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Multi-Antenna UAV Assisted Hybrid FSO/RF Data Collection for IoT: Optimal Design for Fairness

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Xu, Fang Xie, Zhijie Hu, Kai Liu, Yu Song, Shenghui Gulliver, Thomas Aaron Xie, Yiyuan Yang, Yandong Duo, Bin Wang, Yuanchen Southwest University College of Electronic and Information Engineering Chongqing400715 China Hong Kong University of Science and Technology ECE Department Hong Kong Tianjin University School of microelectronics Tianjin300072 China University of Victoria Department of electrical and computer engineering VictoriaBCV8P5C2 Canada AI & HPC Software Department in IEI Beijing China Chengdu University of Technology School of computer and network security department Chengdu610059 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Wireless sensors are often deployed in hard-to-reach locations for data collection. Unmanned aerial vehicles(UAVs) can easily fly over such locations and so it is a promising solution to collect data from remote sensors. This paper considers a UAV-assisted hybrid free space optical (FSO)/radio frequency (RF) data collection network for Internet of Things (IoT) applications. In this network, multiple remote sensors transfer information to a UAV with multiple antennas through RF links using time division multiple access (TDMA). The UAV uses decode and forward (DF) protocol to send information to a base station (BS) via FSO link. Successive interference cancellation (SIC) is employed to decode the information at the BS. Multi-agent deep reinforcement learning (DRL) is modified and applied to obtain a near-optimal scheme, for transmit power allocation, to minimize the maximum outage probability of decoding the information from the sensors under dynamic weather condition/UAV state. Numerical results are presented to illustrate the system design tradeoffs. Furthermore, the validity and superiority of our proposed approach is verified by comparing it with exhaustive search and differential evolution algorithms. © 1965-2011 IEEE.

关键词： Drones

AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks

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arXiv 2021年

作者： Feng, Ruiwei Xie, Yufeng Lai, Minshan Chen, Danny Z. Cao, Ji Wu, Jian Coll. of Computer Science and Technology Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China Polytechnic Institute Zhejiang University Hangzhou China Dept. of Computer Science and Engineering University of Notre Dame Notre Dame United States Coll. of Pharmaceutical Sciences Zhejiang University Hangzhou China School of Public Health Zhejiang University Hangzhou China

Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine. This paper presents a novel Attention-Guided Multi-omics Integration (AGMI) approach for DRP, which first constructs a Multi-edge Graph (MeG) for each cell line, and then aggregates multi-omics features to predict drug response using a novel structure, called Graph edge-aware Network (GeNet). For the first time, our AGMI approach explores gene constraint based multi-omics integration for DRP with the whole-genome using GNNs. Empirical experiments on the CCLE and GDSC datasets show that our AGMI largely outperforms state-of-the-art DRP methods by 8.3%-34.2% on four metrics. Our data and code are available at https://***/yivan-WYYGDSG/AGMI. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

Neural networks enforcing physical symmetries in nonlinear dynamical lattices: The case example of the Ablowitz-Ladik model

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arXiv 2021年

作者： Zhu, Wei Khademi, Wesley Charalampidis, Efstathios G. Kevrekidis, Panayotis G. Department of Mathematics and Statistics University of Massachusetts Amherst AmherstMA01003-4515 United States Computer Science and Software Engineering Department California Polytechnic State University San Luis ObispoCA93407-0403 United States School of Electrical Engineering and Computer Science Oregon State University OR97331-5501 United States Mathematics Department California Polytechnic State University San Luis ObispoCA93407-0403 United States

In this work we introduce symmetry-preserving, physics-informed neural networks (S-PINNs) motivated by symmetries that are ubiquitous to solutions of nonlinear dynamical lattices. Although the use of PINNs have recently attracted much attention in data-driven discovery of solutions chiefly to partial differential equations, we demonstrate that they fail at enforcing important physical laws including symmetries of solutions and conservation laws. Through the correlation of parity symmetries in both space and time of solutions to differential equations with their group equivariant representation, we construct group-equivariant NNs which respect spatio-temporal parity symmetry. Moreover, we adapt the proposed architecture to enforce different types of periodicity (or localization) of solutions to nonlinear dynamical lattices. We do so by applying S-PINNs to the completely integrable Ablowitz-Ladik model, and performing numerical experiments with a special focus on waveforms that are related to rogue structures. These include the Kuznetsov-Ma soliton, and Akhmediev breather as well as the Peregrine soliton. Our numerical results demonstrate the superiority and robustness of the proposed architecture over standard PINNs. Copyright © 2021, The Authors. All rights reserved.

