Digital Signal Processing

Contains reports on twelve research projects.U. S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG76-24117)National Aeronautics and Space Administration (Grant NSG-5157)Joint Services Electronics Program (Contract DAABO7-76-C-1400)U.S. Navy-Office of Naval Research (Contract N00014-77-C-0196)Woods Hole Oceanographic InstitutionU. S. Navy - Office of Naval Research (Contract N00014-75-C-0852)Department of Ocean Engineering, M.I.T.National Science Foundation subcontract to Grant GX 41962 to Woods Hole Oceanographic Institutio

Surrogate time series

Before we apply nonlinear techniques, for example those inspired by chaos theory, to dynamical phenomena occurring in nature, it is necessary to first ask if the use of such advanced techniques is justified "by the data". While many processes in nature seem very unlikely a priori to be linear, the possible nonlinear nature might not be evident in specific aspects of their dynamics. The method of surrogate data has become a very popular tool to address such a question. However, while it was meant to provide a statistically rigorous, foolproof framework, some limitations and caveats have shown up in its practical use. In this paper, recent efforts to understand the caveats, avoid the pitfalls, and to overcome some of the limitations, are reviewed and augmented by new material. In particular, we will discuss specific as well as more general approaches to constrained randomisation, providing a full range of examples. New algorithms will be introduced for unevenly sampled and multivariate data and for surrogate spike trains. The main limitation, which lies in the interpretability of the test results, will be illustrated through instructive case studies. We will also discuss some implementational aspects of the realisation of these methods in the TISEAN (http://www.mpipks-dresden.mpg.de/~tisean) software package.Comment: 28 pages, 23 figures, software at http://www.mpipks-dresden.mpg.de/~tisea

Doctor of Philosophy

dissertationDynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a powerful tool to detect cardiac diseases and tumors, and both spatial resolution and temporal resolution are important for disease detection. Sampling less in each time frame and applying sophisticated reconstruction methods to overcome image degradations is a common strategy in the literature. In this thesis, temporal TV constrained reconstruction that was successfully applied to DCE myocardial perfusion imaging by our group was extended to three-dimensional (3D) DCE breast and 3D myocardial perfusion imaging, and the extension includes different forms of constraint terms and various sampling patterns. We also explored some other popular reconstruction algorithms from a theoretical level and showed that they can be included in a unified framework. Current 3D Cartesian DCE breast tumor imaging is limited in spatiotemporal resolution as high temporal resolution is desired to track the contrast enhancement curves, and high spatial resolution is desired to discern tumor morphology. Here temporal TV constrained reconstruction was extended and different forms of temporal TV constraints were compared on 3D Cartesian DCE breast tumor data with simulated undersampling. Kinetic parameters analysis was used to validate the methods

Consciousness as a State of Matter

We examine the hypothesis that consciousness can be understood as a state of matter, "perceptronium", with distinctive information processing abilities. We explore five basic principles that may distinguish conscious matter from other physical systems such as solids, liquids and gases: the information, integration, independence, dynamics and utility principles. If such principles can identify conscious entities, then they can help solve the quantum factorization problem: why do conscious observers like us perceive the particular Hilbert space factorization corresponding to classical space (rather than Fourier space, say), and more generally, why do we perceive the world around us as a dynamic hierarchy of objects that are strongly integrated and relatively independent? Tensor factorization of matrices is found to play a central role, and our technical results include a theorem about Hamiltonian separability (defined using Hilbert-Schmidt superoperators) being maximized in the energy eigenbasis. Our approach generalizes Giulio Tononi's integrated information framework for neural-network-based consciousness to arbitrary quantum systems, and we find interesting links to error-correcting codes, condensed matter criticality, and the Quantum Darwinism program, as well as an interesting connection between the emergence of consciousness and the emergence of time.Comment: Replaced to match accepted CSF version; discussion improved, typos corrected. 36 pages, 15 fig

High-resolution multipath channel parameter estimation using wavelet analysis

This thesis explores the novel use of wavelet analysis as a high-resolution digital signal processing algorithm for multipath channel parameter estimation. The results obtained from this research indicate that this wavelet-based digital signal processing algorithm overcomes the resolution limitation in conventional high-resolution algorithm. This may provide a more cost-effective means of implementing channel sounding equipments for very high-resolution measurements

Acceleration Methods for MRI

Acceleration methods are a critical area of research for MRI. Two of the most important acceleration techniques involve parallel imaging and compressed sensing. These advanced signal processing techniques have the potential to drastically reduce scan times and provide radiologists with new information for diagnosing disease. However, many of these new techniques require solving difficult optimization problems, which motivates the development of more advanced algorithms to solve them. In addition, acceleration methods have not reached maturity in some applications, which motivates the development of new models tailored to these applications. This dissertation makes advances in three different areas of accelerations. The first is the development of a new algorithm (called B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithm or BARISTA), that solves a parallel MRI optimization problem with compressed sensing assumptions. BARISTA is shown to be 2-3 times faster and more robust to parameter selection than current state-of-the-art variable splitting methods. The second contribution is the extension of BARISTA ideas to non-Cartesian trajectories that also leads to a 2-3 times acceleration over previous methods. The third contribution is the development of a new model for functional MRI that enables a 3-4 factor of acceleration of effective temporal resolution in functional MRI scans. Several variations of the new model are proposed, with an ROC curve analysis showing that a combination low-rank/sparsity model giving the best performance in identifying the resting-state motor network.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120841/1/mmuckley_1.pd

Detecting Differential Rotation and Starspot Evolution on the M dwarf GJ 1243 with Kepler

We present an analysis of the starspots on the active M4 dwarf GJ 1243, using four years of time series photometry from Kepler. A rapid $P = 0.592596\pm0.00021$ day rotation period is measured due to the $\sim$2.2\% starspot-induced flux modulations in the light curve. We first use a light curve modeling approach, using a Monte Carlo Markov Chain sampler to solve for the longitudes and radii of the two spots within 5-day windows of data. Within each window of time the starspots are assumed to be unchanging. Only a weak constraint on the starspot latitudes can be implied from our modeling. The primary spot is found to be very stable over many years. A secondary spot feature is present in three portions of the light curve, decays on 100-500 day timescales, and moves in longitude over time. We interpret this longitude shearing as the signature of differential rotation. Using our models we measure an average shear between the starspots of 0.0047 rad day$^{-1}$, which corresponds to a differential rotation rate of $\Delta\Omega = 0.012 \pm 0.002$ rad day$^{-1}$. We also fit this starspot phase evolution using a series of bivariate Gaussian functions, which provides a consistent shear measurement. This is among the slowest differential rotation shear measurements yet measured for a star in this temperature regime, and provides an important constraint for dynamo models of low mass stars.Comment: 13 pages, 7 figures, ApJ Accepte