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Satellite-UAV Networks for 6G Control: A Sensing-Communication-Computing-Control Closed Loop Perspective

IEEE Network 2025年

作者： Lei, Chengleyang Fang, Xinran Feng, Wei Chen, Yunfei Xiao, Ming Ge, Ning Mao, Shiwen The Department of Electronic Engineering State Key Laboratory of Space Network and Communications Tsinghua University Beijing100084 China The Department of Engineering University of Durham DurhamDH1 3LE United Kingdom The Department of Information Science and Engineering School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The future sixth-generation (6G) networks are envisioned to support machines and robots besides human beings. In order to efficiently support machines and robots working in remote and post-disaster areas, satellites and unmanned aerial vehicles (UAVs) can be utilized. In this article, we investigate how satellite-UAV networks can serve robots for their control tasks. We introduce the typical use cases of the satellite-UAV networks for control and outline the relevant challenges. The control process usually involves sensing, communication, computing, and control components, which forms a sensing-communication-computing-control (SC3) closed loop. In analogy with the reflex arc structure of humans, we reveal the indivisibility of the SC3 closed loops and propose a closed-loop-oriented system design framework. Within this framework, we take the closed-loop control performance as the design target, and highlight the holistic consideration of sensing, communication, computing, and control components in the SC3 closed loop. Furthermore, we discuss the extended SC3 closed loop with multiple sensors and robots to decompose complex control systems. A case study is provided to show the performance gain of the proposed closed-loop-oriented system design. Finally, we outline open issues in designing more efficient satellite-UAV networks for control. ©1986-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

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Child and Adolescent Psychiatry and Mental Health 2024年第1期18卷 60页

作者： Ouyang, Chen-Sen Yang, Rei-Cheng Wu, Rong-Ching Chiang, Ching-Tai Chiu, Yi-Hung Lin, Lung-Chang Department of Information Management National Kaohsiung University of Science and Technology No.1 University Rd. Yanchao District Kaohsiung 824005 Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University #100 Tzyou 1st Rd. Sanmin District Kaohsiung 80756 Taiwan Department of Electrical Engineering I-Shou University No.1 Sec. 1 Syuecheng Rd. Dashu District Kaohsiung 84001 Taiwan Department of Computer and Communication National Pingtung University No.4-18 Minsheng Rd. Pingtung County Pingtung 900391 Taiwan Department of Information Engineering I-Shou University No.1 Sec. 1 Syuecheng Rd. Dashu District Kaohsiung 84001 Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University No.100 Shih-Chuan 1st Road Sanmin District Kaohsiung 807378 Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales. Methods: This approach is mainly based on movement quantization through analysis of variance in patients’ skeletons detected in outpatient videos. The patients’ skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations. Results: The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor “thigh angle”, with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39;accuracy, 91.03%;sensitivity, 90.25%;specificity, 91.86%;and AUC, 94.00%). Conclusions: The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automat

关键词： ADHD Cutoff Machine learning OpenPose Skeleton detection Variance analysis

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Detecting Human-to-AI Author Change in Arabic Text

Detecting Human-to-AI Author Change in Arabic Text

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Amal Boutadjine Fouzi Harrag Mouad Bensouilah Sabrina Karboua Mohamed Deriche Department of Computer Science Faculty of science Setif 1 University-Ferhat ABBAS Setif Algeria Department of Computer Science University of Jijel Jijel Algeria AIRC College of Engineering and Information Technology Ajman University Ajman UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent large language models (LLMs), such as Gemini and ChatGPT, have demonstrated the ability to produce texts that are fluent and human-like when given precise instructions, presenting significant challenges in distinguishing between human-authored and AI-generated content, particularly in morphologically rich languages like Arabic. Despite the fact that these issues have prompted several research on AI content identification, the majority of these earlier investigations framed the problem as a binary classification problem, supposing that a text is either wholly AI-generated or totally human-written. This study introduces a novel methodology for detection where the text to be identified is cowritten by generative LLMs and humans, by identifying transitions in text authorship using advanced machine learning and deep learning techniques. We propose Trans-Detect a sophisticated neural architecture that combines AraBERT with a bidirectional LSTM network and a specialized attention mechanism, specifically designed to capture subtle linguistic variations in hybrid texts. Alongside traditional Random Forest, XGBoost, and LSTM-CNN models, our detector processes Arabic text datasets to identify and predict authorship transitions. The research achieved a macro F1-score of 0.6-0.8 with traditional models, while our proposed neural architecture demonstrated superior performance with an F1-score of 0.98, showing significant improvement in detecting text origin segments and revealing the complex nature of authorship changes. The findings provide a foundation for future research exploring the integration of advanced natural language processing techniques to enhance the accuracy and robustness of style change detection systems, particularly in handling the characteristics of Arabic text.

