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8. Finding Simplex Items in Data Streams

Author

Fan, Zhuochen, primary, Guo, Jiarui, additional, Li, Xiaodong, additional, Yang, Tong, additional, Zhao, Yikai, additional, Wu, Yuhan, additional, Cui, Bin, additional, Xu, Yanwei, additional, Uhlig, Steve, additional, and Zhang, Gong, additional

Published
2023
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9. Super-Resolution Harmonic Retrieval of Non-Circular Signals

Author

Zhang, Yu (author), Wang, Yue (author), Tian, Zhi (author), Leus, G.J.T. (author), Zhang, Gong (author), Zhang, Yu (author), Wang, Yue (author), Tian, Zhi (author), Leus, G.J.T. (author), and Zhang, Gong (author)

Abstract

This paper proposes a super-resolution harmonic retrieval method for uncorrelated strictly non-circular signals, whose covariance and pseudo-covariance present Toeplitz and Hankel structures, respectively. Accordingly, the augmented covariance matrix constructed by the covariance and pseudo-covariance matrices is not only low rank but also jointly Toeplitz-Hankel structured. To efficiently exploit such a desired structure for high estimation accuracy, we develop a low-rank Toeplitz-Hankel covariance reconstruction (LRTHCR) solution employed over the augmented covariance matrix. Further, we design a fitting error constraint to flexibly implement the LRTHCR algorithm without knowing the noise statistics. In addition, performance analysis is provided for the proposed LRTHCR in practical settings. Simulation results reveal that the LRTHCR outperforms the benchmark methods in terms of lower estimation errors., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Signal Processing Systems

Published
2023
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18. Capsule-Guided Multi-View Attention Network for SAR Target Recognition With Small Training Set.

Author

Dai, Qijun, Zhang, Gong, Xue, Biao, and Fang, Zheng

Abstract

Although numerous classification algorithms based on deep learning can effectively extract valuable features from synthetic aperture radar (SAR) images to improve the accuracy of SAR automatic target recognition (ATR), most of them have rigorous constraints in reality and fail to obtain satisfying results with limited training samples. To address the above issues, we propose a novel multi-view attention (ViA) capsule network (CapsNet) for SAR target small sample recognition. In our method, after gaining the primary capsules of SAR images under different views, a capsule-based ViA module is designed to enhance the feature relationships between different views. Then a joint dynamic routing (DR) mechanism is adopted to further capture the robust inter- and intra-image spatial relationships to generate SAR capsules specialized in the final classification. Especially, to alleviate the impact of SAR target angle sensitivity on recognition, rotated cropping (RC) is applied to the original SAR images in advance. Finally, experimental results on the moving and stationary target recognition (MSTAR) and OpenSARShip dataset have demonstrated the superiority and robustness of the proposed method upon limited labeled samples. [ABSTRACT FROM AUTHOR]

Published
2023
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22. First Si-Waveguide-Integrated InGaAs/InAlAs Avalanche Photodiodes on SOI Platform

Author

Zhang, Jishen, primary, Xu, Haiwen, additional, Tan, Kian Hua, additional, Wicaksono, Satrio, additional, Kong, Qiwen, additional, Zhang, Gong, additional, Chen, Yue, additional, Sun, Chen, additional, Wang, Haibo, additional, Wang, Chao, additional, Zheng, Zijie, additional, Jiao, Leming, additional, Zhou, Zuopu, additional, Lim, Charles Ci Wen, additional, Yoon, Soon-Fatt, additional, and Gong, Xiao, additional

Published
2022
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24. First Demonstration of Fully CMOS-compatible Non-volatile Programmable Photonic Switch Enabled by Ferroelectric-SOI Waveguide for Next Generation Photonic Integrated Circuit

Author

Chen, Yue, primary, Zhang, Gong, additional, Zhou, Jiuren, additional, Zheng, Zijie, additional, Jiao, Leming, additional, Wang, Haibo, additional, Zhou, Zuopu, additional, Kumar, Annie, additional, Zhang, Jishen, additional, Wang, Yuxuan, additional, Kong, Qiwen, additional, Sun, Chen, additional, and Gong, Xiao, additional

Published
2022
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39. First InGaAs/InAlAs Single-Photon Avalanche Diodes (SPADs) Heterogeneously Integrated with Si Photonics on SOI Platform for 1550 nm Detection

Author

Zhang, Jishen, primary, Xu, Haiwen, additional, Zhang, Gong, additional, Chen, Yue, additional, Wang, Haibo, additional, Tan, Kian Hua, additional, Wicaksono, Satrio, additional, Wang, Chao, additional, Sun, Chen, additional, Kong, Qiwen, additional, Wen Lim, Charles Ci, additional, Yoon, Soon-Fatt, additional, and Gong, Xiao, additional

