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Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

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Journal of Systems Engineering and Electronics 2019年第2期30卷 238-244页

作者： LI Binquan HU Xiaohui School of Automation Science and Electrical Engineering Beihang University Institute of Software Chinese Academy of Sciences

How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single *** machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model *** a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

关键词： convolutional neural network (CNN) distributed architecture remote sensing images (RSIs) target classification pre-training

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Adaptive Gradient Data Partition and Route Selection for distributed DNN Training 20th

Adaptive Gradient Data Partition and Route Selection for D...

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20th IFIP WG 10.3 International Conference on network and Parallel Computing, NPC 2024

作者： Chai, Bo Tan, Xiaobin Yuan, Shenzhi Jia, Guangge Meng, Qiushi Wang, Weifeng Zhu, Shiyin University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China H3C Technologies Co. Limited Beijing China

ISBN: (纸本)9789819628636

As the scale of distributed training for Deep neural network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-network Aggregation (INA) can accelerate aggregating gradients process, the critical communication bottleneck in distributed DNN training. Due to the limited processing capacity of network switch, existing aggregation approaches have turned to a combination of INA and Parameter Server (PS) methods. However, these approaches employ a fixed partitioning of the gradient data, leading to suboptimal performance. This paper proposes GPRS, an adaptive gradient aggregation framework for distributed DNN training, which adaptively combines the line-rate processing of INA and large memory capacity of PS. We present an adaptive gradient data partition scheme, which divides the gradient data into INA and PS aggregation. We propose a heuristic algorithm that determines the aggregation route selection by assigning priority to the available network paths. To validate our approach, we build a real-world experimental testbed and implement the proposed framework. Experimental results show that it can overcome the limitations of traditional INA-only methods and reduce communication time by 15% ∼ 52% for DNN training of varying scales. © IFIP International Federation for Information processing 2025.

关键词： Data aggregation

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An Artificial neural network for Solving distributed Optimal Control of the Poisson's Equation

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neural processing LETTERS 2019年第1期49卷 159-175页

作者： Ghasemi, Shojaeddin Effati, Sohrab Ferdowsi Univ Mashhad Dept Appl Math Mashhad Iran Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Mashhad Iran

This paper presents a simple and efficient method based on artificial neural network to solve distributed optimal control of Poisson's equation with Dirichlet boundary condition. The trial solutions are used to approximate the state and control variables. These trial solutions are considered by using a single layer neural network. By replacing the trial solutions in objective function and Poisson's equation, then using the weighted residual method, distributed optimal control of Poisson's equation is converted to a linear quadratic optimal control problem. The weights of the trial solutions are computed by solving the new problem. In order to solve the linear quadratic optimal control problem, the Pontryagin maximum principle is used. Finally we apply the proposed method on several examples that in computational experiments, the high efficiency of the presented method is illustrated.

关键词： Optimal control Poisson's equation Artificial neural network Weighted residual method Pontryagins maximum principle

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An attention-guided convolution neural network for denoising of distributed acoustic sensing-vertical seismic profile data

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GEOPHYSICAL PROSPECTING 2023年第6期71卷 914-930页

作者： Li, Yue Zhang, Yipan Dong, Xintong Wang, Hongzhou Jilin Univ Coll Commun Engn Changchun Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Changchun Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Changchun 130061 Peoples R China

distributed acoustic sensing technology is a new type of signal acquisition technology, and this technology has been widely used in obtaining vertical seismic profile data in recent years. distributed acoustic sensing technology has the advantages of high sampling density and strong tolerance to a harsh environment. However, in the real distributed acoustic sensing-vertical seismic profile data, the effective signal will be annihilated by various noises, which significantly complicates data analysis and interpretation. Deep learning approaches have developed rapidly in the noise suppression field in recent years. In order to eliminate the noise in distributed acoustic sensing-vertical seismic profile data, based on traditional convolution neural network, we add channel attention and spatial attention modules to the network to enhance the feature extraction ability of the network and use extended convolution to increase the receptive field to build a more efficient denoising model. In addition, we use different indicators to evaluate the quality of denoising, including signal-to-noise ratio, mean absolute error, kurtosis and skewness. The experimental results show that our method can recover the uplink wave field and downlink wave field, remove horizontal noise, optical system noise, random noise and other noises and improve the overall signal-to-noise ratio before and after denoising by 22 dB, reflecting a good ability of denoising and recovering effective signals.

