Now showing items 1-5 of 5
Modeling Systems from Measurements of their Frequency Response
The problem of modeling systems from frequency response measurements is of interest to many engineers. In electronics, we wish to construct a macromodel from tabulated impedance, admittance or scattering parameters to ...
Endogenous Sparse Recovery
Sparsity has proven to be an essential ingredient in the development of efficient solutions to a number of problems in signal processing and machine learning. In all of these settings, sparse recovery methods are employed ...
Regime Change: Sampling Rate vs. Bit-Depth in Compressive Sensing
The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demonstrated that structured signals can ...
An asymptotic minimax analysis of nonlocal means on edges
This thesis analyzes the non-local means denoising algorithm using the criterion of minimax optimality from statistical decision theory. We show that nonlocal means is minimax suboptimal on images with smooth discontinuities ...
Compressive Sensing for 3D Data Processing Tasks: Applications, Models and Algorithms
Compressive sensing (CS) is a novel sampling methodology representing a paradigm shift from conventional data acquisition schemes. The theory of compressive sensing ensures that under suitable conditions compressible signals ...