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Incorporating auditory models in speech/audio applications

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Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception.

Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.

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Date Created
2011

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Carbonate-ceramic dual-phase membranes for high temperature carbon dioxide separation

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Emission of CO2 into the atmosphere has become an increasingly concerning issue as we progress into the 21st century Flue gas from coal-burning power plants accounts for 40% of all carbon dioxide emissions. The key to successful separation

Emission of CO2 into the atmosphere has become an increasingly concerning issue as we progress into the 21st century Flue gas from coal-burning power plants accounts for 40% of all carbon dioxide emissions. The key to successful separation and sequestration is to separate CO2 directly from flue gas (10-15% CO2, 70% N2), which can range from a few hundred to as high as 1000°C. Conventional microporous membranes (carbons/silicas/zeolites) are capable of separating CO2 from N2 at low temperatures, but cannot achieve separation above 200°C. To overcome the limitations of microporous membranes, a novel ceramic-carbonate dual-phase membrane for high temperature CO2 separation was proposed. The membrane was synthesized from porous La0.6Sr0.4Co0.8Fe0.2O3-d (LSCF) supports and infiltrated with molten carbonate (Li2CO3/Na2CO3/K2CO3). The CO2 permeation mechanism involves a reaction between CO2 (gas phase) and O= (solid phase) to form CO3=, which is then transported through the molten carbonate (liquid phase) to achieve separation. The effects of membrane thickness, temperature and CO2 partial pressure were studied. Decreasing thickness from 3.0 to 0.375 mm led to higher fluxes at 900°C, ranging from 0.186 to 0.322 mL.min-1.cm-2 respectively. CO2 flux increased with temperature from 700 to 900°C. Activation energy for permeation was similar to that for oxygen ion conduction in LSCF. For partial pressures above 0.05 atm, the membrane exhibited a nearly constant flux. From these observations, it was determined that oxygen ion conductivity limits CO2 permeation and that the equilibrium oxygen vacancy concentration in LSCF is dependent on the partial pressure of CO2 in the gas phase. Finally, the dual-phase membrane was used as a membrane reactor. Separation at high temperatures can produce warm, highly concentrated streams of CO2 that could be used as a chemical feedstock for the synthesis of syngas (H2 + CO). Towards this, three different membrane reactor configurations were examined: 1) blank system, 2) LSCF catalyst and 3) 10% Ni/y-alumina catalyst. Performance increased in the order of blank system < LSCF catalyst < Ni/y-alumina catalyst. Favorable conditions for syngas production were high temperature (850°C), low sweep gas flow rate (10 mL.min-1) and high methane concentration (50%) using the Ni/y-alumina catalyst.

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2011

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Synthesis and stability of ceramic-carbonate dual-phase membrane for carbon dioxide separation

Description

Of the potential technologies for pre-combustion capture, membranes offer the advantages of being temperature resistant, able to handle large flow rates, and having a relatively small footprint. A significant amount of research has centered on the use of polymeric and

Of the potential technologies for pre-combustion capture, membranes offer the advantages of being temperature resistant, able to handle large flow rates, and having a relatively small footprint. A significant amount of research has centered on the use of polymeric and microporous inorganic membranes to separate CO2. These membranes, however, have limitations at high temperature resulting in poor permeation performance. To address these limitations, the use of a dense dual-phase membrane has been studied. These membranes are composed of conductive solid and conductive liquid phases that have the ability to selectively permeate CO2 by forming carbonate ions that diffuse through the membrane at high temperature. The driving force for transport through the membrane is a CO2 partial pressure gradient. The membrane provides a theoretically infinite selectivity. To address stability of the ceramic-carbonate dual-phase membrane for CO2 capture at high temperature, the ceramic phase of the membrane was studied and replaced with materials previously shown to be stable in harsh conditions. The permeation properties and stability of La0.6Sr0.4Co0.8Fe0.2O3-δ (LSCF)-carbonate, La0.85Ce0.1Ga0.3Fe0.65Al0.05O3-δ (LCGFA)-carbonate, and Ce0.8Sm0.2O1.9 (SDC)-carbonate membranes were examined under a wide range of experimental conditions at high temperature. LSCF-carbonate membranes were shown to be unstable without the presence of O2 due to reaction of CO2 with the ceramic phase. In the presence of O2, however, the membranes showed stable permeation behavior for more than one month at 900oC. LCGFA-carbonate membranes showed great chemical and permeation stability in the presence of various conditions including exposure to CH4 and H2, however, the permeation performance was quite low when compared to membranes in the literature. Finally, SDC-carbonate membranes showed great chemical and permeation stability both in a CO2:N2 environment for more than two weeks at 900oC as well as more than one month of exposure to simulated syngas conditions at 700oC. Ceramic phase chemical stability increased in the order of LSCF < LCGFA < SDC while permeation performance increased in the order of LCGFA < LSCF < SDC.

