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Binary Separable Convolutional: An Efficient Fast Image Classification Method

Binary Separable Convolutional: An Efficient Fast Image Clas...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Donglin Jing Linbo Tang Yu Pan Wei Tang Shichao Zhou Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The training and running of neural network require large computational space and memory space, which makes it difficult to deploy on a resource-constrained embedded sys-tems. To address this limitation, we introduce a two-stage pipeline: Depth separable, local binary. Our method is divided into two steps. Firstly, a deep separable convolution is applied to each input channel. Then, point-by-point convolution is applied to the feature map obtained by the filter. In the second step, we use local binarization method to initialize the filter corresponding to the input channel into sparse binary. In network training, the sparse binary filter remains fixed and only needs to train convolution of 1×1 size. Our experimental results show that, on the basis of approximate accuracy with the original network, we have reduced the number of convolution parameters by 9x to 10x, and reduced the training time and testing time to by 2x. Our compression method helps to deploy complex neural networks on resource-constrained embedded platform.

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Compressive Multidimensional Harmonic Retrieval with Prior Knowledge

Compressive Multidimensional Harmonic Retrieval with Prior K...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Li, Yinchuan Zhang, Xu DIng, Zegang Wang, Xiaodong School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Electrical Engineering Department Columbia University New York United States Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9781728123455

This paper concerns the problem of estimating multidimensional (MD) frequencies using prior knowledge of the signal spectral sparsity from partial time samples. In many applications, such as radar, wireless communications, and super-resolution imaging, some structural information about the signal spectrum might be known beforehand. Suppose that the frequencies lie in given intervals, the goal is to improve the frequency estimation performance by using the prior information. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to given intervals. We then propose to solve the frequency-selective atomic norm minimization by converting them into semidefinite program based on the MD Vandermonde decomposition. Numerical simulation results are presented to illustrate the good performance of the proposed method. © 2019 IEEE.

关键词： Frequency estimation

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Weighted Anomaly Detection Algorithm Based on Local Density

Weighted Anomaly Detection Algorithm Based on Local Density

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Yibing Tian Linbo Tang Zhen Li Yun Huang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Anomaly detection for hyperspectral imagery (HSI) has drawn a lot of attention lately. In the past decades, several methods for detecting anomalies in HSI have been developed. The Reed-Xiaoli detector (RXD) is one of the most widely used methods for anomaly detection. However, RXD assumes that background follow the Gaussian distribution, not always conformed to the real case. In order to solve this problem, we improve the RX anomaly detection algorithm by weighting changes of background. First, the concentric double-window model is utilized to obtain the local densities of each background pixels. Then, the local densities are applied to weight background mean and covariance matrix to reduce the influence of abnormal pixels in the background. The experimental results show that our method outperforms other approaches in anomaly detection.

关键词： hyperspectral imagery anomaly detection local RXD weighted covariance matrix anomaly detection hyperspectral imagery AKR1B10 gene variance covariance matrix Gaussian distribution

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Ground Target Automatic Recognition and Tracking in Complex Battlefield Environment

Ground Target Automatic Recognition and Tracking in Complex ...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Chen Wu Linbo Tang Chenhui Duan Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

With the developments of science and the changes of modern warfare modes, the precise guidance and attack towards hostile targets in complex battlefield environments has extremely important military needs and research significance. However, the existing guidance mode is often constrained by factors such as battlefield background interference, target appearance variation and limited data links, which in turn affects tracking and strike performance. To this end, we present an integrated recognition and tracking scheme for the ground targets in complex battlefield environment. Specifically, with the missile-target distance information, we design an adaptive scale estimation scheme to tackle the aspect ratio variation during tracking. Furthermore, an efficient tracking monitoring index is advocated to judge the tracking condition as well as forecast the potential tracking failure. Once tracking failure is determined, a deep-learning based detection module is activated to re-detect the target as well as correct the tracking drift. In the strike stage of the mission, the key region of the hostile target would be identified and locked by the missile in order to maximize the attack effect. The proposed recognition and tracking scheme are tested upon extensive actual videos and achieved a tracking accuracy over 85%, which demonstrates its effectiveness as well as provides a novel solution for accurate and robust attack on enemy targets in a complex battlefield environment.

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Research on Crop Planting Area Classification From Remote Sensing Image Based on Deep Learning

Research on Crop Planting Area Classification From Remote Se...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Yun Huang Linbo Tang Donglin Jing Zhen Li Yibing Tian Shichao Zhou Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Remote sensing technology is widely used in agriculture monitoring because of the advantages of large-area simultaneous observation, low cost and dynamic monitoring of time and space. However, a manual visual interpretation method is often used to extract the information behind the remote sensing images, which is time and labor consuming. Moreover, handcraft features such as texture and structure of crop images are applied to classify crop planting area while these features are not robust. In order to reduce the cost and enhance the classification accuracy, we improved the state-of-the-art image semantic segmentation network SegNet in crop planting area classification which can speed up the convergence and reduce the model size largely with small gains in accuracy. The experiment result shows that the classification accuracy of the improved SegNet is slightly increased compared with SegNet, and the computational cost (FLOPs) of the improved SegNet is much less than SegNet.

