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In many physiological studies, variables of interest are not directly accessible, requiring that they be estimated indirectly from noisy measured signals. Here, we introduce two empirical methods to estimate the true physiological signals from indirectly measured, noisy data. The first method is an extension of Tikhonov regularization to large-scale problems, using a sequential update approach. In the second method, we improve the conditioning of the problem by assuming that the input is uniform over a known time interval, and then we use a least-squares method to estimate the input. These methods were validated computationally and experimentally by applying them to flow-through respirometry data. Specifically, we infused CO2 in a flow-through respirometry chamber in a known pattern, and used the methods to recover the known input from the recorded data. The results from these experiments indicate that these methods are capable of sub-second accuracy. We also applied the methods on respiratory data from a grasshopper to investigate the exact timing of abdominal pumping, spiracular opening, and CO2 emission. The methods can be used more generally for input estimation of any linear system. / Master of Science / The goal of an inverse problem is to determine some signal or parameter of interest that is not directly accessible but can be obtained from an observed effect or a processed version that is measurable. Finding the gas exchange signal in animals is an example of an inverse problem. One method to noninvasively measure the gas exchange rate of animals is to put them in a respirometry chamber, flow air through the chamber, and measure the concentration of the respiratory gasses outside the chamber. However, because the gasses mix in the chamber and gradually flow through the gas analyzer, the pattern of the measured gas concentration can be dramatically different than the true pattern of real instantaneous gas exchange of the animal. In this thesis, we present two methods to recover the true signal from the recorded data (i.e., for inverse reconstruction), and we evaluate them computationally and experimentally.

Optical modulation has shown the selective and sensitive signal improvement in high background systems in cell imaging; however, cell applications are still limited due to biocompatibility and delivery issues. Fluorescent proteins have a variety of optically accessible states that make them ideal candidates for investigation of modulatability. Combining the optical modulation technique with the biocompatibility of fluorescent proteins is a major advance. This work focuses on evaluation fluorescent proteins and their optical states for modulation, as well demonstrations of cellular imaging. Herein, we evaluate a green fluorescent protein with interesting photophysical properties favorable for optical modulation. Positive for optical modulation, further investigation of the state dictating modulation reveals the presence of a slow component on the order of milliseconds. To better understand the mechanism responsible modulation, blue fluorescent proteins are created to modify the chromophore environment. Extraction of photophysics confirm the alteration timescales of the modulated state. Motivated by the ability to improve imaging and decode hidden dynamics, demodulation of these proteins demonstrates the selective recovery of signal in the presence of high cellular background. The continued investigation of several other fluorescent proteins identifies modulatable proteins across the visible wavelength region. Additionally, solvent environmental factors show varying timescales which, when combined with mutagenesis, suggest a cis/trans isomerization coupled with a proton transfer. This information of the properties dictating optical modulation allows for the engineering of improved modulatable proteins to study cellular dynamics.

