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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

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A distributed application placement and migration management techniques for Edge and Fog computing environments

arXiv

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arXiv 2021年

作者： Goudarzi, Mohammad Palaniswami, Marimuthu Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Australia The Department of Electrical and Electronic Engineering The University of Melbourne Australia

Fog/Edge computing model allows harnessing of resources in the proximity of the Internet of Things (IoT) devices to support various types of latency-sensitive IoT applications. However, due to the mobility of users and a wide range of IoT applications with different resource requirements, it is a challenging issue to satisfy these applications' requirements. The execution of IoT applications exclusively on one fog/edge server may not be always feasible due to limited resources, while the execution of IoT applications on different servers requires further collaboration and management among servers. Moreover, considering user mobility, some modules of each IoT application may require migration to other servers for execution, leading to service interruption and extra execution costs. In this article, we propose a new weighted cost model for hierarchical fog computing environments, in terms of the response time of IoT applications and energy consumption of IoT devices, to minimize the cost of running IoT applications and potential migrations. Besides, a distributed clustering technique is proposed to enable the collaborative execution of tasks, emitted from application modules, among servers. Also, we propose an application placement technique to minimize the overall cost of executing IoT applications on multiple servers in a distributed manner. Furthermore, a distributed migration management technique is proposed for the potential migration of applications' modules to other remote servers as the users move along their path. Besides, failure recovery methods are embedded in the clustering, application placement, and migration management techniques to recover from unpredicted failures. The performance results demonstrate that our technique significantly improves its counterparts in terms of placement deployment time, average execution cost of tasks, the total number of migrations, the total number of interrupted tasks, and cumulative migration cost. Copyright © 2021, The

关键词： Internet of things

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Spectral Efficiency Maximization for Probabilistic Semantic Communication with Rate Splitting

Spectral Efficiency Maximization for Probabilistic Semantic ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zhouxiang Zhao Zhaohui Yang Mingzhe Chen Xu Gan Chongwen Huang Yao Sun Qianqian Yang Wei Xu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA James Watt School of Engineering University of Glasgow Glasgow UK National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) network with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data, which is represented by substantial knowledge graphs, to multiple users. Due to limited communication resource, the BS needs to utilize semantic communication techniques to compress the large-sized data. In this paper, the semantic communication is enabled by shared probability graphs between the BS and users. The process of semantic compression requires computation power at the BS, which has an impact on limited power budget. Therefore, it is necessary to balance the power between transmission and computation. Based on the probability graph, the semantic rate related to semantic compression ratio is first theoretically formulated. Then, the problem is formulated as an optimization problem with the aim of maximizing the sum semantic rate of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is accordingly proposed to obtain a suboptimal solution. Numerical results validate the effectiveness of the proposed scheme.

关键词： Vehicular and wireless technologies Spectral efficiency Computational modeling Semantics Knowledge graphs Probabilistic logic Numerical models

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PCB Surface Defect Detection Based on TPBAS-YOLOv5

PCB Surface Defect Detection Based on TPBAS-YOLOv5

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

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

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 targets .Although 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.

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Secure Communication of Active RIS Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Li, Xuehua Pei, Yingjie Yue, Xinwei Liu, Yuanwei Ding, Zhiguo The Key Laboratory of Information and Communication Systems Ministry of Information Industry The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

As a revolutionary technology, reconfigurable intelligent surface (RIS) has been deemed as an indispensable part of the 6th generation communications due to its inherent ability to regulate the wireless channels. However, passive RIS (PRIS) still suffers from some pressing issues, one of which is that the fading of the entire reflection link is proportional to the product of the distances from the base station to the PRIS and from the PRIS to the users, i.e., the productive attenuation. To tackle this problem, active RIS (ARIS) has been proposed to reconfigure the wireless propagation condition and alleviate the productive attenuation. In this paper, we investigate the physical layer security of the ARIS assisted non-orthogonal multiple access (NOMA) networks with the attendance of external and internal eavesdroppers. To be specific, the closed-form expressions of secrecy outage probability (SOP) and secrecy system throughput are derived by invoking both imperfect successive interference cancellation (ipSIC) and perfect SIC. The secrecy diversity orders of legitimate users are obtained at high signal-to-noise ratios. Numerical results are presented to verify the accuracy of the theoretical expressions and indicate that: i) The SOP of ARIS assisted NOMA networks exceeds that of PRIS-NOMA, ARIS/PRIS-assisted orthogonal multiple access (OMA);ii) Due to the balance between the thermal noise and residual interference, introducing excess reconfigurable elements at ARIS is not helpful to reduce the SOP;and iii) The secrecy throughput performance of ARIS-NOMA networks outperforms that of PRIS-NOMA and ARIS/PRIS-OMA networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Contactless Breathing Waveform Detection Through RF Sensing: Radar vs. Wi-Fi Techniques

Contactless Breathing Waveform Detection Through RF Sensing:...

