Semi-insulating gallium arsenide (SI-GaAs) has gained great interest in recent years due to its wide application in optoelectronic devices and high-speed integrated circuits. An important feature of SI-GaAs is the high density of deep-level defect states, which control the electrical properties of the substrate by compensating the shallow defects. Over the years, deep-level transient spectroscopy (DLTS) and its variations have been the most effective tools employed for the characterization of deep-level defects. However, most of these techniques require a contact probe and tend to be quite restrictive in their applications’ scope. In this thesis deep-level photo-thermal spectroscopy (DLPTS), an all-optical rate-window-based technique, is presented as a novel noncontact technique for the characterization of deep-level defects in SI-GaAs. The signal-generation mechanism for DLPTS is the super-bandgap excitation of carriers, and the sub-bandgap detection of the defect’s thermal-emission process. Combined with the rate-window detection utilizing lock-in amplifiers, DLPTS measurements are performed in three different modalities: temperature-scan, pulse-rate scan, and time-scan. This work demonstrates that each mode provides unique information about the defect configuration, and, in combination, the modes offer a powerful tool for the study of defect properties and optoelectronic processes in SI-GaAs. A hierarchical carrier-emission theory is proposed to explain the thermal broadening (nonexponentiality) in photo-thermal spectra. The model is studied comparatively with the Gaussian distribution of activation energies, and their similarities demonstrate an ergodic equivalence of random energy distribution and the constrained hierarchical emission process. In addition, a rate-window gated photo-carrier radiometry (PCR) technique is developed. The original diffusion-based PCR theory is modified to reflect the signal domination by trap emission and capture rates in the absence of diffusion. Defect luminescence is collected and analyzed using photo-thermal temperature spectra and resonant detection combined with frequency scans. The study results in the identification of five radiative defect states and the defect-photoluminescence quantum efficiency.

The recognition of time-varying geometry (TVG) objects (in particular, humans) and their actions is a complex task due to common real-world sensing challenges, such as obstacles and environmental variations, as well as due to issues specific to TVG objects, such as self-occlusion. Herein, it is proposed that a multi-camera active-vision system, which dynamically selects camera poses in real-time, be used to improve TVG action sensing performance by selecting camera views on-line for near-optimal sensing-task performance. Active vision for TVG objects requires an on-line sensor-planning strategy that incorporates information about the object itself, including its current action, and information about the state of the environment, including obstacles, into the pose-selection process. Thus, the focus of this research is the development of a novel methodology for real-time sensing-system reconfiguration (active vision), designed specifically for the recognition of a single TVG object and its actions in a cluttered, dynamic environment, which may contain multiple other dynamic (maneuvering) obstacles. The proposed methodology was developed as a complete, customizable sensing-system framework which can be readily modified to suit a variety of specific TVG action-sensing tasks – a 10-stage pipeline real-time architecture. Sensor Agents capture and correct camera images, removing noise and lens distortion, and segment the images into regions of interest. A Synchronization Agent aligns multiple images from different cameras to a single ‘world-time.’ Point Tracking and De-Projection Agents detect, identify, and track points of interest in the resultant 2-D images, and form constraints in normalized camera coordinates using the tracked pixel coordinates. A 3-D Solver Agent combines all constraints to estimate world-coordinate positions for all visible features of the object-of-interest (OoI) 3-D articulated model. A Form-Recovery Agent uses an iterative process to combine model constraints, detected feature points, and other contextual information to produce an estimate of the OoI’s current form. This estimate is used by an Action-Recognition Agent to determine which action the OoI is performing, if any, from a library of known actions, using a feature-vector descriptor for identification. A Prediction Agent provides estimates of future OoI and obstacle poses, given past detected locations, and estimates of future OoI forms given the current action and past forms. Using all of the data accumulated in the pipeline, a Central Planning Agent implements a formal, mathematical optimization developed from the general sensing problem. The agent seeks to optimize a visibility metric, which is positively related to sensing-task performance, to select desirable, feasible, and achievable camera poses for the next sensing instant. Finally, a Referee Agent examines the complete set of chosen poses for consistency, enforces global rules not captured through the optimization, and maintains system functionality if a suitable solution cannot be determined. In order to validate the proposed methodology, rigorous experiments are also presented herein. They confirm the basic assumptions of active vision for TVG objects, and characterize the gains in sensing-task performance. Simulated experiments provide a method for rapid evaluation of new sensing tasks. These experiments demonstrate a tangible increase in single-action recognition performance over the use of a static-camera sensing system. Furthermore, they illustrate the need for feedback in the pose-selection process, allowing the system to incorporate knowledge of the OoI’s form and action. Later real-world, multi-action and multi-level action experiments demonstrate the same tangible increase when sensing real-world objects that perform multiple actions which may occur simultaneously, or at differing levels of detail. A final set of real-world experiments characterizes the real-time performance of the proposed methodology in relation to several important system design parameters, such as the number of obstacles in the environment, and the size of the action library. Overall, it is concluded that the proposed system tangibly increases TVG action-sensing performance, and can be generalized to a wide range of applications, including human-action sensing. Future research is proposed to develop similar methods to address deformable objects and multiple objects of interest.