Published
2021
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42. Achievable Lower Bound on the Optimal Access Bandwidth of (K + 2, K, 2)-MDS Array Code with Degraded Read Friendly

Author

Wu, Ting Yi, Han, Yunghsiang S., Li, Zhengrui, Bai, Bo, Zhang, Gong, Zhang, Xiaoyang, Wu, Xiang, Wu, Ting Yi, Han, Yunghsiang S., Li, Zhengrui, Bai, Bo, Zhang, Gong, Zhang, Xiaoyang, and Wu, Xiang

Abstract

Regenerating codes are designed to reduce the repair bandwidth (access bandwidth) for rebuilding a fail node in an erasure-coded storage system. In practical systems, the fail node is not rebuilt immediately. Before its rebuilding, the data originally stored in the failed node might be accessed by the system. Hence, accessing the data in the failed disk (degraded read) with low latency is crucial for any practical storage system. In this work, to solve this problem, a new class of the regenerating codes based on the maximum distance separable (MDS) array codes is defined, named the MDS array code with the property of degraded read friendly (DRF). For the DRF MDS array codes with 2 redundant nodes and the sub-packetization level of 2, the lower bound of their access bandwidth is derived. A class of the DRF MDS array codes that achieves the derived bound is given to solidify the achievability of the proposed lower bound. © 2021 IEEE.

Published
2021

43. On the Repair Bandwidth and Repair Access of Two Storage Systems: Large-Scale and Uniform Rack-Aware Storage Systems

Author

Li, Zhengrui, Han, Yunghsiang S., Wu, Ting Yi, Hou, Hanxu, Bai, Bo, Zhang, Gong, Li, Zhengrui, Han, Yunghsiang S., Wu, Ting Yi, Hou, Hanxu, Bai, Bo, and Zhang, Gong

Abstract

In this paper, we consider two rack-aware storage systems. First, large-scale rack-aware storage system, which is very common in large-scale storage system, is a rack-aware storage system where all sizes of racks are at least the number of redundant nodes. For such storage system, we prove that any Maximum Distance Separable (MDS) codes can have optimal inter-rack repair bandwidth and give a closed-form representation of all repair schemes with optimal inter-rack repair bandwidth. Furthermore, we show that the optimal repair access and optimal inter-rack repair bandwidth can be attained simultaneously for such storage system. Second, we investigate the rack-aware storage system of all racks with the same size, which is called uniform rack-aware storage system. We prove that, except the trivial cases, we cannot attain optimal inter-rack repair bandwidth and optimal repair access for such storage system at the same time. Specifically, we establish the lower bound of repair access for a repair scheme with optimal interrack repair bandwidth, which is tight for some parameters, and also the tight lower bound of inter-rack repair bandwidth for a repair scheme with optimal repair access. © 2021 IEEE.

Published
2021

44. Multirotor UAV-Borne Repeat-Pass CSM-VideoSAR.

Author

Zhang, Ying, Zhu, Daiyin, Mao, Xinhua, Zhang, Gong, and Leung, Henry

Subjects
SYNTHETIC aperture radar, SYNTHETIC apertures, IMAGING systems, DRONE aircraft
Abstract

A 360° video synthetic aperture radar (VideoSAR) appears as an interactive observation capability to understand the dynamic scenarios. VideoSAR system mounted on an unmanned aerial vehicle (UAV) delivers a highly flexible and cost-effective approach. In this letter, the UAV-borne repeat-pass circular stripmap (CSM) VideoSAR observation is formulated in the format of video area overflown to enhance the wide-area surveillance capability. A completely two-dimensional interpolation-free polar format algorithm is employed, especially in terms of repeat-pass CSM-VideoSAR configuration. We conduct the repeat-pass CSM-VideoSAR experiments on multirotor UAV-borne system in the interests of imaging demonstration and promoting the inspirations on the related open issues. [ABSTRACT FROM AUTHOR]

Published
2022
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46. An Applied Frequency Scaling Algorithm Based on Local Stretch Factor for Near-Field Miniature Millimeter-Wave Radar Imaging.

