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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Flavius Opriţoiu Mircea Vlăduţiu Computer Science and Engineering Department Advanced Computing Systems and Architectures (ACSA) Laboratory Politehnica University Timisoara 2 V.Parvan Blvd Timisoara Romania

This paper presents a systolic array architecture for General Matrix Multiplication. The system was designed and verified using the Verilog description language. The architecture was constructed for educational use aiming to complement the practical activities of computer Architecture classes. The proposed solution can be used as a design space exploration tool for evaluation of matrix multiplication accelerators based on non-stationary systolic arrays. Both the width and format of the processed operands as well as the number of stages of the systolic array’s Processing Elements can be customized. As a case study, the design and performance of a systolic array for accelerating the multiplication of square matrices of signed, 8-bit integers, is presented. The overall architecture was synthesized for the Altera DE2 FPGA board in order to evaluate its performance.

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 IEEE.

关键词： Stereo image processing

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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

Multiagent Reinforcement Learning with an Attention Mechanis...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xu Zhang Ziqi Lin Shimin Gong Bo Gu Dusit Niyato School of Intelligent Systems Engineering Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China School of Computer Science and Engineering Nanyang Technological University Singapore

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much “attention” should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).

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Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

arXiv

引用

arXiv 2023年

作者： Zhang, Xu Lin, Ziqi Gong, Shimin Gu, Bo Niyato, Dusit School of Intelligent Systems Engineering Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China School of Computer Science and Engineering Nanyang Technological University Singapore

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much "attention" should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR). Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Constrained Probabilistic Pareto Dominance for Expensive Constrained Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, Zhiyao Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering and the Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

This paper proposes a new parameterless constraint-handling technique, named constrained probabilistic Pareto dominance (CPPD), for expensive constrained multiobjective optimization problems (CMOPs). In CPPD, when comparing two solutions, in terms of each original objective, we design a new objective for each solution, which is the negative product of two probabilities calculated based on the predicted fitness mean values and the uncertainty information provided by Kriging models: 1) the probability that this solution satisfies all constraints, denoted as PoF, and 2) the probability that this solution is better than the other on the original objective, denoted as PoB. It is evident that for each solution, PoF and PoB indicate its feasibility and its optimality on the corresponding original objective, respectively. Then, Pareto dominance based on new objectives is executed. As a result, both competitive feasible solutions and promising infeasible solutions with good diversity can be preserved by CPPD. These two kinds of solutions can help the population to exploit the located feasible parts and to explore new feasible parts, respectively. Further, based on CPPD, we develop a Pareto-based Kriging-assisted constrained multiobjective evolutionary algorithm (called PEA) to deal with expensive CMOPs with two or three objectives. Finally, PEA is generalized to solve expensive constrained many-objective optimization problems, named PEA+. The effectiveness of CPPD, PEA, and PEA+ is verified by comprehensive experiments. IEEE

关键词： Evolutionary algorithms

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Multi-agent Traffic Prediction via Denoised Endpoint Distribution

Multi-agent Traffic Prediction via Denoised Endpoint Distrib...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yao Liu Ruoyu Wang Yuanjiang Cao Quan Z. Sheng Lina Yao Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China School of Computing Macquarie University Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Data 61 CSIRO & School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatiotemporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents’ spatio-temporal features alongside their intrinsic intentions and un-certainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model’s efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.

