Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

We study regression using functional predictors in situations where these functions contains both phase and amplitude variability. In other words, the functions are misaligned due to errors in time measurements, and these errors can significantly degrade both model estimation and prediction performance. The current techniques either ignore the phase variability, or handle it via preprocessing, that is, use an off-the-shelf technique for functional alignment and phase removal. We develop a functional principal component regression model which has a comprehensive approach in handling phase and amplitude variability. The model utilizes a mathematical representation of the data known as the square-root slope function. These functions preserve the L 2 norm under warping and are ideally suited for simultaneous estimation of regression and warping parameters. Using both simulated and real-world data sets, we demonstrate our approach and evaluate its prediction performance relative to current models. In addition, we propose an extension to functional logistic and multinomial logistic regression.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Coherent Change Detection (CCD) is a process of highlighting an area of activity in scenes (seafloor) under survey and generated from pairs of synthetic aperture sonar (SAS) images of approximately the same location observed at two different time instances. The problem of CCD and subsequent anomaly feature extraction/detection is complicated due to several factors such as the presence of random speckle pattern in the images, changing environmental conditions, and platform instabilities. These complications make the detection of weak target activities even more difficult. Typically, the degree of similarity between two images measured at each pixel locations is the coherence between the complex pixel values in the two images. Higher coherence indicates little change in the scene represented by the pixel and lower coherence indicates change activity in the scene. Such coherence estimation scheme based on the pixel intensity correlation is an ad-hoc procedure where the effectiveness of the change detection is determined by the choice of threshold which can lead to high false alarm rates. In this paper, we propose a novel approach for anomalous change pattern detection using the statistical normalized coherence and multi-pass coherent processing. This method may be used to mitigate shadows by reducing the false alarms resulting in the coherent map due to speckles and shadows. Test results of the proposed methods on a data set of SAS images will be presented, illustrating the effectiveness of the normalized coherence in terms statistics from multi-pass survey of the same scene.

In this paper, we consider the problem of denoising and classification of SONAR signals observed under compositional noise, i.e., they have been warped randomly along the x-axis. The traditional techniques do not account for such noise and, consequently, cannot provide a robust classification of signals. We apply a recent framework that: 1) uses a distance-based objective function for data alignment and noise reduction; and 2) leads to warping-invariant distances between signals for robust clustering and classification. We use this framework to introduce two distances that can be used for signal classification: a) a y-distance, which is the distance between the aligned signals; and b) an x-distance that measures the amount of warping needed to align the signals. We focus on the task of clustering and classifying objects, using acoustic spectrum (acoustic color), which is complicated by the uncertainties in aspect angles at data collections. Small changes in the aspect angles corrupt signals in a way that amounts to compositional noise. As a result, we demonstrate the use of the developed metrics in classification of acoustic color data and highlight improvements in signal classification over current methods.