Yang Luo

TL;DR: Experiments on the benchmark dataset show the proposed framework has efficient convergence speed and achieves improved recognition accuracy, especially for the signals modulated by higher dimensional schemes such as 16 quadrature amplitude modulation and 64-QAM.

TL;DR: Wang et al. as discussed by the authors proposed an efficient DL-AMR model based on phase parameter estimation and transformation, with convolutional neural network (CNN) and gated recurrent unit (GRU) as the feature extraction layers, which can achieve high recognition accuracy equivalent to the existing state-of-the-art models but reduces more than a third of the volume of their parameters.

TL;DR: Wang et al. as mentioned in this paper proposed a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images, which includes an elaborate feature fusion module consisting of a multi-directional fusion pathway (MFP) and an adaptive weighted fusion (AWF) strategy for RSCD to boost the way that information propagates in the network.

TL;DR: Wang et al. as discussed by the authors proposed a space-time multi-channel deep learning system for automatic modulation recognition, which consists of a multichannel input and spatial feature mapping module, a time feature extraction module and a full-connection network classifier module which are connected in sequence.

TL;DR: A novel machine learning model inspired by the human visual system, named as local attention grouping, is proposed to effectively extract key visual features, which is further improved by learning from multiple randomly selected regional images via ensemble learning.