Master of Science (MS), Ohio University, 2003, Electrical Engineering & Computer Science (Engineering and Technology)

In this work, we present two C/C++ implementations of general purpose automatic differentiation (AD) for GNU Octave applications: FAD for forward mode AD and LogAD for reverse mode AD with bisection checkpointing. Both FAD and LogAD accept functions written in the GNU Octave language and work in the Octave environment via dynamically linked functions. FAD evaluates the product of the Jacobian of the input function and an arbitrary vector in time and space that are proportional to the time and space used by the original function. LogAD evaluates the product of an arbitrary vector and the Jacobian of the input function via a checkpointing approach first proposed by Griewank in 1992.

Committee: David Juedes (Advisor) Subjects: Computer Science

Doctor of Philosophy, Case Western Reserve University, 2024, Physics

Adaptive Radiotherapy (ART) facilitates replanning during head and neck radiotherapy. Central to ART is Deformable Image Registration (DIR), which enables quantification of tissue and tumor changes between initial planning and daily images. However, evaluating DIR algorithms for physiological accuracy remains challenging. To address this, we present a proof-of-principle study introducing an automatic DIR evaluation tool that employs deep learning for tissue segmentation and a finite element model for deformation analysis. Our study illustrates the end-to-end training and implementation of the proposed evaluation framework and argues for its clinical feasibility. Efficient automatic liver dome delineation can improve the clinical feasibility of the online breath-hold verification technique for liver SBRT, proposed in a previous study by our institution. We present a proof-of-principle study for a deep learning-based pipeline for the automatic delineation of liver domes in kV triggered images. The pipeline uses a deep learning model for liver dome region segmentation and postprocessing for dome delineation. Cross-validation assesses the generalizability of the pipeline. In MRI, the region behind the knee of a supine patient is vulnerable to artifacts. Building on previous work by our group, we design and develop a prototype single half-cylinder shim coil for knee MRI and investigate its limits. In vivo data show that our method, which is based on Turner's target field for coil design, could be optimized to mitigate B0 non-uniformity in localized regions of interest. Magnetic Resonance Fingerprinting (MRF) offers promise as a quantitative imaging method. A key challenge in MRF are partial volume (PV) effects. Building on the seminal work into PV MRF, we propose a method based on density-based clustering, which is semi-automatic and interpretable. The brain tissue properties calculated by our method aligns closely with literature values, promising increased eff (open full item for complete abstract)

Committee: Michael Martens (Committee Chair); Robert Brown (Committee Member); Bingqi Guo (Committee Member); Ping Xia (Committee Member); Gary Chottiner (Committee Member) Subjects: Artificial Intelligence; Medical Imaging; Physics

Master of Science, Miami University, 2016, Computational Science and Engineering

Automatic modulation classifiers (AMC) are one of the basic building blocks of electronic warfare receivers and cognitive radios. Although many research papers on AMC algorithms have been published, very few results on their implementation are available. This thesis presents a feature-based AMC built upon a software-defined radio platform. The developed AMC can detect signals over a broad spectrum and classify the modulation used. The modulation schemes considered in this thesis are amplitude modulation (AM), frequency modulation (FM), phase-shift keying (PSK), and quadrature amplitude modulation (QAM). Experimental results demonstrate the validity of the developed AMC algorithm and its implementation.

Committee: Chi-Hao Cheng Ph.D (Advisor); Dmitriy Garmatyuk Ph.D (Committee Member); Jason Pennington Ph.D (Committee Member) Subjects: Communication; Computer Engineering; Computer Science; Electrical Engineering; Engineering; Experiments; Technology

Master of Science, The Ohio State University, 1982, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1987, Graduate School

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Master of Science, The Ohio State University, 1985, Graduate School

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Master of Science, The Ohio State University, 1983, Graduate School

Committee: Not Provided (Other) Subjects:

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

Nuclear Magnetic Resonance (NMR) spectroscopy is an analytical technique used to study a wide range of molecules ranging from proteins to small molecules. A crucial aspect of protein NMR studies is the investigation of conformational dynamics because of the insights one can get about the biological activity of proteins. The motions found in biological processes occur on various timescales that only NMR is able to study with atomic resolution. Unfortunately, studying proteins in this matter is time-consuming because the NMR experiments one needs to run are pseudo-3D experiments that take a day to a week to complete. One method to minimize the time needed to acquire multidimensional NMR experiments is called “NMR by Ordered Acquisition using 1H-detection” or NOAH. NOAH aims to concatenate two or more different pulse sequences in order to share one single recovery delay time. The recovery delay is the period that one needs to wait for the magnetization to return to equilibrium on a timescale of seconds, while the timescale for most pulses and delays is of the order of µs to ms. Thus, concatenating two or more sequences leads to substantial time gains provided that there is no loss in sensitivity and assuming that the spectra do not change in any significant way. NMR is also used extensively in small molecule studies. For example, the field of metabolomics aims at identifying and quantifying metabolites in a complex biological system, such as serum, urine, cell extracts, or food. Several software programs exist to aid researchers to achieve this task. Previously, we have published web servers such as COLMAR (Complex Mixture Analysis by NMR) that attempt to automatically or semi-automatically identify metabolites by comparing their chemical shifts with database chemical shifts in 2D NMR experiments (COLMARm) and quantify them (COLMARq). Until now, however, we did not have a web server to do quantification of 1D spectra. Such a tool is useful as it can significantly spee (open full item for complete abstract)

Committee: Rafael Brüschweiler (Advisor); Christopher Jaroniec (Committee Member); Mark Foster (Committee Member) Subjects: Analytical Chemistry; Bioinformatics; Chemistry

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects:

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

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1969, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1960, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1969, Graduate School

Committee: Not Provided (Other) Subjects:

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

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1967, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2008, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects: