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arXiv 2021年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Chen, Cailian Jin, Shi Han, Zhu Poor, H. Vincent School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO SydneyNSW2015 Australia The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multiarmed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better tradeoff by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Distributed reinforcement learning for age of information minimization in real-time IoT systems

arXiv

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arXiv 2021年

作者： Wang, Sihua Chen, Mingzhe Yang, Zhaohui Yin, Changchuan Saad, Walid Cui, Shuguang Poor, H. Vincent The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Department of Engineering King’s College London WC2R 2LS United Kingdom The Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Chinese University of Hong Kong Shenzhen518172 China

In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied. In the considered model, each IoT device monitors a physical process that follows nonlinear dynamics. As the dynamics of the physical process vary over time, each device must find an optimal sampling frequency to sample the real-time dynamics of the physical system and send sampled information to a base station (BS). Due to limited wireless resources, the BS can only select a subset of devices to transmit their sampled information. Thus, edge devices must cooperatively sample their monitored dynamics based on the local observations and the BS must collect the sampled information from the devices immediately, hence avoiding the additional time and energy used for sampling and information transmission. To this end, it is necessary to jointly optimize the sampling policy of each device and the device selection scheme of the BS so as to accurately monitor the dynamics of the physical process using minimum energy. This problem is formulated as an optimization problem whose goal is to minimize the weighted sum of AoI cost and energy consumption. To solve this problem, we propose a novel distributed reinforcement learning (RL) approach for the sampling policy optimization. The proposed algorithm enables edge devices to cooperatively find the global optimal sampling policy using their own local observations. Given the sampling policy, the device selection scheme can be optimized thus minimizing the weighted sum of AoI and energy consumption of all devices. Simulations with real data of PM 2.5 pollution show that the proposed algorithm can reduce the sum of AoI by up to 17.8% and 33.9% and the total energy consumption by up to 13.2% and 35.1%, compared to a conventional deep Q network method and a uniform sampling policy. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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Sample complexity and overparameterization bounds for temporal difference learning with neural network approximation

arXiv

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arXiv 2021年

作者： Cayci, Semih Satpathi, Siddhartha He, Niao Srikant, R. Coordinated Science Laboratory The University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Department of Computer Science ETH Zurich Zurich8006 Switzerland C3.ai Digital Transformation Institute The Department of Electrical and Computer Engineering Coordinated Science Laboratory The University of Illinois at Urbana-Champaign UrbanaIL61801 United States

In this paper, we study the dynamics of temporal difference learning with neural network-based value function approximation over a general state space, namely, Neural TD learning. We consider two practically used algorithms, projection-free and max-norm regularized Neural TD learning, and establish the first convergence bounds for these algorithms. An interesting observation from our results is that max-norm regularization can dramatically improve the performance of TD learning algorithms, both in terms of sample complexity and overparameterization. In particular, we prove that max-norm regularization improves state-of-the-art convergence bounds. The results in this work rely on a novel Lyapunov drift analysis of the network parameters as a stopped and controlled random process. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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A novel Skin lesion prediction and classification technique: ViT-GradCAM

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Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) 2024年第9期30卷 e70040页

作者： Muhammad Shafiq Kapil Aggarwal Jagannathan Jayachandran Gayathri Srinivasan Rajasekhar Boddu Adugna Alemayehu School of Information Engineering Qujing Normal University Qujing China. Key Laboratory of Intelligent Sensor and System Design College of Information Engineering Qujing Normal University Qujing China. Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India. Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India. Lecturer in Software Engineering Wachemo University Hosaina Ethiopia.

BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

关键词： Grad‐CAM Multi‐Headed Self attention (MSA) linear patch projection multilayer perceptron (MLP) skin lesion transformer encoder vision transformer (ViT)

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From multi-view to hollow-3D: Hallucinated hollow-3D R-CNN for 3D object detection

arXiv

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arXiv 2021年

作者： Deng, Jiajun Zhou, Wengang Zhang, Yanyong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China The Department of Computer Science University of Science and Technology of China Hefei230026 China

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN (H23D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed H23D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at https://***/djiajunustc/H-23D_R-CNN. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Counting Monkeypox Lesions in Patient Photographs: Limits of Agreement of Manual Counts and Artificial Intelligence

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Journal of Investigative Dermatology 2023年第2期143卷 347-350.e4页

作者： McNeil, Andrew J. House, David W. Mbala-Kingebeni, Placide Mbaya, Olivier Tshiani Dodd, Lori E. Cowen, Edward W. Nussenblatt, Véronique Bonnett, Tyler Chen, Ziche Saknite, Inga Dawant, Benoit M. Tkaczyk, Eric R. Dermatology Service and Research Service Department of Veterans Affairs Tennessee Valley Healthcare System Nashville TN United States Department of Dermatology Vanderbilt University Medical Center Nashville TN United States Department of Electrical and Computer Engineering School of Engineering Vanderbilt University Nashville TN United States Institut National de Recherche Biomédicale Kinshasa Democratic Republic Congo Clinical Monitoring Research Program Directorate Frederick National Laboratory for Cancer Research Frederick MD United States Clinical Trials Research Section Division of Clinical Research National Institute of Allergy and Infectious Disease Bethesda MD United States Dermatology Branch National Institute of Arthritis and Musculoskeletal and Skin Diseases Bethesda MD United States Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases Bethesda MD United States Biophotonics Laboratory Institute of Atomic Physics and Spectroscopy University of Latvia Riga Latvia Department of Biomedical Engineering School of Engineering Vanderbilt University Nashville TN United States

