This is a study that is focused on developing an individual design methodology for a centrifugal impeller and generating a mixed flow impeller for a small turbojet engine by using this methodology. The structure of the methodology is based on the design, modeling and the optimization processes, which are operated sequentially. The design process consists of engine design and compressor design codes operated together with a commercial design code. Design of Experiment methods and an in-house Neural Network code is used for the modeling phase. The optimization is based on an in-house code which is generated based on multidirectional search algorithm. The optimization problem is constructed by using the inhouse parametric design codes of the engine and the compressor. The goal of the optimization problem is to reach an optimum design which gives the best possible combination of the thrust and the fuel consumption for a small turbojet engine. The final combination of the design parameters obtained from the optimization study are used in order to generate the final design with the commercial design code. On the last part of the thesis a comparison of the final design and a standard radial flow impeller is made in order to clarify the benefit of the study. The results have been showed that a mixed flow compressor design is superior to a standard radial flow compressor in a small turbojet application.

Isolator displacements, floor accelerations, roof displacements, base shear and torsional moments are basic parameters that are considered in the design of seismically isolated structures. The aim of this study is to evaluate the effects of bidirectional earthquake excitations of near fault records on the response of base isolated structures in terms of basic parameters mentioned above. These parameters computed from nonlinear response history analysis (RHA) and they are compared with the parameters computed from equivalent lateral force procedure (ELF). Effect of asymmetry in superstructure is also examined considering mass eccentricity at each floor level. Torsional amplifications in isolator displacements, floor accelerations, roof displacements and base shear are compared for different level of eccentricities. Two buildings with different story heights are used in the analyses.The building systems are modeled in structural analysis program SAP2000. The scaling of ground motion data are taken from the study of &ldquo / Response of Isolated Structures Under Bi-directional Excitations of Near-fault ground Motions&rdquo / (Ozdemir, 2010). Each ground motion set (fault normal and fault parallel) are applied simultaneously for different range of effective damping of lead rubber bearing (LRB) and for different isolation periods.

Professional research project (Au.D.)--University of SouthFlorida, 2002. / Title from PDF of title page. / Document formatted into pages; contains 22 pages. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: The purpose of this study was to determine the prevalence of the binaural interaction component (BIC) in a large sample of normal hearing adults, and to measure the absolute latency and amplitude of the BIC as a function of the click rate of the stimulus and the electrode montage. The BIC is obtained by subtracting the auditory evoked potential waveform obtained with binaural stimulation from the waveform obtained by adding the responses from the left and right monaural stimulation. The tested hypothesis was that the recordings of the BIC vary among normal hearing individuals, and BIC latency and amplitude values change as a function of stimulus rate. Studies of the BIC help to explain the neural correlates of some binaural processes, and to develop an electrophysiological index of binaural processes for objective clinical evaluations.Data was completed and analyzed on 47 adults between the ages of 20 and 41 (mean = 25) with hearing in the normal range (thresholds [ 20 dB HL at 500, 1000, 2000, and 4000 Hz in each ear) and no known neurological disorders. The results revealed a great variability in BIC morphology between subjects. The BIC waveforms were categorized into five distinct groups according to the number of positive and negative peaks present. Chi-square analyses revealed a significant relationship between click rate and BIC category; however, the relationship between recording montage and BIC category was insignificant. An Analysis of Variance (ANOVAs) revealed a significant increase in absolute latency and decrease in absolute amplitude of both negative and positive peaks as click rate increased from 7.7/s to 57.7/s. The results did not reveal a significant change in the type of BIC as an effect of electrode montage.In conclusion, the BIC within the binaural difference waveform may be obtained in the majority of young individuals with normal hearing. Specifically, a slower stimulus rate revealed more components of the waveform, as well as an improvement in the morphology of the BIC compared to a faster stimulus rate. As these findings may aid in the development of an electrophysiological index of binaural neural processes in young individuals with normal hearing, more research should be attempted in the study of BIC in other age groups and patients with different audiograms. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

