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arXiv 2025年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Mao, Shiwen Quek, Tony Q.S. Computer Science and Technology Jilin University Changchun130012 China Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert communication system assisted by the aerial active simultaneous transmitting and reflecting reconfigurable intelligent surface (AASTAR-RIS) to improve the channel capacity while ensuring the transmission covertness. Specifically, we first derive the minimal detection error probability (DEP) under the worst condition that the Warden has perfect channel state information (CSI). Then, we formulate an AASTAR-RIS-assisted satellite-terrestrial covert communication optimization problem (ASCCOP) to maximize the sum of the fair channel capacity for all ground users while meeting the strict covert constraint, by jointly optimizing the trajectory and active beamforming of the AASTAR-RIS. Due to the challenges posed by the complex and high-dimensional state-action spaces as well as the need for efficient exploration in dynamic environments, we propose a generative deterministic policy gradient (GDPG) algorithm, which is a generative deep reinforcement learning (DRL) method to solve the ASCCOP. Concretely, the generative diffusion model (GDM) is utilized as the policy representation of the algorithm to enhance the exploration process by generating diverse and high-quality samples through a series of denoising steps. Moreover, we incorporate an action gradient mechanism to accomplish the policy improvement of the algorithm, which refines the better state-action pairs through the gradient ascent. Simulation results demonstrate that the proposed approach significantly outperforms important benchmarks, and also validate the performance and practicality under different algorithm parameters and environment settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Development of a Remote-Control System for a Mobile Vibration-Driven Robot

Development of a Remote-Control System for a Mobile Vibratio...

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International Conference on CAD Systems in Microelectronics (CADSM)

作者： Vitaliy Korendiy Oleksandr Kachur Rostyslav Predko Department of Technical Mechanics and Dynamics of Machines Lviv Polytechnic National University Lviv Ukraine Department of Computer-Aided Design Lviv Polytechnic National University Lviv Ukraine Department of Robotics and Integrated Mechanical Engineering Technologies Lviv Polytechnic National University Lviv Ukraine

Vibration-driven locomotion system is a relatively new branch of mobile robotics. The design variety of vibration-driven robots is quite wide, while their control systems need constant improvement from the viewpoint of the robot's operational efficiency, accuracy, production, and maintenance costs. The purpose of this paper is the development, simulation, and practical implementation of a novel control system for a mobile vibration-driven robot. The proposed control system is based on Arduino hardware and software. The developed wheeled robot can be remotely controlled using the Bluetooth interface and the mobile phone with the corresponding Android application installed. The experimental prototype of the mobile robot is tested under different operational conditions (supplied voltages and forced frequencies) regulated by the developed control system. The corresponding kinematic parameters of the robot's wheeled platform and the impact body are registered by the WitMotion accelerometers, and the experimental data are processed with the help of the WitMotion and MathCad software. The results of the presented research can be effectively used while designing and implementing mobile robotic systems for inspecting, cleaning, or maintaining the internal surfaces of various pipelines, tubes, vessels, intestines, etc.

关键词： Voltage measurement Intestines Pipelines Oscilloscopes Control systems Robot sensing systems Software

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Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays

arXiv

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arXiv 2023年

作者： Li, Jiahui Sun, Geng Duan, Lingjie Wu, Qingqing The College of Computer Science and Technology Jilin University Changchun130012 China Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore487372 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms and also show that the proposed method can reduce time and energy costs significantly

关键词： Multiobjective optimization

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Ms-Mvs: Multi-Stage Multi-View Stereo Via Offset Regression and Uncertain Distance Estimation

SSRN

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SSRN 2024年

作者： Cao, Mingwei Liu, Bowen Yi, Jun Yu, Ye Zhao, Haifeng Lyu, Zhihan Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Game Design Uppsala University Visby62167 Sweden

In recent years, deep learning-based multi-view stereo (MVS) reconstruction methods have made significant progress. However, they still face challenges such as low accuracy and incomplete scene reconstruction due to the differences between the captured images from various viewpoints. To address these challenges, we propose a multi-stage MVS reconstruction network that utilizes offset regression and uncertainty distance estimation to weaken the impact of the differences. Firstly, the multi-view images are input into a dynamic feature extraction network to obtain robust image features. Secondly, we utilize the uncertainty distance estimation method to dynamically calculate the depth sampling range for each pixel. This method considers the probability distribution and contextual information of adjacent pixels to improve the depth estimation accuracy. Thirdly, we present an offset regression algorithm that combines the advantages of both classification and regression methods. It further refines the predicted depth values, bringing them closer to the ground truth values. Finally, we tested the method on public datasets and compared it to traditional MVS reconstruction methods. Results show that the new method outperforms the current methods in terms of the accuracy and completeness of point clouds while using less memory space. The source code is available at: https://***/liubowen1233/MS-MVS. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Unlocking Past Information: Temporal Embeddings in Cooperative Bird’s Eye View Prediction

arXiv

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arXiv 2024年

作者： Rößle, Dominik Gerner, Jeremias Bogenberger, Klaus Cremers, Daniel Schmidtner, Stefanie Schön, Torsten Department of Computer Science and AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt85049 Germany Department of Electrical Engineering and AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt85049 Germany The School of Engineering and Design Technical University of Munich München80333 Germany The School of Computation Information and Technology Technical University of Munich Garching85748 Germany

Accurate and comprehensive semantic segmentation of Bird’s Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2% and under communication failures by up to 19%. The code will be published on GitHub. © 2024, CC BY-NC-ND.

