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arXiv 2020年

作者： Fu, Jun Chen, Zhibo Chen, Xiaoming Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Beijing Technology and Business University China

In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Phase Transition Analysis for Compressive Sensing Based DoA Estimation in Automotive Radar

Phase Transition Analysis for Compressive Sensing Based DoA ...

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International Radar Symposium (IRS)

作者： Yan Zhang Silin Gao Mingqian Liu Bingchen Zhang Yirong Wu Chinese Academy of Sciences Aerospace Information Research Institute Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Processing Application System Beijing China

ISBN: (数字)9788394942151

ISBN: (纸本)9781728157863

In automotive radar applications, the compressive sensing (CS) based DoA estimation is used in array signal processing in recent years. Sparse reconstruction has the potential to estimate the direction of arrival (DoA) with super resolution. However, failed results may be acquired via sparse reconstruction in inappropriate conditions, namely the critical condition determining success or failure must be taken into consideration. In this paper, the sparsity of the scenario and the signal-to-noise ratio (SNR) are analyzed as the main factors via phase transition diagrams. Other factors affecting the success or failure are also investigated, such as the array configuration and the sparse recovery algorithm. Simulated and experimental results demonstrate the critical conditions, in which the DoA estimation is successful or failed.

关键词： Direction-of-arrival estimation Estimation Radar applications Compressed sensing Signal resolution Signal to noise ratio Automotive engineering

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On the Use of Axial Ratio to Analysis Transmit Distortions in Hybrid Polarimetry and Their Influence to Canonical Scattering Mechanisms 6

On the Use of Axial Ratio to Analysis Transmit Distortions i...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Chen, Shiqiang Hong, Wen Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728129129

Transmit distortions in the hybrid polarimetric (HP) SAR cannot be compensated simply with external calibration methods. Therefore, it is necessary to analysis their influence on HP data. In this study, we have proposed an analysis method based on the Axial Ratio (AR) of the real transmitted polarized wave. Firstly, impact of different distortion sources is quantified with the AR parameter. Next, a case study is illustrated using real GF-3 measured data. At last, the influence of transmit distortions to canonical scattering mechanisms is analyzed. © 2019 IEEE.

关键词： Polarimeters

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Prior-enlightened and motion-robust video deblurring

arXiv

引用

arXiv 2020年

作者： Zhou, Ya Xu, Jianfeng Tasaka, Kazuyuki Chena, Zhibo Li, Weiping CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China KDDI Research Inc. Fujimino-shi Saitama356-8502 Japan

Various blur distortions in video will cause negative impact on both human viewing and video-based applications, which makes motion-robust deblurring methods urgently needed. Most existing works have strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened and MOTION-robust video deblurring model (PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information, explicitly enhancing the scenes’ perception while mitigating the output’s artifacts. On the other hand, we design the priors representing blur distribution, to better handle non-uniform blur in spatio-temporal domain. Besides the classical camera shake caused global blurry, we also prove the generalization for the downstream task suffering from local blur. Extensive experiments demonstrate we can achieve the state-of-the-art performance on well-known REDS and GoPro datasets, and bring machine task gain. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion estimation

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SAR image classification based on spiking neural network through spike-time dependent plasticity and gradient descent

arXiv

引用

arXiv 2021年

作者： Chen, Jiankun Qiu, Xiaolan Ding, Chibiao Wu, Yirong National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Aerospace Information Research Institute Beijing10090 China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing10090 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Suzhou Research Institute Aerospace Information Research Institute Chinese Academy of Sciences Suzhou215123 China The University of Chinese Academy of Sciences Beijing10049 China

At present, the Synthetic Aperture Radar (SAR) image classification method based on convolution neural network (CNN) has faced some problems such as poor noise resistance and generalization ability. Spiking neural network (SNN) is one of the core components of brain-like intelligence and has good application prospects. This article constructs a complete SAR image classifier based on unsupervised and supervised learning of SNN by using spike sequences with complex spatio-temporal information. We firstly expound the spiking neuron model, the receptive field of SNN, and the construction of spike sequence. Then we put forward an unsupervised learning algorithm based on STDP and a supervised learning algorithm based on gradient descent. The average classification accuracy of single layer and bilayer unsupervised learning SNN in three categories images on MSTAR dataset is 80.8% and 85.1%, respectively. Furthermore, the convergent output spike sequences of unsupervised learning can be used as teaching signals. Based on the TensorFlow framework, a single layer supervised learning SNN is built from the bottom, and the classification accuracy reaches 90.05%. By comparing noise resistance and model parameters between SNNs and CNNs, the effectiveness and outstanding advantages of SNN are verified. Code to reproduce our experiments is available at https://***/Jiankun-chen/Supervised-SNN-with-GD. © 2021, CC BY.

