Master of Science in Electrical Engineering (MSEE), Wright State University, 2016, Electrical Engineering

The rich spectral information captured by hyperspectral sensors has given rise to a number of remote sensing applications, ranging from vegetative assessment and crop health monitoring, to military surveillance and combatant identification. However, due to limited spatial resolution, multiple ground materials generally contribute, i.e. mix, to form the spectrum recorded for a single pixel. The unmixing problem considers the inverse problem of determining the underlying material spectra, called endmembers, from sensor measurements. While classical unmixing approaches were deterministic in nature and did not attempt to identify in-scene materials, recent methods use labeled training data to generate statistical models of endmember variabilities and perform statistical unmixing for simultaneous material identification and abundance estimation. However, the computational complexity of statistical unmixing with endmember variability is Ο(N3), cubic in the number N of sensed spectral bands. This large computational demand is at odds with continuous technological improvements that are dramatically increasing the spectral resolution of remote spectroscopy methods. In particular, current sensor technology is transitioning from the hyperspectral realm (hundreds of spectral bands) to the ultraspectral realm (thousands of spectral bands) and eclipsing the ability to perform statistical unmixing. In this thesis we develop a computationally tractable statistical unmixing method. The proposed method uses Markov chains to model endmember variability and the spectral correlation properties present within endmembers. We use a probabilistic graphical model over multiple Markov chains to capture the mixing effects of the spectral sensor and employ sum-product message passing to develop an accelerated statistical unmixing algorithm. The computational complexity, Ο(NM3), of the proposed algorithm is only linear in the number of bands and depends on the number of endmembers M (open full item for complete abstract)

MS, Kent State University, 2008, College of Arts and Sciences / Department of Earth Sciences

The versatility of diffuse spectral reflectance (DSR) was investigated as a complementary methodology to XRD and XRF when studying clay minerals in stratigraphic sequences. The Analytical Spectral Device (ASD) LabSpec® Pro FR UV/VIS/nIR spectrometer provides an innovative nondestructive methodology that is cost effective, portable, quick, and easy to use with samples in the lab or field. LabSpec® Pro FR spectrometer and similar equipment are remarkable research tools underutilized in the area of clay mixtures. This study develops a new methodology that demonstrates the versatility of the LabSpec® Pro FR and the use of DSR as a tool for generating a spectral library and then determining clay mineralogy of various core samples. Samples from two sources were evaluated: (1) sediment from core MNK3, from a slack water Pleistocene lake near St. Louis, in which stratigraphic changes in clay mineralogy occur down core, and (2) the Ordovician Millbrig K-bentonite (samples from AL, GA, KY, TN, and VA), an altered tephra in which the changes occur laterally in a single horizon. DSR spectral data is validated against XRD, ICP-MS, and XRF data. This spectral library was generated from four primary clays and clay mixtures, consisting over 231 two variable mixtures in 5% increments, by weighted percents and is augmented with spectra from the USGS spectral library. Clay mineral standards were obtained from the Clay Mineral Repository and Wards Natural Science. The aim is to close the gap that currently exists for an expanded spectral library of clay mixtures and explore the DSR variability of clay mixtures. PCA (Principal Component Analysis) was used to correlate the spectral data of the library with the two MNK3 and Millbrig sample sets. Stepwise Linear Regression (SLR) analysis was used with the composite library as an identification tool. By combining PCA analysis of unknowns with SLR against our clay mixture library, we identify our components in an objective, quantifiable way. (open full item for complete abstract)

MS, Kent State University, 2025, College of Arts and Sciences / Department of Earth Sciences

