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arXiv 2020年

作者： Cheng, Lei Chen, Zhongtao Shi, Qingjiang Wu, Yik-Chung Theodoridis, Sergios The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The School of Software Engineering Tongji University Shanghai201804 China The Shenzhen Research Institute of Big Data Shenzhen518172 China The National and Kapodistrian University of Athens Greece Greece The Department of Electronic Systems Aalborg University Denmark

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as an essential yet challenging problem. In particular, since the tensor rank controls the complexity of the CPD model, its inaccurate learning would cause overfitting to noise or underfitting to the signal sources, and even destroy the interpretability of model parameters. However, the optimal determination of a tensor rank is known to be a non-deterministic polynomial-time hard (NP-hard) task. Rather than exhaustively searching for the best tensor rank via trial- and-error experiments, Bayesian inference under the Gaussian-gamma prior was introduced in the context of probabilistic CPD modeling, and it was shown to be an effective strategy for automatic tensor rank determination. This triggered flourishing research on other structured tensor CPDs with automatic tensor rank learning. On the other side of the coin, these research works also reveal that the Gaussian-gamma model does not perform well for high-rank tensors and/or low signal-to-noise ratios (SNRs). To overcome these drawbacks, in this paper, we introduce a more advanced generalized hyperbolic (GH) prior to the probabilistic CPD model, which not only includes the Gaussian-gamma model as a special case, but also is more flexible to adapt to different levels of sparsity. Based on this novel probabilistic model, an algorithm is developed under the framework of variational inference, where each update is obtained in a closed-form. Extensive numerical results, using synthetic data and real-world datasets, demonstrate the significantly improved performance of the proposed method in learning both low as well as high tensor ranks even for low SNR cases. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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Correction to: Partially Overlapped Channel Assignment for Cloud-Based Heterogeneous Cellular and Mesh Networks

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Wireless Personal Communications 2021年第3期122卷 2583-2583页

作者： Iqbal, Saleem Qureshi, Kashif Naseer Majeed, Saqib Ghafoor, Kayhan Zrar Jeon, Gwanggil University Institute of Information Technology PMS UAAR Rawalpindi Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

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Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

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Multi-node optical frequency dissemination with post automatic phase correction

arXiv

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arXiv 2020年

作者： Hu, Liang Tian, Xueyang Wu, Guiling Kong, Mengya Shen, Jianguo Chen, Jianping State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai200240 China the College of Physics and electronic information Engineering Zhejiang Normal University Jinhua321004 China

—We report a technique for coherence transfer of laser light through a fiber link, where the optical phase noise induced by environmental perturbation via the fiber link is compensated by remote users with passive phase noise correction, rather than at the input as is conventional. Neither phase discrimination nor active phase tracking is required due to the open-loop design, mitigating some technical problems such as the limited compensation speed and the finite compensation precision as conventional active phase noise cancellation. We theoretically analyze and experimentally demonstrate that the delay effect introduced residual fiber phase noise after noise compensation is a factor of 7 higher than the conventional techniques. Using this technique, we demonstrate the transfer laser light through a 145-km-long, lab-based spooled fiber. After being compensated, the relative frequency instability in terms of overlapping Allan deviation is 1.9 × 10−15 at 1s averaging time and scales down 5.3×10−19 at 10,000 s averaging time. The frequency uncertainty of the light after transferring through the fiber relative to that of the input light is (−0.36 ± 2.6) × 10−18. As the transmitted optical signal remains unaltered until it reaches the remote sites, it can be transmitted simultaneously to multiple remote sites on an arbitrarily complex fiber network, paving a way to develop a multi-node optical frequency dissemination system with post automatic phase noise correction for a number of end users. Copyright © 2020, The Authors. All rights reserved.

关键词： Natural frequencies

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Fundamental limitations of Rayleigh backscattering noise on fiber-based multiple-access optical frequency transfer

arXiv

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arXiv 2020年

作者： Hu, Liang Tian, Xueyang Wu, Guiling Shen, Jianguo Chen, Jianping State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai200240 China College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China

Ultimate limits of performance for single-source-bidirectional architecture e.g., optical frequency transfer are set by delay fluctuations within the host material, e.g., optical fiber or atmospheric channel, which is dynamically induced. The most common manifestation of such phase disorder in the optical fiber is backscattering lights coming from Rayleigh backscattering and fresnel reflection, which is observed in nearly all single-source-bidirectional architectures and can lead to both irreversible coherence losses as well as undesirable interference coupling. While it has been shown that backscattering induced phase noise can be suppressed by adopting acoustic-optic-modulators (AOMs) at the local and remote sites to break the frequency symmetry in both directions. However, this issue can not be avoided for conventional fiber-optic multiple-access coherent optical phase dissemination in which the interference of the signal light with the Rayleigh backscattered light will probably destroy the coherence of the stabilized optical signal. We suppress the backscattering effect by locally breaking the frequency symmetry at the extraction point by inserting an additional AOM. Here, we theoretically analyze and experimentally demonstrate an add-drop one more AOM approach for suppressing the Rayleigh backscattering within the fiber link. Near-complete suppression of backscattering noise is experimentally confirmed through the measurementthe elimination of a common interference term of the signal light and the Rayleigh backscattered light. The results demonstrate that the Rayleigh backscattering light has a limited effect compared to the residual delay-limited fiber phase noise on the system’s performance. Our results also provide new evidence that it is possible to largely suppress Rayleigh and other backscattering noise within a long optical fiber link, where the accumulated phase noise could be large, by using frequency symmetry breaking at each access node to achieve ro

