Data processing

The increasing system design complexity is negatively impacting the overall system design productivity by increasing the cost and time of product development. One key to overcoming these challenges is exploiting Component Based Engineering practices. However it is a challenge to select an optimum component from a component library that will satisfy all system functional and non-functional requirements, due to varying performance parameters and quality of service requirements. In this thesis we propose an integrated framework for component selection. The framework is a two phase approach that includes a system modeling and analysis phase and a component selection phase. Three component selection algorithms have been implemented for selecting components for a Network on Chip architecture. Two algorithms are based on a standard greedy method, with one being enhanced to produce more intelligent behavior. The third algorithm is based on simulated annealing. Further, a prototype was developed to evaluate the proposed framework and compare the performance of all the algorithms.

Research, tests and analysis are presented on several reinforcements placed in the asphalt overlay of a roadway built over soft organic soils. Non-destructive Evaluation (NDE) methods and statistical analysis were used to characterize the pavement before and after rehabilitative construction. Before reconstruction, falling weight deflectometer, rut and ride tests were conducted to evaluate the existing pavement and determine the statistical variability of critical site characteristics. Twenty-four 500ft. test sections were constructed on the roadway including sixteen reinforced asphalt and eight control sections at two test locations that possessed significantly different subsoil characteristics. NDE tests were repeated after reconstruction to characterize the improvements of the test sections. Test results were employed to quantify the stiffness properties of the pavement based on load-deflection data to evaluate the relative performance of the reinforced sections. Statistical analysis of the data showed the stiffness of the reinforced sections was consistently higher than the control sections.

A sudden expansion combustor (SUE) is analyzed using computation fluid dynamics (CFD). CO emissions and NOx emissions are computed for various operating conditions of the SUE combustor using a can type and an annular type geometrical configurations. The goal of this thesis is to see if the SUE combustor is a viable alternative to conventional combustors which utilize swirlers. It is found that for the can type combustor the NOx emissions were quite low compared to other combustor types but the CO emissions were fairly high. The annular combustor shows better CO emissions compared to the can type, but the CO emissions are still high compared to other combustors. Emissions can be improved by providing better mixing in the primary combustion zone. The SUE combustor design needs to be further refined in order for it to be a viable alternative to conventional combustors with swirlers.

Though several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ; they require long battery life, and ultimately fall short on delivering real-time continuous detection of arrhythmia and specifically progressive development of cardiac A-Fib. The focus of this dissertation is the design of a class of adaptive and efficient energy-aware real-time detection models for monitoring, early real-time detection and reporting of progressive development of cardiac A-Fib.... The design promises to have a greater positive public health impact from predicting A-Fib and providing a viable approach to meeting the energy needs of current and future real-time monitoring, detecting and reporting required in wearable computing healthcare applications that are constrained by scarce energy resources.

This dissertation contains results of the candidate's research on the generalized discrete logarithm problem (GDLP) and its applications to cryptology, in non-abelian groups. The projective special linear groups PSL(2; p), where p is a prime, represented by matrices over the eld of order p, are investigated as potential candidates for implementation of the GDLP. Our results show that the GDLP with respect to specic pairs of PSL(2; p) generators is weak. In such cases the groups PSL(2; p) are not good candidates for cryptographic applications which rely on the hardness of the GDLP. Results are presented on generalizing existing cryptographic primitives and protocols based on the hardness of the GDLP in non-abelian groups. A special instance of a cryptographic primitive dened over the groups SL(2; 2n), the Tillich-Zemor hash function, has been cryptanalyzed. In particular, an algorithm for constructing collisions of short length for any input parameter is presented. A series of mathematical results are developed to support the algorithm and to prove existence of short collisions.

An algebraic surface defined by an equation of the form z2 = (x+a1y) ... (x + any) (x - 1) is studied, from both an algebraic and geometric point of view. It is shown that the surface is rational and contains a singular point which is nonrational. The class group of Weil divisors is computed and the Brauer group of Azumaya algebras is studied. Viewing the surface as a cyclic cover of the affine plane, all of the terms in the cohomology sequence of Chase, Harrison and Roseberg are computed.

In this work, we develop an extension of the generalized Fourier transform for exterior domains due to T. Ikebe and A. Ramm for all dimensions n>2 to study the Laplacian, and fractional Laplacian operators in such a domain. Using the harmonic extension approach due to L. Caffarelli and L. Silvestre, we can obtain a localized version of the operator, so that it is precisely the square root of the Laplacian as a self-adjoint operator in L2 with DIrichlet boundary conditions. In turn, this allowed us to obtain a maximum principle for solutions of the dissipative two-dimensional quasi-geostrophic equation the exterior domain, which we apply to prove decay results using an adaptation of the Fourier Splitting method of M.E. Schonbek.

Deterministic and stochastic weighting methods are commonly used methods for estimating missing precipitation rain gauge data based on values recorded at neighboring gauges. However, these spatial interpolation methods seldom check for their ability to preserve site and regional statistics. Such statistics and primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study evaluates the efficacy of traditional interpolation methods for estimation of missing data in preserving site and regional statistics. New optimal spatial interpolation methods intended to preserve these statistics are also proposed and evaluated in this study. Rain gauge sites in the state of Kentucky are used as a case study, and several error and performance measures are used to evaluate the trade-offs in accuracy of estimation and preservation of site and regional statistics.

The demand on transportation infrastructure is dramatically increasing due to population growth causing the transportation systems to be pushed to their limits. With the projected population growth, not only for the U.S. but especially for the higher education field, university campuses are of great importance for transportation engineers. Urban univeristy campuses are considered major trip generators and with the population forecast many challenges are bound to arise. The implementation of an improved transit system provides a lower-cost solution to the continuously increasing congestion problems in university campus road networks and surrounding areas. This paper presents a methodology focused on the development of a hybrid system concentrated in three main aspects of transit functionality : access to bus stop location, reasonable travel time and low cost. Two methods for bus stop locations assessment are presented for two levels of analysis : microscopic and mesoscopic. The resulting travel time from the improved bus stop locations is analyzed and compared to the initial conditions by using a microsimulation platform. The development of a mathematical model targets the overall system's cost minimization, including user and operator cost, while maximizing the service coverage. The results demonstrate the benefits of the bus stop assessment by the two applied methods, as well as, the benefits of the route and headway selection based on the mathematical model. Moreover, the results indicate that the generation of routes using travel time as the impedance factor generates the optimal possible routes to obtain the minimum system's overall cost.

The focus of this thesis is to statistically model violent crime rates against population over the years 1960-2009 for the United States. We approach this question as to be of interest since the trend of population for individual states follows different patterns. We propose here a method which employs cubic spline regression modeling. First we introduce a minimum/maximum algorithm that will identify potential knots. Then we employ least squares estimation to find potential regression coefficients based upon the cubic spline model and the knots chosen by the minimum/maximum algorithm. We then utilize the best subsets regression method to aid in model selection in which we find the minimum value of the Bayesian Information Criteria. Finally, we preent the R2adj as a measure of overall goodness of fit of our selected model. We have found among the fifty states and Washington D.C., 42 out of 51 showed an R2adj value that was greater than 90%. We also present an overall model of the United States. Also, we show additional applications our algorithm for data which show a non linear association. It is hoped that our method can serve as a unified model for violent crime rate over future years.