Fault detection and diagnosis of gear transmission systems have attracted considerable attention in recent years, due to the need to decrease the downtime on production machinery and to reduce the extent of the secondary damage caused by failures. However, little research has been done to develop gear shaft and planetary gear crack detection methods based on vibration signal analysis. In this thesis, an approach to gear shaft and planetary gear fault detection based on the application of the wavelet transform to both the time synchronously averaged (TSA) signal and residual signal is presented. Wavelet approaches themselves are sometimes inefficient for picking up the fault signal characteristic under the presence of strong noise. In this thesis, the autocovariance of maximal energy wavelet coefficients is first proposed to evaluate the gear shaft and planetary gear fault advancement quantitatively. For a comparison, the advantages and disadvantages of some approaches such as using variance, kurtosis, the application of the Kolmogorov-Smirnov test (K-S test), root mean square (RMS) , and crest factor as fault indicators with continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for residual signal, are discussed. It is demonstrated using real vibration data that the early faults in gear shafts and planetary gear can be detected and identified successfully using wavelet transforms combined with the approaches mentioned above. In the second part of the thesis, the planetary gear deterioration process from the new condition to failure is modeled as a continuous time homogeneous Markov process with three states: good, warning, and breakdown. The observation process is represented by two characteristics: variance and RMS based on the analysis of autocovariance of DWT applied to the TSA signal obtained from planetary gear vibration data. The hidden Markov model parameters are estimated by maximizing the pseudo likelihood function using the EM iterative algorithm. Then, a multivariate Bayesian control chart is applied for fault detection. It can be seen from the numerical results that the Bayesian chart performs better than the traditional Chi-square chart.

A concentric pneumatic nebulizer (CPN) and a custom designed flow focusing nebulizer (FFN) are characterized. As will be shown, the classical Nukiyama-Tanasawa and Rizk-Lefebvre models lead to erroneous size prediction for the concentric nebulizer under typical operating conditions due to its specific design, geometry, dimension and different flow regimes. The models are then modified to improve the agreement with the experimental results. The size prediction of the modified models together with the spray velocity characterization are used to determine the overall nebulizer efficiency and also employed as input to a new Maximum Entropy Principle (MEP) based model to predict joint size-velocity distribution analytically. The new MEP model is exploited to study the local variation of size-velocity distribution in contrast to the classical models where MEP is applied globally to the entire spray cross section. As will be demonstrated, the velocity distribution of the classical MEP models shows poor agreement with experiments for the cases under study. Modifications to the original MEP modeling are proposed to overcome this deficiency. In addition, the new joint size-velocity distribution agrees better with our general understanding of the drag law and yields realistic results. / PhD

