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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Rashika Raina Nidhi Simmons David E. Simmons Michel Daoud Yacoub Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast UK Dhali Holdings Ltd. Belfast UK Wireless Technology Laboratory School of Electrical and Computer Engineering University of Campinas Campinas Brazil

In this paper, we establish simple and efficient approximations for the outage probability of a single-user multi-resource allocation system that consists of a machine learning (ML) based outage predictor whose task is to assign resources to the user while minimizing outages. We begin by presenting the outage probability expressions for this system. We then propose the approximations to this system’s outage probability using both the sinc function and the zeroth-order Bessel function of the first kind. These approximations are based on naive upper and lower bounds and stem from understanding how the channel samples de-correlate over time. Our results demonstrate that the outage probability indeed lies within the range defined by the bounds. Moreover, the effectiveness of the proposed outage probability approximations are evident as they exhibit a strong alignment with the trend of the outage probability curve. Finally, because of the simplicity of our approximations, they can be calculated in a computationally efficient manner.

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Triple Point Masking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

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Adaptive Tracking Control of an Omnidirectional Mobile Robot System with Uncertainties and Disturbances

Adaptive Tracking Control of an Omnidirectional Mobile Robot...

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Annual Conference of Industrial Electronics Society

作者： Yaozhong Zhang Jiang Long Zitong Bai Wei Wang School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Northwestern Polytechnical University Xi’ an China Zhongguancun Laboratory Beijing China

This paper addresses the tracking problem of an omnidirectional mobile robot (OMR), which is a second-order electromechanical system with unmatched uncertainties and disturbances. The control inputs are the currents of the motors, and the control objective is the tracking control of the real-time position and orientation for the robot. The main features of this paper are summarized as follows. (1) Both the kinematic and the dynamic models for a standard OMR are presented, which explicitly defines the unknown physical parameters of the system as well as the specific mathematical form of disturbances. (2) A new OMR control scheme is proposed based on adaptive back-stepping techniques. It is shown that the OMR can asymptotically track the desired trajectory with any orientation. (3) In addition to numerical simulations, a real OMR experiment platform is established. The effectiveness of the proposed control scheme has been verified with both simulation and experimental results.

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Channel Estimation for Movable Antenna Aided Wideband Communication Systems

Channel Estimation for Movable Antenna Aided Wideband Commun...

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IEEE Conference on Wireless Communications and Networking

作者： Songqi Cao Lipeng Zhu Zhenyu Xiao Boyu Ning School of Electronic and Information Engineering Beihang University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper proposes a channel estimation method for movable antenna (MA)-aided wideband communication systems to acquire complete channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at several position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem of multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Finally, simulation results demonstrate that the proposed SOMP-based channel estimation method can reconstruct the complete wideband CSI with a high accuracy.

关键词： Antenna measurements Accuracy Transmitting antennas Channel estimation Matching pursuit algorithms Estimation Vectors Broadband antennas Wideband Compressed sensing

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Multiplication Complexity Optimization based on Quantified Boolean Formulas

Multiplication Complexity Optimization based on Quantified B...

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Electronics Design Automation (ISEDA), International Symposium of

作者： Jun Zhu Hongyang Pan Zhufei Chu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China State Key Laboratory of Integrated Chips and System School of Microelectronics Fudan University Shanghai China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

In the field of cryptography and security, minimizing the number of AND gates within logic networks is crucial. Such optimization significantly influences the multiplication complexity of circuits. This paper proposes a Quantified Boolean Formulas (QBF) based resynthesis to dynamically construct local circuits from sub-circuits, with the goal of minimizing the number of AND gates and optimizing the number of XOR gate numbers in well-optimized networks. Experimental results on parts of the EPFL benchmarks suite indicate that the proposed method effectively reduces the number of AND gates by 15.51 % and the number of XOR gates by 3.34%.

关键词： Design automation Circuits Logic gates Benchmark testing Complexity theory Logic Cryptography

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Efficient Multi-Query Oriented Continuous Subgraph Matching 40

Efficient Multi-Query Oriented Continuous Subgraph Matching

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ma, Ziyi Yang, Jianye Zhou, Xu Xiao, Guoqing Wang, Jianhua Yang, Liang Li, Kenli Lin, Xuemin School of Artificial Intelligence Hebei University of Technology China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control China Cyberspace Institute of Advanced Technology Guangzhou University China PengCheng Laboratory Department of New Networks China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (纸本)9798350317152

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings. © 2024 IEEE.

