Synthetic aperture radar (SAR) target recognition faces the challenge that there are very little
labeled data. Although few-shot learning methods are developed to extract more information …

Synthetic aperture radar (SAR) is an imaging modality used for a variety of military and
civilian tasks, many of which could benefit greatly from computer automation. The increase …

In this work, we design a fully complex-valued neural network for the task of iris recognition.
Unlike the problem of general object recognition, where real-valued neural networks can be …

Complex-valued neural networks have emerged boasting superior modeling performance
for many tasks across the signal processing, sensing, and communications arenas …

Complex-valued data are ubiquitous in signal and image processing applications, and
complex-valued representations in deep learning have appealing theoretical properties …

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things
applications, communication and sensing technologies have rapidly evolved from millimeter …

Rapid advancements in 5G communication and the Internet-of-things have prompted the
development of cognitive radio sensing for spectrum monitoring and malicious attack …

Magnetic resonance imaging (MRI) has the ability to produce a series of images that each
have different visual contrast between tissues, allowing clinicians to qualitatively assess …

Although convolutional neural networks (CNN) have shown excellent performance in many
image recognition tasks, it commonly requires a lot of labeled data, and the recognition …

Deep learning‐based Automatic Modulation Recognition (AMR) can improve the recognition
rate compared with traditional AMR methods. However, in practical applications, as training …