In previous studies, evidence of thermal wave behavior was found in heterogeneous materials. Thus, the overall goal of this study was to experimentally verify those results, and develop a parameter estimation scheme to estimate the thermal properties of various heterogeneous materials. Two types of experiments (Experiments 1 and 2) were conducted to verify the existence or non-existence of thermal wave behavior in heterogeneous materials. In Experiment 1 sand, ion exchanger, and sodium bicarbonate were used as test materials, while processed meat (bologna) was used in Experiment 2. The measured temperature profiles of the samples were compared with the parabolic and hyperbolic heat conduction model results. The values of thermal diffusivity and thermal conductivity were obtained using the Box-Kanemasu parameter estimation method which is based on the comparison between temperature measurements and the solutions of the theoretical model. Overall, no clear experimental evidence was found to justify the use of hyperbolic heat conduction models rather than parabolic for the materials tested. Further comprehensive experimentation using different heating rates is warranted to definitely identify the accurate type of heat conduction process associated with such materials, and to describe the physical mechanisms which produce wave-like heat conduction in heterogeneous materials. / Master of Science

Distributed rainfall runoff hydrologic models can be highly effective in improving flood forecasting capabilities at ungauged, interior locations of the watershed. However, their implementation in operational decision-making is hindered by the high dimensionality of the state-parameter space and by lack of methods/understanding on how to properly exploit and incorporate available spatio-temporal information about the system. This dissertation is composed of a sequence of five studies, whose overall goal is to improve understanding on problems relating to parameter identifiability in distributed models and to develop methodologies for their calibration.The first study proposes and investigates an approach for calibrating catchment scale distributed rainfall-runoff models using conventionally available data. The process, called regularization, uses spatial information about soils and land-use that is embedded in prior parameter estimates (Koren et al. 2000) and knowledge of watershed characteristics, to constrain and reduce the dimensionality of the feasible parameter space.The methodology is further extended in the second and third studies to improve extraction of `hydrologically relevant' information from the observed streamflow hydrograph. Hydrological relevance is provided by using signature measures (Yilmaz et al 2008) that correspond to major watershed functions. While the second study applies a manual selection procedure to constrain parameter sets from the subset of post calibrated solutions, the third develops an automatic procedure based on a penalty function optimization approach.The fourth paper investigates the relative impact of using the commonly used multiplier approach to distributed model calibration, in comparison with other spatial regularization strategies and also includes investigations on whether calibration to data at the catchment outlet can provide improved performance at interior locations. The model calibration study conducted for three mid sized catchments in the US led to the important finding that basin outlet hydrographs might not generally contain information regarding spatial variability of the parameters, and that calibration of the overall mean of the spatially distributed parameter fields may be sufficient for flow forecasting at the outlet. This then was the motivation for the fifth paper which investigates to what degree the spatial characteristics of parameter and rainfall fields can be observable in catchment outlet hydrographs.

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

No description available.

No description available.

No description available.

Tissue engineering (TE) combines methods of cells, engineering and materials to improve or replace biological functions of native tissues or organs. Fabricating scaffolds is a vital process in TE for the mechanical support of the cells proliferation with desired functions and intricate structures. A pneumatic dispensing system of 3D printing is used to build soft scaffolds with controllable pore sizes in this research. An effective method is required to help users to systematically select proper parameters to print hydrogel strands with desired widths to fabricate scaffolds. In this research, printing parameters are classified first to build a simplified mathematical model to identify the significant parameters. A factorial experiment is then conducted to investigate effects of selected parameters and their interactions on the strand width. The solution is further verified using single variable experiments with the regression test. Based on the results, a parameters selection method is proposed and evaluated using two verification tests. A comparison test of the scaffolds fabrication is conducted to verify the analytic solution of the proposed theory. It is found that the nozzle sizes, dispensing pressure, and moving speed of a printer head all statistically affect strand widths. Among them, the nozzle size has the most significant influence on strand widths. Factors interactions are mainly embodied in between the nozzle size - moving speed and the nozzle size - dispensing pressure. In addition, a statistical significant linear relationship is found between the moving speed - strand width and the dispensing pressure - strand width. Furthermore, due to the high cost of bio-materials and the high pressure threat of air compressor in the dispensing system, a 3D bio-printing simulation system is developed to demonstrate the system configuration and operation procedures to help new users avoiding operation mistakes in the real world. A haptic-based 3D bio-printing simulation system with the haptic feedback is presented by means of the Phantom Omni haptic interface. The virtual environment is developed using the Worldviz software. The haptic force feedback is calculated based on the spring-damper model and the proxy method. This system is verified using questionnaire survey to provide a flexible, cost-effective, safe, and highly interactive learning environment. / February 2016

Sensor network plays a significant role in determining the performance of network inference tasks. A wireless sensor network with a large number of sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in WSN is developing an efficient protocol which has a significant impact on the convergence of the network. Parameter estimation is one of the most important applications of sensor network. In order to model such large and complex networks for estimation, efficient strategies and algorithms which take less time to converge are being developed. To deal with this challenge, an approach of having multilayer network structure to estimate parameter and reach convergence in less time is estimated by comparing it with known gossip distributed algorithm. Approached Multicast multilayer algorithm on a network structure of Gaussian mixture model with two components to estimate parameters were compared and simulated with gossip algorithm. Both the algorithms were compared based on the number of iterations the algorithms took to reach convergence by using Expectation Maximization Algorithm.Finally a series of theoretical and practical results that explicitly showed that Multicast works better than gossip in large and complex networks for estimation in consensus building strategies.

Contemporary cosmology is a vibrant field, with data and observations increasing rapidly. This allows for accurate estimation of the parameters describing our cosmological model. In this thesis we present new research based on two different types of cosmological observations, which probe the universe at multiple epochs. We begin by reviewing the current concordance cosmological paradigm, and the statistical tools used to perform parameter estimation from cosmological data. We highlight the initial conditions in the universe and how they are detectable using the Cosmic Microwave Background radiation. We present the angular power spectrum data from temperature observations made with the Atacama Cosmology Telescope (ACT) and the methods used to estimate the power spectrum from temperature maps of the sky. We then present a cosmological analysis using the ACT data in combination with observations from the Wilkinson Microwave Anisotropy Probe to constrain parameters such as the effective number of relativistic species and the spectral index of the primordial power spectrum, which we constrain to deviate from scale invariance at the 99% confidence limit. We then use this combined dataset to constrain the primordial power spectrum in a minimally parametric framework, finding no evidence for deviation from a power-law spectrum. Finally we present Bayesian Estimation Applied to Multiple Species, a parameter estimation technique using photometric Type Ia Supernova data to estimate cosmological parameters in the presence of contaminated data. We apply this algorithm to the full season of the Sloan Digital Sky Survey II Supernova Search, and find that the constraints are improved by a factor of three relative to the case where one uses a smaller, spectroscopically confirmed subset of supernovae.