Q
Qizhe Qu
Researcher at University of Electronic Science and Technology of China
Publications - 17
Citations - 492
Qizhe Qu is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Convolutional neural network & Frequency domain. The author has an hindex of 5, co-authored 12 publications receiving 84 citations.
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HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation
TL;DR: Experimental results reveal that ship detection and instance segmentation can be well implemented on HRSID, and this work has constructed a High-Resolution SAR Images Dataset (HRSID).
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JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals
TL;DR: This paper proposes a novel jamming recognition network (JRNet) based on robust power-spectrum features that achieves better and stable recognition performance especially under low JNR conditions with relatively less storage source and a bit more FLOPs and inference time.
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Intra-pulse modulation radar signal recognition based on CLDN network
TL;DR: The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals, and has higher average accuracy and better performance under low SNR condition.
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CSR-Net: A Novel Complex-Valued Network for Fast and Precise 3-D Microwave Sparse Reconstruction
TL;DR: A novel 3-D microwave sparse reconstruction method based on a complex-valued sparse reconstruction network (CSR-Net), which converts complex number operations into matrix operations for real and imaginary parts and outperforms both conventional iterative threshold optimization methods and deep network-based ISTA-NET-plus large margins.
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TPSSI-Net: Fast and Enhanced Two-Path Iterative Network for 3D SAR Sparse Imaging
TL;DR: Wang et al. as discussed by the authors proposed a two-path iterative framework for 3D SAR sparse imaging by mapping the AMP into a layer-fixed deep neural network, each layer of TPSSI-Net consists of four modules in cascade corresponding to four steps of the Onsager optimization.