Research was performed on the development of relationships between the microstructure of nickel and yttria-stabilized zirconia (YSZ) coatings and the processing parameters used for their deposition by atmospheric plasma spraying (APS). Research was also performed on the development of relationships between the microstructure of plasma sprayed Ni-YSZ coatings and the electrochemical performance of metal-supported solid oxide fuel cells (SOFCs) incorporating these coatings as anodes. Three APS processes were used to deposit Ni-YSZ coatings: dry-powder plasma spraying (DPPS), suspension plasma spraying (SPS), and solution precursor plasma spraying (SPPS). These processes differ in the form of the feedstock injected into the plasma. The composition of the Ni-YSZ coatings deposited with each spray process could be controlled through adjustment of the plasma gas composition and stand-off distance, as well as adjustment of feedstock properties including agglomerate size fraction for DPPS, NiO particle size and suspension feed rate in SPS, and the enthalpy of decomposition of the precursors used in SPPS. The porosity of the Ni-YSZ coatings could be controlled through the addition of a sacrificial pore forming material to each feedstock, with coating porosities up to approximately 35% being achieved for each coating type. Metal-supported SOFCs were fabricated to each have anodes deposited with a different plasma spray process, where all anodes had nominally identical composition. The microstructures obtained for each anode type were distinctly different. SPPS led to the most uniform mixing of the smallest Ni and YSZ particles. These anodes most resembled typical structures from anodes fabricated using conventional methods. It was found that the polarization resistance, Rp, associated with the high frequency (> 1 kHz) range of the impedance spectrum correlated to the three phase boundary length (TPBL) density of each anode, with lower Rp values corresponding to higher TPBL densities. It was also found that the Knudsen diffusion coefficient and effective ordinary diffusion coefficient of the porous anodes correlated with the Rp associated with the low frequency (< 1 kHz) range of the impedance spectrum. Therefore, the impedance spectrum can be used to compare microstructural differences among plasma sprayed Ni-YSZ anodes.

The objective of this project is to design a cell orientation control system using a rotating electric field. In particular, the system utilizes two electrostatic phenomena known as electrophoresis and electro-rotation. The device used for creating the electric field was designed and fabricated using the MEMS fabrication technique. The cell orientation control system also includes a vision tracking system that senses the orientation of the cell and a PID controller. Overall, the system is able to control the orientation of the cell with zero steady state error. The objective of this project has been met.

Biogas as a fuel is attractive from a greenhouse standpoint, since biogas is carbon neutral. To be used as such, increasingly stringent emission standards must be met. Current low-emission technologies meet said standards by precisely controlling the air-fuel ratio. Biogas composition can vary substantially, making air-fuel ratio control difficult. This research was conducted as part of a larger project to develop a sensor that accurately measures biogas composition. Biogas was simulated by fuel mixtures consisting of natural gas and CO2; the effects that fuel composition has on emissions and catalyst performance were investigated. Engine-out THC and NOx increased and decreased, respectively, with increasing CO2 in the fuel mixture. Doubling the catalyst residence time doubled the conversion of THC and CO emissions. The effectiveness of the catalyst at converting THC emissions was found to be dependent on the relative proportions of engine-out THC, NOx and CO emissions.

The objective of this project is to design a cell orientation control system using a rotating electric field. In particular, the system utilizes two electrostatic phenomena known as electrophoresis and electro-rotation. The device used for creating the electric field was designed and fabricated using the MEMS fabrication technique. The cell orientation control system also includes a vision tracking system that senses the orientation of the cell and a PID controller. Overall, the system is able to control the orientation of the cell with zero steady state error. The objective of this project has been met.

Biogas as a fuel is attractive from a greenhouse standpoint, since biogas is carbon neutral. To be used as such, increasingly stringent emission standards must be met. Current low-emission technologies meet said standards by precisely controlling the air-fuel ratio. Biogas composition can vary substantially, making air-fuel ratio control difficult. This research was conducted as part of a larger project to develop a sensor that accurately measures biogas composition. Biogas was simulated by fuel mixtures consisting of natural gas and CO2; the effects that fuel composition has on emissions and catalyst performance were investigated. Engine-out THC and NOx increased and decreased, respectively, with increasing CO2 in the fuel mixture. Doubling the catalyst residence time doubled the conversion of THC and CO emissions. The effectiveness of the catalyst at converting THC emissions was found to be dependent on the relative proportions of engine-out THC, NOx and CO emissions.

Creativity is important in the design and manufacture of successful products, yet neither creativity nor the early stages of design are well understood. This lack of understanding limits the tools that can be developed to support the crucial earlier stages of design that ultimately determine product success. My research aims to better understand creativity by studying and quantifying the potential of semantic stimuli (words) presented during concept generation. Natural language was chosen as design stimuli because language provides a systematic framework for stimuli generation. Furthermore, natural language is ubiquitous and intimately related to cognitive functions required in design such as reasoning and memory. Ultimately, the results of this research will assist in the development of early-design support tools. In a series of four experiments, the effects of semantic stimuli oppositely and similarly related to the experiment problem were examined with respect to creativity and designers’ language patterns. Results show that opposite-stimulus concepts were significantly more creative than similar-stimulus concepts. It also was observed that opposite stimuli elicited designer behaviours that may encourage creative concepts. These results suggest that the use of oppositely related stimulus words is a practical method for encouraging creative design.