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2013

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Study of charges present in silicon nitride thin films and their effect on silicon solar cell efficiencies

Description

As crystalline silicon solar cells continue to get thinner, the recombination of carriers at the surfaces of the cell plays an ever-important role in controlling the cell efficiency. One tool to minimize surface recombination is field effect passivation from the

As crystalline silicon solar cells continue to get thinner, the recombination of carriers at the surfaces of the cell plays an ever-important role in controlling the cell efficiency. One tool to minimize surface recombination is field effect passivation from the charges present in the thin films applied on the cell surfaces. The focus of this work is to understand the properties of charges present in the SiNx films and then to develop a mechanism to manipulate the polarity of charges to either negative or positive based on the end-application. Specific silicon-nitrogen dangling bonds (·Si-N), known as K center defects, are the primary charge trapping defects present in the SiNx films. A custom built corona charging tool was used to externally inject positive or negative charges in the SiNx film. Detailed Capacitance-Voltage (C-V) measurements taken on corona charged SiNx samples confirmed the presence of a net positive or negative charge density, as high as +/- 8 x 1012 cm-2, present in the SiNx film. High-energy (~ 4.9 eV) UV radiation was used to control and neutralize the charges in the SiNx films. Electron-Spin-Resonance (ESR) technique was used to detect and quantify the density of neutral K0 defects that are paramagnetically active. The density of the neutral K0 defects increased after UV treatment and decreased after high temperature annealing and charging treatments. Etch-back C-V measurements on SiNx films showed that the K centers are spread throughout the bulk of the SiNx film and not just near the SiNx-Si interface. It was also shown that the negative injected charges in the SiNx film were stable and present even after 1 year under indoor room-temperature conditions. Lastly, a stack of SiO2/SiNx dielectric layers applicable to standard commercial solar cells was developed using a low temperature (< 400 °C) PECVD process. Excellent surface passivation on FZ and CZ Si substrates for both n- and p-type samples was achieved by manipulating and controlling the charge in SiNx films.

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2013

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Adaptive learning and unsupervised clustering of immune responses using microarray random sequence peptides

Description

Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can

Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.

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Date Created
2013

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Growth and characterization of novel thin films for microelectronic applications

Description

I studied the properties of novel Co2FeAl0.5Si0.5 (CFAS), ZnGeAs2, and FeS2 (pyrite) thin films for microelectronic applications ranging from spintronic to photovoltaic. CFAS is a half metal with theoretical spin polarization of 100%. I investigated its potential as a spin