Three problems in compressive sensing, namely, recovery of sparse signals from noise-free measurements, recovery of sparse signals from noisy measurements, and recovery of so called block-sparse signals from noisy measurements, are investigated. In Chapter 2, the reconstruction of sparse signals from noise-free measurements is investigated and three algorithms are developed. The first and second algorithms minimize the approximate L0 and Lp pseudonorms, respectively, in the null space of the measurement matrix using a sequential quasi-Newton algorithm. An efficient line search based on Banach's fixed-point theorem is developed and applied in the second algorithm. The third algorithm minimizes the approximate Lp pseudonorm in the null space by using a sequential conjugate-gradient (CG) algorithm. Simulation results are presented which demonstrate that the proposed algorithms yield improved signal reconstruction performance relative to that of the iterative reweighted (IR), smoothed L0 (SL0), and L1-minimization based algorithms. They also require a reduced amount of computations relative to the IR and L1-minimization based algorithms. The Lp-minimization based algorithms require less computation than the SL0 algorithm. In Chapter 3, the reconstruction of sparse signals and images from noisy measurements is investigated. First, two algorithms for the reconstruction of signals are developed by minimizing an Lp-pseudonorm regularized squared error as the objective function using the sequential optimization procedure developed in Chapter 2. The first algorithm minimizes the objective function by taking steps along descent directions that are computed in the null space of the measurement matrix and its complement space. The second algorithm minimizes the objective function in the time domain by using a CG algorithm. Second, the well known total variation (TV) norm has been extended to a nonconvex version called the TVp pseudonorm and an algorithm for the reconstruction of images is developed that involves minimizing a TVp-pseudonorm regularized squared error using a sequential Fletcher-Reeves' CG algorithm. Simulation results are presented which demonstrate that the first two algorithms yield improved signal reconstruction performance relative to the IR, SL0, and L1-minimization based algorithms and require a reduced amount of computation relative to the IR and L1-minimization based algorithms. The TVp-minimization based algorithm yields improved image reconstruction performance and a reduced amount of computation relative to Romberg's algorithm. In Chapter 4, the reconstruction of so-called block-sparse signals is investigated. The L2/1 norm is extended to a nonconvex version, called the L2/p pseudonorm, and an algorithm based on the minimization of an L2/p-pseudonorm regularized squared error is developed. The minimization is carried out using a sequential Fletcher-Reeves' CG algorithm and the line search described in Chapter 2. A reweighting technique for the reduction of amount of computation and a method to use prior information about the locations of nonzero blocks for the improvement in signal reconstruction performance are also proposed. Simulation results are presented which demonstrate that the proposed algorithm yields improved reconstruction performance and requires a reduced amount of computation relative to the L2/1-minimization based, block orthogonal matching pursuit, IR, and L1-minimization based algorithms. / Graduate

abstract: For a sensor array, part of its elements may fail to work due to hardware failures. Then the missing data may distort in the beam pattern or decrease the accuracy of direction-of-arrival (DOA) estimation. Therefore, considerable research has been conducted to develop algorithms that can estimate the missing signal information. On the other hand, through those algorithms, array elements can also be selectively turned off while the missed information can be successfully recovered, which will save power consumption and hardware cost. Conventional approaches focusing on array element failures are mainly based on interpolation or sequential learning algorithm. Both of them rely heavily on some prior knowledge such as the information of the failures or a training dataset without missing data. In addition, since most of the existing approaches are developed for DOA estimation, their recovery target is usually the co-variance matrix but not the signal matrix. In this thesis, a new signal recovery method based on matrix completion (MC) theory is introduced. It aims to directly refill the absent entries in the signal matrix without any prior knowledge. We proposed a novel overlapping reshaping method to satisfy the applying conditions of MC algorithms. Compared to other existing MC based approaches, our proposed method can provide us higher probability of successful recovery. The thesis describes the principle of the algorithms and analyzes the performance of this method. A few application examples with simulation results are also provided. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

In this paper, we recover the amplitude of a seismic image by approximating the normal (demigrationmigration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time ’wave-equation’ migration code simulating the acoustic wave equation on the SEG-AA salt model.

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Research has shown that a frame for an n-dimensional real Hilbert space offers phase retrieval if and only if it has the complement property. There is a geometric characterization of general frames, the Han-Larson-Naimark Dilation Theorem, which gives us the necessary and sufficient conditions required to dilate a frame for an n-dimensional Hilbert space to a frame for a Hilbert space of higher dimension k. However, a frame having the complement property in an n-dimensional real Hilbert space does not ensure that its dilation will offer phase retrieval. In this thesis, we will explore and provide what necessary and sufficient conditions must be satisfied to dilate a phase retrieval frame for an n-dimensional real Hilbert space to a phase retrieval frame for a k-dimensional real Hilbert.

Constrained by practical and economical considerations, one often uses seismic data with missing traces. The use of such data results in image artifacts and poor spatial resolution. Sometimes due to practical limitations, measurements may be available on a perturbed grid, instead of on the designated grid. Due to algorithmic requirements, when such measurements are viewed as those on the designated grid, the recovery procedures may result in additional artifacts. This paper interpolates incomplete data onto regular grid via the Fourier domain, using a recently developed greedy algorithm. The basic objective is to study experimentally as to what could be the size of the perturbation in measurement coordinates that allows for the measurements on the perturbed grid to be considered as on the designated grid for faithful recovery. Our experimental work shows that for compressible signals, a uniformly distributed perturbation can be offset with slightly more number of measurements.

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