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Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless networks

IEEE Internet of Things Journal 2025年第12期12卷 21225-21239页

作者： Jeong, Seon-Geun Duc, Pham Dang Anh Do, Quang Vinh Noh, Dae-Il Tung, Nguyen Xuan Chien, Trinh Van Pham, Quoc-Viet Hasegawa, Mikio Sekiya, Hiroo Hwang, Won-Joo Department of Information Convergence Engineering Pusan National University Pusan National University Busan46241 Korea Republic of Wireless Communications Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Graduate School of Engineering Chiba University Chiba263-8522 Japan Hanoi100000 Viet Nam School of Computer Science and Statistics Trinity College Dublin DublinD02PN40 Ireland Department of Electrical Engineering Tokyo University of Science Tokyo162-8601 Japan School of Computer Science and Engineering Center for Artificial Intelligence Research Pusan National University Busan46241 Korea Republic of

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, sub-channel assignment, and power allocation in a multi-unmanned aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two sub-problems: 1) a QA-based clustering and 2) sub-channel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two sub-problems. For the first sub-problem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second sub-problem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time. © 2025 IEEE.

关键词： Combinatorial optimization

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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POFM: Profit-Oriented Flow Management in SDN-Based Data center networks

POFM: Profit-Oriented Flow Management in SDN-Based Data Cent...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Wang, You-Chiun Liang, Jhong-Ting Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9798350353464

In a data center network (DCN), there are numerous hosts connected by well-organized switches, usually involving a large amount of data transmission to offer cloud services. How to manage flows in a DCN by deciding packet routes is critical. This paper applies the software-defined networking (SDN) technique to flow management in fat-tree DCNs. Unlike previous studies, we consider that flows have different values and associate flows with profits to reflect their values. The practical profit acquired from each flow will depend on the proportion of successfully sent packets. The paper proposes a profit-oriented flow management (POFM) scheme to maximize the profit obtained while reducing packet loss in flows. In POFM, once a link becomes congested, some flows on that link may be rerouted to other paths. When no alternative path can be found, POFM adjusts the data rates of flows on the congested link based on multiple factors, including their demanded rates, profits, and transmission durations. With simulations, we show that POFM can effectively raise the overall profit and decrease the packet loss rate in the DCN. ©2024 IEEE.

关键词： Packet loss

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Trend Ratio-Based Portfolio Optimization Model Adopting Entanglement-enhanced Quantum-Inspired Evolutionary Computation in the Global Financial Markets

Trend Ratio-Based Portfolio Optimization Model Adopting Enta...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Jiang, Yu-Chi Chang, Ming-Ho Chang, Yu-Yu Wu, Kun-Min Chen, Po-Chun Tong, Yong Feng Lai, Yun-Ting Kuo, Shu-Yu Chou, Yao-Hsin National Taiwan University Department of Electrical Engineering Taiwan National Chi Nan University Department of Computer Science and Information Engineering Taiwan National Taiwan University Center for Quantum Science and Engineering Department of Physics Taiwan

ISBN: (纸本)9798350314588

Financial management is a critical and complicated issue. People tend to invest in the stock market and expect a stable uptrend portfolio to spread the investment risk effectively. As global economies affect each other, investing in global markets is the best way to address systematic risk for effective financial management. Selecting an appropriate portfolio across global markets involves a substantial solution space;thus, an efficient and effective evolutionary computation is proposed. The novel entanglement-enhanced quantum-inspired optimization technique is proposed as an efficient mechanism to search for the relationship between stocks with high-dependency solutions. The novel indicator trend ratio aims to evaluate the investor-desired portfolio with a stable uptrend under a perfect balance between return and risk. Therefore, this study is the first attempt to evaluate the major global markets in the Group of Seven (G7) countries to expand the generality of trend ratio usage. The experiments demonstrate that the proposed novel approach has better and more precise searchability than other state-of-the-art optimization. The proposed intelligent model outperforms the Sharpe ratio, benchmark strategy, and index performance regarding the trend ratio, risk, maximum drawdown, and profit factor. The results reveal that risk dispersion substantially improves investment performance. Under the evaluation of the trend ratio, the proposed system integrates G7 markets as an expansion of a prospective avenue for global asset management © 2023 IEEE.

关键词： Financial markets

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AI-Enabled Multimedia Semantic Communications

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IEEE Communications Magazine 2025年第6期63卷 66-73页

作者： Li, Chenxing Duan, Yiping Wang, Liejun Tao, Xiaoming Tsinghua University Department of Electronic Engineering China State Key Laboratory of Space Network China Beijing National Research Center for Information Science and Technology China School of Computer Science and Technology Xinjiang University China

With increasing demand for mobile communication and the development of artificial intelligence, the development of the sixth-generation mobile communication network (6G) will be oriented toward more intelligent and multimodal services. It is necessary to consider the deep integration of information, communication, and data technologies and build a full life cycle resource management framework for computing, data, artificial intelligence models, and communication. To address the requirements of 6G intelligent applications and multimodal services, this article refers to the visual cognitive mechanism of the human brain to improve multimedia representation and communication efficiency and studies the architecture of semantic communication systems, privacy protection mechanisms, and quality evaluation and guarantee mechanisms. The "semantic-network-semantic" architecture was established to integrate semantic computing, intelligent communication, and endogenous security to improve transmission efficiency, service quality, and security credibility. Through prior semantic knowledge, semantic representation, and encoding transmission methods, the quality of service of semantic communication is improved. To build an efficient 6G semantic communication paradigm and system, the communication mode should be changed, and new ideas for future mobile communication should be provided. © 1979-2012 IEEE.