I studied the properties of novel Co2FeAl0.5Si0.5 (CFAS), ZnGeAs2, and FeS2 (pyrite) thin films for microelectronic applications ranging from spintronic to photovoltaic. CFAS is a half metal with theoretical spin polarization of 100%. I investigated its potential as a spin injector, for spintronic applications, by studying the critical steps involved in the injection of spin polarized electron populations from tunnel junctions containing CFAS electrodes. Epitaxial CFAS thin films with L21 structure and saturation magnetizations of over 1200 emu/cm3 were produced by optimization of the sputtering growth conditions. Point contact Andreev reflection measurements show that the spin polarization at the CFAS electrode surface exceeds 70%. Analyses of the electrical properties of tunnel junctions with a superconducting Pb counter-electrode indicate that transport through native Al oxide barriers is mostly from direct tunneling, while that through the native CFAS oxide barriers is not. ZnGeAs2 is a semiconductor comprised of only inexpensive and earth-abundant elements. The electronic structure and defect properties are similar in many ways to GaAs. Thus, in theory, efficient solar cells could be made with ZnGeAs2 if similar quality material to that of GaAs could be produced. To understand the thermochemistry and determine the rate limiting steps of ZnGeAs2 thin-film synthesis, the (a) thermal decomposition rate and (b) elemental composition and deposition rate of films were measured. It is concluded that the ZnGeAs2 thin film synthesis is a metastable process with an activation energy of 1.08±0.05 eV for the kinetically-limited decomposition rate and an evaporation coefficient of ~10-3. The thermochemical analysis presented here can be used to predict optimal conditions of ZnGeAs2 physical vapor deposition and thermal processing. Pyrite (FeS2) is another semiconductor that has tremendous potential for use in photovoltaic applications if high quality materials could be made. Here, I present the layer-by-layer growth of single-phase pyrite thin-films on heated substrates using sequential evaporation of Fe under high-vacuum followed by sulfidation at S pressures between 1 mTorr and 1 Torr. High-resolution transmission electron microscopy reveals high-quality, defect-free pyrite grains were produces by this method. It is demonstrated that epitaxial pyrite layer was produced on natural pyrite substrates with this method.

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2013

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Energy and quality-aware multimedia signal processing

Description

Today's mobile devices have to support computation-intensive multimedia applications with a limited energy budget. In this dissertation, we present architecture level and algorithm-level techniques that reduce energy consumption of these devices with minimal impact on system quality. First, we present

Today's mobile devices have to support computation-intensive multimedia applications with a limited energy budget. In this dissertation, we present architecture level and algorithm-level techniques that reduce energy consumption of these devices with minimal impact on system quality. First, we present novel techniques to mitigate the effects of SRAM memory failures in JPEG2000 implementations operating in scaled voltages. We investigate error control coding schemes and propose an unequal error protection scheme tailored for JPEG2000 that reduces overhead without affecting the performance. Furthermore, we propose algorithm-specific techniques for error compensation that exploit the fact that in JPEG2000 the discrete wavelet transform outputs have larger values for low frequency subband coefficients and smaller values for high frequency subband coefficients. Next, we present use of voltage overscaling to reduce the data-path power consumption of JPEG codecs. We propose an algorithm-specific technique which exploits the characteristics of the quantized coefficients after zig-zag scan to mitigate errors introduced by aggressive voltage scaling. Third, we investigate the effect of reducing dynamic range for datapath energy reduction. We analyze the effect of truncation error and propose a scheme that estimates the mean value of the truncation error during the pre-computation stage and compensates for this error. Such a scheme is very effective for reducing the noise power in applications that are dominated by additions and multiplications such as FIR filter and transform computation. We also present a novel sum of absolute difference (SAD) scheme that is based on most significant bit truncation. The proposed scheme exploits the fact that most of the absolute difference (AD) calculations result in small values, and most of the large AD values do not contribute to the SAD values of the blocks that are selected. Such a scheme is highly effective in reducing the energy consumption of motion estimation and intra-prediction kernels in video codecs. Finally, we present several hybrid energy-saving techniques based on combination of voltage scaling, computation reduction and dynamic range reduction that further reduce the energy consumption while keeping the performance degradation very low. For instance, a combination of computation reduction and dynamic range reduction for Discrete Cosine Transform shows on average, 33% to 46% reduction in energy consumption while incurring only 0.5dB to 1.5dB loss in PSNR.

