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IEEE International Symposium on Information Theory

作者： Milad Abolpour MohammadJavad Salehi Soheil Mohajer Seyed Pooya Shariatpanahi Antti Tölli Centre for Wireless Communications University of Oulu Finland Department of Electrical and Computer Engineering University of Minnesota USA School of Electrical and Computer Engineering University of Tehran Iran

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

A novel coding design is proposed to enhance information retrieval in a wireless network of users with partial access to the data, in the sense of observation, measurement, computation, or storage. Information exchange in the network is assisted by a multi-antenna base station (BS), with no direct access to the data. Accordingly, the missing parts of data are exchanged among users through an uplink (UL) step followed by a downlink (DL) step. In this paper, new coding strategies, inspired by coded caching (CC) techniques, are devised to enhance both UL and DL steps. In the UL step, users transmit encoded and properly combined parts of their accessible data to the BS. Then, during the DL step, the BS carries out the required processing on its received signals and forwards a proper combination of the resulting signal terms back to the users, enabling each user to retrieve the desired information. Using the devised coded data retrieval strategy, the data exchange in both UL and DL steps requires the same communication delay, measured by normalized delivery time (NDT). Furthermore, the NDT of the UL/DL step is shown to coincide with the optimal NDT of the original DL multi-input single-output CC scheme, in which the BS is connected to a centralized data library.

关键词： Base stations Wireless networks Information retrieval Downlink Libraries Encoding Time measurement

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RS-YOLOv10: Enhancing YOLOv10 for Accurate Small-Object Detection

RS-YOLOv10: Enhancing YOLOv10 for Accurate Small-Object Dete...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Dat Minh-Tien Nguyen Thien Huynh-The Department of Computer and Communications Engineering HCMC University of Technology and Education Ho Chi Minh City Vietnam

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The capability to detect objects in remote sensing images has proven to deliver exceptional results spanning multiple fields of application, setting it apart from other techniques. However, with limited resources, it faces numerous challenges related to noise, poor information quality, and particularly the detection of small objects. To overcome these challenges, we introduce RS-YOLOv10, a model built on YOLOv10, designed to reduce resource consumption and improve accuracy for remote sensing applications. First, to enhance the backbone network, we apply the Efficient Attention Mechanism, which helps the model prioritize key information within the feature map, the model boosts detection efficiency. In addition, we propose the Weighted Focused Feature Pyramid Network (WFFPN), which effectively minimizes the network while enhancing the efficiency and performance of multi-scale feature fusion. When evaluated on the VisDrone2019 dataset, RS-YOLOv10 demonstrated an approximate 10% decrease in the number of parameters relative to the baseline model and significantly improves metrics, with precision increasing by 2.5%, mAP 50 increasing by 1.4%, and mAP 95 increasing by 1.1%. Relative to other cutting-edge models, our model shows superior performance, particularly for smaller object detection.

关键词： Measurement Accuracy Computational modeling Noise Object detection Transformers Sensors Information management Remote sensing Load modeling

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Large Multi-Modality Model-Based V2X communications

Large Multi-Modality Model-Based V2X Communications

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Hyunsoo Kim Seokhyun Jeong Byonghyo Shim Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In the 5G NR and 6G, vehicle-to-everything (V2X) communication has emerged as a crucial technology. In this paper, we present a novel task-oriented communications (ToC) approach that employs large multi-modal models (LMMs) for V2X tasks such as traffic management, The key idea of the proposed LMM-based V2X (LMM-V2X) scheme is to extract essential information relevant to vehicular tasks from the various sensing data. LMM-V2X exchanges only the essential feature vec-tors needed for vehicular tasks, thereby reducing communication overhead and improving task performance. Numerical results demonstrate that the proposed LMM - V2X technique significantly increases average vehicle speed by more than 40 % compared to conventional methods.

关键词： Simulation Sensor phenomena and characterization Feature extraction Data models Numerical models Information and communication technology Data mining Vehicle-to-everything Intelligent sensors Convergence

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Event-driven bi-fidelity control for efficient and reliable robotic manipulation

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Results in engineering 2025年 26卷

作者： Alsaade, Fawaz W. Al-zahrani, Mohammed S. Alsaadi, Fuad E. Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

This paper proposes a novel bi-fidelity control framework for robotic manipulators that integrates a high-fidelity model predictive control (MPC) scheme with a low-fidelity Long Short-Term Memory (LSTM) neural network surrogate. Unlike conventional fixed-schedule approaches, our method employs an event-triggered mechanism that dynamically selects the appropriate controller based on a real-time error metric. This mechanism ensures that the computationally intensive MPC is invoked only when the LSTM approximation deviates beyond acceptable bounds. We rigorously established the near-optimal performance and closed-loop stability of our approach via Lyapunov analysis under mild assumptions, instilling confidence in its reliability. The simulation results on a three-arm manipulator, subject to low-frequency sinusoidal and high-frequency chirp trajectories, demonstrate that the proposed approach achieves up to 80–90% reduction in MPC calls, significantly improving the accuracy of the follow-up and computational efficiency. These findings highlight the potential of integrating learning-based approximations with conventional optimization-based control to achieve reliable and time-efficient performance in complex robotic manipulation tasks. © 2025 The Authors

