The main objective of this thesis is to present a new path planning scheme for solving the shortest (collision-free) path problem for an agent (vehicle) operating in a partially known environment. We present two novel algorithms to solve the planning problem. For both of these approaches we assume that the agent has detailed knowledge of the environment and the obstacles only in the vicinity of its current position. Far away obstacles or the final destination are only partially known and may even change dynamically at each instant of time. The path planning scheme is based on information gathered on-line by the available on-board sensor devices. The solution minimizes the total length of the path with respect to a metric that includes actual path length, along with a risk-induced metric. In order to obtain an approximation of the whole configuration space at different levels of fidelity we use a wavelet approximation scheme. In the first proposed algorithm, the path-planning problem is solved using a multi-resolution cell decomposition of the environment obtained from the wavelet transform. In the second algorithm, we extend the results of the the first one by using the multiresolution representation of the environment in conjunction with a conformal mapping to polar coordinates. By performing the cell decomposition in polar coordinates, we can naturally incorporate sector-like cells that are adapted to the data representation collected by the on-board sensor devices.

The safety of pipelines distributed in the infrastructure of many industries has become very important since the industrial revolution. The guided ultrasonic wave technique can provide the possibility for rapid screening in long pipelines with corrosion. Especially the torsional mode T(0,1) of guided waves has been used in the cases of the pipe in the hidden region substantially. The ability of evaluating the inaccessible areas of the pipe makes the guided ultrasonic wave technique sit high on the roster of non-destructive testing tool for pipe inspection. However, the problem arises when attempting to detect the corrosions at the welded support bracket or under the bitumen coating on the pipe. The signal reflected from the corrosion will be covered by a large signal induced by the welded support or attenuated by the bitumen coating seriously. Therefore, the effects of welded support and bitumen coating on the T(0,1) mode are investigated by the experimental and the simulative methods. The continuous wavelet transform analysis is the signal processing method to extract the hidden signal of corrosion in this dissertation. There are five test pipes in the experiments. The response of the normal welded support is studied on the #1 test pipe. The #2 test pipe is used for attenuation investigation. The reflected signals of the features on the #3, #4, and #5 test pipes are measured and processed by continuous wavelet transform during defect detection process. In addition, the linear hexahedron elements are used to build the finite element models of the 6-inch steel pipe with support bracket and the pipe with bitumen coating. It is found that the effects of support bracket on the reflection comprise mode conversion, delayed appearance, trailing echoes, and frequency dependent behavior. When the T(0,1) mode impinges on to the support bracket, it will convert into the A0 mode inside the support due to the circumferential disturbance on the pipe surface. The reflection of the support bracket is identified as three parts formed by the direct echo, delayed echo and the trailing echo. The constructive interference of the A0 mode reflecting from the boundaries inside the support causes that the reflection spectrum shows two maxima peak at around 20-22 kHz (frequency regime of 0.0) and 32-34 kHz (frequency regime of 4.0) from both the experimental and simulated results. For the bitumen coating, the data collected from the welds and defects under the bitumen coating on the #2 test pipe show the attenuation effect on guided wave propagation and the difficulty of minor corrosion detection. In the finite element model of coated pipe, the results of predicted attenuation curves of T(0,1) mode indicate that the attenuation effect on guided wave propagation is aggravated with the increased value of the thickness, density or damping factor of the coated layer. Especially, in the case of 5-mm, the predicted attenuation curve shows a maximum point. Before this point, the attenuation increases with the operating frequency. For long range pipe inspection, it is the best way to avoid choosing the operating frequency around the corresponding frequency of the point. The measured data of corrosion affected by the welded support or the coated bitumen layer was processed by continuous wavelet transform to form a time-frequency analysis. The corrosion signals were identified in the contour map of the wavelet coefficient successfully. The understanding of the guided wave propagation on the pipe welded with support or pipe coated with bitumen is helpful to interpret the reflected signals. The use of continuous wavelet transform on signal processing techniques can improve the ability of defect detection on pipe with complex geometries.