In Florida, the increased installation of non-traversable medians and directional median opening has produced an increased number of U-turns on multilane highways. Arguments have been advanced by some opponents of median modification projects that the increased numbers of U-turns may result in safety and operational problems on multilane highways. The primary objective of this study is to evaluate the operational effects of U-turn movement on multilane roadways. To achieve this research objective, extensive data were collected. Field measurements were conducted at 40 sites in the Tampa Bay area of Florida to collect traffic operations data. Besides, the crash histories of 179 selected roadway segments in central Florida were investigated. Statistical analysis was conducted based on the collected traffic operations data and crash data to quantitatively evaluate the operational performance of U-turn movement. Delay and travel time were compared for different driveway left- turn alternatives that are widely used in Florida and nationally. Crash rate models were developed to evaluate how the separation distance between a driveway exit and the downstream U-turn bay impacts the safety performance of vehicles making right-turns followed by U-turns (RTUT). With the crash data analysis results, the minimum separation distances under different roadway conditions were determined to facilitate driver use of RTUTs. The capacity of U-turn movement was analyzed under two different situations: (1) U-turns are provided at a signalized intersection; and (2) U-turns are provided at an unsignalized intersection. Adjustment factors were developed to quantify the impacts of the presence of U-turning vehicles on the capacity of a signalized intersection. The critical gaps and follow-up time for U-turn movement at unsignalized intersections were estimated. With the estimated critical gaps and follow-up time, the Harders model was used to determine the capacity of U-turn movem ent at an unsignalized intersection. This study also looks extensively at the minimum roadway width and median width required by vehicles to perform U-turn maneuvers on 4-lane divided roadways. It was found that a roadway width of 46 ft is generally sufficient for most types of design vehicles (except heavy vehicles) to perform a continuous U-turn maneuver without impedance.

In this paper an overview is given on the application of directional basis functions, known under the name Curvelets/Contourlets, to various aspects of seismic processing and imaging. Key concepts in the approach are the use of (i) that localize in both domains (e.g. space and angle); (ii) non-linear estimation, which corresponds to localized muting on the coefficients, possibly supplemented by constrained optimization (iii) invariance of the basis functions under the imaging operators. We will discuss applications that include multiple and ground roll removal; sparseness-constrained least-squares migration and the computation of 4-D difference cubes.

The aim of this thesis is the modeling of surface directional thermal radiation and angular correction on the LST by using empirical and physical methods as well as the analysis of field validation. The work has conducted to some conclusions. The directional emissivity of natural surfaces was obtained from MODIS emissivity product and then used in the split-window algorithm for angular correction on LST. The parameterization models of directional emissivity and thermal radiation were developed. As for the non-isothermal pixels, the daytime-TISI method was proposed to retrieve directional emissivity and effective temperature from multi-angular middle and thermal infrared data. This was validated using an airborne dataset. The kernel-driven BRDF model was checked in the thermal infrared domain and its extension was used to make angular normalization on the LST. A new model, namely FovMod that concerns on the footprint of ground sensor, was developed to simulate directional brightness temperature of row crop canopy. Based on simulation result of the FovMod, an optimal footprintfor field validation of LST was obtained. This thesis has systematically investigated the topic of directional thermal radiation and angular correction on surface temperature and its findings will improve the retrieval accuracy of temperature and emissivity from remotely sensed data and will also provide suggestion for the future design of airborne or spaceborne multi-angular thermal infrared sensors and also for the ground measurement of surface parameters.

Generalized linear mixture models (GLMM) are widely used in statistical applications to model count and binary data. We consider the problem of nonparametric likelihood estimation of mixing distributions in GLMM's with multiple random effects. The log-likelihood to be maximized has the general form l(G)=Σi log∫f(yi,γ) dG(γ) where f(.,γ) is a parametric family of component densities, yi is the ith observed response dependent variable, and G is a mixing distribution function of the random effects vector γ defined on Ω. The literature presents many algorithms for maximum likelihood estimation (MLE) of G in the univariate random effect case such as the EM algorithm (Laird, 1978), the intra-simplex direction method, ISDM (Lesperance and Kalbfleish, 1992), and vertex exchange method, VEM (Bohning, 1985). In this dissertation, the constrained Newton method (CNM) in Wang (2007), which fits GLMM's with random intercepts only, is extended to fit clustered datasets with multiple random effects. Owing to the general equivalence theorem from the geometry of mixture likelihoods (see Lindsay, 1995), many NPMLE algorithms including CNM and ISDM maximize the directional derivative of the log-likelihood to add potential support points to the mixing distribution G. Our method, Direct Search Directional Derivative (DSDD), uses a directional search method to find local maxima of the multi-dimensional directional derivative function. The DSDD's performance is investigated in GLMM where f is a Bernoulli or Poisson distribution function. The algorithm is also extended to cover GLMM's with zero-inflated data. Goodness-of-fit (GOF) and selection methods for mixed models have been developed in the literature, however their application in models with nonparametric random effects distributions is vague and ad-hoc. Some popular measures such as the Deviance Information Criteria (DIC), conditional Akaike Information Criteria (cAIC) and R2 statistics are potentially useful in this context. Additionally, some cross-validation goodness-of-fit methods popular in Bayesian applications, such as the conditional predictive ordinate (CPO) and numerical posterior predictive checks, can be applied with some minor modifications to suit the non-Bayesian approach. / Graduate / 0463 / rabihsaab@gmail.com

