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检索条件"机构=Beijing Key Laboratory of Embedded•Real-Time Information: Processing Technology"

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A time Deterministic Dynamic Segmentation Scheduling Algorithm 9

A Time Deterministic Dynamic Segmentation Scheduling Algorit...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Gu, Haonan Wu, Jing Guo, Yi Wuhan University of Science and Technology College of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

This paper proposed a soft real-time scheduling algorithm 'time-deterministic dynamic partitioning scheduling' for multimedia tasks in embedded devices. We established a new model based on the characteristics of embedded devices. We developed a pre-scheduling mechanism to select a suitable scheduling strategy based on the actual situation of the task set in the offline cache and to rearrange the task order so as to guarantee the reliability and real-time performance of the system while having higher flexibility and stability. The mechanism also completes the tasks assigned by the system in a stable manner with high utilization. © 2022 IEEE.

关键词： Scheduling algorithms

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Design and implementation of a configurable parallel FFT processor in onboard SAR imaging system based on FPGA

Design and implementation of a configurable parallel FFT pro...

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IET International Radar Conference (IRC 2023)

作者： Ming Xu Jiawei Zhang Yongrui Li Yifei Yin Ao Zhang Heng Dong Liang Chen Hao Shi Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing 100081 People's Republic of China Shanghai Institute of Satellite Engineering Shanghai 201109 People's Republic of China

ISBN: (数字)9781837240982

Spaceborne Synthetic Aperture Radar (SAR) plays an important role in the field of emergency disaster reduction. In order to meet the stringent timeliness requirements in onboard SAR data processing, this paper proposes a configurable four-channel parallel FFT processor, which serves as the core of SAR imaging processing. This processor is capable of supporting up to 128K-point FFT operations. Additionally, it supports radix-2 time decimation FFT concatenation processing, radix-4 time decimation FFT concatenation processing, radix-2 frequency decimation FFT concatenation processing, and radix-4 frequency decimation FFT concatenation processing, all of which can be flexibly configured based on data structure . This processor is implemented on the Xilinx Virtex-7 XC7VX690T FPGA and can achieve a maximum FFT processing capability of 128K points at a frequency of 300MHz. The evaluation and analysis results demonstrate that the processor reaches a higher processing rate, effectively meeting the demanding requirements for efficient spaceborne processing.

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Radar HRRP Open Set Recognition Using Hierarchical Prototype Learning

Radar HRRP Open Set Recognition Using Hierarchical Prototype...

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IEEE International Conference on Radar

作者： Yichen Liu Yanhua Wang Liang Zhang Binhua Yu Xiaojing Yang Yu-ang Zhang Le Zheng Difan Zou School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Department of Computer Science University of Hong Kong Hongkong China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Hongkong China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

High-resolution range profiles (HRRP) are increasingly employed in radar automatic target recognition (RATR). In practical applications of RATR, the environment is open and dynamic, and the recognition model is likely to encounter targets of unseen categories. However, traditional methods focus on a close-set scenario, where all categories in the test set are known during training. To bridge this gap, this paper proposes an open-set recognition (OSR) method based on hierarchical classification, developed from prototype learning. This method improves the generalization performance for recognizing known categories and identifies unknown categories while providing information about how the unknown categories relate to the known ones. Specifically, first, a four-layer category hierarchy is built based on prior knowledge to guide the training and testing stages. Next, we propose a hierarchical prototype loss (HPL) to constrain the feature space extracted by the prototype network so that the feature distribution of objects is consistent with the hierarchical category structure. Lastly, the trained prototype network makes predictions following the hierarchical structure. This operation could provide relationship information between the unknown and known categories. Extensive experiments on measured HRRP data validate the effectiveness of our proposed method for open-set recognition.

关键词： Training Bridges Target recognition Prototypes Radar Feature extraction Testing

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SAR-DARTS: a convolutional neural architecture search for SAR scene classification

SAR-DARTS: a convolutional neural architecture search for SA...

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IET International Radar Conference (IRC 2023)

作者： Tian Zhou Yinsheng Xu Qihai Li Baogui Qi He Chen Liang Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing 100081 People's Republic of China Shanghai Institute of Satellite Engineering Shanghai 200240 People's Republic of China

ISBN: (数字)9781837240982

In recent years, the collaborative service architecture of cloud computing and edge computing to reduce the demand for computing, storage and other capabilities of artificial intelligence methods has developed rapidly, but their application in synthetic aperture radar (SAR) image scene classification is still immature. In this paper, we propose a neural architecture search and pruning model for SAR scene classification. By introducing network morphisms and path regularization method, we can reduce the unfair competition between cells on the basis of expanding the search space. The model approach is applied to the FUSAR-Ship and OpenSARShip datasets for performance evaluation. The proposed method can achieve higher accuracy with fewer parameters.

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Generative Multi-View HRRP Recognition Based on Cascade Generation and Fusion Network

Generative Multi-View HRRP Recognition Based on Cascade Gene...