This dissertation develops a "neighborhood" based neural network model utilizing wavelet analysis and Self-organizing Map (SOM) to predict building baseline energy use. Wavelet analysis was used for feature extraction of the daily weather profiles. The resulting few significant wavelet coefficients represent not only average but also variation of the weather components. A SOM is used for clustering and projecting high-dimensional data into usually a one or two dimensional map to reveal the data structure which is not clear by visual inspection. In this study, neighborhoods that contain days with similar meteorological conditions are classified by a SOM using significant wavelet coefficients; a baseline model is then developed for each neighborhood. In each neighborhood, modeling is more robust without unnecessary compromises that occur in global predictor regression models. This method was applied to the Energy Predictor Shootout II dataset and compared with the winning entries for hourly energy use predictions. A comparison between the "neighborhood" based linear regression model and the change-point model for daily energy use prediction was also performed. We also studied the application of the non-parametric nearest neighborhood points approach in determining the uncertainty of energy use prediction. The uncertainty from "local" system behavior rather than from global statistical indices such as root mean square error and other measures is shown to be more realistic and credible than the statistical approaches currently used. In general, a baseline model developed by local system behavior is more reliable than a global baseline model. The "neighborhood" based neural network model was found to predict building baseline energy use more accurately and achieve more reliable estimation of energy savings as well as the associated uncertainties in energy savings from building retrofits.

The Texas-Louisiana Shelf Circulation and Transport Study featured moorings that covered the shelf during 1992 to 1994, and captured the oceanic response on the shelf to category 4 Hurricane Andrew in August of 1992. Eighty-one current meters distributed over 31 moorings along several contours of isobaths provided excellent spatial and temporal coverage over the shelf. The low-frequency variability (2 days and longer) of current observations and tide gauges to the West of the storm are analyzed after the passage of Andrew, focusing on the region outside of direct hurricane forcing. Wavelet analyses are utilized to investigate the dominant periods excited by the storm over the shelf and their temporal evolution after forcing has subsided. Subsequent to the storm's passage, the observations and wavelet transforms show a two-to-four day period coastal trapped wave that propagate westward at speeds near 6 m/s and then around the Texas bend along the bathymetry. The signal remains detectable in observations as far south as Port Isabel, Texas. The prominent frequencies determined from wavelet analysis are compared with predicted coastal trapped wave dispersion modes and show good agreement in the predicted group speed and cross-shelf structure of the first mode. The energies calculated from the data indicate a largely barotropic shelf wave response which is corroborated in the observed currents and by theory.

Many hydrocarbon detection techniques have been developed for decades and one of the most efficient techniques for hydrocarbon exploration in recent years is well known as amplitude versus offset analysis (AVO). However, AVO analysis does not always result in successful hydrocarbon finds because abnormal seismic amplitude variations can sometimes be caused by other factors, such as alternative lithology and residual hydrocarbons in certain depositional environments. Furthermore, not all gas fields are associated with obvious AVO anomalies. Therefore, new techniques should be applied to combine with AVO for hydrocarbon detection. In my thesis, I, through case studies, intend to investigate and validate the wave decomposition technique as a new tool for hydrocarbon detection which decomposes seismic wave into different frequency contents and may help identify better the amplitude anomalies associated with hydrocarbon occurrence for each frequency due to seismic attenuation. The wavelet decomposition analysis technique has been applied in two geological settings in my study: clastic reservoir and carbonate reservoir. Results from both cases indicate that the wavelet decomposition analysis technique can be used for hydrocarbon detection effectively if the seismic data quality is good. This technique can be directly applied to the processed 2D and 3D pre-stack/post-stack data sets (1) to detect hydrocarbon zones in both clastic and carbonate reservoirs by analyzing the low frequency signals in the decomposed domain and (2) to identify thin beds by analyzing the high frequency signals in the decomposed domain. In favorable cases, the method may possibly help separate oil from water in high-porosity and high-permeability carbonate reservoirs deeply buried underground. Therefore, the wavelet analysis would be a powerful tool to assist geological interpretation and to reduce risk for hydrocarbon exploration.