Remote sensing and watershed modeling are a rapid and non-invasive approach to monitoring agricultural systems in the current climate crisis with a shortage of food production in many areas. The soil matrix is very complex with varying land cover and soil materials, making it difficult to identify unique spectral curves and model specific soil constituents. This study seeks to estimate soil carbon in farms with multiple soil types based on model simulations and the decomposition of visible and near-infrared spectral response signals. First, the current study applied Kent State University's Varimax-Rotated Principal Component Analysis (KSU VPCA) through the Semi-Automated Averaging Code to unmix groundcover spectral signals and detect carbon, and nitrogen. The KSU VPCA of a reflectance obtained from the FieldSpec4 SR and the Harmonized Sentinel-2A/B imagery through stepwise multiple regression explained variance of 45-68% in soil percentage of total carbon (R = 0.68-0.83, and an R2 =0.45-0.68), 45-67% in soil percentage of total nitrogen (R = 0.67-0.82, and an R2 =0.45-0.67), and 16-40% in permanganate oxidizable carbon (R = 0.29-0.63, and an R2 = 0.16-0.40). The six-eigen vector KSU VPCA correlated strongly with spectral signals of a mixture of minerals and plant-laden pigments. The KSU VPCA of plant materials explained an 81% variance in crop vegetation biomass. To predict groundcover, Random Forest Classifiers (RFC) were developed based on the first derivatives of reflectance (RFC2), and KSU VPCA scores of reflectance (RFC3). RFC2 predicted dried crop residues with the highest precision of 100% while RFC3 predicted soil with the highest recall score of 95%. Secondly, the study applied the Agricultural Policy eXtender model to assess the impacts of cover crops and tillage on soil carbon stocks in a maize -wheat-soybean crop rotation. The reduced tillage with cover crops improved soil organic carbon (50-110%) compared to conventional tillage (5-48%). A tri-state fer (open full item for complete abstract)

Master of Science (MS), Bowling Green State University, 2020, Communication Disorders

Objective. To explore how listeners use three aspects of the acoustic signal in the novel context of formant space configurations to determine speaker gender. Methods. The software Madde, Praat, and Audacity were used to synthesize 210 sound files that each contain the vowels /i, æ, ɑ, u/ separated by brief silences (i.e., the formant space configuration context). The 210 files were created by combining 10 values for fundamental frequency, seven sets of formant frequencies (vocal tract length), and three values for source spectral tilt. The lowest values for formant frequencies (longest value for vocal tract length) and fundamental frequency each correspond to the values for the average male. The highest values for formant frequencies (shortest vocal tract length) and fundamental frequency each correspond to the values for the average female. The values for source spectral tilt approximate the voice qualities of breathy, normal, and pressed. Twenty-three listeners judged the gender of the “speaker” of the synthesized sounds as female or male. Results. Increases in fundamental frequency and formant frequencies (decreases in vocal tract length) correlated with increased likelihood of judgement of female. An interaction between source spectral tilt and formant frequencies (vocal tract length) revealed that an increase in the steepness of source spectral tilt increased likelihood of judgement of female only when formant frequencies were high (vocal tract length was short). An interaction between formant frequencies (vocal tract length) and fundamental frequency revealed listeners were more sensitive to changes in fundamental frequency when formant frequencies were high (vocal tract length was short). Conclusions. Both fundamental frequency and formant frequencies are strong cues to speaker gender. The contribution of other cues, such as source spectral tilt were subtle. The observed interactions point to gender aspects of speech perception as a complex phenomen (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2016, Biochemistry Program, Ohio State

This dissertation describes the research that was conducted on the development of label-free quantitation procedures for the identification and quantitation of proteome differences determined from shotgun proteomics experiments. Chapter 1 introduces common approaches of which their basic understanding of is imperative for all proteomic scientists. This introductory chapter also describes label-free quantitation approaches, which is built upon in following chapters. Chapter 2 outlines a novel approach to perform label-free spectral count quantitation from shotgun proteomic experiments. This approach, termed MultiSpec, utilizes open-source statistical platforms; namely edgeR, DESeq and baySeq, to statistically select protein candidates for further investigation. The results from these three statistical approaches are combined to provide a single ranked list of differentially expressed proteins. The statistical results from multiple proteomic pipelines are integrated and cross-validated by means of collapsing protein groups. Chapter 3 highlights the efficient application of negative binomial based tag-count analysis of large-scale proteomics. This chapter illustrates the efficacy of edgeR to perform spectral count quantitation across a large number of samples. Chapter 4 demonstrates the use of precursor abundance (MS1) quantitation, an alternative to spectral count quantitation, to quantitate proteome differences in chromatin-bound androgen receptor protein complexes pivotal in directing proper gene expression in the context of localized human prostate cancer. Also presented in chapter 4, precursor intensities were used to determine proteome differences between the prostate proteomes of a transgenic mouse model of prostatic intraepithelial neoplasia (PIN). In a collaborative effort, these data were overlaid with RNA sequencing and Chromatin-Immunoprecipitation sequencing data to identify a proteome set of putative androgen receptor regulated proteins.