关键词： Uncertainty Navigation Soft sensors Robot kinematics Predictive models Transformers Road traffic Trajectory Complexity theory Spatiotemporal phenomena

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cVTS: A Constrained Voronoi Tree Search Method for High Dimensional Analog Circuit Synthesis 23

cVTS: A Constrained Voronoi Tree Search Method for High Dime...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Aidong Zhao Xianan Wang Zixiao Lin Zhaori Bi Xudong Li Changhao Yan Fan Yang Li Shang Dian Zhou Xuan Zeng State Key Laboratory of Integrated Chips and Systems Microelectronics Department Fudan University Shanghai China Department of Electrical Engineering University of Texas at Dallas USA Department of Electrical and Computer Engineering University of California at San Diego USA School of Data Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350323481

A constrained Voronoi tree-based domain decomposition method for high-dimensional Bayesian optimization is proposed to solve large scale analog circuit synthesis problems, which can be formulated as high-dimensional heterogeneous black-box optimization. Hierarchical Voronoi tree progressively breaks down the design space into partitions with implicit performance boundaries such that promising regions are efficiently explored. Fast exploitation is ensured in Voronoi nest via local Bayesian optimization with a few observations. A slice-enhanced Gibbs sampling method is proposed to sample acquisition function cMES in irregular polyhedrons with design constraints. Compared with state-of-the-art methods, cVTS achieves significant speed up without loss of accuracy.

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Design and Implementation of a Compact CPW-Fed UWB Monopole Antenna 2

Design and Implementation of a Compact CPW-Fed UWB Monopole ...

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2nd International Conference on Advanced Electrical engineering, ICAEE 2022

作者： Mokhtari, Mokhtar Mansoul, Ali Challal, Mouloud Oussaid, Rachid Usthb Telecom Division Cdta Faculty of Electronics and Computer Science Algiers Algeria Telecom Division Cdta Algiers Algeria Institute of Electrical and Electronic Engineering University m'Hamed Bougara - Boumerdes Signals and Systems Laboratory Boumerdes Algeria Usthb Faculty of Electronics and Computer Sciences Algiers Algeria

ISBN: (数字)9781665417419

ISBN: (纸本)9781665417419

In this paper, a compact ultra-wideband patch antenna is presented and discussed. The antenna, printed on Rogers RT5580, consists of leaf-shaped radiating element excited through tapered coplanar waveguide feed line, and semi-elliptical partial ground plane. It covers a frequency range starting from 2.8 to 15.1 GHz. The antenna prototype is fabricated, and a good agreement is obtained between the experimental and simulation results indicating that the proposed structure, having a reasonably compact size of 20x32 mm2 is suitable for a wide variety of applications due to its large operating frequency. © 2022 IEEE.

关键词： Monopole antennas

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EEG Classification Using Contrastive Learning and Riemannian Tangent Space Representations

EEG Classification Using Contrastive Learning and Riemannian...

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Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Ahmed Tibermacine Imad Eddine Tibermacine Meftah Zouai Abdelaziz Rabehi Department of Computer Science University of Biskra Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Zian Achour University of Djelfa Djelfa Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

This paper introduces a novel advanced framework for the classification of Electroencephalography (EEG) signals through the integration of Riemannian geometry and a bespoke contrastive learning framework. Initially, the EEG signals are segmented and converted into covariance matrices, which are regularized to ensure positive definiteness, thus enabling their interpretation as Symmetric Positive Definite (SPD) matrices. These SPD matrices are then projected onto the Riemannian manifold, facilitating the extraction of discriminative features by utilizing Euclidean geometric operations within the tangent space. A specialized neural network architecture, TangentSpaceNet, is devised to map these features into a lower-dimensional space. In this space, a customized contrastive loss function, predicated on Euclidean distance, is implemented to enhance class separability. This approach substantially improves the robustness and accuracy of EEG signal classification. Empirical assessments highlight the effectiveness of the suggested approach, showcasing significant improvements in classifying performance 93% accuracy, 94%precision, 94% recall, and 94% F1-score. The novel fusion of Riemannian geometry and contrastive learning presents a sturdy and adaptable structure for analyzing EEG signals, presenting considerable prospects for utilization in brain-computer interfaces, cognitive neuroscience, and the identification of neurological conditions.

关键词： Geometry Training Accuracy Computational modeling Contrastive learning Brain modeling Feature extraction Electroencephalography Covariance matrices Matrix converters

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