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arXiv 2023年

作者： Lin, Zheng Zhu, Guangyu Deng, Yiqin Chen, Xianhao Gao, Yue Huang, Kaibin Fang, Yuguang The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Computer Science Fudan University Shanghai200438 China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Pok Fu Lam Hong Kong The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States The School of Control Science and Engineering Shandong University Shandong Jinan250061 China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple edge devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training and reduces the dimension of activations’ gradients for back propagation (BP) via last-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at edge devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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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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Pedestrian Positioning Algorithm Based on Zero-Velocity Correction

Pedestrian Positioning Algorithm Based on Zero-Velocity Corr...

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Automation, Robotics and computer engineering (ICARCE), International Conference on

作者： Shiying Huang Haolin Li Xueyan Zhang Hongwei Liu Tianhua Fu Department of Instrumentation Science and Opto-Electronics Engineering Beihang University Beijing China Department of Computer Science Beihang University Beijing China Harbin Medical University Harbin China Beijing Institute of Precision Machinery and Control Beijing China Beijing Experimental Factory Co. Ltd Beijing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Pedestrian positioning technology is a key technology that has emerged in recent years in the field of navigation. It is of great significance in indoor, underground and other navigation fields. In this paper, aiming at the problem of error accumulation in pedestrian positioning systems based on MEMS devices, a pedestrian positioning method based on the inertial navigation scheme is studied, and the zero-velocity correction method is used to suppress the accumulation of velocity errors, thereby improving the positioning accuracy. The effectiveness of the method in this paper is verified by experiments.

关键词： Micromechanical devices Meters Pedestrians Accuracy Automation Inertial navigation Hardware Robots

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A Novel DWT-based Encoder for Human Pose Estimation

A Novel DWT-based Encoder for Human Pose Estimation

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2022 Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2022

作者： De Magistris, Giorgio Romano, Matteo Starczewski, Janusz Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Computational Intelligence Czstochowa University of Technology al. Armii Krajowej 36 Częstochowa42-200 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The proposed approach for pose estimation is based on the construction of a Convolutional Neural Network with an encoding-decoding structure and a spatial pyramid based on WASP structure in its bottleneck and a Discrete wavelet transform encoder. These techniques already shown their capabilities to solve the main problems in state of the art related to: different Field of view (FoV) required to analyze the different possible sizes of a specific subject. we want to solve the faulty structure of the modern CNN based Neural Networks in the encoding part using DWT encoder and WASP. This Work also have the objective of demonstrating from a more general point of view which could be the advantages of a Discrete Wavelet Transform (DWT) encoder in any CNN-based approach for Pose Estimation and Object detection in any form, such as for several subjects in the same image or in the internal video due to the almost redundant use of the usual most famous encoding structures for CNN such as ResNet-101, U-Net or VGG16-19. we will do our tests using a U-net Based CNN in order to evaluate the importance of the results of the Discrete Wavelet Transform encoder also in the decoding part through the cropping of theme at the last layers of the network. This is necessary due to the loss of border's pixels during encoding that could be useful for the result's evaluation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Discrete wavelet transforms

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The Future of Beyond 5G Sensing: Transforming Activity Recognition with Reconfigurable Intelligent Surfaces

The Future of Beyond 5G Sensing: Transforming Activity Recog...

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Activity and Behavior Computing (ABC), International Conference on

作者： Sherief Hashima Hamada Rizk Hirozumi Yamaguchi Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Engineering Dept Egyptian Atomic Energy Authority Cairo Egypt Graduate School of Information Science and Technology Osaka University Suita Japan Computer and Control Engineering Department Tanta University Tanta Egypt

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

The advent of Beyond 5G (B5G) and the anticipated arrival of 6G have spurred a remarkable impact on various aspects of human life. Next-generation Human Activity Recognition (HAR) systems are poised to advance healthcare, create smart environments, and enhance overall well-being. The imperative for next-gen HAR systems lies in their capability to be intelligent, privacy-preserving, and deeply accurate. These systems, leveraging the cutting-edge capabilities of B5G and 6G, such as Re-configurable Intelligent Surface (RIS), aim to revolutionize the monitoring process and intelligently discern various human activities. Hence, this paper introduces BSgActiv, a smart RIS-enhanced HAR system. BSgActiv utilizes fractional wavelet transform to effectively highlight time and frequency features of activities from the measured channel state information (CSI) reflected from RIS. Afterward, these features are utilized to train a recurrent neural network that records the temporal characteristics of the input, hence promoting activity recognition. Moreover, BSgActiv integrates diverse modules that enhance the deep model's overall observation and ability to withstand and perform well in the presence of noise. BSgActiv evaluations contain two different realistic scenarios, including both non-line-of-sight and multi-floor setups, proofing its efficacy. Particularly, BSgActiv overcomes benchmark techniques and delivers an activity recognition accuracy of 89.7%.