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IEEE International Conference on Radar

作者： Qiang Zhou Bingqian Yu Yanhua Wang Liang Zhang Le Zheng Difan Zou Xin Zhang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Department of Computer Science University of Hong Kong Hongkong China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Hongkong China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

High-resolution range profile (HRRP) is critical for radar target recognition. However, HRRP data for non-cooperative targets are often sparsely collected, leading to limited performance of HRRP target recognition methods. To address the issue of sparse view target recognition, we propose a generative multi-view HRRP recognition (GMVR) model. Firstly, by leveraging the pattern learning and generation ability of deep generative models (DGMs), we propose a viewpoint feature expansion module (VFEM) to generate multi-view features adjacent to sparse viewpoints. Subsequently, based on feature-level fusion, we proposed a multi-view feature fusion module (MVFM). Aided by attention mechanisms, the generated features can be adaptively integrated, thereby reducing the decision bias caused by single-viewpoint HRRP samples. Additionally, we employ transfer learning to mitigate the small sample problem caused by sparse viewpoints. Experimental results demonstrate that the proposed method outperforms various comparison methods, achieving optimal performance.

关键词： Adaptation models Attention mechanisms Target recognition Transfer learning Radar Pattern recognition

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Cascaded mixed pyramid pooling for surface defect segmentation in solid oxide fuel cell 3

Cascaded mixed pyramid pooling for surface defect segmentati...

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3rd International Conference on Electronic information Engineering, Big Data, and Computer technology, EIBDCT 2024

作者： Wang, Yaokun Fu, Xiaowei Li, Xi Xu, Wei College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9781510680449

To address the issues of defect leakage and mis-segmentation in the surface defect segmentation of solid oxide fuel cells, we propose a cascaded mixed pyramid pooling segmentation method. In the encoding part, attention-based depth-wise separable convolution is implemented to replace the traditional convolution. This reduces the computational complexity and achieves hierarchical feature extraction. Secondly, cascaded sampling is applied to the last three layers of the encoder to promote multi-level feature fusion, and mixed pyramid pooling consisting of atrous convolution and strip pooling is adopted to achieve feature extraction for long-range band defects. Lastly, an adaptive loss function is constructed to supervise the training process, balancing the relative importance among the losses and improving the learning of defect features by the network. The experiments demonstrate that the proposed method improves segmentation accuracy, as evidenced by better results in mean absolute error, F1 score, intersection over union, and Pratt's figure of merit. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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A model compression method based on multi-teacher distillation for SAR scene classification

A model compression method based on multi-teacher distillati...

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IET International Radar Conference (IRC 2023)

作者： Qihai Li Jiawei Zhang Tian Zhou Baogui Qi He Chen Liang Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing 100081 People's Republic of China Shanghai Institute of Satellite Engineering Shanghai 200240 People's Republic of China

ISBN: (数字)9781837240982

In recent years, with the advancement of deep learning, convolutional neural networks tailored for processing image content have gained increasing attention within the remote sensing imagery domain. However, deep convolutional neural networks that exhibit exceptional performance often encompass a substantial number of parameters, thereby necessitating heightened computational resources and time for network training and prediction. Given the constraints posed by resource limitations in the realm of remote sensing, deploying such high-performance networks onto these devices becomes a challenging endeavor. To address this set of challenges, this paper introduces a methodology grounded in target differential probability for multi - teacher knowledge distillation. This approach effectively reduces the model's complexity while preserving the network models' commendable performance. By conducting a statistical analysis of the model's output probability distribution and computing the differential probabilities between teacher and student networks, we regulate the output pro portions of teachers within the multi-teacher model. As a result, a high-performing student network is cultivated. In aggregate, experimental validation on two distinct datasets substantiates the efficacy of this approach in facilitating efficient knowledge transfer, consequently yielding heightened performance outcomes.

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition embedded Autoencoder

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

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

作者： Weijia Wang Zhuchang Qi Lue Wang Wei Yang Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi'an Institute of Modern Control Technology Xi'an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method.

关键词： Sensitivity Target recognition Convolution Fuses Autoencoders Dual band Scattering Radar Radar signal processing Signal resolution

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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model

Dual-Band HRRP Recognition via Wavelet Packet Decomposition ...

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

作者： Wei Yang Zhuchang Qi Heke Wu Yang Li Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Xi'an Institute of Modern Control Technology Xi’an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets; we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule.

关键词： Sensitivity Limiting Target recognition Fuses Redundancy Dual band Radar Feature extraction Wavelet packets Frequency response

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SAR Target Recognition Using Improved Monogenic-Based Feature Extraction Framework

SAR Target Recognition Using Improved Monogenic-Based Featur...

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Feng Yao, Weijun Li, Yang Chen, Wei Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Beijing China School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab Beijing China

ISBN: (纸本)9781665498142

Applying computer vision methods to synthetic aperture radar (SAR) image recognition is a research trend in recent years, and a series of valuable results have been achieved. In order to use machine learning classifiers for recognition, it is necessary to extract effective features, and most of these features are directly extracted based on grayscale SAR images. SAR data is usually rare, and more difficult to collect than optical images. Therefore, the problem of recognition using a small-size training set for SAR is more challenging to practical pattern recognition methods. The monogenic signal is an extended version of analytic signals in high-dimensional space which has attracted attention. In this paper, a new recognition framework which is based on feature dimensionality augmentation using combined multi-scale monogenic components and histogram of oriented gradient (HOG) feature is proposed. Proposed feature is named as MONO-HOG. This letter focuses on recognition under both standard operating condition (SOC) and small-sample scene. Experiments on moving and stationary target automatic recognition (MSTAR) data set show that our proposed framework has satisfying performance. © 2021 IEEE.

关键词： feature extraction monogenic signal small sample synthetic aperture radar (SAR) target recognition

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