关键词： Accuracy Text detection Detectors Writing Linguistics Predictive models Feature extraction Transformers Robustness Random forests

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Incorporating Onboard Visual Sensing and Geographic information for Lane-Level Vehicle Localization

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IEEE Sensors Journal 2025年

作者： Lin, Huei-Yung Li, Jun-Yi Su, Ming-Wei National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering 168 University Road Chiayi621 Taiwan

With the recent advances in deep learning and artificial intelligence, vehicular technologies are progressing rapidly toward fully autonomous driving. Critical components of advanced driver assistance systems (ADAS) have been extensively investigated in the past decade. In this paper, we address the challenge of precise vehicle localization using visual perception and geographic information. The proposed method incorporates drivable area detection, lane line detection, and image-map matching to achieve lane-level localization accuracy. It takes advantage of digital maps and images from the onboard cameras to simultaneously optimize vehicle localization and lane detection. A confidence estimation is proposed to generate virtual lane lines for the lane marking identification. Our networks are pre-trained on the Cityscapes, CULane and TuSimple datasets, and fine-tuned with the newly proposed TRoad and TLane. In the experiments, the performance evaluated using MAE and RMSE have demonstrated a significant improvement over the conventional GPS/GNSS-based vehicle localization techniques. Source code and datasets are available at https://***/Wally0924/Enhancing-Vehicle-Localization-Using-Lane-Detection-and-Geographic-information. © 2001-2012 IEEE.

关键词： Advanced driver assistance systems

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MoCo: Urban User Mobile Contact Detection Based on Cellular Signaling Trace

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IEEE Transactions on Mobile Computing 2025年

作者： Duan, Sijing Lyu, Feng Zhang, Jing Lu, Huali Yang, Peng Wu, Huaqing Zhang, Yaoxue Shen, Xuemin Central South University School of Computer Science and Engineering Hunan ChangSha410083 China Tsinghua University Department of Computer Science and Technology Beijing China Huazhong University of Science and Technology School of Electronic Information and Communications Hubei Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada Tsinghua University Department of Computer Science and Technology Haidian District Beijing100084 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Mobile contact exhibits user co-traveling events within the same transportation tool, which is crucial for resident profiling, face-to-face interaction detection, etc. In this paper, we investigate urban user mobile contact detection with cellular signaling traces, which is cost-efficient to enable large-scale detection. Specifically, we develop a data collection platform to collect substantial user signaling traces, covering different types of road scenarios within a city. With the collected traces, we perform systematic data analysis to reveal several technical challenges, which are sparsity of signaling trajectory, remote base station noise, and fuzzy matching difficulties. To address challenges, we propose a mobile contact detection method named MoCo. In MoCo framework, we first conduct data denoising to remove the noise from remote base stations. Then, we devise a spatio-temporal filter to eliminate unlikely mobile contact traces in both spatial and temporal domains, reducing the computational overhead. Finally, we design a detection network that integrates the submodules of data alignment, feature encoder, spatio-temporal representation learner, and user mobile contact detector. Extensive evaluation results demonstrate the superiority of MoCo in comparison with state-of-the-art baselines. Robust experiments show that MoCo can work efficiently in different transportation modes and urban densities. © 2002-2012 IEEE.