关键词： data processing noise seismics signal processing

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Dynamic overload control for distributed call processors using the neural-network method

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IEEE TRANSACTIONS ON neural networkS 1998年第6期9卷 1377-1387页

作者： Wu, S Wong, KYM RIKEN Brain Sci Inst Lab Informat Synth Hirosawa 2-1 Wako Saitama 35101 Japan Hong Kong Univ Sci & Technol Dept Phys Kowloon Peoples R China

Overload control of call processors in telecom networks is used to protect the network of call processing computers from excessive load during traffic peaks, and involves techniques of predictive control with limited local information. Here we propose a neural-network algorithm, in which a group of neural controllers are trained using examples generated by a globally optimal control method. Simulations show that the neural controllers have better performance than local control algorithms in both the throughput and the response to traffic upsurges. Compared with the centralized control algorithm, the neural control significantly decreases the computational time for making decisions and can be implemented in real time.

关键词： adaptive control neural networks overload control radial basis function telecommunication network traffic upsurge

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Enhanced neural network Implementation for Temperature Profile Extraction in distributed Brillouin Scattering-Based Sensors

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IEEE SENSORS JOURNAL 2022年第7期22卷 6871-6878页

作者： Madaschi, Andrea Morosi, Jacopo Brunero, Marco Boffi, Pierpaolo Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy Cohaerentia Srl I-20133 Milan Italy

In this work two neural network (NN) based solutions are proposed to recover the distributed temperature profile of a sensing fiber, measured using a commercial Brillouin Optical Time-Domain Analysis (BOTDA) interrogator. A detailed analysis in terms of temperature accuracy and processing speed is carried out for both the proposed methods, comparing the results with the ones obtained from the application of classical fitting techniques, namely cross-correlation (CORR), Lorentzian fitting (LF) and Pseudo-Voigt fitting (PV), through both simulations and real measurements carried out in laboratory environment. The results show that the first NN implementation, which aims to maximize the accuracy of the temperature profile and the processing speed, can handle different width of frequency acquisition window but needs to be optimized for a specific frequency acquisition scanning step. The second NN implementation, however, can also handle different values of the acquisition scanning step with a minor performance drop. Simulations and experimental data show a massive advantage of NN implementations in terms of processing speed with respect to classical fitting techniques, with a slightly better accuracy of the estimated temperature profiles.

关键词： Artificial neural networks Temperature sensors Scattering Temperature measurement Signal to noise ratio Time-frequency analysis Optical sensors neural network Brillouin BOTDA fiber optic distributed sensor temperature

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A 16 x 16 cellular neural network universal chip: The first complete single-chip dynamic computer array with distributed memory and with gray-scale input-output

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ANALOG INTEGRATED CIRCUITS AND SIGNAL processing 1998年第3期15卷 227-237页

作者： Cruz, JM Chua, LO Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

This paper presents a 16 x 16 Cellular neural network Universal Chip with analog input and output ports, which can read in and process gray-scale images in the analog domain. The chip contains about 5,000 analog multi... 详细信息

关键词： CNN CNN universal chip computer array CMOS analog nonlinear dynamics

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Radar pulse compression for point target and distributed target using neural network

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2007年第1期23卷 183-201页

作者： Duh, Fun-Bin Juang, Chia-Feng Feng Chia Univ Dept Elect Engn Taichung 407 Taiwan Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan

An important study of the responses to the point target and the distributed target of the radar echoes processed by a neural network pulse compression (NNPC) algorithm is presented in this paper. For whatever the purpose of a radar system, both of the point target and distributed target echoes are received simultaneously. It is always necessary and helpful to discriminate them clearly while detecting the desired target, which will reduce the influence for each other in pulse compression processing. However, in most of the pulse compression algorithms, it is only considered the radar purpose to process one type of the targets but neglect the other. This will make either the presence of a point target's range sidelobes masking and corrupting the observation of the weak distributed target nearby or a distributed target with extended range interfering with the detection of the neighboring point target. By completely considering the interactions of a point target with a distributed target, we acquire all the possible data occurred in the procedure. Using these valid data, we can train the backpropagation (BP) network to construct it as a well performance of NNPC. To compare with the traditional algorithms such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter based on 13-element Barker code (B13 code), the proposed NNPC provides the requirements of high signal-to-sidelobe ratio, low integrated sidelobe level (ISL), and high target discrimination ratio. Simulation results show that this NNPC algorithm has significant advantages in targets discrimination ability, range resolution ability, and noise rejection performance while processing the interaction of point target with distributed target, which are superior to the traditional algorithms.