关键词： Solitons

Overview of Energy Consumption Optimization in Mobile Edge Computing

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Journal of Physics: Conference Series 2022年第1期2209卷

作者： Bingyi Hu Jixun Gao Yanxin Hu Huaichen Wang Jialei Liu School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454000 China School of Computer Henan University of Engineering Zhengzhou 451191 China School of Software Engineering Anyang Normal University Anyang 455000 China

The energy consumption of traditional mobile network is mainly reflected by task calculation and data transmission. The edge server in mobile edge computing is placed at the edge of the network closer to the mobile terminal device. When the computing resources of the mobile terminal device are insufficient, the task can be offloaded to the edge server for processing. This paper discusses the energy consumption optimization in the Mobile Edge Computing (MEC) system, which is composed of the edge server and mobile devices at the edge of the network. Firstly, the system model of MEC is elaborated. Secondly, the energy consumption optimization for mobile devices and base stations is studied. Which oriented to the mobile device energy consumption optimization is divided into multiple users - single MEC server scenarios of energy consumption optimization and multiple users - the energy optimization of MEC server scenario, and enumerate the oriented to the base station and a new method for the optimization of energy consumption for mobile equipment energy consumption optimization method, in addition to the insufficiencies of these methods and the prospect of the future are also explained.

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Dynamic Compressing Prompts for Efficient Inference of Large Language Models

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arXiv 2025年

作者： Hu, Jinwu Zhang, Wei Wang, Yufeng Hu, Yu Xiao, Bin Tan, Mingkui Du, Qing School of Software Engineering South China University of Technology China Pazhou Lab Guangzhou China School of Future Technology South China University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Department of Health Technology and Informatics Hong Kong Polytechnic University Hong Kong School of Software Engineering South China University of Technology Guangzhou China Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

Large Language Models (LLMs) have shown outstanding performance across a variety of tasks, partly due to advanced prompting techniques. However, these techniques often require lengthy prompts, which increase computational costs and can hinder performance because of the limited context windows of LLMs. While prompt compression is a straightforward solution, existing methods confront the challenges of retaining essential information, adapting to context changes, and remaining effective across different tasks. To tackle these issues, we propose a task-agnostic method called Dynamic Compressing Prompts (LLM-DCP). Our method reduces the number of prompt tokens while aiming to preserve the performance as much as possible. We model prompt compression as a Markov Decision Process (MDP), enabling the DCP-Agent to sequentially remove redundant tokens by adapting to dynamic contexts and retaining crucial content. We develop a reward function for training the DCP-Agent that balances the compression rate, the quality of the LLM output, and the retention of key information. This allows for prompt token reduction without needing an external black-box LLM. Inspired by the progressive difficulty adjustment in curriculum learning, we introduce a Hierarchical Prompt Compression (HPC) training strategy that gradually increases the compression difficulty, enabling the DCP-Agent to learn an effective compression method that maintains information integrity. Experiments demonstrate that our method outperforms state-of-the-art techniques, especially at higher compression rates. The code for our approach will be available at https://***/Fhujinwu/DCP. © 2025, CC BY.

关键词： Curricula

Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks

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arXiv 2022年

作者： Grabowski, Bartosz Glomb, Przemyslaw Masarczyk, Wojciech Plawiak, Pawel Yıldırım, Özal Acharya, U. Rajendra Tan, Ru-San Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Warsaw University of Technology Pl. Politechniki 1 Warsaw00-661 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Department of Software Engineering Firat University Elazig Turkey School of Science and Technology Singapore University of Social Sciences Singapore Faculty of Business Education Law and Arts University of Southern Queensland Australia School of Engineering Ngee Ann Polytechnic 535 Clementi Road 599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan Kumamoto University Kumamoto Japan Department of Cardiology National Heart Centre Singapore169609 Singapore Duke-NUS Medical School 169857 Singapore

Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and classification tasks. Moreover, we combined the two approaches, using unlabeled and labeled data, to train the model. We tested the model on the MIT-BIH Arrhythmia database. Our method showed high effectiveness in detecting cardiac arrhythmia based on modified Lead II ECG records, as well as achieved high quality of ECG signal approximation. For the former, our method attained overall accuracy of 87.33% and balanced accuracy of 80.54%, on par with reference approaches. For the latter, application of self-supervised learning allowed for training without the need for expert labels. The regression model yielded satisfactory performance with fairly accurate prediction of QRS complexes. Transferring knowledge from regression to the classification task, our method attained higher overall accuracy of 87.78%. © 2022, CC BY.

