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Adaptive Sparse Channel Estimation for RIS-Assisted Mmwave Massive MIMO Systems

IEEE Transactions on Vehicular technology 2025年

作者： Shao, Shuying Lv, Tiejun Du, Siying Zeng, Jie Tan, Fangqing Lin, Zhipeng School of Information and Communication Engineering Beijing100876 China Shandong Institute of Petroleum and Chemical Technology Department of Computer Science and Engineering Dongying257061 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Guilin University of Electronic Technology School of Information and Communication Guilin541004 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China

The integration of reconfigurable intelligent surfaces (RISs) with millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems holds significant promise for future wireless communication methods aiming to achieve high-speed data transmission and expand communication coverage. However, channel estimation (CE) in RIS-assisted wireless communication systems faces many challenges due to the limited signal processing capability of the RIS. Existing methods often fail to fully capture the non-uniform sparsity of mmWave channels. To address this limitation, we propose an adaptive sparse CE scheme involving two progressive algorithms, i.e., normalized least mean squares–log sum (NLMS-LOG) and normalized least mean squares–normalized least mean fourth–log sum (NLMS-NLMF-LOG), for RIS-assisted mmWave massive MIMO systems. The proposed scheme selectively adjusts the channel coefficients to improve the accuracy of the estimation, enabling more efficient exploitation of the inherent non-uniform sparsity in mmWave channels. The NLMS-LOG algorithm innovatively incorporates a log-sum penalty term into the cost function of the NLMS algorithm, thereby increasing the convergence rate. Building upon NLMS-LOG, the NLMS-NLMF-LOG algorithm employs a mixed error function to achieve superior performance across different SNR conditions. The simulation results demonstrate that the proposed scheme outperforms existing methods, such as the NLMS, sparse exponential forgetting window least mean square, and sparse hybrid adaptive filtering algorithms, in terms of CE accuracy and convergence speed. © 1967-2012 IEEE.

关键词： Cost functions

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Autonomous dynamic formation for maritime target tracking using multi-agent reinforcement learning

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Wang, Hua Li, Jiaxin Tao, Hao Liu, Junnan Li, Chaochao Wang, Ke Xu, Mingliang School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou450001 China National Supercomputing Center in Zhengzhou Zhengzhou450001 China China Ship Development and Design Center Wuhan430064 China School of Geo-Science & Technology Zhengzhou University Zhengzhou450001 China

In various maritime missions such as escort and roundup, dynamic formation target tracking plays a crucial role. Most existing dynamic formation methods require user intervention before formation changes, resulting in poor flexibility and low automation. And they do not consider variations in the abilities of individual members. To address the above issue, we propose an autonomous dynamic formation planning method based on multi-agent reinforcement learning, integrating formation configuration into the strategy. This method can automatically adjust the formation based on the current state of the formation, providing greater flexibility and adaptability. Simultaneously, a staged reward function is devised for the training process to guide agents in progressively learning dynamic formation tasks. Finally, we validate the effectiveness and generalization of our proposed method through various experiments. © 2025 Elsevier Ltd

关键词： Reinforcement learning

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Quantum Linear Multistep Method for Using a Quantum Oracle with Differential Equations

arXiv

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arXiv 2025年

作者： Park, Kyoung Keun Choi, Kwangyeul Kim, Minwoo Song, Giwon Kim, Taehyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Inter university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

Differential equations are a crucial mathematical tool used in a wide range of applications. If the solution to an initial value problem (IVP) can be transformed into an oracle, it can be utilized in various fields such as search and optimization, achieving quadratic speedup with respect to the number of candidates compared to its classical counterpart. In the past, attempts have been made to implement such an oracle using the Euler method. In this study, we propose a quantum linear multistep method (QLMM) that applies the linear multistep method, commonly used to numerically solve IVPs on classical computers, to generate a numerical solution of the IVP for use in a quantum oracle. We also propose a method to find the optimal form of QLMM for a given IVP. Finally, through computer simulations, we derive the QLMM formulation for an example IVP and show that the solution from the optimized QLMM can be used in an optimization problem. © 2025, CC BY.