关键词： Predictive control systems

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Autonomous Hardware-Trojan Generation Method Using Reinforcement Learning for Random Forest-Based Hardware-Trojan Detection

Autonomous Hardware-Trojan Generation Method Using Reinforce...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Ikegami, Yuka Hasegawa, Kento Hidano, Seira Fukushima, Kazuhide Togawa, Nozomu Department of Computer Science and Communications Engineering Waseda University Japan KDDI Research Inc. Japan

ISBN: (纸本)9798331521165

In recent years, the risk of inserting malicious circuits called hardware Trojan (HT) in the IC design process has become a serious concern. Although machine learning methods are effective in detecting HTs, their performance is not guaranteed for more difficult HTs. In order to make practical use of HT detection methods, they must be able to adapt to the daily updates of hard-to-detect HTs, and it is quite necessary to efficiently generate them. In this paper, we propose an autonomous HT generation method using reinforcement learning for random forest-based HT detection. The proposed method uses reinforcement learning to generate HTs efficiently and autonomously. The reinforcement learning algorithm used repeatedly generates new HTs and observes the HT detection accuracy, and learns a policy for modifying parameters during HT composition in order to minimize the HT detection accuracy. From the evaluation experiments, we confirmed that the proposed method generates HTs that are difficult to detect. © 2025 IEEE.

关键词： Reinforcement learning

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A Novel Hyper Chaotic-Cellular Automata Based Medical Image Encryption Technique 3

A Novel Hyper Chaotic-Cellular Automata Based Medical Image ...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： El-Shafai, Walid Naguib, Ahmed Shokair, Mona Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt 6 October University Faculty of Electrical Engineering Egypt

ISBN: (纸本)9798350323511

The security of healthcare and telemedicine systems stands as a matter of paramount importance, necessitating extensive research. In the upcoming years, the medical industry envisions widespread adoption of advanced telemedicine applications. With healthcare smart devices becoming increasingly interconnected through the Internet, enabling convenient access from any location, the protection of sensitive and private information, such as personal medical images, emerges as a critical focal point. As a result, the encryption of medical images assumes a pivotal role within the domain of telemedicine and healthcare applications. This article introduces a highly efficient cryptographic system designed to enhance the protection of medical images by harnessing the power of cellular automata techniques in conjunction with hyper-chaotic procedures. In the proposed medical image encryption technique, a layered cellular automata approach is embraced, and a pseudorandom sequence derived from a four-dimensional hyper-chaotic system is employed as the cryptographic key. The study encompasses both grayscale and color images. Several assessment criteria, including the Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), average entropy, correlation coefficients, Structural Similarity (SSIM), Feature Similarity (FSIM), and Peak Signal-to-Noise Ratio (PSNR), are employed to evaluate the suggested cryptosystem. These evaluations vividly underscore the robust security performance exhibited by the proposed approach. © 2023 IEEE.

关键词： Entropy

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Scheduling of Integrated 5G and Time Sensitive Network for Deterministic Communication

Scheduling of Integrated 5G and Time Sensitive Network for D...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Shih, Yuan-Yao Liu, Hou-Chen Chuang, Ching-Chih Pang, Ai-Chun National Chung Cheng University AIM-HI and Department of Communications Engineering Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan National Pingtung University Department of Information Management Taiwan

ISBN: (纸本)9798350339918

Various modern industrial applications necessitate deterministic communication, assuring that each packet of a data flow arrives at a specific time. Most existing Time-Sensitive Networking (TSN) scheduling mechanisms enforce temporary isolation (any two flows cannot simultaneously be in the same queue in a network device) to ensure such deterministic property. This approach has no significant side effects in a wired Ethernet environment. However, recent calls have been made for incorporating TSN and wireless technology, such as 5th generation (5G) network, due to the device's mobility and desire for flexibility of many emerging industrial applications. In wireless environments, the fickle nature of wireless communication will cause the existing approach to require more network resources to maintain the deterministic property, resulting in decreased schedulability. Thus, this paper aims to propose a more efficient scheduling mechanism for deterministic communication under integrated 5G and TSN environments. First, we parameterize the residence time of the bridges and analyze the requirements of deterministic communication. Then, based on the analysis, we extend and develop new scheduling rules to improve schedulability while maintaining determinism. Finally, Our evaluation via performing a set of experiments in realistic settings showcases the improved schedulability of our method with the preservation of the deterministic property. © 2023 IEEE.

