Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.

Current representation schemes for automatic text classification treat documents as syntactically unstructured collections of words (Bag-of-Words) or `concepts' (Bag-of-Concepts). Past attempts to encode syntactic structure have treated part-of-speech information as another word-like feature, but have been shown to be less effective than non-structural approaches. We propose a new representation scheme using Holographic Reduced Representations (HRRs) as a technique to encode both semantic and syntactic structure, though in very different ways. This method is unique in the literature in that it encodes the structure across all features of the document vector while preserving text semantics. Our method does not increase the dimensionality of the document vectors, allowing for efficient computation and storage. We present the results of various Support Vector Machine classification experiments that demonstrate the superiority of this method over Bag-of-Concepts representations and improvement over Bag-of-Words in certain classification contexts.

Les satellites de télédétection sont devenus incontournables pour la société civile. En effet, les images satellites ont été exploitées avec succès pour traiter plusieurs applications, notamment la surveillance de l'environnement et de la prévention des catastrophes naturelles. Dans les dernières années, l'augmentation de la disponibilité de très haute résolution spatiale (THR) d'images de télédétection abouti à de nouvelles applications potentiellement pertinentes liées au suivi d'utilisation des sols et à la gestion environnementale. Cependant, les capteurs optiques, en raison du fait qu'ils acquièrent directement la lumière réfléchie par le soleil, ils peuvent souffrir de la présence de nuages dans le ciel et / ou d'ombres sur la terre. Il s'agit du problème des données manquantes, qui induit un problème important et crucial, en particulier dans le cas des images THR, où l'augmentation des détails géométriques induit une grande perte d'informations. Dans cette thèse, de nouvelles méthodologies de détection et de reconstruction de la région contenant des données manquantes dans les images THR sont proposées et appliquées sur les zones contaminées par la présence de nuages et / ou d'ombres. En particulier, les contributions méthodologiques proposées comprennent: i) une stratégie multirésolution d'inpainting visant à reconstruire les images contaminées par des nuages ; ii) une nouvelle combinaison d'information radiométrique et des informations de position spatiale dans deux noyaux spécifiques pour effectuer une meilleure reconstitution des régions contaminés par les nuages en adoptant une régression par méthode a vecteurs supports (RMVS) ; iii) l'exploitation de la théorie de l'échantillonnage compressé avec trois stratégies différentes (orthogonal matching pursuit, basis pursuit et une solution d'échantillonnage compressé, basé sur un algorithme génétique) pour la reconstruction d'images contaminés par des nuages; iv) une chaîne de traitement complète qui utilise une méthode à vecteurs de supports (SVM) pour la classification et la détection des zones d'ombre, puis une régression linéaire pour la reconstruction de ces zones, et enfin v) plusieurs critères d'évaluation promptes à évaluer la performance de reconstruction des zones d'ombre. Toutes ces méthodes ont été spécialement développées pour fonctionner avec des images très haute résolution. Les résultats expérimentaux menés sur des données réelles sont présentés afin de montrer et de confirmer la validité de toutes les méthodes proposées. Ils suggèrent que, malgré la complexité des problèmes, il est possible de récupérer de façon acceptable les zones manquantes masquées par les nuages ou rendues erronées les ombres.

Current representation schemes for automatic text classification treat documents as syntactically unstructured collections of words (Bag-of-Words) or `concepts' (Bag-of-Concepts). Past attempts to encode syntactic structure have treated part-of-speech information as another word-like feature, but have been shown to be less effective than non-structural approaches. We propose a new representation scheme using Holographic Reduced Representations (HRRs) as a technique to encode both semantic and syntactic structure, though in very different ways. This method is unique in the literature in that it encodes the structure across all features of the document vector while preserving text semantics. Our method does not increase the dimensionality of the document vectors, allowing for efficient computation and storage. We present the results of various Support Vector Machine classification experiments that demonstrate the superiority of this method over Bag-of-Concepts representations and improvement over Bag-of-Words in certain classification contexts.

The vector finite element method has gained great attention since overcoming the deficiencies incurred by the scalar basis functions for the vector Helmholtz equation. Most implementations of vector FEM have been non-adaptive, where a mesh of the domain is generated entirely in advance and used with a constant degree polynomial basis to assign the degrees of freedom. To reduce the dependency on the users' expertise in analyzing problems with complicated boundary structures and material characteristics, and to speed up the FEM tool, the demand for adaptive FEM grows high. For efficient adaptive FEM, error estimators play an important role in assigning additional degrees of freedom. In this proposal study, hierarchical vector basis functions and four error estimators for p-refinement are investigated for electromagnetic applications.

The subject of this dissertation is to develop a rigorous transverse-mode integral equation formulation for analyzing TE/TM electromagnetic mode field solutions for dielectric waveguides. The main topics are composed of two related parts. The first part deals with scalar problems. In which we propose a transverse-mode integral-equation formulation for problems such as mode solutions of the ridged microwave waveguides. This same technique also applies to EM waves scattering off the facet of dielectric slab waveguides terminating in free space. For both problems we constructed a specifically chosen basis for the unknown tangential field functions, and we were able to reduce the kernel matrix size by more than one half without noticeable degradation of the field solutions. In the second part of the thesis, we apply a full-vector integral-equation formulation to analyze modal characteristics of the complex, two-dimensional, rectangular-like dielectric waveguide that is divisible into vertical slices of one-dimensional layered structures. The entire electromagnetic vector mode field solution is completely determined by the y-component electric and magnetic field functions on the interfaces between slices. These interfacial functions are governed by a system of vector-coupled transverse-mode integral equations (VCTMIE) whose kernels are made of orthonormal sets of both TE-to-y and TM-to-y modes from each slice. Finally, we show the numerical results to present the stable and quick convergence of this method as well as to improve the Gibb¡¦s phenomenon in the recreation of the transverse fields.

