13 research outputs found
An Approach to Ground Moving Target Indication Using Multiple Resolutions of Multilook Synthetic Aperture Radar Images
Ground moving target indication (GMTI) using multiple resolutions of synthetic aperture radar (SAR) images to estimate the clutter scattering statistics is shown to outperform conventional sample matrix inversion space-time adaptive processing GMTI techniques when jamming is not present. A SAR image provides an estimate of scattering from nonmoving targets in the form of a clutter scattering covariance matrix for the GMTI optimum processor. Since the homogeneity of the scattering statistics are unknown, using SAR images at multiple spatial resolutions to estimate the clutter scattering statistics results in more confidence in the final detection decision. Two approaches to calculating the multiple SAR resolutions are investigated. Multiple resolution filter bank smoothing of the full-resolution SAR image is shown to outperform an innovative approach to multilook SAR imaging. The multilook SAR images are calculated from a single measurement vector partitioned base on synthetic sensor locations determined via eigenanalysis of the radar measurement parameters
Design of Machine Learning Algorithms with Applications to Breast Cancer Detection
Machine learning is concerned with the design and development of algorithms and
techniques that allow computers to 'learn' from experience with respect to some class
of tasks and performance measure. One application of machine learning is to improve
the accuracy and efficiency of computer-aided diagnosis systems to assist physician,
radiologists, cardiologists, neuroscientists, and health-care technologists. This thesis
focuses on machine learning and the applications to breast cancer detection. Emphasis
is laid on preprocessing of features, pattern classification, and model selection.
Before the classification task, feature selection and feature transformation may be
performed to reduce the dimensionality of the features and to improve the classification
performance. Genetic algorithm (GA) can be employed for feature selection based
on different measures of data separability or the estimated risk of a chosen classifier.
A separate nonlinear transformation can be performed by applying kernel principal
component analysis and kernel partial least squares.
Different classifiers are proposed in this work: The SOM-RBF network combines
self-organizing maps (SOMs) and radial basis function (RBF) networks, with the RBF
centers set as the weight vectors of neurons from the competitive layer of a trained
SaM. The pairwise Rayleigh quotient (PRQ) classifier seeks one discriminating boundary
by maximizing an unconstrained optimization objective, named as the PRQ criterion,
formed with a set of pairwise const~aints instead of individual training samples.
The strict 2-surface proximal (S2SP) classifier seeks two proximal planes that are not
necessary parallel to fit the distribution of the samples in the original feature space or
a kernel-defined feature space, by ma-ximizing two strict optimization objectives with
a 'square of sum' optimization factor. Two variations of the support vector data description
(SVDD) with negative samples (NSVDD) are proposed by involving different
forms of slack vectors, which learn a closed spherically shaped boundary, named as the
supervised compact hypersphere (SCH), around a set of samples in the target class. \Ve
extend the NSVDDs to solve the multi-class classification problems based on distances
between the samples and the centers of the learned SCHs in a kernel-defined feature
space, using a combination of linear discriminant analysis and the nearest-neighbor rule.
The problem of model selection is studied to pick the best values of the hyperparameters
for a parametric classifier. To choose the optimal kernel or regularization
parameters of a classifier, we investigate different criteria, such as the validation error
estimate and the leave-out-out bound, as well as different optimization methods, such
as grid search, gradient descent, and GA. By viewing the tuning problem of the multiple
parameters of an 2-norm support vector machine (SVM) as an identification problem
of a nonlinear dynamic system, we design a tuning system by employing the extended
Kalman filter based on cross validation. Independent kernel optimization based on
different measures of data separability are a~so investigated for different kernel-based
classifiers.
Numerous computer experiments using the benchmark datasets verify the theoretical
results, make comparisons among the techniques in measures of classification
accuracy or area under the receiver operating characteristics curve. Computational
requirements, such as the computing time and the number of hyper-parameters, are
also discussed.
All of the presented methods are applied to breast cancer detection from fine-needle
aspiration and in mammograms, as well as screening of knee-joint vibroarthrographic
signals and automatic monitoring of roller bearings with vibration signals. Experimental
results demonstrate the excellence of these methods with improved classification
performance.
For breast cancer detection, instead of only providing a binary diagnostic decision
of 'malignant' or 'benign', we propose methods to assign a measure of confidence
of malignancy to an individual mass, by calculating probabilities of being benign and
malignant with a single classifier or a set of classifiers
Bibliography of Lewis Research Center technical publications announced in 1984
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1984. All the publications were announced in the 1984 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses
Bibliography of Lewis Research Center technical publications announced in 1993
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses