Signal Processing Commons^™

Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest

Theses and Dissertations

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

Hyperspectral Unmixing: A Theoretical Aspect And Applications To Crism Data Processing, Yuki Itoh

Doctoral Dissertations

Hyperspectral imaging has been deployed in earth and planetary remote sensing, and has contributed the development of new methods for monitoring the earth environment and new discoveries in planetary science. It has given scientists and engineers a new way to observe the surface of earth and planetary bodies by measuring the spectroscopic spectrum at a pixel scale. Hyperspectal images require complex processing before practical use. One of the important goals of hyperspectral imaging is to obtain the images of reflectance spectrum. A raw image obtained by hyperspectral remote sensing usually undergoes conversion to a physical quantity representing the intensity of …

Machine Learning With Topological Data Analysis, Ephraim Robert Love

Doctoral Dissertations

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …

Laser Illuminated Imaging: Beam And Scene Deconvolution Algorithm, Benjamin W. Davis

Theses and Dissertations

Laser illuminated imaging systems deal with several physical challenges that must be overcome to achieve high-resolution images of the target. Noise sources like background noise, photon counting noise, and laser speckle noise will all greatly affect the imaging systems ability to produce a high-resolution image. An even bigger challenge to laser illuminated imaging systems is atmospheric turbulence and the effect that it will have on the imaging system. The illuminating beam will experience tilt, causing the beam to wander off the center of the target during propagation. The light returning to the detector will similarly be affected by turbulence, and …

Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung

Theses and Dissertations

This dissertation focuses on improving the ability to detect dim stellar objects that are in close proximity to a bright one, through statistical image processing using short exposure images. The goal is to improve the space domain awareness capabilities with the existing infrastructure. Two new algorithms are developed. The first one is through the Neighborhood System Blind Deconvolution where the data functions are separated into the bright object, the neighborhood system, and the background functions. The second one is through the Dimension Reduction Blind Deconvolution, where the object function is represented by the product of two matrices. Both are designed …

Mitigating The Effects Of Boom Occlusion On Automated Aerial Refueling Through Shadow Volumes, Zachary C. Paulson

Theses and Dissertations

In flight refueling of Unmanned Aerial Vehicles (UAVs) is critical to the United States Air Force (USAF). However, the large communication latency between a ground-based operator and his/her remote UAV makes docking with a refueling tanker unsafe. This latency may be mitigated by leveraging a tanker-centric stereo vision system. The vision system observes and computes an approaching receiver's relative position and orientation offering a low-latency, high frequency docking solution. Unfortunately, the boom -- an articulated refueling arm responsible for physically pumping fuel into the receiver -- occludes large portions of the receiver especially as the receiver approaches and docks with …

Rss-Based Device-Free Passive Detection And Localization Using Home Automation Network Radio Frequencies, Tiffany M. Phan

Theses and Dissertations

This research provided a proof of concept for a device-free passive (DfP) system capable of detecting and localizing a target through exploitation of a home automation network’s radio frequency (RF) signals. The system was developed using Insteon devices with a 915 MHz center frequency. Without developer privileges, limitations of the Insteon technology like no intrinsic received signal strength (RSS) field and silent periods between messages were overcome by using software-defined radios to simulate Insteon devices capable of collecting and reporting RSS, and by creating a message generation script and implementing a calibrated filter threshold to reduce silent periods. Evaluation of …

Offline And Online Density Estimation For Large High-Dimensional Data, Aref Majdara

Dissertations, Master's Theses and Master's Reports

Density estimation has wide applications in machine learning and data analysis techniques including clustering, classification, multimodality analysis, bump hunting and anomaly detection. In high-dimensional space, sparsity of data in local neighborhood makes many of parametric and nonparametric density estimation methods mostly inefficient.

This work presents development of computationally efficient algorithms for high-dimensional density estimation, based on Bayesian sequential partitioning (BSP). Copula transform is used to separate the estimation of marginal and joint densities, with the purpose of reducing the computational complexity and estimation error. Using this separation, a parallel implementation of the density estimation algorithm on a 4-core CPU is …

Bayesian Dictionary Learning For Single And Coupled Feature Spaces, Li He

Doctoral Dissertations

Over-complete bases offer the flexibility to represent much wider range of signals with more elementary basis atoms than signal dimension. The use of over-complete dictionaries for sparse representation has been a new trend recently and has increasingly become recognized as providing high performance for applications such as denoise, image super-resolution, inpaiting, compression, blind source separation and linear unmixing. This dissertation studies the dictionary learning for single or coupled feature spaces and its application in image restoration tasks. A Bayesian strategy using a beta process prior is applied to solve both problems.

