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3rd IEEE International Conference on Robotic Computing (IRC)

作者： Soria, Enrica Schiano, Fabrizio Floreano, Dario Department of Computer Science and Technology the Key Laboratory of Embedded System Service Computing Shanghai China

ISBN: (纸本)9781538692455

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which consists in the analysis of the effects of limited sensing on the behavior of the swarm. In nature, the central sensor modality often used for achieving flocking is vision. In this work, we study how the reduction in the field of view and the orientation of the visual sensors affect the performance of the Reynolds flocking algorithm used to control the swarm. To quantify the impact of limited visual sensing, we introduce different metrics such as (i) order, (ii) safety, (iii) union and (iv) connectivity. As Nature suggests, our results confirm that lateral vision is essential for coordinating the movements of the individuals. Moreover, the analysis we provide will simplify the tuning of the Reynolds flocking algorithm which is crucial for real-world deployment and, especially for aerial swarms, it depends on the envisioned application. We achieve the results presented in this paper through extensive Monte-Carlo simulations and integrate them with the use of genetic algorithm optimization.

关键词： Robot sensing systems Visualization Animals Nickel Azimuth

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Prescribed-time synchronization for multiweighted complex networks: A switching control scheme

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systems & Control Letters 2025年 203卷

作者： Linlong Xu Xiwei Liu Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 201804 PR China

This letter deals with an interesting intersection phenomenon of prescribed-time stability (PTSta) for dynamical systems, and develops a novel switching control scheme to investigate prescribed-time synchronization (PTSyn) for multiweighted and directly coupled complex networks. Different from most previous works that scholars only pay attention to designing the regulation function to ensure PTSta, we aim to select various parameters and uncover the mathematical mechanism of intersecting system state curves. We rigorously prove that, if the settling time is larger than 1, then no matter what the initial value is, the intersection exists only once before the settling time; otherwise, there is no intersection. Moreover, an energy consumption evaluation function is also put forward for PTSta, whose exact value is also calculated via exponential integrals. Then, this intersection theory is applied on the PTSyn of multiweighted complex networks, which is beneficial to construct the switching control scheme or choose the optimal parameters to reduce the energy cost. The rearranging variables’ order technique is utilized to conduct the multiweighted complex networks and obtain the synchronization criterion. Finally, four simulations are presented to verify theoretical results.

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Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

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Frontiers of computer Science 2020年第6期14卷 55-69页

作者： Qianchen YU Zhiwen YU Zhu WANG Xiaofeng WANG Yongzhi WANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China Shanxi Provincial Key Laboratory for Embedded System Xi'an 710072China College of Computer Science and Engineering North Minzu UniversityYinchuan 750021China College of Earth Exploration Science and Technology Jilin UniversityChangchun 130025China

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been ***,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined *** propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in *** Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been ***,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the *** this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout *** conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

关键词： graph convolutional neural network graph classification overlapping community detection nonparametric Bayesian generative model relational infinite latent feature model Indian buffet process uncollapsed Gibbs sampler posterior inference quality estimation

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Cross-Modal Representation Reconstruction for Zero-Shot Classification

Cross-Modal Representation Reconstruction for Zero-Shot Clas...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yu Wang Shenjie Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education P. R. China Mojie Technology (Ningbo)

ISBN: (纸本)9781728176055;9781728176062

Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CM-RR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.

关键词： Training Visualization Semantics Signal processing algorithms Signal processing Feature extraction Iterative algorithms

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Mobility-Aware User Association and Computation Offloading in Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Qiu, Bin Feng, Ke Li, Xian Xiao, Hailin Zhang, Zhongshan Guilin University of Technology School of Computer Science and Engineering Guilin541004 China Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541004 China Guangxi Modern Polytechnic College Hechi530700 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Wuhan430062 China School of Cyberspace Science and Technology Guangdong519088 China

The integration of mobile edge computing (MEC) into ultra-dense network (UDN) has emerged as a promising solution for providing uninterrupted task offloading services to computation-demanding mobile devices (MDs), leveraging densely deployed MEC servers. However, the mobility of MDs can significantly affect the computation offloading decision and system performance in UDN-MEC. To address this issue, we develop a joint optimization algorithm for mobility-aware user association and computation offloading to minimizize the total system cost, including delay and energy consumption in UDN-MEC. Specifically, a polynomial fitting algorithm based on an extended Kalman filter (PFA-EKF) is designed to predict the nonlinear moving trajectories of the MDs. Furthermore, we present a mobility-aware user-association rule based on a breadth-first search (UAR-BFS) that can realize balanced matching offloading in the overlapping coverage area of densely deployed small base stations. Accordingly, we propose an improved deep deterministic policy gradient-based greedy algorithm (IDDPG-GA) to address the joint optimization problem of multiuser dynamic offloading and resource allocation, in which an intelligently adjusted bias factor is proposed to enhance collaborative behavior among MDs. Extensive numerical results and comparisons demonstrate the promising performance improvements of the proposed scheme compared with existing benchmark schemes in various scenarios. © 2025 IEEE.

关键词： Computation offloading

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Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment

Cross-Domain Semantic Segmentation of Urban Scenes via Multi...

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International Conference on Pattern Recognition

作者： Bin Zhang Shengjie Zhao Rongqing Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education China School of Software Engineering Tongji University Shanghai China

Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.

关键词： Image segmentation Solid modeling Semantics Neural networks Virtual reality Feature extraction Robustness

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The behavioral sign of account theft: Realizing online payment fraud alert 29

The behavioral sign of account theft: Realizing online payme...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Cheng Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University China

ISBN: (纸本)9780999241165

As a matter of fact, it is usually taken for granted that the occurrence of unauthorized behaviors is necessary for the fraud detection in online payment services. However, we seek to break this stereotype in this work. We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users' high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account's historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than 80% of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than 94% of fraudulent transactions while maintaining a very low false alarm rate (less than 0.1%). © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Crime

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Intelligent Energy-Efficient Resource Allocation for Multi-UAV-Assisted Mobile Edge Computing Networks

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China Communications 2025年第4期22卷 339-355页

作者： Hu Han Shen Le Zhou Fuhui Wang Qun Zhu Hongbo The China Information Consulting and Designing Institute CO. Ltd.Nanjing 210000China The Jiangsu Key Laboratory of Wireless Communications(Nanjing University of Posts and Telecommunications) Nanjing 210003China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education(Nanjing University of Posts and Telecommunications)Nanjing 210003China The College of Artificial Intelligence(Nanjing University of Aeronautics and Astronautics) Nanjing 210000China The Department of Computer Science(San Francisco State University) San FranciscoCAUSA

The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency ***,the multi-UAVassisted MEC network remains largely *** this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground *** considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is *** address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,*** results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.

关键词： dynamic trajectory optimization intelligent resource allocation unmanned aerial vehicle uav assisted uav assisted mec energy efficiency smart applications mobile edge computing mec deep reinforcement learning

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LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

arXiv

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arXiv 2023年

作者： Huang, Kai Zhao, Junqiao Zhu, Zhongyang Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Local geometric information, i.e., normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://***/tiev-tongji/LOG-LIO. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical radar

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