Author

Xue, Biao, Zhang, Gong, Leung, Henry, and Wang, Lin

Subjects
*RADAR, *SYNTHETIC aperture radar, *ALGORITHMS, *SPACE-based radar, *CHANNEL estimation, *CELL migration, *SURVEILLANCE radar, *AZIMUTH
Abstract

The frequency scaling algorithm (FSA) is a popular imaging algorithm for dechirped SAR data. To obtain a large azimuth detection area, the miniature millimeter-wave (mmW) linear-frequency-modulated continuous-wave (LFMCW) surveillance radar requires a wider azimuth beamwidth, which leads to additional range frequency aliasing in FSA. Because of the adoption of the dechirp-on-receive technique, the sampling frequency is much smaller than the range bandwidth during near-field imaging, which further aggravates the aliasing effects. The target cannot be well focused, and it makes the weak targets submerged in the background. To acquire high-quality SAR images, an improved FSA using the local stretch operation is proposed. The aliasing bandwidth properties introduced by the FS operation and the desired objective range cell migration (RCM) factor are used in this proposed local-stretch FSA (LSFSA). The initial RCM factor is adjusted by the stretch operation to eliminate the frequency aliasing to a certain level without increasing the computing load. The LSFSA is suitable for solving the problem of range frequency aliasing in near-field side-looking SAR and high squint SAR with wide azimuth beamwidth. The proposed method is validated using reasonable simulations and convincing experiments. [ABSTRACT FROM AUTHOR]

Published
2022
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47. Efficient Angle Estimation for MIMO Systems via Redundancy Reduction Representation.

Author

Zhang, Yu, Wang, Yue, Tian, Zhi, Leus, Geert, and Zhang, Gong

Subjects
MIMO systems, COVARIANCE matrices, DIELECTRIC loss, REDUNDANCY in engineering, ANGLES, SIGNAL-to-noise ratio
Abstract

This paper proposes an efficient direction of departure (DOD) and direction of arrival (DOA) estimation method for multi-input multi-output (MIMO) systems. For uncorrelated scenarios, the redundancy of the covariance matrix is first exploited by establishing its concise representation through redundancy reduction, which transforms the original large-size covariance matrix into a smaller-size matrix without loss of useful angle information. Then, the resulting transformed matrix, which retains a salient structure, permits efficient two-dimensional (2D) angle estimators working on a reduced-size problem for DOD and DOA estimation. Compared with conventional subspace-based methods, the proposed method incorporating an appropriate 2D angle estimator is more computationally efficient and can achieve higher estimation accuracy for small numbers of snapshots and low signal-to-noise ratios, which are verified by simulation results. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction

Author

Wang, Yue (author), Zhang, Yu (author), Tian, Zhi (author), Leus, G.J.T. (author), Zhang, Gong (author), Wang, Yue (author), Zhang, Yu (author), Tian, Zhi (author), Leus, G.J.T. (author), and Zhang, Gong (author)

Abstract

This paper develops an enhanced low-rank structured covariance reconstruction (LRSCR) method based on the decoupled atomic norm minimization (D-ANM), for super-resolution two-dimensional (2D) harmonic retrieval with multiple measurement vectors. This LRSCR-D-ANM approach exploits a potential structure hidden in the covariance by transferring the basic LRSCR to an efficient D-ANM formulation, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. The new LRSCR-D-ANM method builds upon the existence of a generalized Vandermonde decomposition of its solution, which otherwise cannot be guaranteed by the basic LRSCR unless a very conservative condition holds. Further, a low-complexity solution of the LRSCR-D-ANM is provided for fast implementation with negligible performance loss. Simulation results verify the advantages of the proposed LRSCR-D-ANM over the basic LRSCR, in terms of the wider applicability and the lower complexity., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Circuits and Systems

Published
2020
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50. Deep Learning Prediction of Incoming Rainfalls: An Operational Service for the City of Beijing China

Author

Zhang Gong, Yang Guowei, Wenpeng Zhang, Gu Zhu, Qixun Wang, Song Kuan, Jianzhong Liu, Xiaochen Yu, Wenjun Liu, Xu Chunmeng, Ying Wang, and Chen Shi

Subjects
Service (systems architecture), Meteorology, business.industry, Computer science, Deep learning, Weather forecasting, Magnitude (mathematics), 02 engineering and technology, 010501 environmental sciences, computer.software_genre, 01 natural sciences, law.invention, Beijing, Anticipation (artificial intelligence), law, Temporal resolution, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Weather radar, Artificial intelligence, business, computer, 0105 earth and related environmental sciences
Abstract

We developed a deep learning prototype for the purpose of rainfall prediction for the city of Beijing China. It predicts rainfall dynamics in the next two hours with spatial reso-lution of 1km and temporal resolution of 6 minutes. That is a magnitude better than traditional weather forecasting. The computation time is less than 10 seconds, several magnitudes of less computation time than traditional forecasting. This deep learning network combines the strengths of known structures such as U-Net, ResNet, Sqeeze-and-Excitation, and the spatial Attention mod-ule. We rely solely on full convolutional layers instead of RNN layers as used in other weather prediction ef-forts. Meteorologically assessment metrics suggest better performance by the FCN approach. The prototype is now up and running in anticipation for the summer rain season of 2019. This might be the first such service employed by a capital weather service.

Published
2019

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