关键词： Initial value problems

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification

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computers in Biology and Medicine 2025年 185卷 109593-109593页

作者： Khan, Zeshan Aslam Waqar, Muhammad Raja, Muhammad Junaid Ali Asif Chaudhary, Naveed Ishtiaq Khan, Abeer Tahir Mehmood Anwar Raja, Muhammad Asif Zahoor International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Rawal Institute of Health Sciences Islamabad Pakistan

Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria. The plasmodium falciparum infection, the disease origin causes the intestinal illness. Fortunately, advances in artificial intelligence techniques have made it possible to use visual data sets to quickly and effectively diagnose malaria which has also proven to be cost and time effective. In literature, several DL approaches have previously been used with good precision but suffer from computational inefficiency and interpretability. Therefore, this research proposes a generalized fractional order-based explainable lightweight convolutional neural network model to overcome these limitations. The fractional order optimization algorithms have proven worth in terms of estimation accuracy and convergence speed for different applications. The proposed fractional order optimizer-based model offers an improved solution to malaria disease diagnosis with a percentage accuracy of 95 % using the standard NIH dataset and outperforms the existing complex models concerning speed and effectiveness. The proposed fractionally optimized lightweight CNN model has shown substantial performance on the external MP-IDB dataset and M5 test set as well by achieving a generalized test accuracy of 92 % and 90.4 % which verifies the robustness and generalizability of the proposed solution under available circumstances. Moreover, the efficacy of the proposed lightweight architecture is endorsed through evaluation metrics of precision, recall, and F1-score. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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Nonsaturated large magnetoresistance and transport dynamics in a n-doped cryogenic WSe2 field-effect device

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Physical Review B 2025年第12期111卷 125307-125307页

作者： Wei-Chen Lin Ching-Chen Yeh Jia-Zhu Zou Nhat Anh Nguyen Phan Inayat Uddin Hai Yen Le Thi Kenji Watanabe Takashi Taniguchi Gil-Ho Kim Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Physics National Taiwan University Taipei 106 Taiwan Department of Electrical and Computer Engineering Sungkyunkwan University (SKKU) Suwon 164 Republic of Korea Sungkyunkwan Advanced Institute of Nano Technology (SAINT) Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan

Interest in the two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) continues to intensify, driven by their suitable band gaps to supplant silicon as next-generation semiconductor materials. Among various TMDs, tungsten diselenide (WSe2) is renowned for its superior electrical properties in carrier density and mobility under ambient conditions. Despite its notable attributes, the behavior of monolayer WSe2 in the electron-doped regime under cryogenic conditions remains largely uncharted, particularly concerning its magnetotransport properties. In this study, we reveal the transport mechanisms of monolayer WSe2 from high temperatures down to the cryogenic regime. As evident by Efros–Shklovskii variable-range hopping (E-S VRH) in the cryogenic regime, strong Coulomb interactions arise between electrons. Above 8 K, an uncommon nonsaturated quadratic large magnetoresistance (MR) can be explained by the wave-function shrinkage model, which is consistent with the E-S VRH transport mechanism. Notably, the nonsaturated quadratic large MR shows a magnitude up to 1740% at 13 T. These findings underscore the potential applications for monolayer WSe2 in cryogenic field-effect devices, magnetic sensors, and memory devices and mark a significant advance in magnetotransport research.

关键词： Monolayers

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Comparative Study on Electromagnetic Performance of Slotted and Slotless PM Synchronous Motors According to Aspect Ratios

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IEEE Transactions on Magnetics 2025年

作者： Hong, Hyeonbin Lee, Jae-Kwang Jung, Dong-Hoon Lee, Ju Kim, Hyunwoo Hanyang University Department of Electrical Engineering Seoul04763 Korea Republic of Korea Electronics Technology Institute Intelligent Mechatronics Research Center Bucheon14502 Korea Republic of Andong National University Department of Electrical and Materials Engineering Andong36729 Korea Republic of Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN47907 United States