关键词： Multimedia services

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Pattern Recognition Accuracy Optimization of Unsupervised Spiking Neural network Using Y-Doped AlN Memristors

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IEEE Transactions on Electron Devices 2023年第8期70卷 4473-4478页

作者： Fu, Yi Huang, Chu-Chun Lin, Zi-Yi Lee, Chi-Ching Wang, Jer-Chyi Chang Gung University Department of Electronic Engineering Guishan Taoyuan33302 Taiwan Chang Gung University Department of Computer Science and Information Engineering Guishan Taoyuan33302 Taiwan Chang Gung University Department of Computer Science and Information Engineering Artificial Intelligence Research Center Guishan Taoyuan33302 Taiwan Linkou Branch Genomic Medicine Core Laboratory Chang Gung Memorial Hospital Guishan Taoyuan33305 Taiwan Linkou Branch Department of Neurosurgery Chang Gung Memorial Hospital Guishan Taoyuan33305 Taiwan Ming Chi University of Technology Department of Electronic Engineering Taishan New Taipei City243303 Taiwan

Inspired by the biological nervous system, the unsupervised spiking neural network (SNN) with the spike-timing-dependent plasticity (STDP) learning rule has been considered as the next-generation artificial neural network. To construct an SNN with high pattern recognition accuracy, hardware with balance synaptic behavior needs to be developed. Here, yttrium (Y)-doped aluminum nitride (AlN) memristors were proposed as artificial synapses in SNNs to investigate the dependence between the doping concentration and the pattern recognition accuracy. With the doping of Y in AlN films, both the memory characteristics and synaptic behaviors of the AlN memristors were optimized. In addition, the STDP parameters of the memristors were extracted and fed into the SNN system to simulate the pattern recognition capability. The optimized pattern recognition accuracies of 75.89% and 60.21% for the MNIST and ETH-80 datasets, respectively, were achieved for the AlN memristors with a Y-doping concentration of 3.4%, which is promising for implementation in future neuromorphic computing system and artificial intelligence. © 1963-2012 IEEE.

关键词： Aluminum nitride

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A Lightweight Multi-domain Multi-attention Progressive network for Single Image Deraining 24

A Lightweight Multi-domain Multi-attention Progressive Netwo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhong, Junliu Li, Zhiyi Xiang, Dan Han, Maotang Li, Changsheng Gan, Yanfen School of Computer Science Guangzhou Maritime University Guangdong Guangzhou China Network and Information Service Center Guangdong University of Foreign Studies South China Business College Guangdong Guangzhou China School of Low Altitude Equipment and Intelligent Control Guangzhou Maritime University Guangdong Guangzhou China School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce Guangdong Guangzhou China

ISBN: (纸本)9798400706868

Currently, the information processing in a spatial domain alone has intrinsic limitations that hinder the deep network's effectiveness (performance) improvement in a single image deraining. Moreover, the deraining networks' structures and learning processes are becoming increasingly intricate, leading to challenges in structural lightweight, and training and testing efficiency. We propose a lightweight multi-domain multi-attention progressive network (M2PN) to handle these challenges. For performance improvement, the M2PN backbone applies a simple progressive CNN-based structure consisting of the S same recursive M2PN modules. This recursive backbone with a skip connection mechanism allows for better gradient flow and helps to effectively capture low-to-high-level/scales spatial features in progressive structure to improve contextual information acquisition. To further complement acquired spatial information for better deraining, we conduct spectral analysis on the frequency energy distribution of rain steaks, and theoretically present the relationship between the spectral bandwidths and the unique falling characteristics and special morphology of rain steaks. We present the frequency-channel attention (FcA) mechanism and the spatial-channel attention (ScA) mechanism to fuse frequency-channel features and spatial features better to distinguish and remove rain steaks. The simple recursive network structure and effective multi-domain multi-attention mechanism serve as the M2PN to achieve superior performance and facilitate fast convergence during training. Furthermore, the M2PN structure, with a small network component quantity, shallow network channels, and few convolutional kernels, requires only 168K parameters, which is 1 to 2 orders of magnitude lower than the existing SOTA networks. The experimental results demonstrate that even with such a few network parameters, M2PN still achieves the best overall performance. © 2024 ACM.

关键词： Image enhancement

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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A New Portfolio Optimization Model Considering Hybrid Trading Strategies

A New Portfolio Optimization Model Considering Hybrid Tradin...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Kuo, Shu-Yu Jiang, Yu-Chi Lai, Yun-Ting Chou, Yao-Hsin Center for Quantum Science and Engineering National Taiwan University Department of Physics Taiwan National Taiwan University Department of Electrical Engineering Taiwan National Chi Nan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350314588

Computational intelligence (CI) has been extensively used in financial technology areas to help make smart decisions within enormous solution spaces. This study proposes a new portfolio optimization model using hybrid strategies to concurrently consider long and short investments and compose effective portfolios assessed by an emerging indicator to reach an excellent balance between return and risk. It exploits the trend ratio investment strategy to a detailed extent to evaluate the steady uptrend and downtrend portfolios and provides an in-depth discussion of the U.S. stock market. The proposed method combines long and short selling through a single fund to enhance investment efficiency. Quantum-inspired CI is adopted to construct a near-optimal portfolio efficiently and effectively, and sliding windows are applied to rebalance the portfolio and dynamically discover appropriate periods. The experiment conducts comprehensive statistical tests to show that hybrid strategies significantly improve traditional approaches. The comparison with other traditional methods shows outstanding performances in MDD, PF, RoMaD, return, and trend ratio. The proposed hybrid portfolio optimization application can be complementary, offset the volatility and increase the return, and show great potential in the financial industry. © 2023 IEEE.

关键词： Investments

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