关键词： Gradient methods

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An Energy Efficient Carry-Free Inner Product Unit

An Energy Efficient Carry-Free Inner Product Unit

引用

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

作者： Yan, Wen Ercegovac, Milos D. Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of California Computer Science Department Los AngelesCA United States

ISBN: (纸本)9781728123455

An energy efficient truncated inner product unit is proposed in this paper. The proposed unit is pipelined and processes the m pairs of n-bit operands in serial, so that only one unit is required and it can be reused in each recurrence. Truncated inner product is produced so that no accumulation is needed for the lower part. The array reduction unit is bit-level pipelined to improve throughput and reduce the initial delay. A two-level [4:2] adder is proposed to accumulate redundant vectors with non-uniform input arrival. The proposed inner product is compared with other truncated inner product units using fully merged arithmetic, partially merged arithmetic and the counter based inner product for different precision and vector length. Our proposed inner product unit has significant reduction in area, power and energy. © 2019 IEEE.

关键词： Pipelines

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Two-Step Bistatic Spaceborne Sliding-Spotlight SAR Imaging Agorithm Based on Accurate Range Model

Two-Step Bistatic Spaceborne Sliding-Spotlight SAR Imaging A...

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Jianbing Xiang Xiaolei Lv Xikai Fu Ye Yun University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The slant range errors caused by traditional hyperbolic range equation (THRE) with stop-and-go assumption will lead to image defocusing in high resolution spaceborne sliding-spotlight Bistatic SAR system (ST-BiSAR). In this paper, an accurate bistatic slant range model based on uniform acceleration curve motion (UARM) is proposed, which is precisely fitted with the actual range history. Then, a two-step imaging algorithm based on UARM and method of reversion series (MSR) is introduced to eliminate aliasing phenomenon and realize focus. Finally, simulation results verify the correctness and effectives of the proposed range model and imaging algorithms.

关键词： Mathematical model Imaging Satellites Synthetic aperture radar Acceleration Manganese Azimuth

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Beyond triplet loss: Meta prototypical N-tuple loss for person re-identification

arXiv

引用

arXiv 2020年

作者： Zhang, Zhizheng Lan, Cuiling Zeng, Wenjun Chen, Zhibo Chang, Shih-Fu The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Anhui Hefei China Microsoft Research Asia Beijing China Columbia University New YorkNY United States

Person Re-identification (ReID) aims at matching a person of interest across images. In convolutional neural network (CNN) based approaches, loss design plays a vital role in pulling closer features of the same identity and pushing far apart features of different identities. In recent years, triplet loss achieves superior performance and is predominant in ReID. However, triplet loss considers only three instances of two classes in per-query optimization (with an anchor sample as query) and it is actually equivalent to a two-class classification. There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID. In this paper, we introduce a multi-class classification loss, i.e., N-tuple loss, to jointly consider multiple (N) instances for per-query optimization. This in fact aligns better with the ReID test/inference process, which conducts the ranking/comparisons among multiple instances. Furthermore, for more efficient multi-class classification, we propose a new meta prototypical N-tuple loss. With the multi-class classification incorporated, our model achieves the state-of-the-art performance on the benchmark person ReID datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Unambiguous Imaging for Moving Targets in Maritime Scenarios with Dual Receive Channel Mode of GF-3 Satellite 6

Unambiguous Imaging for Moving Targets in Maritime Scenarios...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Yang, Junying Qiu, Xiaolan Zhong, Lihua DIng, Chibiao Huang, Lijia Chen, Hao Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing100190 China Fifth Laboratory Institute of Communication and Tracking Technology Beijing100190 China

ISBN: (纸本)9781728129129

Gaofen-3 (GF-3) is the first Chinese multichannel synthetic aperture radar (SAR) sensor that can operate in the dual receive channel (DRC) mode. Different from the traditional single-channel SAR system, the multichannel SAR system can overcome the inherent limitation to achieve high-resolution and wide-swath (HRWS) at the same time. However, the key challenge it faces is false target suppression. Especially for the moving vessels on the ocean, the existence of false targets will increase false alarm probability and affect the interpretation of SAR images. In this paper, the method of integration of detection, velocity estimation, location, and imaging for moving targets in the HRWS SAR system is proposed as well as applied to get an unambiguous image. The simulation and GF-3 real data experimental results show the validity of the method. © 2019 IEEE.

关键词： Synthetic aperture radar

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ScanSAR Radiometric Correction and Analysis of GaoFen-3 6

ScanSAR Radiometric Correction and Analysis of GaoFen-3

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Zhong, Lihua Qiu, Xiaolan Han, Bing Hu, Yuxin Chen, Aiping DIng, Chibiao Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing100190 China Beijing Institute of Telemetry and Telecommunications Technology Beijing100094 China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129129

GaoFen-3(GF-3) is China's first multi-mode c-band radar imaging satellite launched on August 10, 2016. In order to meet the requirements for global ocean observing and wave detection, three wide swath modes were designed using Scan-beam synthetic aperture radar (ScanSAR) imaging mode, which are narrow scan, wide scan and global observation. The resolution varies from 50m to 500 m and the swath varies from 300 km to 650 km. Due to the Mosaic of sub-swath image, ScanSAR has a high requirement for radiation accuracy from processing algorithm. In this paper, GF-3 ScanSAR imaging processing gain and radiation correction methods are introduced, and the processing results of measured data collected in the amazon rainforest are analyzed to verify the radiation accuracy of accuracy ScanSAR. © 2019 IEEE.

关键词： Synthetic aperture radar

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