关键词： barker code distributed target least squares inverse filter linear programming filter neural network pulse compression point target sidelobe

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FedGraphMRI-net: A federated graph neural network framework for robust MRI reconstruction across non-IID data

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BIOMEDICAL SIGNAL processing AND CONTROL 2025年 102卷

作者： Ahmed, Shahzad Jinchao, Feng Manan, Malik Abdul Yaqub, Muhammad Ali, Muhammad Usman Raheem, Abdul Beijing Univ Technol Fac Informat Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Hunan Univ Sch Biomed Sci Changsha Peoples R China COMSATS Univ Islamabad Dept Comp Sci Sahiwal Campus Sahiwal Pakistan Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

Accurate MRI reconstruction from undersampled k-space data is essential in medical imaging. Still, it is often dependent on conditional models closely tied to specific imaging operators, which limits their adaptability to different imaging protocols and equipment. This dependence leads to suboptimal performance under varying conditions. Centralized approaches also pose data privacy concerns, as they require data sharing across institutions. To address these challenges, we introduce FedGraphMRI-Net, a federated learning framework specifically designed for MRI reconstruction in non-Independent and Identically distributed (non-IID) settings. Our approach leverages graph-based neural networks to capture both local and global anatomical correlations, ensuring patient privacy and adaptability to diverse, site-specific data distributions. FedGraphMRI-Net employs a graph clustering strategy via the Louvain algorithm to partition global MRI data into sub-graphs, each representing localized anatomical features and spatial relationships. Experimental results demonstrate that FedGraphMRI-Net achieves superior MRI reconstruction performance, obtaining PSNR scores of 43.8 f 1.1, 44.1 f 1.0, and 45.0 f 1.1 dB, and SSIM values of 98.5 f 0.2 %, 98.3 f 0.2 %, and 98.8 f 0.1 % for T1, T2, and PDweighted scans on the IXI dataset. On the fastMRI dataset with 4x acceleration, the model achieved PSNR scores of 42.0 f 1.5, 40.8 f 1.4, and 43.2 f 1.6 dB, along with SSIM values of 98.5 f 0.4 %, 97.9 f 0.3 %, and 98.8 f 0.1 % for T1, T2, and FLAIR scans. FedGraphMRI-Net outperforms state-of-the-art models in cross-domain generalization and high acceleration scenarios, offering a robust, scalable, and privacy-preserving MRI reconstruction solution for varied clinical environments.

关键词： Federated learning MRI reconstruction Graph neural network Deep learning Privacy-preserving Medical imaging

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Deep embedded lightweight CNN network for indoor objects detection on FPGA

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JOURNAL OF PARALLEL AND distributed COMPUTING 2025年 201卷

作者： Afif, Mouna Ayachi, Riadh Said, Yahia Atri, Mohamed Univ Monastir Fac Sci Monastir Lab Condensed Matter & Nanosci Monastir Tunisia Univ Monastir Fac Sci Monastir Monastir Tunisia Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 73213 Saudi Arabia King Khalid Univ Coll Comp Sci Abha Saudi Arabia

Indoor object detection and recognition present an active research axis in computer vision and artificial intelligence fields. Various deep learning-based techniques can be applied to solve object detection problems. With the appearance of deep convolutional neural networks (DCNN) a great breakthrough for various applications was achieved. Indoor object detection presents a primary task that can assist Blind and Visually Impaired persons (BVI) during their navigation. However, building a reliable indoor object detection system used for edge device implementations still presents a serious challenge. To address this problem, we propose in this work to build an indoor object detection system based on DCNN network. Cross-stage partial network (CSPNet) was used for the detection process and a lightweight backbone based on EfficientNet v2 was used as a network backbone. To ensure a lightweight implementation of the proposed work on FPGA devices, various optimization techniques have been applied to compress the model size and reduce its computation complexity. The proposed indoor object detection system was implemented on a Xilinx ZCU 102 board. Training and testing experiments have been conducted on the proposed indoor objects dataset that counts 11,000 images containing 25 landmark classes and in indoor objects detection dataset. The proposed work achieved 82.60 mAP and 28 FPS for the original version and 80.04 with 35 FPS as processing speed for the compressed version.

关键词： Indoor objects detection Blind and visually impaired Deep learning Embedded implementation VITIS AI Xilinx ZCU 102

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