Compressed encoding of vibration signals using extremum sampling

Adaptive compressed MRI sampling based on wavelet encoding

Introduction: The main idea of Compressed Sensing is to exploit the fact that there is some structure and redundancy in most signals of interest. Clearly, the more we known about the signal and the more the information we encode into the signal processing algorithm, the better performance we can achieve. In this paper, we propose an adaptive compressed MRI sensing scheme that combined wavelet e...

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...

Unmanned aerial vehicle field sampling and antenna pattern reconstruction using Bayesian compressed sensing

Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A pr...

Digital Encoding of Television Signals Using

Compressed-Sampling-Based Image Saliency Detection in the Wavelet Domain

When watching natural scenes, an overwhelming amount of information is delivered to the Human Visual System (HVS). The optic nerve is estimated to receive around 108 bits of information a second. This large amount of information can’t be processed right away through our neural system. Visual attention mechanism enables HVS to spend neural resources efficiently, only on the selected parts of the...