No description available.

No description available.

Cognitive Radio (CR) is an emerging wireless communications technology that addresses the inefficiency of current radio spectrum usage. CR also supports the evolution of existing wireless applications and the development of new civilian and military applications. In military and public safety applications, there is no information available about the signal present in a frequency band and hence there is a need for a CR receiver to identify the modulation format employed in the signal. The automatic modulation classifier (AMC) is an important signal processing component that helps the CR in identifying the modulation format employed in the detected signal. AMC algorithms developed so far can classify only signals from a single user present in a frequency band. In a typical CR scenario, there is a possibility that more than one user is present in a frequency band and hence it is necessary to develop an AMC that can classify signals from multiple users simultaneously. One of the main objectives of this dissertation is to develop robust multiuser AMC's for CR. It will be shown later that multiple antennas are required at the receiver for classifying multiple signals. The use of multiple antennas at the transmitter and receiver is known as a Multi Input Multi Output (MIMO) communication system. By using multiple antennas at the receiver, apart from classifying signals from multiple users, the CR can harness the advantages offered by classical MIMO communication techniques like higher data rate, reliability, and an extended coverage area. While MIMO CR will provide numerous benefits, there are some significant challenges in applying conventional MIMO theory to CR. In this dissertation, open problems in applying classical MIMO techniques to a CR scenario are addressed. A blind equalizer is another important signal processing component that a CR must possess since there are no training or pilot signals available in many applications. In a typical wireless communication environment the transmitted signals are subjected to noise and multipath fading. Multipath fading not only affects the performance of symbol detection by causing inter symbol interference (ISI) but also affects the performance of the AMC. The equalizer is a signal processing component that removes ISI from the received signal, thus improving the symbol detection performance. In a conventional wireless communication system, training or pilot sequences are usually available for designing the equalizer. When a training sequence is available, equalizer parameters are adapted by minimizing the well known cost function called mean square error (MSE). When a training sequence is not available, blind equalization algorithms adapt the parameters of the blind equalizer by minimizing cost functions that exploit the higher order statistics of the received signal. These cost functions are non convex and hence the blind equalizer has the potential to converge to a local minimum. Convergence to a local minimum not only affects symbol detection performance but also affects the performance of the AMC. Robust blind equalizers can be designed if the performance of the AMC is also considered while adapting equalizer parameters. In this dissertation we also develop Single Input Single Output (SISO) and MIMO blind equalizers where the performance of the AMC is also considered while adapting the equalizer parameters. / Ph. D.

