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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Fangyuan Li Jiahu Qin Shaoshuai Mou Yu Kang Department of Automation University of Science and Technology of China Hefei China School of Aeronautics and Astronautics Purdue University West Lafayette Indiana United States State Key Laboratory of Fire Science University of Science and Technology of China Hefei China Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing China

ISBN: (纸本)9781509035502

Some applications of the interconnected embedded systems such as sensor networks rely on all nodes in the network to execute certain tasks simultaneously. To meet this demand for simultaneity, a multi-timer model based fully distributed task synchronization algorithm is proposed in this paper. In this multi-timer model, each node containing an embedded system is characterized by a timer. The microcontrollers (MCUs) within the interconnected embedded systems are switched to the assigned tasks by timer interrupts. Each timer decides when to trigger interrupts by only using the information from its neighbors. Task synchronization is realized by using the proposed synchronization algorithm. Some simulation examples are presented in the end to verify the effectiveness of the proposed synchronization algorithm.

关键词： Embedded systems Synchronization Clocks Nickel Switches Algorithm design and analysis Time-frequency analysis

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W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network

arXiv

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arXiv 2020年

作者： Zhao, Hongwei Peng, Chengtao Liu, Lei Li, Bin School of Information Science and Technology University of Science and Technology of China Hefei Anhui230022 China Department of Precision Machinery and Instrumentation University of Science and Technology of China HefeiAnhui230022 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230026 China

Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a W-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the W-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e_ophtha_EX and DiaRetDb1. We achieved F1-score of 94.76% and 95.73% for OD segmentation, and 92.80% and 94.14% for exudates segmentation. To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. Our results demonstrated that by choosing the optimal weights of each task, the MTL based W-net outperformed separate models trained individually on each task. Code and pre-trained models will be available at: https://***/FundusResearch/MTL_for_OD_and_***. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Some Reflections and Simulations on the Resolution and Focusing Approach of Microwave Photonic SAR with an Across-Band Bandwidth

Some Reflections and Simulations on the Resolution and Focus...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Min Chen Xiaolan Qiu Xuejiao Wen Hui Chen Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Chinese Academy of Sciences The Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Beijing China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Aerospace Information Research Institute Suzhou China State key Laboratory of Millimeter Waves Southeast University Nanjing China

Microwave photonic (MWP) SAR technology can realize large bandwidth even across multiple wavebands and therefore improves the imaging resolution significantly. However, when targets are illuminated by the electromagnetic wave with such an across-band bandwidth, their scattering characteristics will change with signal frequency, which is not taken into consideration for conventional SAR imaging. In this paper, in order to investigate the resolution and optimize the imaging of MWP SAR, we first design several typical structures and conduct electromagnetic simulations on them, from which, we obtain the variation of scattering characteristics within an across-band bandwidth. Then, we use different focusing approaches for one-dimensional imaging processing and adopt different evaluation indicators to evaluate the performance of these methods comprehensively. Finally, with detailed analysis, we give suggestions on the selection of compression methods for different structures at different SNRs.

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Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation

arXiv

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arXiv 2023年

作者： Li, Ziqiang Wang, Chaoyue Rui, Xue Xue, Chao Leng, Jiaxu Li, Bin Big Data and Decision Lab University of Science and Technology of China China The University of Sydney Australia JD Explore Academic China School of computer science Chongqing University of Posts and Telecommunications China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From Scratch (LFS) methods often generate images that closely resemble the training data while Transfer Learning (TL) methods try to improve performance by leveraging prior knowledge from GANs pre-trained on large-scale datasets. However, current TL methods may not allow for sufficient control over the degree of knowledge preservation from the source model, making them unsuitable for setups where the source and target domains are not closely related. To address this, we propose a novel pipeline called Peer is your Pillar (PIP), which combines a target few-shot dataset with a peer dataset to create a data-unbalanced conditional generation. Our approach includes a class embedding method that separates the class space from the latent space, and we use a direction loss based on pre-trained CLIP to improve image diversity. Experiments on various few-shot datasets demonstrate the advancement of the proposed PIP, especially reduces the training requirements of few-shot image generation. © 2023, CC BY.

关键词： Large dataset

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Multi-aspect Tomographic SAR Imaging Approach via Distributed Compressed Sensing and Joint Sparsity

Multi-aspect Tomographic SAR Imaging Approach via Distribute...

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IEEE International Conference on Radar

作者： Bang Du Zhe Zhang Xiaolan Qiu Bin Lei Chibiao Ding Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute (of Chinese Academy of Sciences) University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China Aerospace Information Research Institute (of Chinese Academy of Sciences) Beijing China

ISBN: (纸本)9781665468893

Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same target scene from various baselines. Canonical TomoSAR observations are from a single aspect, which has been well studies already. However, modern SAR sensors such as Unmanned Aerial Vehicle (UAV) allow us to achieve multi-aspect TomoSAR data of the same target scene. This paper proposes a novel framework named “Multi-aspect TomoSAR,” which takes advantage of rich TomoSAR data from multiple observation aspects. We derive the multi-aspect TomoSAR signal model using distributed compressed sensing (DCS) and adopt a simultaneous sparse approximation algorithm named SOMP to solve the joint sparsity model. Numerical results on synthetic simulated data show that the multi-aspect estimation can provide more accurate estimation, yield a promising perspective. Experimental results on real airborne data will be reported in the journal version of this work later.

