Compressed Sensing Based Distributed Sampling Method for Vibration Signal from Train Rotating Parts

Random Sampling and Signal Reconstruction Based on Compressed Sensing

Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure a...

Random Sampling and Signal Bregman Reconstruction Based on Compressed Sensing

Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted from as little as possible of the data by applying CS, and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the struc...

A Block-Wise random sampling approach: Compressed sensing problem

The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initiali...

Notes from Compressed Sensing and Contemporary Sampling

Noise driven compressed sensing method for space time signal processing

In contrary to the existing work related with compressed sensing based STAP technique, which adopts the original sensing matrix, the proposed noise driven compressed sensing method is to construct a new sensing matrix with weak coherence through incorporating the measurement noise. The proposed method tries to build an equivalent system of the classical model in compressed sensing, resulting in...