Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Descriptive discriminant analysis for repeated measures data

Background: Linear discriminant analysis (DA) encompasses procedures for classifying observations into groups (predictive discriminant analysis, PDA) and describing the relative importance of variables for distinguishing between groups (descriptive discriminant analysis, DDA) in multivariate data. In recent years, there has been increased interest in DA procedures for repeated measures data. PDA procedures that assume parsimonious repeated measures mean and covariance structures have been developed, but corresponding DDA procedures have not been proposed. Most DA procedures for repeated measures data rest on the assumption of multivariate normality, which may not be satisfied in biostatistical applications. For example, health-related quality of life (HRQOL) measures, which are increasingly being used as outcomes in clinical trials and cohort studies, are likely to exhibit skewed or heavy-tailed distributions. As well, measures of relative importance based on discriminant function coefficients (DFCs) for DDA procedures have not been proposed for repeated measures data. Purpose: The purpose of this research is to develop repeated measures discriminant analysis (RMDA) procedures based on parsimonious covariance structures, including compound symmetric and first order autoregressive structures, and that are robust (i.e., insensitive) to multivariate non-normal distributions. It also extends these methods to evaluate the relative importance of variables in multivariate repeated measures (i.e., doubly multivariate) data. Method: Monte Carlo studies were conducted to investigate the performance of the proposed RMDA procedures under various degrees of group mean separation, repeated measures correlation structures, departure from multivariate normality, and magnitude of covariance mis-specification. Data from the Manitoba Inflammatory Bowel Disease Cohort Study, a prospective longitudinal cohort study about the psychosocial determinants of health and well-being, are used to illustrate their applications. Results: The conventional maximum likelihood (ML) estimates of DFCs for RMDA procedures based on parsimonious covariance structures exhibited substantial bias and error when the covariance structure was mis-specified or when the data followed a multivariate skewed or heavy-tailed distribution. The DFCs of RMDA procedures based on robust estimators obtained from coordinatewise trimmed means and Winsorized variances, were less biased and more efficient when the data followed a multivariate non-normal distribution, but were sensitive to the effects of covariance mis-specification. Measures of relative importance for doubly multivariate data based on linear combinations of the within-variable DFCs resulted in the highest proportion of correctly ranked variables. Conclusions: DA procedures based on parsimonious covariance structures and robust estimators will produce unbiased and efficient estimates of variable relative importance of variables in repeated measures data and can be used to test for change in relative importance over time. The choice among these RMDA procedures should be guided by preliminary descriptive assessments of the data

A Nonparametric Approach to Segmentation of Ladar Images

The advent of advanced laser radar (ladar) systems that record full-waveform signal data has inspired numerous inquisitions which aspire to extract additional, previously unavailable, information about the illuminated scene from the collected data. The quality of the information, however, is often related to the limitations of the ladar camera used to collect the data. This research project uses full-waveform analysis of ladar signals, and basic principles of optics, to propose a new formulation for an accepted signal model. A new waveform model taking into account backscatter reflectance is the key to overcoming specific deficiencies of the ladar camera at hand, namely the ability to discern pulse-spreading effects of elongated targets. A concert of non-parametric statistics and familiar image processing methods are used to calculate the orientation angle of the illuminated objects, and the deficiency of the hardware is circumvented. Segmentation of the various ladar images performed as part of the angle estimation, and this is shown to be a new and effective strategy for analyzing the output of the AFIT ladar camera

Local Analysis of Dissipative Dynamical Systems

Linear transformation techniques such as singular value decomposition (SVD) have been used widely to gain insight into the qualitative dynamics of data generated by dynamical systems. There have been several reports in the past that had pointed out the susceptibility of linear transformation approaches in the presence of nonlinear correlations. In this tutorial review, local dispersion along with the surrogate testing is proposed to discriminate nonlinear correlations arising in deterministic and non-deterministic settings.Comment: 85 Pages, 13 Figure

A markovian approach to unsupervised change detection with multiresolution and multimodality SAR data

In the framework of synthetic aperture radar (SAR) systems, current satellite missions make it possible to acquire images at very high and multiple spatial resolutions with short revisit times. This scenario conveys a remarkable potential in applications to, for instance, environmental monitoring and natural disaster recovery. In this context, data fusion and change detection methodologies play major roles. This paper proposes an unsupervised change detection algorithmfor the challenging case of multimodal SAR data collected by sensors operating atmultiple spatial resolutions. The method is based on Markovian probabilistic graphical models, graph cuts, linear mixtures, generalized Gaussian distributions, Gram-Charlier approximations, maximum likelihood and minimum mean squared error estimation. It benefits from the SAR images acquired at multiple spatial resolutions and with possibly different modalities on the considered acquisition times to generate an output change map at the finest observed resolution. This is accomplished by modeling the statistics of the data at the various spatial scales through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images that are defined on the pixel grid at the finest resolution and would be collected if all the sensors could work at that resolution. A Markov random field framework is adopted to address the detection problem by defining an appropriate multimodal energy function that is minimized using graph cuts