关键词： Atmospheric modeling Airborne radar Estimation Tomography Autonomous aerial vehicles Sparse representation Numerical models

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ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Qiu, Xiaolan Gao, Silin Wang, Yue Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an offline optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Fuzzy-logic based adaptive weighting filter for strap-down inertial navigation systems

Fuzzy-logic based adaptive weighting filter for strap-down i...

引用

World Congress on Intelligent Control and Automation (WCICA)

作者： Wenjun Lv Yu Kang Zhijun Li Yun-Bo Zhao Department of Automation & CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui Province China College of Automation Science and Engineering South China University of Technology Guangzhou Guangdong Province China College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang Province China

Strap-down inertial navigation systems are widely applicable in both military and civil industries. The outputs of gyroscope and accelerometer are polluted by noises. Different form the conventional technologies, the weighting filter presented in this work is superior to both the complementary filter and Kalman filter. Furthermore, to eliminate the negative effects brought by the variation of noise statistical characteristics, a fuzzy-logic based regulator is introduced to adjust the weight coefficient adaptively. Finally, experiments are conducted to validate the efficiency of the filter.

关键词： Noise Gyroscopes Accelerometers Kalman filters Accuracy Mathematical model Educational institutions

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Rpsa: Riemannian Principal Skewness Analysis

SSRN

引用

SSRN 2023年

作者： Geng, Xiurui Gao, Jingyu Aerospace Information Research Institute Chinese Academy of Sciences Beijing100864 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100864 China

By transforming the determination of the principal skewness direction of data into the solution of the eigenpair (eigenvalue and eigenvector) of its coskewness tensor, the principal skewness analysis (PSA) has been introduced into the blind source separation (BSS) community. Different from the eigenpair problem of a matrix which has been extensively and thoroughly studied, the solution of the eigenpairs of a coskewness tensor is a recently emerging research topic and can only be obtained by means of optimization at present. Generally, the iteration direction and the iteration step size are the two most critical factors in optimization methods. As the iteration direction, the conventional gradient has been widely used to solve the eigenpairs of a coskewness tensor in the existing methods. However, in this paper, we have found that compared with the conventional gradient, Riemannian gradient as the search direction is more conducive to avoid the current eigenvector converging to the ones that have been obtained. In addition, in order to obtain the rest locally maximal eigenpairs of a coskewness tensor, a new step-size adjustment strategy is also designed in this study. Experiments on simulated and real data demonstrate the effectiveness and superiority of the presented method. © 2023, The Authors. All rights reserved.

关键词： Tensors

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K-Wise Multi-Graph Matching

SSRN

引用

SSRN 2023年

作者： Zhu, Xinwen Zhu, Liangliang Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences Beijing100864 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100864 China

Multi-graph matching, which aims to find the correspondences of multiple graphs, is an extension of conventional two-graph matching. There are mainly two categories of multi-graph matching methods, i.e., the pairwise-based methods and the tensor-based methods. Both of them try to find the correspondences between graphs by maximizing the values of the similarities between the corresponding features, e.g., nodes, edges, and so on. The difference is that for a set of features from different graphs to be matched, the pairwise-based methods consider the similarities of every two features, while the tensor-based methods directly consider the whole similarity among them. Therefore, the measurement of similarities in the tensor-based methods is usually much more flexible than that in the pairwise-based methods. However, the time and space complexities of the tensor-based methods grow geometrically as the number of graphs to be matched increases. In this paper, through a comprehensive investigation, we propose a k-wise multi-graph matching method which includes the aforementioned two kinds of methods as special cases. Generally, by choosing different value of k, it could be found that the proposed algorithm can strike a balance between flexible similarity measurement and low computational complexity. In addition, a factorization of k-wise global affinity matrix is proposed to save the computational cost of our method. Experiments with synthetic and real data demonstrate the superiority of our method. © 2023, The Authors. All rights reserved.

关键词： Tensors

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InSAR DEM Reconstruction Based on Backprojection Algorithm in Two Converse Flights

InSAR DEM Reconstruction Based on Backprojection Algorithm i...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Xiaoning Hu Maosheng Xiang Bingnan Wang Xikai Fu University of Chinese Academy of Sciences National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing China National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distortions such as shadow and layover in the mountainous terrain, which will reduce the quality of generated DEM. Fusion of two or more different aspects of InSAR data can deal with this problem. We propose an InSAR DEM reconstruction method based on backprojection (BP) algorithm in two converse flights. This method utilizes the feature of BP algorithm that geocoding has been realized in imaging process to simplify the fusion process of multi-aspect InSAR data. In addition, an iterative DEM extraction method is introduced to improve DEM accuracy. Experimental results verify the effectiveness of the proposed method.

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