关键词： Birds

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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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Development of AC Voltage Stabilizer with Microcontroller-Based Control System

Development of AC Voltage Stabilizer with Microcontroller-Ba...

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International computer Sciences and Information Technologies (CSIT)

作者： Andriy Holovatyy Andrzej Łukaszewicz Vasyl Teslyuk Nazariy Ripak Department of Computer-Aided Design Systems Lviv Polytechnic National University Lviv Ukraine Department of Machinery Design and Operation Faculty of Mechanical Engineering Bialystok University of Technology Bialystok Poland Department of Automated Control Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798350334326

In the paper, the model and prototype of the AC stabilizer have been developed based on the AVR ATmega328P microcontroller. The block diagram of the device, the electronic circuit and the algorithms of the system operation have been developed. In particular, the algorithm includes software in C in the Arduino IDE. The AC voltage stabilizer is designed using the modular principle, which makes it possible to quickly upgrade. The AC voltage stabilizer makes it possible to maintain the supply voltage on the load within acceptable limits with significant deviations, has the ability to adjust the voltage range of the network to operate the stabilizer, manual adjustment potentiometers.

关键词： Semiconductor device modeling Microcontrollers Systems operation Software algorithms Prototypes Software Voltage control

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Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation

Enhancing Realistic Floating Car Observers in Microscopic Tr...

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International Conference on Intelligent Transportation

作者： Jeremias Gerner Dominik Rößle Daniel Cremers Klaus Bogenberger Torsten Schön Stefanie Schmidtner Department of Electrical Engineering and AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany Department of Computer Science and AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany School of Computation Information and Technology Technical University of Munich Garching Germany School of Engineering and Design Technical University of Munich München Germany

We present a system that enables a realistic detection of traffic participants using Floating Car Observers (FCOs) directly within microscopic simulations. Point clouds are utilized to transform the two-dimensional simulation into a three-dimensional environment. In this environment, vehicles can be equipped with up to four camera sensors. Employing computer vision strategies, we identify which traffic participants would be detected by real-world sensor systems. We utilize the resulting system to generate datasets. In this process, various vehicles move within a simulation, recording which traffic participants would be recognized by the approach. Additionally, for each step of the simulation, an image from the current simulation and the position vectors of the traffic participants are documented. We employ the dataset to train neural networks, enabling them to replicate the results achieved using the CV method. The trained Vision Transformer and ResNet architectures achieve accuracies of up to 90%. Compared to the CV approach, the neural networks facilitate as much as 18-fold speed enhancement. We have made the source code, datasets, and trained models openly accessible at: ***/urbanAIthi/SUMO_FCO.

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Bridging Gaps in HCI: Advancing Education, Research, and Careers in Asia

Bridging Gaps in HCI: Advancing Education, Research, and Car...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Gamage, Dilrukshi Cheng, Shiwei Mudliar, Preeti Lu, Zhicong Zhao, Shengdong Ahmed, Nova Ma, Xiaojuan Lee, Uichin Wang, Ding Department of Computation and Intelligent Systems University of Colombo School of Computing Colombo Sri Lanka Zhejiang University of Technology Hangzhou China International Institute of Information Technology Bangalore Bangalore India Department of Computer Science City University of Hong Kong Hong Kong School of Creative Media City University of Hong Kong Hong Kong Design Inclusion and Access Lab. Department of Electrical and Computer Engineering North South University Dhaka Dhaka Bangladesh Hong Kong University of Science and Technology Hong Kong Hong Kong School of Computing KAIST Daejeon South Korea Google Atlanta GA United States

ISBN: (纸本)9798400713958

Asia’s Human-computer Interaction (HCI) landscape is rapidly evolving, yet it faces distinct challenges in curriculum development, research establishment, and career navigation. This panel discussion, hosted by Asia SIGCHI Committee (ASC) at CHI2025, will bring together distinguished experts to address these challenges and explore strategies for fostering a robust and inclusive HCI community in Asia. Panelists will discuss the integration of culturally relevant content in HCI education, approaches to enhancing research visibility on international platforms, and initiatives to bridge gaps between academic training and industry expectations. Through an engaging discussion and audience interaction, the panel aims to identify actionable solutions, promote collaboration, and inspire future initiatives that strengthen HCI’s growth across the region. Participants will gain valuable insights into overcoming systemic barriers while building sustainable and impactful HCI programs tailored to Asia’s unique sociocultural dynamics. This panel seeks to advance the global relevance and contributions of HCI in Asia. © 2025 Copyright held by the owner/author(s).

关键词： Asia Curricula HCI HCI Education HCI Teaching

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Availability Audit of IoT System Data Reserved by 3-2-1 Backup Strategy based on Fault Tree and State Transition Diagram Analysis 15

Availability Audit of IoT System Data Reserved by 3-2-1 Back...

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15th IEEE International Scientific and Technical Conference on computer Sciences and Information Technologies, CSIT 2020

作者： Lobur, Mykhaylo Shcherbovskykh, Serhiy Stefanovych, Tetyana Lviv Polytechnic National University Department of Computer-Aided Design Lviv Ukraine Lviv Polytechnic National University Department of Mechanical Engineering Lviv Ukraine

The model for availability audit of data reserved by a 3-2-1 backup strategy is proposed. The model takes into account technical failures and force majeure such as fire, flooding, intruder actions and so on. Fault tre... 详细信息

ISBN: (纸本)9781728174433

关键词： Trees (mathematics)

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