Doctor of Philosophy, The Ohio State University, 2015, Speech and Hearing Science

This study seeks to further our understanding of auditory development by investigating “spectral attention bands” (spectral region of attention for an expected target) and the ability to integrate or segregate information across frequency bands in children. The ability to attend to a target signal and discriminate speech from noise is of special importance in children. On a daily basis children must listen and attend to important auditory information in noisy classroom environments. A comparison of spectral attention bandwidth in children and adults might clarify where aspects of processing/listening efficiency breaks down. The current three experiments investigate the shape of spectral attention bands in children age 5 to 8 as compared to adults and indicate that the spectral attending listening strategy may effect understanding speech in noise. This study indicates that children do in fact listen differently than adults, using less efficient listening strategies that may lead them to be more susceptible to noise. This study also shows that between the ages of 5 and 8, enough substantial refinements in listening strategies occur to see a change to more adult-like performance in the older child age range.

PHD, Kent State University, 2013, College of Arts and Sciences / Department of Earth Sciences

The main goal of this research is to provide physical evidence of reverse flow(s), from the Arctic to the North Pacific Ocean, after the Last Glacial Maximum (LGM). This is primarily essential to studies concerned with understanding how the fluctuations in strength of the Southern Ocean Wind (SOW), in conjunction with an open Bering Strait, alter the direction of water flow through the Bering Strait. Visible and Near Infrared (VNIR) derivative spectroscopy; quotient normalization and varimax rotated principal component analysis of diffuse spectral reflectance (DSR) measurements from 234 surface core samples and 2 piston cores, in addition to the USGS spectral library, were used to extract and identify these lithological compositions (in order of importance) within the study location. These compositions are chlorite + muscovite; goethite + phycoerythrin + phycocyanin; smectite; calcite+dolomite; and illite + Chlorophyll a. The Geostatistical tool, kriging, was utilized in creating the sedimentary maps of all the components. These maps were used to determine these components' modern spatial patterns. This aided in the evaluation and downcore interpretation of the component most suited for this study. The illite in illite + Chlorophyll a assemblage was deemed to be the appropriate water mass tracer for a reverse flow from the Arctic into the North Pacific; this is because of its prominence and abundance in the Mackenzie River drainage basin and on the west Arctic Sea shelf. The illite denotes these periods of meltwater pulses (MWP): MWP 1A, ~14,600 and 13,800 Cal yrs. BP, separated by the Older Dryas; MWP 1B, ~11,000-9,200 Cal yrs. BP; and MWP 1C, ~8,000 Cal yrs. BP. The timing of these pulses along with previously published data on the Bering Sea shelf and the North Pacific Ocean enabled these deductions: 1) the initial opening of the Bering Strait and the flow direction after the LGM; 2) the source of these meltwater pulses and the mechanism that might drive (open full item for complete abstract)

MS, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science

Support Vector Machines (SVM) have been used extensively in different areas of science and engineering for classification and regression tasks. In this thesis we test their efficacy once again by applying them to two very different problems. The first task consists of classifying land cover in remote sensing images. For this task we incorporate 2 similarity measures namely the Spectral Information Divergence and Spectral Angle Mapper into the SVM kernel. We then compare these kernels and aggregations of these kernels with the standard Gaussian Radial Basis Function (RBF) kernel. Our results suggest that the aggregate kernels are more effective at land cover classification than the RBF kernel.We then apply the SVM algorithm to the problem of RNA-protein interaction prediction. Here our goal is to build a stable classifier for predicting RNA binding amino acids along a protein chain. We investigate the prediction ability of features such as predicted solvent accessibility, relative solvent accessibility, hydrophobicity and the position specific scoring matrix (PSSM). We conclude that while these features all contribute to classifier accuracy, most of the information is encoded and comes from the PSSM matrix.

