136 citations · 268 across the 9 of their papers we have counts for
10 papers
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
Chenwei Wang, Xiaoyu Liu, Yulin Huang +4
Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends h…
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
Chenwei Wang, Siyi Luo, Jifang Pei +4
Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful a…
A deep deformable residual learning network for SAR images segmentation
Chenwei Wang, Jifang Pei, Yulin Huang +1
Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Re…
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network
Chenwei Wang, Jifang Pei, Xiaoyu Liu +4
Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, wh…
Global in Local: A Convolutional Transformer for SAR ATR FSL
Chenwei Wang, Yulin Huang, Xiaoyu Liu +3
Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR) automatic target recognition (ATR) for years. However, under the limited SAR images, the widt…
Ensemble Wrapper Subsampling for Deep Modulation Classification
Sharan Ramjee, Shengtai Ju, Diyu Yang +3
Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output s…
Histogram Transform Ensembles for Large-scale Regression
Hanyuan Hang, Zhouchen Lin, Xiaoyu Liu +1
We propose a novel algorithm for large-scale regression problems named histogram transform ensembles (HTE), composed of random rotations, stretchings, and translations. First of al…
Best-scored Random Forest Classification
Hanyuan Hang, Xiaoyu Liu, Ingo Steinwart
We propose an algorithm named best-scored random forest for binary classification problems. The terminology "best-scored" means to select the one with the best empirical performanc…
Fast Deep Learning for Automatic Modulation Classification
Sharan Ramjee, Shengtai Ju, Diyu Yang +3
In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communica…
Deep Neural Network Architectures for Modulation Classification
Xiaoyu Liu, Diyu Yang, Aly El Gamal
In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by genera…