关键词： Natural frequencies

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DeepKeyGen: A deep learning-based stream cipher generator for medical image encryption and decryption

arXiv

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arXiv 2020年

作者： Ding, Yi Tan, Fuyuan Qin, Zhen Cao, Mingsheng Choo, Kim-Kwang Raymond Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China ChengduSichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Guangdong523808 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China ChengduSichuan610054 China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249-0631 United States

The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients’ medical imaging data. In this paper, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three datasets, namely: the Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset, and the BraTS18 dataset. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control

arXiv

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arXiv 2020年

作者： Mao, Weichao Zhang, Kaiqing Zhu, Ruihao Simchi-Levi, David Başar, Tamer Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Cornell SC Johnson College of Business IthacaNY14853 United States Institute for Data Systems and Society Department of Civil and Environmental Engineering Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States

We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain variation budgets. We propose Restarted Q-Learning with Upper Confidence Bounds (RestartQ-UCB), the first model-free algorithm for non-stationary RL, and show that it outperforms existing solutions in terms of dynamic regret. Specifically, RestartQ-UCB with Freedman-type bonus terms achieves a dynamic regret bound of (formula presented ), where S and A are the numbers of states and actions, respectively, ∆ > 0 is the variation budget, H is the number of time steps per episode, and T is the total number of time steps. We further present a parameter-free algorithm named Double-Restart Q-UCB that does not require prior knowledge of the variation budget. We show that our algorithms are nearly optimal by establishing an information-theoretical lower bound of (formula presented ), the first lower bound in non-stationary RL. Numerical experiments validate the advantages of RestartQ-UCB in terms of both cumulative rewards and computational efficiency. We demonstrate the power of our results in examples of multi-agent RL and inventory control across related products. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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Impact of thermal radiation on fractional viscoelastic nanofluid flow with joint heat mass transfer using a neural network based approach

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Journal of Radiation Research and Applied Sciences 2025年第3期18卷

作者： Shajar Abbas Taoufik Saidani Muhammad Ramzan S. Saleem Nashwan Adnan Othman Dilsora Abduvalieva Faiza Benabdallahi Ilyas Khan Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan AS (Ayub Shah) Institute of Mathematics and Research Centre Multan Pakistan Center for Scientific Research and Entrepreneurship Northern Border University 73213 Arar Saudi Arabia Department of Mathematics College of Science King Khalid University Abha 61413 Saudi Arabia Center for Artificial Intelligence (CAI) King Khalid University Abha 61421 Saudi Arabia Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor Avenue 27 Tashkent 100070 Uzbekistan Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Mathematical Sciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Mathematics in Applied Sciences and Engineering Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq

This study aims to enhance the predictive modeling of free convection flow in viscoelastic nanofluids influenced by thermal radiation, concentrating especially on improving the understanding of heat and mass transfer behavior. To achieve this, a novel computational framework is developed by incorporating the Prabhakar fractional operator, it successfully records memory effects and non-local behavior of-ten neglected in classical models. The governing fractional partial differential equations are solved using Levenberg-Marquardt backpropagation algorithm-trained artificial neural networks. Training data are generated analytically through the Laplace transform, with a 70 %30 % training-validation split. The proposed model demonstrates high predictive accuracy, achieving a mean squared error below 10 −4 . Sensitivity analysis reveals that the fractional parameter reduces velocity fluid, while concentration and temperature respond strongly to chemical reactions and thermal conditions. The main contribution of this work lies in combining fractional calculus with ANN-based optimization to provide a reliable, efficient tool for modeling and enhancing heat transfer in nanofluid-based engineering systems.

关键词： Thermal radiation Viscoelasticity Fractional operator Method of the laplace transform Chemical reaction

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Robot Hand Interaction Using Plastic Deformation Control with Inner Position Loop

Robot Hand Interaction Using Plastic Deformation Control wit...

引用

IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Kenichi Murakami Koki Ishimoto Taku Senoo Masatoshi Ishikawa Data Science Research Division Information Technology Center The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Department of Information Physics and Computing The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Electrical Systems and Control Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima City Hiroshima Japan

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

In this study, the inner position loop addition for plastic deformation control of the error robustness of the model and actual parameters is proposed. The inner position loop is a type of position feedback control based on the estimation of model parameters and dynamics of robots. This position control system with proportional and derivative gains modifies the trajectory of the end effector close to the desired trajectory. The robustness of the proposed control scheme is evaluated by comparing it with plastic deformation control without an inner position loop in both simulation and actual environments. Finally, the stable reaction with the proposed control is demonstrated for the case where a human pushes the robot finger.

关键词： Strain Plastics Force Deformable models Robots Friction Trajectory

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