Engineering Commons^™

Computer-Aided Classification Of Impulse Oscillometric Measures Of Respiratory Small Airways Function In Children, Nancy Selene Avila

Open Access Theses & Dissertations

Computer-aided classification of respiratory small airways dysfunction is not an easy task. There is a need to develop more robust classifiers, specifically for children as the classification studies performed to date have the following limitations: 1) they include features derived from tests that are not suitable for children and 2) they cannot distinguish between mild and severe small airway dysfunction.

This Dissertation describes the classification algorithms with high discriminative capacity to distinguish different levels of respiratory small airways function in children (Asthma, Small Airways Impairment, Possible Small Airways Impairment, and Normal lung function). This ability came from innovative feature selection, …

Novel Classification Of Slow Movement Objects In Urban Traffic Environments Using Wideband Pulse Doppler Radar, Berta Rodriguez Hervas

Open Access Theses & Dissertations

Every year thousands of people are involved in traffic accidents, some of which are fatal. An important percentage of these fatalities are caused by human error, which could be prevented by increasing the awareness of drivers and the autonomy of vehicles. Since driver assistance systems have the potential to positively impact tens of millions of people, the purpose of this research is to study the micro-Doppler characteristics of vulnerable urban traffic components, i.e. pedestrians and bicyclists, based on information obtained from radar backscatter, and to develop a classification technique that allows automatic target recognition with a vehicle integrated system. For …

Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea

Open Access Theses & Dissertations

The main contribution of this dissertation is the development of a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution …

Psychophysical Similarity Based Feature Selection For Nodule Retrieval In Ct, Ravi K. Samala

Open Access Theses & Dissertations

The emerging paradigms in cancer research indicate the need for a multi-perspective and multi-modal screening approach for early lung cancer diagnosis to increase the probability of clinical resection. Currently no standalone screening methodology is proved to suffice for a clinical diagnosis. Computed tomography has been proved to present abnormality at an early stage with less impact on survival rate in population studies. Nevertheless, because of its non-invasive characteristic, it can be used for diagnosis, prognosis and visualization of tumor. Studies have shown that Computer aided diagnosis (CAD) as a second reader can perform in a similar capacity as a radiologist. …

Reduced Dimensionality Hyperspectral Classification Using Finite Mixture Models, Vikram Jayaram

Open Access Theses & Dissertations

Classification of HSI data is a challenging problem for two main reasons. First, with limited spatial resolution of HSI sensors and/or the distance of the observed scene, the images invariably contain pixels composed of several materials. It is desirable to resolve the contributions of the constituents from the observed image without relying on high spatial resolution images. Remote sensing cameras have been designed to capture a wide spectral range motivating the use of post-processing techniques to distinguish materials via their spectral signatures. Secondly, available training data for most pattern recognition problems in HSI processing is severely inadequate. Under the framework …