Point processes are common in many physical applications found in engineering and biology. These processes can be observed in one-dimension as a time series or two-dimensions as a spatial point pattern with extensive amounts of literature devoted to their analyses. However, if the observed process is a hybrid of spatial and temporal point process, very few practical methods exist. In such cases, practitioners often remove the temporal component and analyze the spatial dependencies. This marginal spatial analysis may lead to misleading results if time is an important factor in the process. In this dissertation we extend the current analysis of spatial point patterns to include a temporal dimension. First- and second-order intensity measures for analyzing spatiotemporal point patterns are explicitly defined. Estimation of first-order intensities are examined using 3-dimensional smoothing techniques. Conditions for weak stationarity are provided so that subsequent second-order analysis can be conducted. We consider second-order analysis of spatiotemporal point patterns first in the space-time domain through an extension of Ripley's Κ-function. An alternative analysis is given in the frequency domain though construction of a spatiotemporal periodogram. The methodology provided is tested through simulation of spatiotemporal point patterns and by analysis of a real data set. The biological application concerns the estimation of the homerange of groups of the endangered red-cockaded woodpecker in the Fort Bragg area of North Carolina. Monthly or bimonthly point patterns of the bird distribution are analyzed and integrated over a 23 month period. / Ph. D.

License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition. / Master of Science

No / Abstract: We develop a novel approach to estimate the n unknown constituent frequencies of a sinusoidal signal that comprises of unknown number, n, of sinusoids of unknown phases and unknown amplitudes. The approach has been applied to multiple sinusoidal signals in the presence of white Gaussian noise with varying signal to noise ratio (SNR). The approach is based on eigenspace analysis of Hankel matrix formed with the samples from averaged frequency spectrum of the signal obtained through multiple measurements. The eigenspace analysis is based on the newly developed 3M relationship which reflects and exploits the relationship between the consecutive sets of Maximum, Middle and Minimum eigenvalues of square symmetric matrix of the Hankel matrix. The 3M relationship exhibits a pattern in line with the order of the Hankel matrix and leads to parametric estimation of the constituent sinusoids. This paper also presents the relationship equation between the size of 3M relationship pattern and the dimensions of the Hankel matrix. The performance of the developed approach has been tested to correctly estimate multiple constituent frequencies within a noisy signal.

In today's highly competitive and deregulated electricity market, companies in the generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers' load profiles. This strategic information is vital in load forecasting, demand-side management planning and long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: 1) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. 2) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for load profile characterization and classification. 3) The frequency domain load profile statistics and forecasting models. Two different models were introduced in this dissertation: the first one is the wavelet load forecast model and the other one is a stochastic model that incorporates local weather condition and frequency domain load profile statistics to perform medium term load profile forecast. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately. / Ph. D. / In today’s highly competitive and deregulated electricity market, companies in the electricity power generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers’ electricity consumption behavior. This strategic information is vital in forecasting and managing the future electricity demand. This information is also very important in utility company’s long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of electric load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: I) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. II) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for categorizing the load profile data. III) The frequency domain load profile statistics and forecasting models. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately.

Supersonic jet exhaust plumes produce noise in jet engines, which has been a problem in the aerospace field. Researchers are working on ways to reduce this turbulent mixing noise, with little modification to the engine and nozzle. Prior work has shown that total temperature non-uniformity is a noise reduction technique which introduces a stream of cold flow into the heated jet. This method has been shown to cause changes in the exhaust plume and result in a 2±0.5 dB reduction of peak sound pressure levels. The goal of this work is to reveal underlying changes in the spatial-temporal structure of plume instability and turbulence caused by non-uniform total temperature distributions. Studies have demonstrated several methods of jet noise reduction by modifying the turbulent mixing in the exhaust plume. Large-scale turbulent structures have been shown to be the dominant source of noise in heated supersonic jets, especially over long, streamwise distances. Therefore, a large field-of-view measurement is desirable for studying these structures. Time-Resolved Doppler Global Velocimetry (TR-DGV) with a sampling frequency of 50 kHz is used to collect flow velocity data that is resolved in both time and space. The experiments for data collection were performed on a heated supersonic jet at the Virginia Tech Advanced Propulsion and Power Laboratory. A converging-diverging nozzle with a diameter Reynolds number of 850,000 was used to generate a perfectly expanded, heated flow of Mach 1.5 and a nozzle pressure ratio (NPR) of 3.67. The unheated plume was introduced at the center of the nozzle, with a total temperature ratio (TTR) of 2. Comparison of the mean velocity fields shows that the introduction of the cooler temperature flow in the thermally non-uniform case results in a velocity deficit of about 10% compared to the thermally uniform case. The method of spectral proper orthogonal decomposition (SPOD) was used to reveal the large-scale, coherent noise producing mechanisms. SPOD results indicate that the thermally non-uniform case showed a decrease in turbulent kinetic energy compared to the uniform case at all frequencies. Coherent fluctuations start developing further upstream in the thermally non-uniform case. The addition of the unheated plume results in a disruption in the propagation of the Mach waves from the shear layer into the ambient. The results indicate that the total temperature non-uniformity results in a modified exhaust plume and mean flow distribution at the nozzle exit, compared to that of a thermally uniform flow, which past studies have indicated is a method to reduce jet noise. / Master of Science / Supersonic jet exhaust plumes produce noise in jet engines, which has been a problem in the aerospace field. Researchers are working on ways to reduce this turbulent mixing noise, with little modification to the engine and nozzle. Traditionally, nozzles produce a single stream of uniform temperature flow. This work identifies a method of reducing jet noise, known as thermal non-uniformity. A stream of cold flow is introduced at the center of the nozzle. Applying this method to jet engines can result in quieter aircraft. Large-scale turbulent structures are the dominant noise producing source in supersonic free jets. To further understand the relationship between coherent structures and acoustic jet noise, spectral analysis is used to educe these structures from the flow. This study uses velocity data collected using Time-Resolved Doppler Global Velocimetry (TR-DGV). The study compares the results of a thermally uniform and a thermally non-uniform heated supersonic jet of Mach 1.5. The goal of this study is to determine the effects of thermal non-uniformity on large-scale coherent structures using a modal decomposition analysis known as spectral proper orthogonal decomposition (SPOD). The results from this study show that the thermally non-uniform cases contained less turbulent kinetic energy compared to the thermally uniform cases. Coherent fluctuations start developing further upstream in the thermally non-uniform case. The addition of the unheated plume results in a disruption in the propagation of the Mach waves from the shear layer into the ambient. The results indicate that the total temperature non-uniformity results in a modified exhaust plume and mean flow distribution at the nozzle exit, compared to that of a thermally uniform flow, which past studies have indicated is a method to reduce jet noise.