关键词： Urban transportation

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

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Multimedia Tools and Applications 2024年第41期83卷 89281-89295页

作者： Lee, Chao-Yang Khanum, Abida Sung, Tien-Wen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan College of Computer Science and Mathematics Fujian University of Technology No3 Xueyuan Road Fuzhou350011 China

The study introduces a groundbreaking two-stage deep hybrid learning architecture, Robust Autonomous Driving Control (RADC), designed to address the formidable challenge of ensuring safe and efficient autonomous driving in adverse weather conditions, including heavy rain and snow, in complex scenarios. In the first stage, our proposal utilizes an encoder as a variational autoencoder (VAE) model. This encoder leverages the VAE to extract feature information from the perceptual data surrounding the environment. Moving on to the second stage, we build a decoder as an Inception-Bidirectional Long Short-Term Memory (IBL) model. This decoder combines the VAE latent features obtained in stage one with additional control vehicle information tasks, including steering, speed, etc. Our approach involves predicting driving behavior along a predetermined route, allowing autonomous vehicles to stay centered on the road, simulating diverse driving scenarios, and achieving significant reductions in route-following time. This framework utilizes deep hybrid learning methods and harnesses Nvidia GPU capabilities to evaluate the effectiveness of InceptionNet, ResNet-50, MobileNet, DenseNet, and VGG16 convolutional neural networks. It enhances vehicle route following with improved metrics such as reduced inference time, heightened accuracy, and minimized lane changes in training. The RADC demonstrates exceptional performance, notably achieving a mean square error of 0.0464, 0.0346, and a 6-millisecond inference time in challenging weather like hard rain and snow. The proposed model is validated through comprehensive experiments in the Airsim environment, showcasing notable interpretability, generalization, and robustness capabilities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Detection Welding Performance of Industrial Robot Using Machine Learning

Detection Welding Performance of Industrial Robot Using Mach...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Duongthipthewa, Onjira Meesublak, Koonlachat Takahashi, Atsushi Mitsantisuk, Chowarit Kasetsart University Department of Electrical Engineering 50 Ngamwongwan Rd Chatuchak Bangkok10900 Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency 112 Thailand Science Park Phahonyothin Road Khlong Nueng Khlong Luang Pathumthani12120 Thailand Tokyo Institute of Technology Department of Information and Communications Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan

ISBN: (纸本)9798350326413

Automated welding robots have become an essential component in manufacturing industries due to their precision and increased productivity. Furthermore, they eliminate the need for human workers to be exposed to toxic fumes and bright light during welding processes, ensuring worker safety. However, any failure in welding robots can lead to poor product quality and impact a company's credibility. Therefore, it is critical to predict the welding performance of these machines to maintain product quality. This study proposes the use of spectrogram image classification to classify the quality of welding products. Using a TM-1400 WGIII welding robot, 180 welding datasets were collected to determine the conditions of the welding robot based on different welding wire lengths. Current and voltage signals of the welding robot were studied to identify suitable input parameters and machine learning algorithms were utilized to improve the accuracy of the predictive welding quality. The experiment resulted in an accuracy of 89% based on a 70% training sets and 30% testing sets of data, indicating that spectrogram image classification is an effective technique to monitor the condition of welding robots, improve welding quality, and increase product quality control. This method can be beneficial for both small- and large-scale industries such as automotive, energy, and construction. © 2023 IEEE.

关键词： Image enhancement

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Optimizing MDS-coded cache-enable wireless network:a blockchain-based cooperative deep reinforcement learning approach

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High technology Letters 2021年第2期27卷 129-138页

作者： Zhang Zheng Yang Ruizhe Yu Fei Richard Zhang Yanhua Li Meng Faculty of Information Technology Beijing University of TechnologyBeijing 100124P.R.China Department of Systems and Computer Engineering Carleton UniversityOttawa K1S5B6Canada

Mobile distributed caching(MDC)as an emerging technology has drawn attentions for its ability to shorten the distance between users and data in the wireless ***,the DC network state in the existing work is always assumed to be either static or real-time *** be more realistic,a periodically updated wireless network using maximum distance separable(MDS)-coded DC is studied,in each period of which the devices may arrive and *** the efficient optimization of the system with large scale,this work proposes a blockchain-based cooperative deep reinforcement learning(DRL)approach,which enhances the efficiency of learning by cooperating and guarantees the security in cooperation by the practical Byzantine fault tolerance(PBFT)-based blockchain *** results are presented,and it illustrates that the proposed scheme can dramatically reduce the total file download delay in DC network under the guarantee of security and efficiency.

