Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Autoregressive Spectral Estimation in Noise with Application to Speech Analysis

Electrical Engineerin

Shedding Light on Hearing in Coma: Investigating the Applicability of Functional Near-infrared Spectroscopy for Assessing Auditory Function and Aiding Prognosis in Patients with Acute Disorders of Consciousness

There is a critical need for a bedside neuroimaging tool to aid in the prediction of functional recovery outcomes for patients with acute disorders of consciousness (DoC) in the early days following severe brain injury. Current neurobehavioral examinations and prognosis tools have limitations in predicting good outcomes, leading to potential mistreatment or premature withdrawal of life support. Functional near-infrared spectroscopy (fNIRS) is a viable candidate for such purposes due to its portability and cost-effectiveness. Auditory processing, viewed as a multi-level and multifaceted brain function, could provide a sensitive and specific marker of residual cognitive function in unresponsive patients. This study aimed to investigate the effectiveness of fNIRS for hierarchical assessment of auditory function and evaluate its applicability for predicting recovery outcomes in acute DoC. The capability of fNIRS for such an application was demonstrated by validating it against fMRI in a healthy population and cross-validating it in an entirely unresponsive patient with cognitive-motor dissociation. An innovative fNIRS-focused method was developed to quantify patients’ auditory function, and a data-driven method was explored to improve the sensitivity and specificity of auditory scores. Using these analytical tools, a direct association was found between auditory function and recovery outcome in a small patient cohort. Based on the study’s findings, the crucial role of methodological considerations in the use of fNIRS was discussed, and specific modifications in the stimulus and optical montage designs were suggested to enhance the method’s reliability

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion