Performance and Robustness Analysis of Co-Prime and Nested Sampling

Coprime and nested sampling are well known deterministic sampling techniques that operate at rates significantly lower than the Nyquist rate, and yet allow perfect reconstruction of the spectra of wide sense stationary signals. However, theoretical guarantees for these samplers assume ideal conditions such as synchronous sampling, and ability to perfectly compute statistical expectations. This thesis studies the performance of coprime and nested samplers in spatial and temporal domains, when these assumptions are violated. In spatial domain, the robustness of these samplers is studied by considering arrays with perturbed sensor locations (with unknown perturbations). Simplified expressions for the Fisher Information matrix for perturbed coprime and nested arrays are derived, which explicitly highlight the role of co-array. It is shown that even in presence of perturbations, it is possible to resolve $O(M^2)$ under appropriate conditions on the size of the grid. The assumption of small perturbations leads to a novel ``bi-affine" model in terms of source powers and perturbations. The redundancies in the co-array are then exploited to eliminate the nuisance perturbation variable, and reduce the bi-affine problem to a linear underdetermined (sparse) problem in source powers. This thesis also studies the robustness of coprime sampling to finite number of samples and sampling jitter, by analyzing their effects on the quality of the estimated autocorrelation sequence. A variety of bounds on the error introduced by such non ideal sampling schemes are computed by considering a statistical model for the perturbation. They indicate that coprime sampling leads to stable estimation of the autocorrelation sequence, in presence of small perturbations. Under appropriate assumptions on the distribution of WSS signals, sharp bounds on the estimation error are established which indicate that the error decays exponentially with the number of samples. The theoretical claims are supported by extensive numerical experiments.

Nested Sampling and its Applications in Stable Compressive Covariance Estimation and Phase Retrieval with Near-Minimal Measurements

Compressed covariance sensing using quadratic samplers is gaining increasing interest in recent literature. Covariance matrix often plays the role of a sufficient statistic in many signal and information processing tasks. However, owing to the large dimension of the data, it may become necessary to obtain a compressed sketch of the high dimensional covariance matrix to reduce the associated storage and communication costs. Nested sampling has been proposed in the past as an efficient sub-Nyquist sampling strategy that enables perfect reconstruction of the autocorrelation sequence of Wide-Sense Stationary (WSS) signals, as though it was sampled at the Nyquist rate. The key idea behind nested sampling is to exploit properties of the difference set that naturally arises in quadratic measurement model associated with covariance compression. In this thesis, we will focus on developing novel versions of nested sampling for low rank Toeplitz covariance estimation, and phase retrieval, where the latter problem finds many applications in high resolution optical imaging, X-ray crystallography and molecular imaging. The problem of low rank compressive Toeplitz covariance estimation is first shown to be fundamentally related to that of line spectrum recovery. In absence if noise, this connection can be exploited to develop a particular kind of sampler called the Generalized Nested Sampler (GNS), that can achieve optimal compression rates. In presence of bounded noise, we develop a regularization-free algorithm that provably leads to stable recovery of the high dimensional Toeplitz matrix from its order-wise minimal sketch acquired using a GNS. Contrary to existing TV-norm and nuclear norm based reconstruction algorithms, our technique does not use any tuning parameters, which can be of great practical value. The idea of nested sampling idea also finds a surprising use in the problem of phase retrieval, which has been of great interest in recent times for its convex formulation via PhaseLift, By using another modified version of nested sampling, namely the Partial Nested Fourier Sampler (PNFS), we show that with probability one, it is possible to achieve a certain conjectured lower bound on the necessary measurement size. Moreover, for sparse data, an l1 minimization based algorithm is proposed that can lead to stable phase retrieval using order-wise minimal number of measurements.

A GLOBAL SAMPLING OF PIANO MUSIC FROM 1978 TO 2005

Pianists of the twenty-first century have a wealth of repertoire at their fingertips. They busily study music from the different periods -- Baroque, Classical, Romantic, and some of the twentieth century -- trying to understand the culture and performance practice of the time and the stylistic traits of each composer so they can communicate their music effectively. Unfortunately, this leaves little time to notice the composers who are writing music today. Whether this neglect proceeds from lack of time or lack of curiosity, I feel we should be connected to music that was written in our own lifetime, when we already understand the culture and have knowledge of the different styles that preceded us. Therefore, in an attempt to promote today’s composers, I have selected piano music written during my lifetime, to show that contemporary music is effective and worthwhile and deserves as much attention as the music that preceded it. This dissertation showcases piano music composed from 1978 to 2005. A point of departure in selecting the pieces for this recording project is to represent the major genres in the piano repertoire in order to show a variety of styles, moods, lengths, and difficulties. Therefore, from these recordings, there is enough variety to successfully program a complete contemporary recital from the selected works, and there is enough variety to meet the demands of pianists with different skill levels and recital programming needs. Since we live in an increasingly global society, music from all parts of the world is included to offer a fair representation of music being composed everywhere. Half of the music in this project comes from the United States. The other half comes from Australia, Japan, Russia, and Argentina. The composers represented in these recordings are: Lowell Liebermann, Richard Danielpour, Frederic Rzewski, Judith Lang Zaimont, Samuel Adler, Carl Vine, Nikolai Kapustin, Akira Miyoshi and Osvaldo Golijov. With the exception of one piano concerto, all the works are for solo piano. This recording project dissertation consists of two 60 minute CDs of selected repertoire, accompanied by a substantial document of in-depth program notes. The recordings are documented on compact discs that are housed within the University of Maryland Library System.