Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item MECHANICAL CHARACTERIZATION OF NORMAL AND CANCEROUS BREAST TISSUE SPECIMENS USING ATOMIC FORCE MICROSCOPY(2014) Roy, Rajarshi; Desai, Jaydev P.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Breast cancer is one of the most common malignancies among women worldwide. Conventional breast cancer diagnostic methods involve needle-core biopsy procedures, followed by careful histopathological inspection of the tissue specimen by a pathologist to identify the presence of cancerous lesions. However, such inspections are primarily qualitative and depend on the subjective impressions of observers. The goal of this research is to develop approaches for obtaining quantitative mechanical signatures that can accurately characterize malignancy in pathological breast tissue. The hypothesis of this research is that by using contact-mode Atomic Force Microscopy (AFM), it is possible to obtain differentiable measures of stiffness of normal and cancerous tissue specimens. This dissertation summarizes research carried out in addressing key experimental and computational challenges in performing mechanical characterization on breast tissue. Firstly, breast tissue specimens studied were 600 um in diameter, about six times larger than the range of travel of conventional AFM X-Y stages used for imaging applications. To scan tissue properties across large ranges, a semi automated image-guided positioning system was developed that can be used to perform AFM probe-tissue alignment across distances greater than 100 um at multiple magnifications. Initial tissue characterization results indicate that epithelial tissue in cancer specimens display increased deformability compared to epithelial tissue in normal specimens. Additionally, it was also observed that the tissue response depends on the patient from whom the specimens were acquired. Another key challenge addressed in this dissertation is accurate data analysis of raw AFM data for characterization purposes. Two sources of uncertainty typically influence data analysis of AFM force curves: the AFM probe's spring constant and the contact point of an AFM force curve. An error-in-variable based Bayesian Changepoint algorithm was developed to quantify estimation errors in the tissue's elastic properties due to these two error sources. Next, a parametric finite element modeling based approach was proposed in order to account for spatial heterogeneity in the tissue response. By using an exponential hyperelastic material model, it was shown that it is possible to obtain more accurate material properties of tissue specimens as opposed to existing analytical contact models. The experimental and computational strategies proposed in this dissertation could have a significant impact on high-throughput quantitative studies of biomaterials, which could elucidate various disease mechanisms that are phenotyped by their mechanical signatures.Item RELIABILITY TESTING & BAYESIAN MODELING OF HIGH POWER LEDS FOR USE IN A MEDICAL DIAGNOSTIC APPLICATION(2013) Sawant, Milind Mahadeo; Christou, Aristos; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)While use of LEDs in fiber optics and lighting applications is common, their use in medical diagnostic applications is rare. Since the precise value of light intensity is used to interpret patient results, understanding failure modes is very important. The contributions of this thesis is that it represents the first measurements of reliability of AlGaInP LEDs for the medical environment of short pulse bursts and hence the uncovering of unique failure mechanisms. Through accelerated life tests (ALT), the reliability degradation model has been developed and other LED failure modes have been compared through a failure modes and effects criticality analysis (FMECA). Appropriate ALTs and accelerated degradation tests (ADT) were designed and carried out for commercially available AlGaInP LEDs. The bias conditions were current pulse magnitude and duration, current density and temperature. The data was fitted to both an Inverse Power Law model with current density J as the accelerating agent and also to an Arrhenius model with T as the accelerating agent. The optical degradation during ALT/ADT was found to be logarithmic with time at each test temperature. Further, the LED bandgap temporarily shifts towards the longer wavelength at high current and high junction temperature. Empirical coefficients for Varshini's equation were determined, and are now available for future reliability tests of LEDs for medical applications. In order to incorporate prior knowledge, the Bayesian analysis was carried out for LEDs. This consisted of identifying pertinent prior data and combining the experimental ALT results into a Weibull probability model for time to failure determination. The Weibull based Bayesian likelihood function was derived. For the 1st Bayesian updating, a uniform distribution function was used as the Prior for Weibull á-â parameters. Prior published data was used as evidence to get the 1st posterior joint á-â distribution. For the 2nd Bayesian updating, ALT data was used as evidence to obtain the 2nd posterior joint á-â distribution. The predictive posterior failure distribution was estimated by averaging over the range of á-â values. This research provides a unique contribution in reliability degradation model development based on physics of failure by modeling the LED output characterization (logarithmic degradation, TTF â<1), temperature dependence and a degree of Relevance parameter `R' in the Bayesian analysis.