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Ya Tu
Researcher at Harbin Engineering University
Publications - 15
Citations - 923
Ya Tu is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Deep learning & Artificial neural network. The author has an hindex of 11, co-authored 14 publications receiving 373 citations.
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Journal ArticleDOI
Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks
TL;DR: This work proposes a smart approach to programmatic data augmentation method by using the auxiliary classifier generative adversarial networks (ACGANs) and shows that it can gain 0.1~6% increase in the classification accuracy in the ACGAN-based data set.
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Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer
TL;DR: The investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL, and develops a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waves while being represented in an image data format.
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An Improved Neural Network Pruning Technology for Automatic Modulation Classification in Edge Devices
TL;DR: A new filter-level pruning technique based on activation maximization (AM) that omits the less important convolutional filter that achieves equal or higher classification accuracy in the RadioML2016.10a dataset.
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Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks
TL;DR: The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, and iterative methods show greater attack performances than that of one-step method.
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Complex-Valued Networks for Automatic Modulation Classification
TL;DR: This correspondence presents the design of several key building blocks for complex-valued networks, such as complex convolution, complex batch-normalization, complex weight initialization, and complex dense strategies, and validates the superior performance in AMC achieved by the complex- valued networks.