引用

International Conference on Communications and Networking, ComNet

作者： Umer Saeed Dingchang Zheng Behzad Ali Shah Syed Ikram Shah Sana Ullah Jan Jawad Ahmad Qammer H. Abbasi Syed Aziz Shah Wadii Boulila Research Centre for Intelligent Healthcare Coventry University Coventry United Kingdom College of Electrical and Mechanical Engineering National University of Sciences and Technology Islamabad Pakistan School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom James Watt School of Engineering University of Glasgow Glasgow United Kingdom RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia

Human breathing detection plays a vital role in healthcare, safety, and various other applications. This research paper explores the use of radio-frequency (RF) sensing technologies, specifically radar and Wi-Fi, for detecting human breathing patterns. Abnormal breathing patterns can indicate respiratory or cardiovascular diseases, and early detection is crucial for timely diagnosis and treatment. Radar-based systems utilize low-power RF pulses to capture subtle chest movements associated with breathing, while software-defined radio (SDR)-based systems analyze Wi-Fi signals to detect variations caused by human chest motion. Deep learning algorithms, namely residual neural network (ResNet) and deep multilayer perceptron (DMLP), are employed to classify breathing patterns based on the collected data. ResNet attained classification accuracy up to 90% on radar-based spectrogram images data, while DMLP attained classification accuracy up to 99% on SDR-based channel state information data. The proposed approaches offer non-intrusive, remote-operable, and cost-effective solutions for breathing detection. The research demonstrates the potential of RF sensing technologies in healthcare, eldercare, sleep monitoring, and emergency response systems, paving the way for enhanced well-being and safety.

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Multi-Robot System for Autonomous Cooperative Counter-UAS Missions: Design, Integration, and Field Testing

Multi-Robot System for Autonomous Cooperative Counter-UAS Mi...

引用

IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Antonella Barisic Marlan Ball Noah Jackson Riley McCarthy Nasib Naimi Luca Strässle Jonathan Becker Maurice Brunner Julius Fricke Lovro Markovic Isaac Seslar David Novick Jonathan Salton Roland Siegwart Stjepan Bogdan Rafael Fierro Laboratory for Robotics and Intelligent Control Systems (LAR-ICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA ETH Zurich Autonomous Systems Lab (ASL) Zurich Switzerland Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (纸本)9781665456814

With the rapid development of technology and the proliferation of uncrewed aerial systems (UAS), there is an immediate need for security solutions. Toward this end, we propose the use of a multi-robot system for autonomous and cooperative counter-UAS missions. In this paper, we present the design of the hardware and software components of different complementary robotic platforms: a mobile uncrewed ground vehicle (UGV) equipped with a LiDAR sensor, an uncrewed aerial vehicle (UAV) with a gimbal-mounted stereo camera for air-to-air inspections, and a UAV with a capture mechanism equipped with radars and camera. Our proposed system features 1) scalability to larger areas due to the distributed approach and online processing, 2) long-term cooperative missions, and 3) complementary multimodal perception for the detection of multirotor UAVs. In field experiments, we demonstrate the integration of all subsystems in accomplishing a counter-UAS task within an unstructured environment. The obtained results confirm the promising direction of using multi-robot and multi-modal systems for C-UAS.

关键词： Scalability Robot vision systems Autonomous aerial vehicles Cameras Software Safety Security

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NSPG-Miner: Mining Repetitive Negative Sequential Patterns

arXiv

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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

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A distributed deep reinforcement learning Technique for application placement in edge and fog computing environments

arXiv

引用

arXiv 2021年

作者： Goudarzi, Mohammad Palaniswami, Marimuthu Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia Department of Electrical and Electronic Engineering University of Melbourne Australia

Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by placement of their tasks on edge and/or cloud servers. Recently, several Deep Reinforcement Learning (DRL)-based placement techniques have been proposed in fog/edge computing environments, which are only suitable for centralized setups. The training of well-performed DRL agents requires manifold training data while obtaining training data is costly. Hence, these centralized DRL-based techniques lack generalizability and quick adaptability, thus failing to efficiently tackle application placement problems. Moreover, many IoT applications are modeled as Directed Acyclic Graphs (DAGs) with diverse topologies. Satisfying dependencies of DAG-based IoT applications incur additional constraints and increase the complexity of placement problem. To overcome these challenges, we propose an actor-critic-based distributed application placement technique, working based on the IMPortance weighted Actor-Learner Architectures (IMPALA). IMPALA is known for efficient distributed experience trajectory generation that significantly reduces exploration costs of agents. Besides, it uses an adaptive off-policy correction method for faster convergence to optimal solutions. Our technique uses recurrent layers to capture temporal behaviors of input data and a replay buffer to improve the sample efficiency. The performance results, obtained from simulation and testbed experiments, demonstrate that our technique significantly improves execution cost of IoT applications up to 30% compared to its counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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