In this thesis, we generalize the classical discrete wavelet transform, and construct wavelet transforms that are shift-invariant, time-varying, undecimated, and signal dependent. The result is a set of powerful and efficient ...

Classical Gaussian, Markov, and Poisson models have played a vital role in the remarkable success of statistical signal processing. However, a host of signals---images, network traffic, financial times series, seismic ...

In the design of a robust control system, one needs a nominal model together with a quantitative bound on the uncertainty that results from under-modeling and disturbances. In this thesis we do not intentionally seek a ...

Wavelet transforms provide a new technique for time-scale analysis of non-stationary signals. Wavelet analysis uses orthonormal bases in which computations can be done efficiently with multirate systems known as filter ...

Orthonormal wavelet bases provide an alternative technique for the analysis of non-stationary signals. Unlike the Gabor representation, the basis functions in the wavelet representation all have the same band width on a ...

The SVD (Singular Value Decomposition) is a critical matrix factorization in many real-time computations from an application domain which includes signal processing and robotics; and complex data matrices are encountered ...

Since the I/O subsystem is the bottleneck in external mergesort, I/O parallelism can result in substantial performance improvements. Concurrency can be introduced by overlapping I/O requests at different disks, and the ...

The optimal detector for a binary detection problem under a variety of criteria is the likelihood ratio test. Despite this simple characterization of the detector, analytic performance analysis in most cases is difficult ...

In this thesis, we present a theoretical study of biorthogonal Coifman wavelet systems, a family of biorthogonal wavelet systems with vanishing moments equally distributed between scaling functions and wavelet functions. ...