In this work, our goal is to track visual targets using residual vector quantization (RVQ). We compare our results with principal components analysis (PCA) and tree structured vector quantization (TSVQ) based tracking. This work is significant since PCA is commonly used in the Pattern Recognition, Machine Learning and Computer Vision communities. On the other hand, TSVQ is commonly used in the Signal Processing and data compression communities. RVQ with more than two stages has not received much attention due to the difficulty in producing stable designs. In this work, we bring together these different approaches into an integrated tracking framework and show that RVQ tracking performs best according to multiple criteria on publicly available datasets. Moreover, an advantage of our approach is a learning-based tracker that builds the target model while it tracks, thus avoiding the costly step of building target models prior to tracking.

Accurate demand forecasting is a key capability for a manufacturing organization, more so, a semiconductor manufacturer. Many crucial decisions are based on demand forecasts. The semiconductor industry is characterized by very short product lifecycles (10 to 24 months) and extremely uncertain demand. The pace at which both the manufacturing technology and the product design changes, induce change in manufacturing throughput and potential demand. Well known methods like exponential smoothing, moving average, weighted moving average, ARMA, ARIMA, econometric methods and neural networks have been used in industry with varying degrees of success. We propose a novel forecasting technique which is based on Support Vector Regression (SVR). Specifically, we formulate ν-SVR models for semiconductor product demand data. We propose a 3-phased input vector modeling approach to comprehend demand characteristics learnt while building a standard ARIMA model on the data. Forecasting Experimentations are done for different semiconductor product demand data like 32 & 64 bit CPU products, 32bit Micro controller units, DSP for cellular products, NAND and NOR Flash Products. Demand data was provided by SRC(Semiconductor Research Consortium) Member Companies. Demand data was actual sales recorded at every month. Model performance is judged based on different performance metrics used in extant literature. Results of experimentation show that compared to other demand forecasting techniques ν-SVR can significantly reduce both mean absolute percentage errors and normalized mean-squared errors of forecasts. ν-SVR with our 3-phased input vector modeling approach performs better than standard ARIMA and simple ν-SVR models in most of the cases.

<p>This thesis treats the problem of source coding in situations where the encoded data is subject to errors. The typical scenario is a communication system, where source data such as speech or images should be transmitted from one point to another. A problem is that most communication systems introduce some sort of error in the transmission. A wireless communication link is prone to introduce individual bit errors, while in a packet based network, such as the Internet, packet losses are the main source of error.</p><p>The traditional approach to this problem is to add error correcting codes on top of the encoded source data, or to employ some scheme for retransmission of lost or corrupted data. The source coding problem is then treated under the assumption that all data that is transmitted from the source encoder reaches the source decoder on the receiving end without any errors. This thesis takes another approach to the problem and treats source and channel coding jointly under the assumption that there is some knowledge about the channel that will be used for transmission. Such joint source--channel coding schemes have potential benefits over the traditional separated approach. More specifically, joint source--channel coding can typically achieve better performance using shorter codes than the separated approach. This is useful in scenarios with constraints on the delay of the system.</p><p>Two different flavors of joint source--channel coding are treated in this thesis; multiple description coding and channel optimized vector quantization. Channel optimized vector quantization is a technique to directly incorporate knowledge about the channel into the source coder. This thesis contributes to the field by using channel optimized vector quantization in a couple of new scenarios. Multiple description coding is the concept of encoding a source using several different descriptions in order to provide robustness in systems with losses in the transmission. One contribution of this thesis is an improvement to an existing multiple description coding scheme and another contribution is to put multiple description coding in the context of channel optimized vector quantization. The thesis also presents a simple image coder which is used to evaluate some of the results on channel optimized vector quantization.</p>

Η παρούσα μελέτη εξετάζει τη δυναμική σχέση μεταξύ των τιμών των ζωοτροφών και παραγωγού, καταναλωτή για τέσσερα είδη κρέατος: μοσχάρι, χοιρινό, αρνί και κοτόπουλο. Η σχετική βιβλιογραφία δείχνει ότι πολλοί παράγοντες επιδρούν στις τιμές των αγροτικών προϊόντων αλλά οι τιμές των ζωοτροφών είναι ο κυριότερος. Αυτό συμβαίνει γιατί οι ζωοτροφές αποτελούν πρώτη ύλη για την παραγωγή κρέατος και κατ΄επέκταση θα επηρέασουν τις τιμές παραγωγού και καταναλωτή. Τα δεδομένα αποτελούνται από 279 μηνιαίες τιμές που εκτείνονται από τον Ιανουάριο 1990 έως τον Ιανουάριο 2013. Χρησιμοποιώντας Johansen cointegration tests, Granger causality tests και impulse response functions τα εμπειρικά αποτελέσματα επιβεβαιώνουν πως οι τιμές των ζωοτροφών, οι τιμές παραγωγού και οι τιμές καταναλωτή δεν είναι ανεξάρτητες μεταξύ τους. / The present paper studies the relationship among feed prices, producer prices and consumer prices of meat: beef, pork, poultry and lamb. The literature indicates that there are many factors which affect agricultural commodity prices but the feed prices are the main. This is why feed has a principal role in the production of meat and will affect producer and consumer prices. The data consists of 279 monthly observations extending from January 1990 to January 2013. Using Johansen cointegration tests, Granger causality tests and impulse response functions, the empirical findings confirm that feed prices, consumer prices and producer prices are interdependent.