关键词： 5G mobile communication systems

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Optimal Semantic-Aware Sampling and Transmission in Energy Harvesting Systems Through the AoII

Optimal Semantic-Aware Sampling and Transmission in Energy H...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Zakeri, Abolfazl Moltafet, Mohammad Codreanu, Marian Centre for Wireless Communications - Radio Technologies University of Oulu Finland University of California Santa Cruz Department of Electrical and Computer Engineering United States Linköping University Department of Science and Technology Sweden

ISBN: (纸本)9798350370218

We study a real-time tracking problem in an energy harvesting status update system with a Markov source and an imperfect channel, considering both sampling and transmission costs. The problem's primary challenge stems from the non-observability of the source due to the sampling cost. By using the age of incorrect information (AoII) as a semantic-aware performance metric, our main goal is to find an optimal policy that minimizes the time average AoII subject to an energy-causality constraint. To this end, a stochastic optimization problem is formulated and solved by modeling it as a partially observable Markov decision process (POMDP). More specifically, to solve the main problem, we use the notion of a belief state and cast the problem as a belief MDP problem. Then, for the perfect channel setup, we effectively truncate the corresponding belief space and solve the MDP problem using the relative value iteration method. For the general setup, a deep reinforcement learning policy is proposed. The simulation results show the efficacy of the derived policies in comparison to an AoI-optimal policy and an opportunistic baseline policy. © 2023 IEEE.

关键词： age of incorrect information partially observable Markov decision process Real-time tracking semantic communication

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Bi-level Optimal Planning of Voltage Regulator in Distribution Systems Considering Maximization of Incentive-based Photovoltaic Energy Integration

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2008-2017页

作者： Xu Xu Youwei Jia Chun Sing Lai Minghao Wang Zhao Xu Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHung HomHong Kong SARChina Department of Electrical and Electronic Engineering University Key Laboratory of Advanced Wireless Communications of Guangdong ProvinceSouthern University of Science and TechnologyShenzhen 515100China Department of Electronic and Computer Engineering Brunel Institute of Power SystemsBrunel University LondonLondonUB83PH United Kingdom Department of Electrical Engineering Shenzhen Research InstituteThe Hong Kong Polytechnic UniversityHung HomHong Kong SARChina

This paper focuses on optimal voltage regulator(VR)planning to maximize the photovoltaic(PV)energy integration in distribution *** describe the amount of dynamic PV energy that can be integrated into the power system,the concept of PV accommodation capability(PVAC)is introduced and modeled with *** proposed planning model is formulated as a Benders decomposition based bi-level stochastic optimization *** the upper-level problem,VR planning decisions and PVAC are determined via mixed integer linear programming(MILP)before considering *** in the lower-level problem,the feasibility of first-level results is checked by critical network constraints(*** magnitude constraints and line capacity constraints)under uncertainties considered by time-varying loads and PV *** this paper,these uncertainties are represented in the form of operational scenarios,which are generated by the Gaussian copula theory and reduced by a well-studied backward-reduction *** modified IEEE 33-node distribution grid is utilized to verify the effectiveness of the proposed *** results demonstrate that a PV energy integration can be significantly enhanced after optimal voltage regulator planning.

关键词： Bi-level stochastic optimization problem critical network constraints photovoltaic energy integration uncertainties voltage regulator planning

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CLIPREC: Graph-Based Domain Adaptive Network for Zero-Shot Referring Expression Comprehension

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IEEE Transactions on Multimedia 2024年 26卷 2480-2492页

作者： Ke, Jingcheng Wang, Jia Chen, Jun-Cheng Jhuo, I-Hong Lin, Chia-Wen Lin, Yen-Yu National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering Hsinchu300093 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115201 Taiwan Microsoft SeattleWA98052 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan

Referring expression comprehension (REC) is a cross-modal matching task that aims to localize the target object in an image specified by a text description. Most existing approaches for this task focus on identifying only objects whose categories are covered by training data. This restricts their generalization to unseen categories and practical usage. To address this issue, we propose a domain adaptive network called CLIPREC for zero-shot REC, which integrates the Contrastive Language-Image Pretraining (CLIP) model for graph-based REC. The proposed CLIPREC is composed of a graph collaborative attention module with two directed graphs: one for objects in an image and the other for their corresponding categorical labels. To carry out zero-shot REC, we leverage the strong common image-text feature space from the CLIP model to correlate the two graphs. Furthermore, a multilayer perceptron is introduced to enable feature alignment so that the CLIP model is adapted to the expression representation from the language parser, resulting in effective reasoning from expressions involving both seen and unseen object categories. Extensive experimental and ablation results on several widely-adopted benchmarks show that the proposed approach performs favorably against state-of-the-art approaches for zero-shot REC. © 1999-2012 IEEE.

关键词： Object detection

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