关键词： 6G mobile communication Wavelet transforms Time-frequency analysis Accuracy 5G mobile communication Reconfigurable intelligent surfaces Feature extraction

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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An Economic Scheduling Method for Coordinated Integrated Energy Systems and Transportation Networks

An Economic Scheduling Method for Coordinated Integrated Ene...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Wenjie Qiao Yinghua Han Fangyuan Si Ning Zhang Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern University Shenyang China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China Department of Electrical Engineering Tsinghua University Beijing China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China

Integrated energy systems (IES) improve overall energy efficiency by synchronizing resources and networks across multiple regions, the high penetration level of electric vehicles (EVs) may pose a threat without coordinating with the IES. Thus, this paper proposes a coordinated IES and transportation network (TN) operation model toward economic scheduling, with the secure operation conditions are comprehensively considered. Specifically, the composition networks of IES and the TN are modeled separately, then a transactive relationship model is proposed to get the networks bridged. To reduce the computational intractability, several approximation and relaxation methods are utilized to transform the model into a mixed-integer-quadratic problem (MIQP). Results show that the coordinated scheduling of IES and TN can lower 4.3% (105.266k$) of consumption compared with the independent scheduling.

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RISense: 6G-Enhanced Human Activity Recognition System with RIS and Deep LDA

RISense: 6G-Enhanced Human Activity Recognition System with ...

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International Conference on Mobile Data Management

作者： Hamada Rizk Sherief Hashima Graduate School of Information Science and Technology Osaka University Suita Japan Computer and Control Engineering Department Tanta University Tanta Egypt Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Engineering Dept Egyptian Atomic Energy Authority Cairo Egypt

ISBN: (数字)9798350374551

ISBN: (纸本)9798350374568

Human Activity Recognition (HAR) systems hold great potential in aiding disabled and elderly individuals to live independently. Various approaches have been suggested for identifying human activities, including sensors, cameras, wearables, and contactless microwave sensing. This latter method is gaining significant attention due to its ability to address privacy concerns arising from cameras and alleviate discomfort caused by wearables. However, current microwave sensing techniques have a key limitation, requiring controlled and ideal conditions to achieve accurate activity detection. In this paper, we propose RISense, a deep learning-aided system for HAR using Re-configurable Intelligent Surface (RIS). RISense introduces novel modules designed to generate a human activity representational space that ensures separability between activity classes, even in the presence of noisy and distorted Channel State Information (CSI) measurements. These representations are then fed into a Recurrent Neural Network (RNN), which learns the sequential changes in features to accurately estimate the user activity. The evaluation of RISense across two realistic settings, encompassing both non-line-of-sight and multi-floor scenarios, showcases its efficacy. Specifically, RISense achieves an activity recognition accuracy of 98.7%. This performance overcomes the accuracy of state-of-the-art systems by over 15%. These findings highlight the superior efficacy of the proposed methodologies, including integrating RIS and advanced learning techniques.

关键词： 6G mobile communication Accuracy Recurrent neural networks Microwave theory and techniques Feature extraction Cameras Human activity recognition

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Robust Reinforcement Learning-based Vehicle control with Object Avoidance

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IFAC-PapersOnLine 2024年第10期58卷 134-139页

作者： Attila Lelkó Balázs Németh András Mihály Olivier Sename Péter Gáspár HUN-REN Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Műegyetem rkp. 3. H-1111 Budapest Hungary Grenoble Institute of Technology 621 avenue Centrale 38400 Saint-Martin-d’Héres France

This paper presents a control design framework for robust reinforcement learning (RL) based vehicle control enhanced with object avoidance using a model predictive approach. The proposed integration method is applied for motion control of autonomous road vehicles. In the motion control, longitudinal and lateral dynamics are incorporated, and the high-performance motion of the vehicle, through the RL-based control agent is achieved. ’H. co based robust control is used as a baseline parallel to the RL-agent to ensure stable operation. The object avoidance and limited error path following are achieved using a model-based predictive algorithm. Finally, the control signals of the three controllers are combined using a supervisor layer, which solves a constrained optimization task to ensure high performance and safe motion. The effectiveness of the proposed control method using simulation scenarios is illustrated.

关键词： Autonomous Vehicles Robust control Motion control Reinforcement learning control Learning adaptation in autonomous vehicles

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LMI-Based Non-Iterative Design Strategies for Guaranteed Cost Static Output Feedback control for Linear Systems

LMI-Based Non-Iterative Design Strategies for Guaranteed Cos...

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International Conference on Electrical, control and Instrumentation engineering (ICECIE)

作者： Hidetoshi Oya Takuya Nakagawa Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

This paper proposes a non-iterative LMI-based design strategy for guaranteed cost static output feedback controllers for linear systems. The proposed design approach is based on the well-known necessary and sufficient condition for the existence of the static output feedback stabilizing control for linear systems. In this paper, for linear systems we show the non-iterative LMI-based design procedure for static output feedback controllers which minimize the upper bound of a given quadratic cost function, and the effectiveness of the proposed static output feedback controller with guaranteed cost is presented through a simple numerical example.

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