Abstract Spread spectrum communication systems may be affected by other types of signals called outliers. These coexisting signals are typically narrow (or concentrated) in the considered domain. This thesis considers two areas of outlier detection, namely the concentrated interference suppression (IS) and concentrated signal detection. The focus is on concentrated signal extraction using blind, iterative and low-complex consecutive mean excision (CME) -based algorithms that can be applied to both IS and detection. A summary of results obtained from studying the performance of the existing IS methods, namely the CME, the forward CME (FCME) and the transform selective IS algorithms (TSISA), is presented. Accurate threshold parameter values for the FCME algorithm are defined. These accurate values are able to control the false alarm rate. The signal detection capability of the CME algorithms is studied and analyzed. It is noticed that the CME algorithms are able to detect signals, but they are not able to estimate signal parameters such as the bandwidth. The presented generic shape-based analysis leads to the limits of detection in which the concentrated signals can be detected. These limits enable checking fast whether the signal is detectable or not without time consuming computer simulations. The performance of the TSISA method is evaluated. Simulation results demonstrate that the TSISA method is able to suppress several types of concentrated interfering signals with a reasonable computational complexity. Finally, new CME-based methods are proposed and evaluated. The proposed methods are the extended TSISA method for IS and the localization algorithm based on double-thresholding (LAD), LAD with normalized thresholds (LAD NT), LAD with adjacent cluster combining (LAD ACC) and two-dimensional (2-D) LAD methods for detection. The simulations indicate that the extended TSISA method has a good performance against several types of concentrated interfering signals. The narrowband signal detection capability of the LAD methods is studied. Numerical results show that the proposed LAD methods are able to detect and localize signals in their domain, and they are able to estimate the number of narrowband signals and their parameters, including, for example, bandwidths and signal-to-noise ratio (SNR) values. The simulations show that the LAD methods outperform the CME algorithms, and ACC and 2-D LAD methods outperform the original LAD method. The LAD methods are also proposed to be used for spectrum sensing purposes in cognitive radios.

The purpose of this investigation was to examine the supposition that chronic pain patients (CPPs) have altered pain perception. Two models were examined that led to opposing predictions as to how CPPs would respond to painful stimuli (i.e., the hypervigilance and adaptation-level models). Both predictions have been supported by past research but because of methodological variation and the type of pain disorder studied, it has remained unclear under what circumstances the predictions of these two models may be met. The responses of pain patients to painful stimuli have been found to vary for patients-with different clinical presentations (i.e. those with and without medically incongruent signs and symptoms). Therefore, the present investigation sought to compare the responses to radiant heat stimuli of sixty CPPs (thirty with and thirty without a medically incongruent pain presentation) to thirty age and sex matched normal control subjects (i.e., pain-tree individuals). Signal detection theory methodology was used in order to separately evaluate sensory sensitivity and the response bias to report sensations as painful. In addition, cognitive and affective factors were assessed in order to identity potential psychological correlates of altered pain perception. The results of this study indicated that the presence of a medically incongruent pain presentation distinguished patients on their subjective report of disability and to a lesser extent cognitive appraisal and affective distress regarding their pain condition. They did not differ in their responses to painful stimuli. In a post hoc analysis where CPPs were classified into 'organic' and 'functional’ diagnostic groups, significant differences in pain threshold and the response bias to report pain were found. Patients classified as 'organic' had significantly higher pain thresholds compared to normal control subjects and patients classified as 'functional'. Differences in pain threshold were primarily represented by the response' bias to report sensations as painful rather than sensory sensitivity to the stimuli. The 'functional' group had a slightly lower pain threshold than the normal control group but this difference was not significant. The results are discussed in light of the two models of pain perception. The two methods used to classify pain patients are discussed according to their orthogonal characteristics on sensory, cognitive, and affective components. / Arts, Faculty of / Psychology, Department of / Graduate

No description available.

One of two Project Reports; Part A: http://hdl.handle.net/11375/17663 / No abstract provided / Thesis / Master of Engineering (ME)

No description available.

No description available.

Signal detection and modulation classification are becoming increasingly important in a variety of wireless communication systems such as those involving spectrum management and electronic warfare and surveillance, among others. The majority of the signal detection and modulation classification algorithms available in the literature assume that the additive noise has a Gaussian distribution. However, while this is a good model for thermal noise, various studies have shown that the noise experienced in most radio channels, due to a variety of man-made and natural electromagnetic sources, is non-Gaussian and exhibits impulsive characteristics. Unfortunately, conventional signal processing algorithms developed for Gaussian noise conditions are known to perform poorly in the presence of non-Gaussian noise. For this reason, the main goal of this dissertation is to develop statistical signal processing algorithms for the detection and modulation classification of signals in radio channels where the additive noise is non-Gaussian. One of the major challenges involved in the design of these algorithms is that they are expected to operate with limited or no prior knowledge of the signal of interest, the fading experienced by the signal, and the distribution of the noise added in the channel. Therefore, this dissertation develops new techniques for estimating the parameters that characterize the additive non-Gaussian noise process, as well as the fading process, in the presence of unknown signals. These novel estimators are an integral contribution of this dissertation. The signal detection and modulation classification problems considered here are treated as hypothesis testing problems. Using a composite hypothesis testing procedure, the unknown fading and noise process parameters are first estimated and then used in a likelihood ratio test to detect the presence or identify the modulation scheme of a signal of interest. The proposed algorithms, which are developed for different non-Gaussian noise models, are shown to outperform conventional algorithms which assume Gaussian noise conditions and also algorithms based on other impulsive noise mitigation techniques. This dissertation has three major contributions. First, in environments where the noise can be modeled using a Gaussian mixture distribution, a new expectation-maximization algorithm based technique is developed for estimating the unknown fading and noise distribution parameters. Using these estimates, a hybrid likelihood ratio test is used for modulation classification. Second, a five-stage scheme for signal detection in symmetric α stable noise environments, based on a class of robust filters called the matched myriad filters, is presented. New algorithms for estimating the noise distribution parameters are also developed. Third, a modulation classifier is proposed for environments in which the noise can be modeled as a time-correlated non-Gaussian random process. The proposed classifier involves the use of a whitening filter followed by likelihood-based classification. A new H_â filter-based technique for estimating the whitening filter coefficients is presented. / Ph. D.

No description available.

Call number: LD2668 .T4 1986 W34 / Master of Science / Electrical and Computer Engineering

Thesis (M.A.)--University of North Carolina Wilmington, 2008. / Title from PDF title page (viewed September 25, 2008) Includes bibliographical references.