Doctor of Philosophy (PhD), Wright State University, 2007, Engineering PhD

The lack of a priori knowledge about the waveform of interest, the multitudes of signals the receiver might receive, and the noise energy that occupies the same portion of the frequency spectrum as the signal makes the design of a modern wideband receiver very challenging. Especially, the receiver must be able to detect a weak signal in the presence of a strong one, which requires a high two-signal instantaneous dynamic range (IDR). To fulfill this requirement, the receiver must detect genuine weak signals and avoid the detection of strong signals' sidelobes and noise and spurs generated from the receiver system. The other major trend in modern receiver signals is the shift towards wider bandwidths. Analog wideband receiver designs can provide accommodation of the technology-stressing bandwidths, but come up to a cost of reduced flexibility. Digital approaches, alternatively, provide flexibility in receiver signal processing, but they are limited by analog-to-digital converter resolution and power consumption. In this dissertation, design and performance evaluation of a 1-GHz signal bandwidth digital receiver, which uses a Kaiser Window function and a compensation technique, is presented. The Kaiser Window reduces the spectral leakage by eliminating the discontinuities at the time window edges and the compensation uncovers the weak signal for extension of the two-signal IDR of the receiver. An exhaustive study of configuration of ADC, FFT, window function, and compensation for a maximum achievable two-signal IDR of the receiver is conducted. It is shown that using a 4-bit ADC and a 256-point FFT of 12-point kernel function, a maximum two-signal IDR of 9 dB is obtained. The IDR is extended to 14 dB by using the Kaiser window and to 18 dB by using the compensation. Furthermore, using a 4-bit ADC and an ideal 256-point FFT, a maximum two-signal IDR of 11 dB is obtained. The IDR is extended to 17 dB by using the Kaiser window and to 22 dB by using the compensation. A co (open full item for complete abstract)

PhD, University of Cincinnati, 2011, Arts and Sciences: Mathematical Sciences

Narrative comprehension is a fundamental cognitive skill that involves the coordination of different functional brain regions. To investigate the network structure among the brain regions supporting this cognitive function, a Spectral Bayesian Network with Bayesian model averaging is developed based on the spectral density estimation of the functional Magnetic Resonance Imaging (fMRI) time series recorded from multiple brain regions. In this approach, the neural interactions and temporal dependence among different brain regions are measured by spectral density matrices after a Fourier transform of the fMRI signals to the frequency domain. A Bayesian model averaging method is then applied to build the network structure from a set of candidate networks. Using this model, brain networks of three distinct age groups are constructed to assess the dynamic change of network connectivity with respect to age. Networks of multivariate time series are also simulated from vector autoregressive models to compare the performances of the SBN with existing methods in learning network structure from time series data. In addition to the network modeling of the functional interactions among brain regions, the quantification of the functional connectivity between two brain regions is also very important for understanding how the functions of the human brain develop. Using spectral coherence and partial spectral coherence, the overall and direct functional connectivity strengths among the language-related neural circuits are computed based on fMRI time series data collected in 313 children ranging in age from 5 to 18 years in a story comprehension experiment. The age or gender effects on both the pair wise direct link and connection strength are studied to access children's development of brain functions for story comprehension. In addition, the connectivity differences between the left and right hemispheres, and the connections in both hemispheres that are directly related to the child (open full item for complete abstract)

Master of Science, The Ohio State University, 2008, Nuclear Engineering

The objective of this work is to provide more accurate neutronic models of the OSU research reactor and also to provide a more complete characterization of the energy-dependent neutron flux spectrum present at the various experimental facilities of the reactor. This work will allow for a better predictive capability, so that experimental results can be better anticipated. Two models are analyzed, one with a very detailed core geometry, the other with a more homogenized core. The two models were refined and made to be more accurate in various ways. Also, experimental benchmarking of these computer models is included, specifically using material activation experiments and spectral deconvolution (or unfolding) codes. In these programs, the wires' post-irradiation activities, their response functions, and an initial guess of the energy-dependent neutron flux spectrum are input into an iterative process, which outputs a energy-dependent neutron flux spectrum. The experimental and predicted energy-dependent neutron flux spectra are then compared to illustrate the differences between models.

