R
Rui Qin
Researcher at Chongqing University of Posts and Telecommunications
Publications - 5
Citations - 48
Rui Qin is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Feature (linguistics). The author has an hindex of 1, co-authored 2 publications receiving 12 citations.
Papers
More filters
Posted Content
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing.
TL;DR: This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques.
Journal ArticleDOI
SAR Ship Detection Based on End-to-End Morphological Feature Pyramid Network
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end lightweight network called morphological feature pyramid Yolo v4-tiny for SAR ship detection, where a morphological network is introduced to preprocess the SAR images for speckle noise suppression and edge enhancement, providing spatial highfrequency information for target detection.
Journal ArticleDOI
RRSARNet: A Novel Network for Radar Radio Sources Adaptive Recognition
TL;DR: In this paper, a novel network based on meta-transfer learning, called RRSARNet, was proposed to achieve effective adaptive RRS recognition in the context of low signal-to-noise ratio (SNR).
Journal ArticleDOI
LW-CMDANet: A Novel Attention Network for SAR Automatic Target Recognition
TL;DR: Experimental results show that the proposed novel multidomain feature subspace fusion representation learning method can achieve better or competitive performance than that of many current existing state-of-the-art methods in terms of recognition accuracy and computational cost.
Journal ArticleDOI
Lightweight detection network for arbitrary-oriented vehicles in UAV imagery via precise positional information encoding and bidirectional feature fusion
TL;DR: Wang et al. as mentioned in this paper proposed a lightweight YOLO-based arbitrary-oriented vehicle detector via precise positional information encoding and bidirectional feature fusion to address the vehicle targets in dynamic scenarios, such as uncertain background, dramatically varying arrangement density, multi-scale, and arbitrary oriented.