Firstly, we illustrate how to generalize the existing beta …

Effects Of Channel Mismatches On Beamforming And Signal Detection, Christopher I. Allen

Theses and Dissertations

Tuner gain measurements of a multichannel receiver are reported. A linear regression model is used to characterize the gain, as a function of channel number, tuner set-on frequency, and intermediate frequency. Residual errors of this model are characterized by a t distribution. Very strong autocorrelation of tuner gain at various frequencies is noted. Tuner performance from one channel to the next is diverse; several defects at specific frequencies are noted. The Wilcoxon signed rank test is used to test normality of tuner gain among devices; normality is rejected. Antenna directivity and phase pattern measurements are also reported. An antenna element …

Frequency Diversity For Improving Synthetic Aperture Radar Imaging, Jawad L. Farooq

Theses and Dissertations

In this work, a novel theoretical framework is presented for using recent advances in frequency diversity arrays (FDAs). Unlike a conventional array, the FDA simultaneously transmits a unique frequency from each element in the array. As a result, special time and space properties of the radiation pattern are exploited to improve cross-range resolution. The idealized FDA radiation pattern is compared with and validated against a full-wave electromagnetic solver, and it is shown that the conventional array is a special case of the FDA. A new signal model, based on the FDA, is used to simulate SAR imagery of ideal point …

Bit-Error-Rate-Minimizing Channel Shortening Using Post-Feq Diversity Combining And A Genetic Algorithm, Gokhan Altin

Theses and Dissertations

In advanced wireline or wireless communication systems, i.e., DSL, IEEE 802.11a/g, HIPERLAN/2, etc., a cyclic prefix which is proportional to the channel impulse response is needed to append a multicarrier modulation (MCM) frame for operating the MCM accurately. This prefix is used to combat inter symbol interference (ISI). In some cases, the channel impulse response can be longer than the cyclic prefix (CP). One of the most useful techniques to mitigate this problem is reuse of a Channel Shortening Equalizer (CSE) as a linear preprocessor before the MCM receiver in order to shorten the effective channel length. Channel shortening filter …

Blind Deconvolution Through Polarization Diversity Of Long Exposure Imagery, Steven P. James

Theses and Dissertations

The purpose of the algorithm developed in this thesis was to create a post processing method that could resolve objects at low signal levels using polarization diversity and no knowledge of the atmospheric seeing conditions. The process uses a two-channel system, one unpolarized image and one linearly polarized image, in a GEM algorithm to reconstruct the object. Previous work done by Strong showed that a two-channel system using polarization diversity on short exposure imagery could produce images up to twice the diffraction limit. In this research, long exposure images were simulated and a simple Kolmogorov model used. This allowed for …

Verification Of A Decision Level Fusion Algorithm Using A Proven Atr System And Measured Sar Data, James Douglas Thompson

Theses and Dissertations

Decision level fusion (DLF) algorithms combine outputs of multiple single sensors to make one confident declaration of a target. This research compares performance results of a DLF algorithm using measured data and a proven ATR system with results from simulated data and a modeled ATR system. This comparison indicates that DLF offers significant performance improvements over single sensor looks. However, results based on simulated data and a modeled ATR are slightly optimistic and overestimate results from measured data and a proven ATR system by nearly 10% over all targets tested.

Multiframe Shift Estimation, Stephen A. Bruckart

Theses and Dissertations

The purpose of this research was to develop a fundamental framework for a new approach to multiframe translational shift estimation in image processing. This thesis sought to create a new multiframe shift estimator, to theoretically prove and experimentally test key properties of it, and to quantify its performance according to several metrics. The new estimator was modeled successfully and was proven to be an unbiased estimator under certain common image noise conditions. Furthermore its performance was shown to be superior to the cross correlation shift estimator, a robust estimator widely used in similar image processing cases, according to several criteria. …

Determination Of Structure From Motion Using Aerial Imagery, Paul R. Graham

Theses and Dissertations

The structure from motion process creates three-dimensional models from a sequence of images. Until recently, most research in this field has been restricted to land-based imagery. This research examines the current methods of land-based structure from motion and evaluates their performance for aerial imagery. Current structure from motion algorithms search the initial image for features to track though the subsequent images. These features are used to create point correspondences between the two images. The correspondences are used to estimate the motion of the camera and then the three-dimensional structure of the scene. This research tests current algorithms using synthetic data …

