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Στόχος της παρούσας διπλωματικής είναι η παρουσίαση ενός συστήματος ανίχνευσης και διαχείρισης παρασίτων σε δεδομένα εγκεφαλογραφήματος (EEG). Το σύστημα αυτό σε πραγματικό χρόνο ανιχνεύει της ύπαρξη παρασίτων κατά την διάρκεια της καταγραφής, αξιοποιώντας ένα προ-εκπαιδευμένο μοντέλο. Τα παράσιτα που ανιχνεύτηκαν μπορούν να διαχειριστούν με αρκετές τεχνοτροπίες ανάλογα με τις ανάγκες της εκάστοτε εφαρμογής. Στην παρούσα διπλωματική παρουσιάζεται μια τεχνοτροπία η οποία αφαιρεί ένα οφθαλμικό παράσιτο με αξιοποίηση τανυστών. Συγκεκριμένα, στην διπλωματική αυτή παρουσιάζονται οι ανάγκες διαχείρισης ροών δεδομένων και πως αυτές αντιμετωπίζονται στην περίπτωση των δεδομένων εγκεφαλογραφήματος. Ο όγκος των δεδομένων καθώς και ο ρυθμός μετά- δοσής τους είναι καθοριστικοί για την διαχείριση και ανάλυση της εισερχόμενης στο σύστημα ροής. Στην διπλωματική αυτή παρουσιάζονται οι γενικές στρατηγικές που έχουν σχεδιαστεί για την διαχείριση χρονοσειρών μεγάλου όγκου και παρουσιάζεται η εφαρμογή τους σε δεδομένα εγκεφαλογραφήματος. Το προτεινόμενο λοιπόν σύστημα μπορεί σε πραγματικό χρόνο να διαχειριστεί ροές δεδομένων εγκεφαλογραφήματος και να διαχωρίσει σε πραγματικό χρόνο περιόδους που υπάρχει κάποιο παράσιτο στο ληφθέν σήμα. Επίσης προ- τείνεται μια μέθοδος που σε offline ανάλυση μπορεί να αφαιρέσει έναν τύπο παρασίτου και συγκεκριμένα το οφθαλμικό παράσιτο. / This diploma thesis presents a system able to detect and manage artifacts in EEG data streams.

In this thesis we utilise a form for the Hubble constant first proposed by Berman (1983) to study a variety of cosmological models. In particular we investigate the Robertson-Walker spacetimes, the Bianchi I spacetime, and the scalar-tensor theory of gravitation of Lau and Prokhovnik (1986). The Einstein field equations with variable cosmological constant and gravitational constant are discussed and the Friedmann models are reviewed. The relationship between observation and the Friedmann models is reviewed. We present a number of new solutions to the Einstein field equations with variable cosmological constant and gravitational constant in the Robertson-Walker spacetimes for the assumed form of the Hubble parameter. We explicitly find forms for the scale factor, cosmological constant, gravitational constant, energy density and pressure in each case. Some of the models have an equation of state for an ideal gas. The gravitational constant may be increasing in certain regions of spacetime. The Bianchi I spacetime, which is homogeneous and anisotropic, is shown to be consistent with the Berman (1983) law by defining a function which reduces to the scale factor of Robertson-Walker. We illustrate that the scalar-tensor theory of Lau and Prokhovnik (1986) also admits solutions consistent with the Hubble variation proposed by Berman. This demonstrates that this approach is useful in seeking solutions to the Einstein field equations in general relativity and alternate theories of gravity. / Thesis (M.Sc.)-University of Natal, 1992.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 61-63).

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references (leaves 158-162). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--Ohio State University, 2009. / Title from first page of PDF file. Includes vita. Includes bibliographical references (p. 128-131).

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / Linear filtering methods are well known and have been successfully applied to many engineering problems. However, they become unpractical when the parameter space is very large. The recently proposed assumption of system separability allows the development of computationally efficient alternatives to classical filtering methods in this scenario. In this work, we show that system separability calls for multilinear system representation and filtering. Based on this parallel, the proposed filtering framework consists of a multilinear extension of the classical Wiener-Hopf (WH) filter that exploits the separability property to solve the supervised multilinear filtering problem. System identification and antenna beamforming computer simulations were conducted to assess the performance of the proposed method. Our numerical results show our approach has smaller computational complexity and that it provides better estimation accuracy than the classical WH filter, which ignores the multilinear system structure. / MÃtodos de filtragem linear estÃo bem estabelecidos e tÃm sido aplicados em diversos problemas de engenharia. Entretanto, eles tornam-se impraticÃveis quando o espaÃo de parÃmetros à grande. A recente hipÃtese de separabilidade de sistema permite o desenvolvimento de mÃtodos computacionalmente eficientes neste cenÃrio. Neste trabalho, nÃs mostramos que a separabilidade de um sistema leva à sua representaÃÃo multilinear. Em vista disso, o mÃtodo de filtragem proposto consiste em uma extensÃo multilinear do filtro de Wiener-Hopf (WH) clÃssico, que explora a separabilidade para resolver o problema de filtragem multilinear supervisionada. SimulaÃÃes computacionais de identificaÃÃo de sistemas e formataÃÃo de feixes de antenas foram realizadas para a avaliaÃÃo do desempenho do mÃtodo proposto. Nosso resultados numÃricos mostram que nossa abordagem possui menor complexidade computacional e que ela fornece melhor acurÃcia de estimaÃÃo que o filtro de WH clÃssico, que ignora a estrutura multilinear do sistema.

Please read the abstract in the front section of this document / Thesis (PhD (Mathematics))--University of Pretoria, 2006. / Mathematics and Applied Mathematics / unrestricted

Clustering is the process of finding underlying group structure in data. As the scale of data collection continues to grow, this “big data” phenomenon results in more complex data structures. These data structures are not always compatible with traditional clustering methods, making their use problematic. This thesis presents methodology for analyzing samples of four-way and higher data, examples of these more complex data types. These data structures consist of samples of continuous data arranged in multidimensional arrays. A large emphasis is placed on clustering this data using mixture models that leverage tensor-variate distributions to model the data. Parameter estimation for all these methods are based on the expectation-maximization algorithm. Both simulated and real data are used for illustration. / Thesis / Doctor of Science (PhD)