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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Super-resolution compressive spectral imaging via two-tone adaptive coding

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Photonics Research 2020年第6期8卷 395-411页

作者： CHANG XU TINGFA XU GE YAN XU MA YUHAN ZHANG XI WANG FENG ZHAO GONZALO R.ARCE Key Laboratory of Photoelectronic Imaging Technology and System School of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China Department of Electrical and Computer Engineering University of DelawareNewarkDelaware 19716USA

Coded apertures with random patterns are extensively used in compressive spectral imagers to sample the incident scene in the image *** samplings,however,are inadequate to capture the structural characteristics of the underlying signal due to the sparsity and structure nature of sensing matrices in spectral *** paper proposes a new approach for super-resolution compressive spectral imaging via adaptive *** this method,coded apertures are optimally designed based on a two-tone adaptive compressive sensing(CS)framework to improve the reconstruction resolution and accuracy of the hyperspectral imager.A liquid crystal tunable filter(LCTF)is used to scan the incident scene in the spectral domain to successively select different spectral *** output of the LCTF is modulated by the adaptive coded aperture patterns and then projected onto a lowresolution detector *** coded aperture patterns are implemented by a digital micromirror device(DMD)with higher resolution than that of the *** to the strong correlation across the spectra,the recovered images from previous spectral channels can be used as a priori information to design the adaptive coded apertures for sensing subsequent spectral *** particular,the coded apertures are constructed from the a priori spectral images via a two-tone hard thresholding operation that respectively extracts the structural characteristics of bright and dark regions in the underlying ***-resolution image reconstruction within a spectral channel can be recovered from a few snapshots of low-resolution *** no additional side information of the spectral scene is needed,the proposed method does not increase the system *** on the mutual-coherence criterion,the proposed adaptive CS framework is proved theoretically to promote the sensing efficiency of the spectral *** and experiments are provided to demonstrate and assess the proposed adaptive cod

关键词： resolution spectral image

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Tunable unidirectional compact acoustic amplifier via space-time modulated membranes

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Physical Review B 2020年第2期102卷 024309-024309页

作者： Xiaohui Zhu Junfei Li Chen Shen Guangyu Zhang Steven A. Cummer Longqiu Li State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang 150001 China Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA

Space-time modulation has recently attracted considerable attention as it enables new possibilities in wave manipulation and control. Here we propose a tunable unidirectional acoustic amplifier based on a space-time modulated membrane system. The transfer matrix method is applied to design this system and optimize the modulation parameters for the realization of unidirectional amplification. Efficient one-way amplification of acoustic waves is demonstrated theoretically and numerically within a frequency range from 2260 to 2520 Hz, with overall dimension being less than 1/3 of the corresponding wavelength. Across this operation band, the amplification factor in the positive direction varies smoothly from 3.0 to 5.8, while the transmitted wave energy in the negative direction does not change significantly. Our work identifies a feasible approach to realize a tunable unidirectional acoustic amplifier via space-time modulated membranes, which offers a design platform for a number of applications in sensing, imaging, and communication.

关键词： Acoustic metamaterials Acoustic wave phenomena Sound waves

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Enhanced graph convolutional and attention fusion network for hyperspectral image classification

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Journal of Supercomputing 2025年第8期81卷

作者： Hao, Yuqi Sun, Yu Zheng, Jianfeng Li, Xiaohui Yu, Xiaodong College of Computer Science and Information Engineering Harbin Normal University Heilongjiang Province Harbin China Department of Municipal and Environmental Engineering Heilongjiang Institute of Construction Technology Heilongjiang Province Harbin China Heilongjiang Key Laboratory of Analysis On Machine Learning and Dynamic System Harbin Normal University Heilongjiang Province Harbin China

In the field of hyperspectral image (HSI) classification, graph neural networks have become a mainstream method. However, graph convolutional networks (GCNs) focus on spatial features and ignore spectral information, while graph attention networks (GATs) are good at extracting spectral features but insufficient at capturing the complete spatial context between pixels. To address these limitations, we propose the enhanced graph convolution and attention fusion network (EGCAFN). Firstly, the module implements the fusion of GCN and GAT: It combines the topology-aware capability of GCN and the attention mechanism of GAT and adaptively adjusts the weights of different features through a node importance assessment mechanism to accurately capture the remote dependencies between pixels. Secondly, the depth-separable convolution and multi-scale feature fusion (DSF) module are introduced to efficiently extract multi-scale information while reducing computational complexity. Finally, the global–local attention model (GLAM) is introduced to solve the feature over-dispersion problem by integrating global and local information by combining the efficient channel attention (ECA) and spatial attention mechanisms. The overall accuracy (OA) of our EGCAFN on Indian Pines, Pavia University, Salinas and Botswana datasets is 94.59%, 96.03%, 97.04% and 99.84%, respectively. And the classification accuracy is improved on all four datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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