Massive multiple-input multiple-output (MIMO) is a promising technology to increase link
capacity and energy efficiency. However, these benefits are based on available channel …

Recent deep network-based compressive sensing (CS) methods have achieved great
success. However, most of them regard different sampling matrices as different independent …

Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor
sampling rate, potentially bringing context-awareness to a wider range of devices …

Deep learning-based image compressive sensing (CS) methods have achieved great
success in the past few years. However, most of them are content-independent, with a …

Compressed domain image classification performs classification directly on compressive
measurements acquired from the single-pixel camera, bypassing the image reconstruction …

In the study of image compressed sensing (CS), various priors are explored for
regularization to achieve better reconstruction and provide different additional information …

Billions of sensor-enabled computing devices open tremendous opportunities for AI-
powered context-aware services. Yet, democratizing AI so that heterogeneous devices can …

Deep network-based image Compressive Sensing (CS) has attracted much attention in
recent years. However, there still exist the following two issues: 1) Existing methods typically …

The majority of digital images are stored in compressed form. Generally, image classification
using convolution neural network (CNN) is done in uncompressed form rather than …

Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate,
potentially bringing context-awareness to a wider range of devices. Nevertheless, practical …