Les ataxies cérébelleuses héréditaires causent une atteinte progressive de l’équilibre et de la marche. Malgré l’amélioration de la performance et de l’accessibilité des tests génétiques, environ la moitié des patients demeurent sans diagnostic précis, ce qui a un impact sur la prise en charge. Dans ce mémoire de maîtrise, nous abordons l’enjeu du sous-diagnostic chez les patients atteints d’ataxie cérébelleuse via l’élaboration d’une nouvelle classification pour les ataxies récessives et la caractérisation détaillée de l’ataxie spinocérébelleuse 34. Le premier chapitre est une revue systématique de la littérature concernant les ataxies récessives. Au total, 2354 références et 130 articles complets ont été révisés afin d’identifier un groupe de 45 pathologies récessives où l’atteinte cérébelleuse est au coeur du phénotype et 29 pathologies multisystémiques additionnelles où l’ataxie est un élément secondaire, mais qui devraient être incluses dans le diagnostic différentiel du patient ataxique. Le deuxième chapitre présente les résultats d’un groupe de travail dédié à la classification des ataxies récessives. En se basant sur les résultats de la revue systématique, 12 experts internationaux se sont entendus sur des critères d’inclusion ainsi que sur deux classifications basées sur la symptomatologie clinique et les mécanismes cellulaires impliqués. Une approche clinique au patient ataxique est proposée. Le troisième chapitre présente une caractérisation en profondeur de l’ataxie spinocérébelleuse 34, causée par des mutations du gène ELOVL4. Nous avons étudié une famille multi-générationnelle présentant un syndrome cérébelleux tardif associé à des déficits exécutifs et une possible atteinte visuospatiale, attentionnelle et psychiatrique. L’analyse immunohistochimique de fibroblastes dermiques a démontré la délocalisation et l’agrégation de la protéine. Un système de classification basé sur des descriptions phénotypiques étoffées est essentiel pour les cliniciens et les chercheurs afin d’organiser adéquatement les groupes de maladies complexes. Le travail présenté constitue une avancée concrète pour améliorer l’approche diagnostique aux patients avec ataxie héréditaire / Hereditary cerebellar ataxias are neurodegenerative disorders associated with progressive motor incoordination and gait imbalance. Despite significant progress in the availability and performance of genetic tests, around half of patients remain without a molecular diagnosis, which has major counselling and management consequences. In this master thesis, we address the issue of underdiagnosis in patients with hereditary ataxias through the development of a novel classification system for recessive cerebellar ataxias and the in-depth characterization Spinocerebellar ataxia 34. The first chapter is a systematic review of the literature regarding recessive cerebellar ataxias. We revised 2354 references and 130 full-text articles to identify a group of 45 recessive disorders in which cerebellar ataxia is at the core of the clinical phenotype and 29 additional complex or multisystem disorders where ataxia is a secondary feature and which should be included in the differential diagnosis of ataxia. The second chapter presents the work of a dedicated task force on the classification of recessive cerebellar ataxias. Based on the results of the systematic review, 12 international ataxia experts agreed on revised inclusion criteria and on classifications based on clinical symptoms and pathogenic cellular mechanisms. We also propose a general clinical approach to the ataxic patient. The third chapter shows the clinical and biochemical characterization of a rare dominant ataxia, Spinocerebellar ataxia 34 caused by ELOVL4 mutations. We studied a multi-generational family with a late-onset cerebellar syndrome associated with executive deficits, and apparent visuospatial, attention, and psychiatric dysfunction. Immunohistochemistry of dermal fibroblasts showed the first evidence of ELOVL4 protein pathology in this disorder with mislocalization and aggregation of the protein. Classification systems based on detailed phenotypic descriptions are essential for both clinicians and researchers to understand complex groups of disorders. The work presented here advances our understanding of hereditary ataxias and constitutes a pragmatic diagnostic tool for clinicians.

Firefighting robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks. There has been difficulty in developing firefighting robots that autonomously locate a fire inside of a structure that is not in the direct robot field of view. The commonly used sensors for robots cannot properly function in fire smoke-filled environments where high temperature and zero visibility are present. Also, the existing obstacle avoidance methods have limitations calculating safe trajectories and solving local minimum problem while avoiding obstacles in real time under cluttered and dynamic environments. In addition, research for characterizing fire environments to provide firefighting robots with proper headings that lead the robots to ultimately find the fire is incomplete. For use on intelligent firefighting robots, this research developed a real-time local obstacle avoidance method, local dynamic goal-based fire location, appropriate feature selection for fire environment assessment, and probabilistic classification of fire, smoke and their thermal reflections. The real-time local obstacle avoidance method called the weighted vector method is developed to perceive the local environment through vectors, identify suitable obstacle avoidance modes by applying a decision tree, use weighting functions to select necessary vectors and geometrically compute a safe heading. This method also solves local obstacle avoidance problems by integrating global and local goals to reach the final goal. To locate a fire outside of the robot field of view, a local dynamic goal-based 'Seek-and-Find' fire algorithm was developed by fusing long wave infrared camera images, ultraviolet radiation sensor and Lidar. The weighted vector method was applied to avoid complex static and unexpected dynamic obstacles while moving toward the fire. This algorithm was successfully validated for a firefighting robot to autonomously navigate to find a fire outside the field of view. An improved 'Seek-and-Find' fire algorithm was developed using Bayesian classifiers to identify fire features using thermal images. This algorithm was able to discriminate fire and smoke from thermal reflections and other hot objects, allowing the prediction of a more robust heading for the robot. To develop this algorithm, appropriate motion and texture features that can accurately identify fire and smoke from their reflections were analyzed and selected by using multi-objective genetic algorithm optimization. As a result, mean and variance of intensity, entropy and inverse difference moment in the first and second order statistical texture features were determined to probabilistically classify fire, smoke, their thermal reflections and other hot objects simultaneously. This classification performance was measured to be 93.2% accuracy based on validation using the test dataset not included in the original training dataset. In addition, the precision, recall, F-measure, and G-measure were 93.5 - 99.9% for classifying fire and smoke using the test dataset. / Ph. D.