关键词： caching technology blockchain deep reinforcement learning(DRL)

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Joint Optimization of Communication Latency and Platoon Control Based on Uplink RSMA for Future V2X Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Gang Hu, Jiewen Ma, Zheng Fan, Pingzhi Yu, F. Richard Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As an effective solution to address road congestion and improve traffic efficiency, vehicle platooning has received a lot of attention in recent years. However, vehicle-to-vehicle (V2V) communication latency can largely impact on the performance of vehicle platooning, which has not been well investigated. Rate-splitting multiple access (RSMA), as one of the promising technologies for 6 G, holds great potential for application in vehicle platooning to reduce the communication latency and thereby enhance platoon control performance. Motivated by the above considerations, platoon control and communication latency are jointly considered in this paper. We first introduce the communication latency into the vehicle dynamics model, and then derive a distributed model predictive control (DMPC)-based platoon control model. To reduce the communication latency, uplink RSMA is introduced for inter-platoon V2V communication. Since the latency minimization problem is non-convex and challenging to solve directly, we first employ a bisection method to give a latency upper bound and transform the initial problem into a non-convex feasibility problems in each iteration. Subsequently, through theoretical derivation, the bandwidth allocation factor is expressed in terms of transmission power, which reduces the number of optimization variables and transforms the problem into a convex problem. Then we can obtain the optimal solution by solving a convex problem in each iteration. Finally, the simulation results demonstrate that the proposed RSMA significantly outperforms traditional frequency division multiple access (FDMA) and non-orthogonal multiple access (NOMA), and can significantly reduce communication latency, enhance platoon control safety, improve driving comfort, and lower the performance requirement on vehicle maximum torque. © 2025 IEEE.

关键词： Traffic congestion

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Exploiting NOMA and RIS in Integrated Sensing and Communication

Exploiting NOMA and RIS in Integrated Sensing and Communicat...

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作者： Zuo, Jiakuo Liu, Yuanwei Zhu, Chenming Zou, Yixuan Zhang, Dengyin Al-Dhahir, Naofal China Information Consulting and Designing Institute Company Ltd. Institute of Communications and Information Technology Nanjing210003 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210003 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

A novel integrated sensing and communication (ISAC) system is proposed, where a dual-functional base station is utilized to transmit the superimposed non-orthogonal multiple access (NOMA) communication signal for serving communication users and sensing targets simultaneously. Furthermore, a new reconfigurable intelligent surface (RIS)-aided-sensing structure, where a dedicated RIS is deployed to provide virtual line-of-sight (LoS) links for radar targets, is also proposed to address the significant path loss or blockage of LoS links for the sensing task. Based on this setup, the beampattern gain at the RIS for the radar target is derived and adopted as a sensing metric. The objective of this paper is to maximize the minimum beampattern gain by jointly optimizing active beamforming at the base station (BS), power allocation coefficients among NOMA users and passive beamforming at the RIS. To tackle the non-convexity of the formulated optimization problem, the beampattern gain and constraints are first transformed into more tractable forms. Then, an iterative block coordinate descent (IBCD) algorithm is proposed by employing successive convex approximation (SCA), Schur complement, semidefinite relaxation (SDR) and sequential rank-one constraint relaxation (SRCR) methods. To reduce the complexity of the proposed IBCD algorithm, a low-complexity iterative alternating optimization (IAO) algorithm is proposed. Particularly, the active beamforming is optimized by solving a semidefinite programming (SDP) problem and the closed-form solutions of the power allocation coefficients are derived. Numerical results show that: i) the proposed RIS-NOMA-ISAC system always outperforms the RIS-ISAC system without NOMA in beampattern gain and illumination power;ii) the low-complexity IAO algorithm achieves a comparable performance to that achieved by the IBCD algorithm. iii) high beampattern gain can be achieved by the proposed joint optimization algorithms in underloaded and overloaded

关键词： Optimization

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