Source Shortest Paths for graphs with non-negative edges and is based on the iterative extraction of nodes from a priority queue. This property limits the explicit parallelism of the algorithm and any attempt to utilize the remaining parallelism results in significant slowdowns due to synchronization overheads. To deal with these problems, we employ the concept of Helper Threads (HT) to extract parallelism on a non-traditional fashion and Transactional Memory (TM) to efficiently orchestrate the concurrent threads' accesses to shared data structures. Results demonstrate that the proposed implementation is able to achieve performance speedups (reaching up to 1.84 for 14 threads), indicating that the two paradigms could be efficiently combined. © 2009 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

NSPG-Miner: Mining Repetitive Negative Sequential Patterns

arXiv

引用

arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/PatternMining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

来源：评论

学校读者我要写书评

暂无评论

Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

引用

IEEE Internet of Things Journal 2025年

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial Intelligence Autonomous Vehicles Deep leaning Edge computing Internet of Vehicles

来源：评论

学校读者我要写书评

暂无评论

OPEN FAULTS IN BICMOS GATES

引用

IEEE TRANSACTIONS ON computer-AIDED DESIGN OF INTEGRATED CIRCUITS AND systems 1995年第5期14卷 567-575页

作者： MA, SC MCCLUSKEY, EJ Center for Reliable Computing Computer Systems Laboratory Department of Electrical Engineering and Computer Science University of Stanford Stanford CA USA

Opens in BICMOS structures are analyzed here. It is shown that some opens cannot be detected by stuck-fault or other functional tests, since some transistors in BiCMOS gates do not affect the logical function of the gate. A switch-level model for CMOS circuits is extended to include bipolar devices. With this switch-level model, opens that cannot be detected by stuck-faults or other functional tests are easily identified. It is also shown that, in BICMOS circuits, an open defect in one transistor can accelerate the wearout of another nondefective transistor.

关键词： BiCMOS integrated circuits Circuit faults Circuit testing Semiconductor device modeling Logic testing Delay MOSFETs Switching circuits Acceleration CMOS technology

来源：评论

学校读者我要写书评

暂无评论

Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

引用

IEEE Internet of Things Journal 2025年

作者： Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose Federated Graph Learning via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions from each IoT institution are effectively integrated into the global model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability © 2014 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Dexterous Manipulation through Imitation Learning: A Survey

arXiv

引用

arXiv 2025年

作者： An, Shan Meng, Ziyu Tang, Chao Zhou, Yuning Liu, Tengyu Ding, Fangqiang Zhang, Shufang Mu, Yao Song, Ran Zhang, Wei Hou, Zeng-Guang Zhang, Hong School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Control Science and Engineering Shandong University Jinan250061 China State Key Laboratory of General Artificial Intelligence Beijing100086 China Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Informatics University of Edinburgh EH8 9AB United Kingdom Department of Computer Science University of Hong Kong 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Grippers

来源：评论

学校读者我要写书评

暂无评论

Reverse aperiodic resonance in low- to high-dimensional bistable systems: A complement to stochastic resonance studies in logic circuits

引用

Communications in Nonlinear Science and Numerical Simulation 2025年 149卷

作者： Shen, Mengen Yang, Jianhua Sanjuán, MiguelA.F. Chen, Huatao Wang, Zhongqiu Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment School of Mechanical and Electrical Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China Nonlinear Dynamics Chaos and Complex Systems Group Departamento de Física Universidad Rey Juan Carlos Tulipán s/n Madrid Móstoles28933 Spain Division of Dynamics and Control School of Mathematics and Statistics Shandong University of Technology Zibo255000 China School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China

As circuits continue to shrink in size, noise has emerged as a critical challenge in achieving optimal performance. Stochastic resonance in logic circuits offers an innovative approach to harness noise constructively;however, current implementations are limited to basic logical functions such as OR, AND, NOR, and NAND, restricting broader applications. This paper introduces a three-dimensional (3D) coupling model to investigate the counterintuitive phenomena that arise in nonlinear systems under noise. Compared to the one-dimensional Langevin equation and the two-dimensional Duffing equation, the 3D coupling model features more adjustable parameters and coupling interactions, enhancing the system's dynamic behavior. The study demonstrates that increasing noise intensity triggers reverse aperiodic resonance, leading to signal phase reversal and amplitude amplification. This phenomenon is attributed to the motion of Brownian particles in a bistable potential well. Additionally, reverse aperiodic resonance addresses the lack of logical negation in traditional stochastic resonance systems by introducing noise-driven phase reversal, providing a novel alternative to conventional inverters. © 2025 Elsevier B.V.

关键词： Sequential circuits

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Network Enabled Fluid Antenna systems: A Two-Stage Approach

arXiv

引用

arXiv 2025年

作者： He, Changpeng Lu, Yang Chen, Wei Ai, Bo Wong, Kai-Kit Niyato, Dusit State Key Laboratory of Advanced Rail Autonomous Operation China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an "unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into a unsupervised loss function to train the two-stage GNN. The numerical results demonstrates that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately. © 2025, CC BY.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：