Translation And Rotation Invariant Multiscale Image Registration, Jennifer L. Manfra

Theses and Dissertations

The most recent research involved registering images in the presence of translations and rotations using one iteration of the redundant discrete wavelet transform. We extend this work by creating a new multiscale transform to register two images with translation or rotation differences, independent of scale differences between the images. Our two-dimensional multiscale transform uses an innovative combination of lowpass filtering and the continuous wavelet transform to mimic the two-dimensional redundant discrete wavelet transform. This allows us to obtain multiple subbands at various scales while maintaining the desirable properties of the redundant discrete wavelet transform. Whereas the discrete wavelet transform produces …

An Objective Evaluation Of Four Sar Image Segmentation Algorithms, Jason B. Gregga

Theses and Dissertations

Because of the large number of SAR images the Air Force generates and the dwindling number of available human analysts, automated methods must be developed. A key step towards automated SAR image analysis is image segmentation. There are many segmentation algorithms, but they have not been tested on a common set of images, and there are no standard test methods. This thesis evaluates four SAR image segmentation algorithms by running them on a common set of data and objectively comparing them to each other and to human segmentors. This objective comparison uses a multi-metric a approach with a set of …

Video Compression Using Wavelets And Hierarchical Motion Estimation, Andrew Peter Byrne

Theses : Honours

This thesis investigates the benefits and the significant compression that can be obtained from data that has been decomposed using a wavelet transform. A video compression algorithm was developed that employs the wavelet transform and a hierarchical motion estimation algorithm which itself utilises benefits of the wavelet transform. Using MATLAB, a popular software tool for matrix based computation and analysis, several functions were developed which together formed the video compression algorithm. A variety of tests were conducted on a sample video sequence to ascertain the strengths and weaknesses of the techniques employed. The results, although not the same as the …

Vhdl Design And Simulation For Embedded Zerotree Wavelet Quantisation, Hung Huynh

Theses : Honours

This thesis discusses a highly effective still image compression algorithm – The Embedded Zerotree Wavelets coding technique, as it is called. This technique is simple but achieves a remarkable result. The image is wavelet-transformed, symbolically coded and successive quantised, therefore the compression and transmission/storage saving can be achieved by utilising the structure of zerotree. The algorithm was first proposed by Jerome M. Shapiro in 1993, however to minimise the memory usage and speeding up the EZW processor, a Depth First Search method is used to transverse across the image rather than Breadth First Search method as initially discussed in Shapiro's …

Automatic Target Cueing Of Hyperspectral Image Data, Terry A. Wilson

Theses and Dissertations

Modern imaging sensors produce vast amounts data, overwhelming human analysts. One such sensor is the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The AVIRIS sensor simultaneously collects data in 224 spectral bands that range from 0.4µm to 2.5µm in approximately 10nm increments, producing 224 images, each representing a single spectral band. Autonomous systems are required that can fuse "important" spectral bands and then classify regions of interest if all of this data is to be exploited. This dissertation presents a comprehensive solution that consists of a new physiologically motivated fusion algorithm and a novel Bayes optimal self-architecting classifier …

Representations, Approximations, And Algorithms For Mathematical Speech Processing, Laura R. Suzuki

Theses and Dissertations

Representing speech signals such that specific characteristics of speech are included is essential in many Air Force and DoD signal processing applications. A mathematical construct called a frame is presented which captures the important time-varying characteristic of speech. Roughly speaking, frames generalize the idea of an orthogonal basis in a Hilbert space, Specific spaces applicable to speech are L₂(R) and the Hardy spaces H_p(D) for p> 1 where D is the unit disk in the complex plane. Results are given for representations in the Hardy spaces involving Carleson's inequalities (and its extensions), …

Applications Of Unsupervised Clustering Algorithms To Aircraft Identification Using High Range Resolution Radar, Dzung Tri Pham

Theses and Dissertations

Identification of aircraft from high range resolution (HRR) radar range profiles requires a database of information capturing the variability of the individual range profiles as a function of viewing aspect. This database can be a collection of individual signatures or a collection of average signatures distributed over the region of viewing aspect of interest. An efficient database is one which captures the intrinsic variability of the HRR signatures without either excessive redundancy typical of single-signature databases, or without the loss of information common when averaging arbitrary groups of signatures. The identification of 'natural' clustering of similar HRR signatures provides a …