Most mobile nodes are operated using batteries, protocols which conserve energy are of interest. The Dynamic Directional Power Control Protocol (DDPC) is a protocol that dynamically varies the energy used in directional transmission to increase the battery life of the transmitter without sacrificing connectivity with the receiver. The advantage of DDPC is that it takes into account the remaining battery power of a node before changing its transmission power. DDPC can achieve a higher network lifetime when compared to a network where nodes use a fixed transmit power level. Meanwhile DDPC dynamically reduces the energy consumed by a node in transmission. It can also reach nodes far from the transmitter by using directional antennas.

Generalized linear mixture models (GLMM) are widely used in statistical applications to model count and binary data. We consider the problem of nonparametric likelihood estimation of mixing distributions in GLMM's with multiple random effects. The log-likelihood to be maximized has the general form l(G)=Σi log∫f(yi,γ) dG(γ) where f(.,γ) is a parametric family of component densities, yi is the ith observed response dependent variable, and G is a mixing distribution function of the random effects vector γ defined on Ω. The literature presents many algorithms for maximum likelihood estimation (MLE) of G in the univariate random effect case such as the EM algorithm (Laird, 1978), the intra-simplex direction method, ISDM (Lesperance and Kalbfleish, 1992), and vertex exchange method, VEM (Bohning, 1985). In this dissertation, the constrained Newton method (CNM) in Wang (2007), which fits GLMM's with random intercepts only, is extended to fit clustered datasets with multiple random effects. Owing to the general equivalence theorem from the geometry of mixture likelihoods (see Lindsay, 1995), many NPMLE algorithms including CNM and ISDM maximize the directional derivative of the log-likelihood to add potential support points to the mixing distribution G. Our method, Direct Search Directional Derivative (DSDD), uses a directional search method to find local maxima of the multi-dimensional directional derivative function. The DSDD's performance is investigated in GLMM where f is a Bernoulli or Poisson distribution function. The algorithm is also extended to cover GLMM's with zero-inflated data. Goodness-of-fit (GOF) and selection methods for mixed models have been developed in the literature, however their application in models with nonparametric random effects distributions is vague and ad-hoc. Some popular measures such as the Deviance Information Criteria (DIC), conditional Akaike Information Criteria (cAIC) and R2 statistics are potentially useful in this context. Additionally, some cross-validation goodness-of-fit methods popular in Bayesian applications, such as the conditional predictive ordinate (CPO) and numerical posterior predictive checks, can be applied with some minor modifications to suit the non-Bayesian approach. / Graduate / 0463 / rabihsaab@gmail.com

The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural computation in the design of intelligent machines, including portable, battery-powered, and energy harvesting applications. Floating-gate transistor technology is a powerful tool for neuromorphic engineering because it allows dense implementation of synapses with nonvolatile storage of synaptic weights, cancellation of process mismatch, and reconfigurable system design. A novel neuromorphic hardware system, featuring compact and efficient channel-based model neurons and floating-gate transistor synapses, was developed. This system was used to model a variety of network topologies with up to 100 neurons. The networks were shown to possess computational capabilities such as spatio-temporal pattern generation and recognition, winner-take-all competition, bistable activity implementing a "volatile memory", and wavefront-based robotic path planning. Some canonical features of synaptic plasticity, such as potentiation of high frequency inputs and potentiation of correlated inputs in the presence of uncorrelated noise, were demonstrated. Preliminary results regarding formation of receptive fields were obtained. Several advances in enabling technologies, including methods for floating-gate transistor array programming, and the creation of a reconfigurable system for studying adaptation in floating-gate transistor circuits, were made.