Master of Science (MS), Ohio University, 2010, Physics and Astronomy (Arts and Sciences)

We present multiwavelength observations of the ultraluminous blazar-type radio loud quasar PKS 0528+134 obtained in September 2009. Our main goal was to characterize this blazar in a quiescence state. We performed optical observations at the 1.3-m McGraw-Hill telescope of the MDM Observatory and also collected radio and optical data from the GASP. In the X-ray regime we collected data from the XMM-Newton Satellite in the 0.2 – 10 keV range. We also obtained gamma-ray data from the Fermi Large Area Telescope (LAT) in the 100 MeV – 300 GeV range. We found no evidence of significant flux or spectral variability in the radio, X-ray and gamma-ray regime. However, significant flux variability was found in the optical region, especially in the R and B bands, and we also found a spectral softening trend. We produced four SEDs with the data we were able to gather, and for the Leptonic combined SSC+ERC jet model that we used, acceptable fits were produced.

Master of Science (MS), Ohio University, 2012, Physics and Astronomy (Arts and Sciences)

BL-Lacertae is the prototype of BL Lac objects. It has been observed during the months of May, October and December 2011 with the aim of studying the intranight color variations on short timescales using the method of Color-Magnitude Diagram analysis and to look for the time-lag between the variations at different optical wavelengths using the method of Discrete Correlation Function analysis. Quasi-simultaneous measurements in the UBVRI bands have been performed using the 1.3 m optical telescope at MDM observatory at Kitt-Peak, AZ. A flare is observed during May 2011 in optical data that is also seen in the same time period in the gamma ray data, which is taken directly from the Fermi LAT website. BL Lacertae showed the trend Redder when Brighter and the transition to Bluer when brighter in the higher flux states, i.e. when the magnitude of R band is less than 13.5. A time-lag between the variations at optical wavelength band B and R is found to be (0.01 +/- 0.0056) day (less than 2ς). Using the value of time-lag, the lower limit on the magnetic field in the jet is found to be B > 1.24 G.

Doctor of Philosophy, The Ohio State University, 2025, Mathematics

This thesis consists of two self-contained works on nonlinear differential equations arising in models of multilayer channel flows. In Chapter 2 we consider the linear stability of traveling waves in a thin-film model for two-fluid Couette flow when a thin layer of the more viscous fluid resides next to the stationary wall. We prove that in a neighborhood of a bifurcation point from a flat state, characterized by a positive integer kb, the principal branch (kb = 1) is spectrally stable while all other branches (kb > 1) are spectrally unstable. For larger amplitude traveling waves, we establish a number of conditional theorems where the conditions were checked with help of computer assist for a set of parameter values. Using these theorems, we rigorously confirm earlier numerical evidence (D. Papageorgiou & S. Tanveer, Proc. Roy. Soc. Lond. A, doi:10.1098/rspa.2019.0367) on stability and instability of traveling waves over a range of parameters. In Chapter 3 we study traveling front solutions for a coupled system of PDEs with quadratic nonlinearity which models a stratified three-layer channel flow down an inclined channel. We prove a set of conditional theorems for the existence of traveling fronts, whose conditions have been verified through computer assist in the case of second-order dissipation in a number of examples. For fourth-order dissipation, which is appropriate when surface tension is included while inertia and density stratification are absent, we prove conditional theorems as well, though we still have to check that conditions are applicable.

Doctor of Philosophy, Case Western Reserve University, 2025, EMC - Aerospace Engineering

Hypersonic stagnation point injection (SPI) experiments are performed using a 7◦ half-angle cone with a 19 mm radius spherical nose and a single, 1.93 mm radius sonic jet in the center of the model directed in to a Mach 6 quiet and a Mach 5.8 noisy air free stream. Injected gases include air, argon, helium, CO2, and SF6. The primary data consists of high-speed schlieren imaging at 76 kHz. Spectral analyses of the schlieren data is used to observe and define three key modes fundamental to the motions of the flow structures: (1) the longitudinal mode, (2) the vortex-coupled mode, and (3) the vortex-shedding mode. The proper orthogonal decomposition (POD) is used to track the mode energy fractions as a function of thrust coefficient while the dynamic mode decomposition (DMD) is used to gain insight into the nature of the motions present in each mode. The spectral proper orthogonal decomposition (SPOD) is used to probe specific frequencies present in the jet reservoir pressure and other spectra. As the thrust coefficient increases, the longitudinal mode becomes the dominant visible motion and the highest energy POD mode while the vortex-coupled mode becomes less energetic. The wavenumber of the Kelvin-Helmholtz instability (KHI) along the contact surface is shown to increase with thrust coefficient and under noisy flow conditions, and is shown to depend on the molar mass and speed of sound of the injected gas. This work provides the first hypersonic SPI experimental data and analyses on how different modes evolve with varying thrust coefficient and injected gas and the first observations and characterization of the KHI in an SPI flow.