published_or_final_version / Education / Doctoral / Doctor of Philosophy

The International UDC Seminar 2009 “Classification at a Crossroads - multiple directions to usability” took place in the Koninklijke Bibliotheek in The Hague on 29-30 October 2009. It was a truly international event as it attracted 135 delegates from 32 countries comprising librarians, LIS lecturers, researchers, information consultants, Web technologists and indexing specialists.

This dissertation presents a technique for the identification and classification of late-type stars and for the estimation of M star metallicities. The technique uses broad-band, V and I, CCD images to identify red stars and two intermediate-band CCD images to classify these as carbon or M types. One of the intermediate passbands is centered on a TiO absorption band at 7750Å and the other is centered on a CN absorption band at 8100Å. Color-color plots of V-I versus the intermediate-band index, 77-81, clearly distinguishes carbon from M stars. Observations of both early- and late-type stars were used to define the 77-81 system based upon the intermediate-band filters. The TiO bandstrength deduced from the 77-81 color as a function of V-I color was investigated for field giants and giants in 12 globular clusters. A linear correlation between [Fe/H] and the V-I color at a given TiO bandstrength was found. This correlation can be used to estimate the metallicity of M giants. The stellar population of a field in Baade's Window was examined using this technique. Many late-M stars and no carbon stars were found. The color-color diagram for Baade's Window suggests a range of metallicities for the M giants of [Fe/H] ≈ -0.4 to > +0.2. The stellar population of the Sagittarius Dwarf Irregular galaxy (Sagdig) was examined using the 77-81 system. A method for estimating reddening based upon the color mode of foreground stars was developed for the analysis of the Sagdig data. Sagdig is estimated to be ~ 1.3 megaparsecs distant. Bright blue and red stars in Sagdig are evidence for recent star formation. Carbon stars were identified in Sagdig. They display a bimodal luminosity and color distribution which suggests distinct epochs of star forming activity between 1 and 10 Gyr ago. The spatial distribution of carbon stars and bright red stars in Sagdig shows this galaxy to be much larger than previously thought.

The purpose of this thesis was to provide a revision and characterization of the species of nongeniculate coralline algae previously ascribed to the now defunct genus Porolithon / to provide a modern account of selected taxa from the genus Hydrolithon and descriptions of taxa found to conform the generic delimitation of Hydrolithon and to use a phenetic cluster analysis to determine the taxonomic relationships between the various taxa ascribed to the genera Polorithon and Hydrolithon.

Empowerment, which has been described as both a means of effecting higher and more efficient quality work outputs and a means of facilitating greater freedom in the workplace combines elements of philosophy, psychology, and management theory. The perceptions of the present empowerment level of 3500 employees of a division of a major corporation were analyzed using data from an empowerment survey. The results were examined using correlational and factorial measures to test the structure of the survey. ANOVA and pair-wise comparisons were used to examine group differences on five subscales of the survey based on employee level in the organization. Significant differences were found in almost all categories. Rank order for the three levels differed from previous findings, perhaps due to empowerment thrusts.