Arrays of fully hydrogel-supported, artificial hair cell (AHC) sensors based on bilayer membrane mechanotransduction are designed and characterized to determine sensitivity to multiple stimuli. The work draws upon key engineering design principles inspired by the characteristics of biological hair cells, primarily the use of slender hair-like structures as flow measurement elements. Many hair cell microelectromechanical (MEMS) devices to sense fluid flow have already been built based on this principle. However, recent developments in lipid bilayer applications, namely physically encapsulated bilayers and hydrogel interface bilayers, have facilitated the development of AHCs made primarily from biomolecular materials. The most current research in this field of "membrane based AHCs," shows promise, yet still lacks the modularity to create large sensor arrays similar to those in nature. This paper presents a novel bilayer based AHC platform, developed for array implementation by applying some of the core design principles of biological hair cells. These principles are translated into key design, fabrication and material considerations toward improved sensor sensitivity and modularity. Single hair cell responses to base excitation and short air pulses are to investigate the dynamic coupling between hair and bilayer membrane transducer. In addition, a spectral analysis of the AHC system under varying voltages and air flow velocities helps to build simple, predictive models for the sensitivity properties of the AHC. And finally, based on these results, we implement a spatial sensing strategy that involves mapping frequency content to stimulus location by "tuning" linear, three-unit arrays of AHCs. Individual AHC sensors characterization results demonstrate peak current outputs in the nanoamp range and measure flow velocities as high as 72 m/s. Characterization of the AHC response to base excitation and air pulses show that membrane current oscillates with the first three bending modes of the hair. Output magnitudes reflect of vibrations near the base of the hair. A 2 degree-of-freedom Rayleigh-Ritz approximation of the system dynamics yields estimates of 19 N/m and 0.0011 Nm/rad for the equivalent linear and torsional stiffness of the hair's hydrogel base, although double modes suggest non-symmetry in the gel's linear stiffness. The sensor output scales linearly with applied voltage (1.79 pA/V), avoiding a higher-order dependence on electrowetting effects. The free vibration amplitude of the sensor also increases in a linear fashion with applied airflow pressure (3.39 pA/m s??). Array sensing tests show that the bilayers' consistent spectral responses allow for an accurate localization of the airflow source. However, temporal variations in bilayer size affect sensitivity properties and make airflow magnitude estimation difficult. The overall successful implementation of the array sensing method validates the sensory capability of the bilayer based AHC. / Master of Science

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Prognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Prognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.

At the time of writing, stress and stress related disease have become the most common reasons for absence in the workplace in Sweden. The purpose of the work presented here is to identify and notify people managing unhealthy levels of stress. Since symptoms of mental stress manifest through functions of the Sympathetic Nervous System (SNS), they are best measured through monitoring of SNS changes and phenomena. In this study, changes in the sympathetic control of heart rate were recorded and analyzed using heart rate variability analysis and a simple runner’s heart rate sensor connected to a smartphone. Mental stress data was collected through stressful video gaming. This was compared to data from non-stressful activities, physical activity and extremely stressful activities such as public speaking events. By using the period between heartbeats and selecting features from the frequency domain, a simple machine learning algorithm could differentiate between the types of data and thus could effectively recognize mental stress. The study resulted in a collection of 100 data points, an algorithm to extract features and an application to continuously collect and classify sequences of heart periods. It also revealed an interesting relationship in the data between different subjects. The fact that continuous stress monitoring can be achieved using minimally intrusive sensors is the greatest benefit of these results, especially when connsidering its potential value in the identification and prevention of stress related disease.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / Prognostic technology uses a series of algorithms, combined forms a prognostic-based inference engine (PBIE) for the identification of deterministic behavior embedded in completely normal appearing telemetry from fully functional equipment. The algorithms used to define normal behavior in the PBIE from which deterministic behavior is identified can be adapted to quantify normal spacecraft telemetry behavior while in orbit about a moon or planet or during interplanetary travel. Time-series analog engineering data (telemetry) from orbiting satellites and interplanetary spacecraft are defined by harmonic and non-harmonic influences, which shape it behavior. Spectrum analysis can be used to understand and quantify the fundamental behavior of spacecraft analog telemetry and relate the behavior's frequency and phase to its time-series behavior through Fourier analysis.