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2012

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On the ordering of communication channels

Description

This dissertation introduces stochastic ordering of instantaneous channel powers of fading channels as a general method to compare the performance of a communication system over two different channels, even when a closed-form expression for the metric may not be available.

This dissertation introduces stochastic ordering of instantaneous channel powers of fading channels as a general method to compare the performance of a communication system over two different channels, even when a closed-form expression for the metric may not be available. Such a comparison is with respect to a variety of performance metrics such as error rates, outage probability and ergodic capacity, which share common mathematical properties such as monotonicity, convexity or complete monotonicity. Complete monotonicity of a metric, such as the symbol error rate, in conjunction with the stochastic Laplace transform order between two fading channels implies the ordering of the two channels with respect to the metric. While it has been established previously that certain modulation schemes have convex symbol error rates, there is no study of the complete monotonicity of the same, which helps in establishing stronger channel ordering results. Toward this goal, the current research proves for the first time, that all 1-dimensional and 2-dimensional modulations have completely monotone symbol error rates. Furthermore, it is shown that the frequently used parametric fading distributions for modeling line of sight exhibit a monotonicity in the line of sight parameter with respect to the Laplace transform order. While the Laplace transform order can also be used to order fading distributions based on the ergodic capacity, there exist several distributions which are not Laplace transform ordered, although they have ordered ergodic capacities. To address this gap, a new stochastic order called the ergodic capacity order has been proposed herein, which can be used to compare channels based on the ergodic capacity. Using stochastic orders, average performance of systems involving multiple random variables are compared over two different channels. These systems include diversity combining schemes, relay networks, and signal detection over fading channels with non-Gaussian additive noise. This research also addresses the problem of unifying fading distributions. This unification is based on infinite divisibility, which subsumes almost all known fading distributions, and provides simplified expressions for performance metrics, in addition to enabling stochastic ordering.

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2014

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Adaptive methods within a sequential Bayesian approach for structural health monitoring

Description

Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to

Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage.

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Date Created
2013

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New passive methodology for power cable monitoring and fault location

Description

The utilization of power cables is increasing with the development of renewable energy and the maintenance replacement of old overhead power lines. Therefore, effective monitoring and accurate fault location for power cables are very important for the sake of a

The utilization of power cables is increasing with the development of renewable energy and the maintenance replacement of old overhead power lines. Therefore, effective monitoring and accurate fault location for power cables are very important for the sake of a stable power supply.

The recent technologies for power cable diagnosis and temperature monitoring system are described including their intrinsic limitations for cable health assessment. Power cable fault location methods are reviewed with two main categories: off-line and on-line data based methods.

As a diagnostic and fault location approach, a new passive methodology is introduced. This methodology is based on analyzing the resonant frequencies of the transfer function between the input and output of the power cable system. The equivalent pi model is applied to the resonant frequency calculation for the selected underground power cable transmission system.

The characteristics of the resonant frequencies are studied by analytical derivations and PSCAD simulations. It is found that the variation of load magnitudes and change of positive power factors (i.e., inductive loads) do not affect resonant frequencies significantly, but there is considerable movement of resonant frequencies under change of negative power factors (i.e., capacitive loads).

Power cable fault conditions introduce new resonant frequencies in accordance with fault positions. Similar behaviors of the resonant frequencies are shown in a transformer (TR) connected power cable system with frequency shifts caused by the TR impedance.

The resonant frequencies can be extracted by frequency analysis of power signals and the inherent noise in these signals plays a key role to measure the resonant frequencies. Window functions provide an effective tool for improving resonant frequency discernment. The frequency analysis is implemented on noise laden PSCAD simulation signals and it reveals identical resonant frequency characteristics with theoretical studies.

Finally, the noise levels of real voltage and current signals, which are acquired from an operating power plant, are estimated and the resonant frequencies are extracted by applying window functions, and these results prove that the resonant frequency can be used as an assessment for the internal changes in power cable parameters such as defects and faults.

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Date Created
2015