关键词： Directed graphs

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NOMA-Empowered Federated Learning for Enhancing Uplink Efficiency in Wireless Networks

NOMA-Empowered Federated Learning for Enhancing Uplink Effic...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Hung Hoang Le Toan-Van Nguyen Tung Dinh Van Tien Hoa Nguyen Hyunseung Choo School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam School of Computer Science and Engineering International University HCM Vietnam Vietnam National University HCM Vietnam Superintelligence Laboratory Sungkyunkwan University Suwon Korea

This paper proposes an uplink non-orthogonal multiple access (NOMA)-assisted federated learning (FL) framework to enhance the stable and accurate convergence in wireless networks. In this system, clients upload their local models securely to the base station through uplink NOMA to improve the uplink transmissions efficiency. We mathematically formulate the total latency optimization problem, taking into account the quality-of-service (QoS) requirements for clients, the power allocation at the base station (BS), and the frequency considerations. Simulation results show the that of the proposed FL framework achieves an 11% higher test accuracy and reduces latency by 25% compared to FedAvg and FedProx.

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Fabric computing: Concepts, opportunities, and challenges

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The Innovation 2022年第6期3卷 67-76页

作者： Min Chen Jia Liu Pan Li Hamid Gharavi Yixue Hao Jingyu Ouyang Jiayu Hu Long Hu Chong Hou Iztok Humar Lei Wei Guang-Zhong Yang Guangming Tao Wuhan National Laboratory for Optoelectronics and School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China National Institute of Standards and Technology(NIST) GaithersburgMD 20899-8920USA School of Optics and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Faculty of Electrical Engineering University of LjubljanaTrzaska cesta 251000 LjubljanaSlovenia School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Medical Robotics Institute Shanghai Jiao Tong UniversityShanghai 200030China State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

With the advent of the Internet of Everything,people can easily interact with their environments *** idea of pervasive computing is becoming a reality,but due to the inconvenience of carrying silicon-based entities and a lack of fine-grained sensing capabilities for human-computer interaction,it is difficult to ensure comfort,esthetics,and privacy in smart *** by the rapid developments in intelligent fabric technology in the post-Moore era,we propose a novel computing approach that creates a paradigm shift driven by fabric computing and advocate a new concept of non-chip sensing in living *** discuss the core notion and benefits of fabric computing,including its implementation,challenges,and future research opportunities.

关键词： computing computer smart

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Energy Consumption Prediction of Electric Bus Based on CNN-XGBoost

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 1153-1167页

作者： Fang, Weidong Chi, Yufeng Pan, Jeng-Shyang School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Chaoyang University of Technology Taichung413310 Taiwan

The existing energy consumption prediction models for pure electric buses do not comprehensively consider various energy consumption influencing factors, and most models only use data from a single operating route. This paper proposes an electric bus trip energy consumption prediction model based on CNN-XGBoost to improve the prediction accuracy. Using the actual driving data of eight pure electric buses, firstly, we analyze the change rule of energy consumption under different ambient temperatures and propose the energy consumption influencing factors stratification to analyze the influence of driving conditions on energy consumption. Then, we innovatively input the speed-related three types of joint probability distribution matrix into the CNN model to predict the motor energy consumption and then input the predicted motor energy consumption and the influencing factors, such as ambient temperature and line information, into the XGBoost model to predict the energy consumption of bus trip. Finally, it is compared and analyzed with various prediction models such as SVR and LGBM, and the results show that the R2 of the CNN-XGBoost model is 0.923, the prediction performance is significantly higher than other models. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Normal distribution

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Edge Graph Intelligence: Reciprocally Empowering Edge Networks With Graph Intelligence

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IEEE Communications Surveys and Tutorials 2025年

作者： Zeng, Liekang Ye, Shengyuan Chen, Xu Zhang, Xiaoxi Ren, Ju Tang, Jian Yang, Yang Shen, Xuemin Sherman Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Guangzhou511453 China Ministry of Education School of Computer Science and Engineering Sun Yat-sen University The Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou510275 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Midea Group Shanghai201702 China Guangzhou511453 China Peng Cheng Laboratory Shenzhen518055 China Terminus Group Beijing100027 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services. Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph Intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them - GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning, 2) emerging techniques centering on the closed loop between graph intelligence and edge networks, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI. © 1998-2012 IEEE.

关键词： Contrastive Learning

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