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Three-level Memory Access Architecture for FPGA-based real-time Remote Sensing Image processing System

Three-level Memory Access Architecture for FPGA-based Real-t...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Ning Zhang Xin Wei Lei Chen He Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Recently, extensive convolutional neural network (CNN)-based methods have been used for real-time remote sensing image processing system. However, the huge storage requirement for model and input image bring a great challenge to hardware implement. In this paper, we propose a three-level memory access architecture for FPGA-based real-time remote sensing image processing system to meet the storage requirement. Moreover, in the proposed architecture, the computational throughput is well matched to the storage bandwidth, which improve the efficiency of arithmetic processing on the hardware platform. In the experiment, we applied the architecture to the hardware implementation of CNN, and implement on Xilinx ZYNQ xc7z035 platform. Our design is evaluated on a main part of the modified YOLOv2 framework [1]. The experimental results demonstrate that this design can access the off-chip memory efficiently, and can provide the required parameters and input data for the CNN on-chip pipeline processing.

关键词： Convolutional Neural Network FPGA Memory Access Image processing Field programmable gate arrays Image processing Storage requirements ARCHITECTURE Building construction three-level Hardware Platform Memory Memory Access

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SOPC-based Implementation of Convolutional Neural Network

SOPC-based Implementation of Convolutional Neural Network

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xing Feng Mingfei Jia He Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Convolutional Neural Network (CNN) is an algorithm widely used in the field of deep learning. Due to the large number of intensive parallel data operations, the use of CPU to implement the CNN serially consumes too much time. In view of the above research background, the System-on-a-Programmable-Chip (SOPC) implementation and acceleration modules of CNN are designed by using the Zynq-7035 development platform launched by Xilinx as the experimental platform. The experiment results show that our method is effective and efficiency for the target image classification on the development platform.

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Ship Detection in Synthetic Aperture Radar Imagery Based on Discriminative Dictionary Learning

Ship Detection in Synthetic Aperture Radar Imagery Based on ...

引用

Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Yun Wang Liang Chen Hao Shi Bocheng Zhang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

The ship target detection technology based on SAR image has important significance in military and civil field applications and is one of the research hotspots at this stage. In this paper, the research work on typical problems in SAR image ship target detection is carried out. A ship target detection algorithm based on discriminative dictionary learning is proposed, which mainly includes image denoising, candidate region extraction and candidate region identification. Firstly, an adaptive non-local filtering method is used to denoise the SAR image. Then the gradient feature map reconstruction algorithm is used to extract the candidate regions. Finally, the category constrained discriminative dictionary learning method is used to classify the candidate regions. The algorithm is based on GF-3 and Terra SAR data. The experimental results show that the proposed algorithm has strong robustness and adaptability.

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FPGA-based reconfigurable matrix inversion implementation for inverse filtering of multichannel SAR imaging

FPGA-based reconfigurable matrix inversion implementation fo...

引用

IET International Radar Conference 2018, IRC 2018

作者： Li, HuiXing Feng, YangKai Hu, ShanQing Li, BingYi Xie, YiZhuang Wu, MengChao Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai China

In multi-channel synthetic aperture radar (SAR), the azimuth non-uniform sampling tends to result in a large number of virtual point targets, which are not expected. Inverse filter algorithm provides a new idea for solving this problem. This way can be abstracted as a matrix inversion in essence, which becomes the key factor that affects the real-time and accuracy of multi-channel pre-processing. This study presents the implementation of matrix inversion method on field programmable gate array (FPGA), based on lower and upper triangular matrix (LU) decomposition algorithm. In this process, the efficient parallelism of FPGA and the rich floating-point intellectual property (IP) cores are fully utilised to speed up the process of inverting the matrix with a data type of 32-bit single-precision floating-point. In this design, the parallelism of the algorithm was fully considered and a hierarchical iterative processing strategy was adopted to realise the reconfigurable storage and computing unit both. At the same time, in order to achieve the balance of resources and efficiency, a reusable structure was proposed also, using the pipeline technology and appropriate data scheduling. Finally, Modelsim platform is used to observe the simulation results, and the performance can be detected combined with MATLAB platform. At last, the computational accuracy is up to 10−7, and the speedup ratio can reach about 1.461 × 103 © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： MATLAB

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Slow-time MIMO Radar Clutter Mitigation Based on Robust Pulse-to-Pulse Coding

Slow-Time MIMO Radar Clutter Mitigation Based on Robust Puls...

引用

Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Feng Xu Junqi Xue Xiaopeng Yang Fawei Yang Zhanze Wang Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Slow-time MIMO radar is investigated to deal with some cost sensitive applications like UAVs detection to avoid the use of arbitrary waveform generators and digital receivers. But this method suffers from the deterioration of Signal to Noise Ratio (SNR) caused by superposition of clutter. To solve this problem, a robust pulse-to-pulse coding method is proposed to mitigate clutter as well as suppress the sidelobe level in Doppler domain. In our proposed method, the problem of optimizing weighting vector is transformed into a typical MVDR problem and the design of new weighting vector is obtained by the combination of MVDR solution and a sequence of weight coefficients which are calculated by Fourier inversion. This proposed method can mitigate the clutter adaptively while the sidelobe is suppressed. Both simulation and experiments results validate the reliability and effectiveness of this method.

关键词： Clutter Doppler effect MIMO communication MIMO radar Transmitters Encoding Demodulation

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