关键词： Electrocardiography

Secure and Transparent Public-key Management System for Vehicular Social Networks

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Secure and Transparent Public-key Management System for Vehi...

International Conference on computer and Communications (ICCC)

作者： Abdul Noor Youxi Wu Salabat Khan School of Artificial Intelligence Hebei University of Technology Tianjin China College of Computer Science and Software engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Vehicular Social Networks (VSNs) are expected to become a reality soon, where commuters having common interests in the virtual community of vehicles, drivers, passengers can share information, both about road conditions and their surroundings. This will improve transportation efficiency and public safety. However, social networking exposes vehicles to different kinds of cyber-attacks. This concern can be addressed through an efficient and secure key management framework. This study presents a Secure and Transparent Public-key Management (ST-PKMS) based on blockchain and notary system, but it addresses security and privacy challenges specific to VSNs. ST-PKMS significantly enhances the efficiency and trustworthiness of mutual authentication. In ST-PKMS, each vehicle has multiple short-lived anonymous public-keys, which are recorded on the blockchain platform. However, public-keys get activated only when a notary system notarizes it, and clients accept only notarized public-keys during mutual authentication. Compromised vehicles can be effectively removed from the VSNs by blocking notarization of their public-keys; thus, the need to distribute Certificate Revocation List (CRL) is eliminated in the proposed scheme. ST-PKMS ensures transparency, security, privacy, and availability, even in the face of an active adversary. The simulation and evaluation results show that the ST-PKMS meets real-time performance requirements, and it is cost-effective in terms of scalability, delay, and communication overhead.

关键词： Privacy Social networking (online) Scalability Public key Blockchain Security Proposals

Synchronization and H∞ synchronization of multi-weighted coupled neural networks with event-triggered communication 38

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Synchronization and H∞ synchronization of multi-weighted co...

38th Chinese Control Conference, CCC 2019

作者： Wang, Yi-Hao Huang, Yan-Li Ren, Shun-Yan Lu, Jian-Mou Liu, Dong-Fang Tianjin Key Laboratory of Optoelectronic Detection Technology and System School of Computer Science and Technology Tianjin Polytechnic University Tianjin300387 China School of Mechanical Engineering Tianjin Polytechnic University Tianjin300387 China Tianjin Tianhang Wisdom Technology Co. Ltd Tianjin300452 China

ISBN: (纸本)9789881563972

Synchronization and H∞ synchronization of multi-weighted coupled neural networks (MWCNNs) with event-triggered communication are researched in this paper. Firstly, we derive a synchronization criterion for MWCNNs with event-triggered communication by using Lyapunov stability theory and some inequality techniques. Then, we use the similar method to get a criterion for ensuring the H∞ synchronization of the considered network at the level of disturbance attenuation. Finally, two numerical examples are verified to demonstrate the effectiveness of the obtained synchronization and H∞ synchronization criteria. © 2019 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Synchronization Couplings Manganese Neural networks Lyapunov methods Complex networks Attenuation

Multisequences with high joint nonlinear complexity from function fields ∗

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arXiv 2020年

作者： Yan, Yang Wang, Qiuyan Wu, Chenhuang School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin300387 China School of Computer Science and Technology Tianjin Polytechnic University Tianjin300387 China Provincial Key Laboratory of Applied Mathematics Putian University

Multisequences over finite fields play a pushing role in the applications that relate to parallelization, such as word-based stream ciphers and pseudorandom vector generation. It is interesting to study the complexity measures for multisequences. In this paper, we propose three constructions of multisequences over finite fields from rational function fields and Hermitian function fields. We also analyze the joint nonlinear complexity of these multisequences. Moreover, the length and dimension of these multisequences are flexible. Copyright © 2020, The Authors. All rights reserved.

关键词： Rational functions