Based on digital image processing techniques, the goal of this work is develop a method to automatically detect license plates. To achieve this goal, this thesis uses wavelet transform to first find the position of the license plate. A number of image processing techniques are then developed to identify each character on the license plate. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach, which is the followed by a simple conclusion.

The Ultra-Wideband (UWB) communication technology has been extensively attended in recent years. In this thesis, we propose the improved selective-Rake receiver and analyze the performance on UWB channels. The UWB transmission channels are modeled with statistical methods and its fading characteristics are discussed. Different impulse radio properties for the UWB communication system are analyzed. The system performance and design complexity issues of selective-Rake receiver (SRake) are studied. Rake receiver has difficulties achieving desired system performance in the dense multipath environment. The main ideas of SRake receiver are to obtain the SNR level on known multipath channel and to determine the desired number of Rake fingers. Matched filters and maximum likelihood detectors are utilized in the implementation of the SRake to estimate the signal time delay. The CLEAN algorithm is then used in selecting the paths with relatively high energy. Furthermore, we also propose a noise cancellation scheme for performance improvement in the SRake receiver. In the noise cancellation scheme, the multiresolution property of wavelet transform is used for filtering the noise interference caused by the rapid fluctuation factor. In addition, a two-stage search is combined with the original CLEAN algorithm to increase the accuracy of path selection. From our simulation results on the UWB channels, the improved SRake receiver, with noise cancellation and two-stage search, indeed has high SRake output SNR and better path accuracy than the original SRake receiver.

The purpose of this research is the estimation for the trend of diastolic pressure of surgical patients utilizing non-invasive photo-plethysmography (PPG) signal. There are two major ways to measure blood pressure during medical treatment : invasive and non-invasive methods. Both them have advantages and disadvantages. For invasive method, it is used for surgical operations. Although it can response the true situation of arterial blood pressure, the damage and uncomfortable are the most disadvantages for itself. And for non-invasive method, it is convenient to use but the measurement results have lower accuracy. In this research, we try to estimate the continuous trend of diastolic pressure utilizing PPG signal. And calculate the accuracy of the results.

As the digital technology advances with each passing day and the internet is evolving so quickly, the use of digital images is increasing on the demand. More information is showed in terms of digital patterns or images in our daily life. Besides retrieving image data from a given image database by context, we can alternatively do that by the image features we prescribed. This method is then called content-based image retrieval, CBIR. The wavelet transform possesses the power of multi-resolutional analysis for digital images. It¡¦s bands are mutually independent so that good results can often be obtained from partial analyses. Although wavelet transform is usually used for image compression and texture analysis, it has also many recent applications in the area of image retrieval. In this research, we propose the use of some new image roughness features to represent the variation of image textures. After an image is transformed on the wavelet, we collect the roughness features as well as wavelet energy features from each band. These features are then used to sort out desired images. We can show that the features as used in this work can be extracted even when the images are altered by some rotation, partial magnification or viewpoint changes.

The main advantage of wavelet transform relative to its Fourier analysis counterpart is its suitability to deal with transient signals. Furthermore, wavelet packet transform has very good frequency analytic ability with the result that it is developing in very fast speed and widespread researched and used in industry and academia. We study the characteristics of fiber optic sensors by applying wavelet transform. Hence, in this paper, the traditional Fourier analysis is taken as a basis, and the wavelet packet analysis is taken as a comparison. The major objects include: (1) calibration of hydrophones; (2) vibration measurement. In calibration of hydrophones, the experimental results show a 2.72 dB re V/£gPa inaccuracies and a 5.3 dB re V/£gPa standard deviation by Fourier analysis, but 0.5 dB inaccuracies and 1.6 dB re V/£gPa standard deviation by wavelet packet analysis. It shows that the wavelet packet analysis has better analytic ability than that of traditional Fourier analysis. In vibration measurement, we utilize FBG interferometers to measure stable vibration. The experimental results denote that wavelet packet analysis has excellent frequency analytic ability as Fourier analysis. Besides, in obtaining transient characteristic signals induced by falling stones, the results appear that wavelet packet analysis has better resolution and identification capability relative to Fourier analysis.