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high torque density. PMSM are generally classified into slotted and slotless types, each offering distinct advantages. A key design parameter of PMSM is the aspect ratio, defined as the ratio of the stator outer diameter to the stack length, which significantly affects electromagnetic performance and application suitability. In this paper, a comparative study of 1.35kW slotted and slotless PMSM is proposed based on aspect ratios under the same volume, using a finite element analysis (FEA). Although the volume of PMSM primarily determines electromagnetic performance, such as electromagnetic torque and loss, the aspect ratio is also crucial because of its impact on magnetic leakage flux. Constraints, including the volume of PMSM, magnet thickness, and current density, are selected to compare electromagnetic performances based on aspect ratio. Optimal models of slotted and slotless PMSM are designed, and their electromagnetic performances are compared. The optimal slotless PMSM achieves a torque ripple approximately 31.08%p lower than that of the optimal slotted PMSM. Furthermore, the total harmonic distortion (THD) of the induced voltage is reduced by approximately 20.16%p. To verify this study, the 1.35kW optimal slotless PMSM is manufactured and experimentally tested, showing errors of less than 5% compared to FEA results. © 1965-2012 IEEE.

关键词： Aspect ratio

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Silicon carbide-graphene nanocomposite electrodes for enhanced desalination via capacitive deionization

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Ceramics International 2025年

作者： Yousef, Ayman Hameed, R.M. Abdel Maafa, Ibrahim M. Abutaleb, Ahmed El-Halwany, M.M. Department of Chemical Engineering College of Engineering and Computer Sciences Jazan University Jazan45142 Saudi Arabia Chemistry Department Faculty of Science Cairo University Giza Egypt Engineering and Technology Research Center Jazan University P.O.Box 114 Jazan82817 Saudi Arabia Department of Mathematics and Physics Engineering Faculty of Engineering Mansoura University El-Mansoura Egypt Faculty of Engineering Mansoura National University Egypt

Graphene nanoflakes [GNFs] were ultrasonically modified with silicon carbide nanopowder with subsequent hydrothermal heating at 120 °C. A number of these SC-GNFs nanocomposites was fabricated based on altering their metallic carbide content [5, 10 and 15 wt%] in relation to that of carbonaceous substrate. The chemical composition, morphology and interaction with water droplets of SC-GNFs nanostructures were examined using suitable physical characterization tools. The ability of these carbide containing graphene nanomaterials to desalinate water samples was evaluated through varied electrochemical measurements. Outstanding specific capacitances could be measured at different SC-GNFs nanocomposites when related to those of bare components of SiC and GNFs. Incorporating 10 wt% SiC between graphene nanoflakes could significantly promote their capacitance by about nine folds. Galvanostatic charge-discharge experiments of SC-GNFs nanocomposite revealed good stability attitude within longer periods. Increased salt removal efficiencies were also demonstrated at all SC-GNFs nanopowders up to 83.33 % when contrasted to those of SiC [10.42 %] and GNFs [12.50 %]. These findings at graphene nanoflakes surfaces after their treatment with SiC nanoparticles might suggest further studies for relevant metallic carbides onto convenient carbonaceous based supports for valuable CDI systems. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Deionized water

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A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging

arXiv

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arXiv 2025年

作者： Wang, Junjia Xiong, Bo Zhou, You Cao, Xun Ma, Zhan School of Electronic Science and Engineering Nanjing University Nanjing210023 China National Engineering Research Center of Visual Technology Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China Medical School Nanjing University Nanjing210093 China

The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into another. The remarkable performance of generative networks, such as CycleGAN, offers an unsupervised learning approach for virtual coloring, overcoming the limitations of high-quality paired data required in supervised learning. Nevertheless, large-scale color transformation necessitates processing large field-of-view images in patches, often resulting in significant boundary inconsistency and artifacts. Additionally, the transformation between different colorized modalities typically needs further efforts to modify loss functions and tune hyperparameters for independent training of networks. In this study, we introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities, termed the Value Mapping Generative Adversarial Network (VM-GAN). Meanwhile, we present a confidence-based tiling method to address the challenge of boundary inconsistency arising from patch-wise processing. Experimental results on diverse data with varying staining protocols demonstrate that our method achieves superior quantitative indicators and improved visual perception. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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