Doctor of Philosophy, Case Western Reserve University, 2025, EECS - System and Control Engineering

Far from equilibrium, fluctuation-driven neural systems self-organize across multiple scales towards efficient information processing and robust adaptations to external environments. Exploiting multiscale self-organization in systems neuroscience and artificial intelligence requires a computational framework targeted at modeling the effective non-equilibrium dynamics of stochastic neural trajectories. Non-equilibrium thermodynamics and representational geometry offer theoretical foundations for this framework, but we also need scalable data-driven techniques for modeling the collective properties of high-dimensional neural networks from partial subsampled observations. Renormalization is a coarse-graining technique, which is central to the study of emergent scaling properties of many-body and nonlinear dynamical systems. While coarse-graining is widely applied to complex systems in physics and machine learning, coarse-graining complex dynamical networks is an unsolved problem affecting many computational sciences. The recent development of diffusion-based renormalization---inspired by quantum statistical mechanics---coarse-grains complex networks near entropy transitions marked by maximal changes in specific heat, or information transmission. Here I explore diffusion-based renormalization of dissipative neural system by generating symmetry-breaking representations across multiple scales and offering scalable algorithmic using tensor networks. Diffusion-guided renormalization is the key innovation bridging microscale diffusion and mesoscale dynamics of dissipative neural systems. For microscale diffusion, I developed a scalable graph inference algorithm that discovers community structure from subsampled neural network activity. Using community-based node orderings, diffusion-guided renormalization efficiently models higher-order interactions and generate a renormalization group flow through metagraphs and joint probability functions. Towards mesoscales, diffusi (open full item for complete abstract)

MS, University of Cincinnati, 2024, Engineering and Applied Science: Computer Science

Discriminating between quantum states and channels is an important task in the field of quantum computation and information. There is a wide variety of approaches through measurement, metrics, and statistical distances. We analyze the existing results to compare properties, main purpose and computability of most common discriminating measures. We also offer a new approach through spectral shift function, a well-known object in the perturbation theory of self-adjoint operators.

Master of Science, The Ohio State University, 1966, Graduate School

Master of Science, The Ohio State University, 1979, Graduate School

Doctor of Philosophy, The Ohio State University, 2024, Nuclear Engineering

In this work, we apply the spherical harmonic expansion technique to the nonclassical transport equation, deriving a system of equations for the nonclassical flux moments, which in this work are called nonclassical spherical harmonic approximations (NSHA). The nonclassical transport equation represents a recently developed mathematical model that enables the modeling of transport problems where the particle flux does not undergo exponential attenuation. Our approach to solving the resulting system involves employing a Spectral Approach (SA) technique to represent the nonclassical angular flux moments as a truncated series of Laguerre polynomials with respect to the free-path variable s. This formulation yields a structured system of equations for the nonclassical angular flux moments akin to the classical P_N equations. Notably, we demonstrate that the NSHA framework is reduced to the classical P_N equation in a special case, highlighting the versatility and applicability of the proposed approach. To validate the derivation and assess the accuracy of NSHA, numerical results for slab geometry test problems are provided. These numerical simulations serve to verify the accuracy and efficacy of the derived equations in capturing the nonclassical transport behavior and further underscore the potential applications of NSHA in practical transport problems. We also analyze the effects of different boundary conditions, including Marshak Boundary Conditions, Mark Boundary Conditions, Asymptotic/Variational (A-V) Boundary Conditions, and Federighi-Pomraning (F-P) Boundary Conditions. Through this analysis, we aim to provide an initial assessment of how these boundary conditions influence the performance and accuracy of the NSHA. By examining these different approaches, we can explore their impact on nonclassical particle transport.