abstract: Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a classification framework that can handle high-dimensional feature spaces, multiple classes and interaction between features. The proposed representations are useful for classification and interpretation of the TS data of varying complexity. The first contribution handles the problem of time warping with a feature-based approach. An interval selection and local feature extraction strategy is proposed to learn a bag-of-features representation. This is distinctly different from common similarity-based time warping. This allows for additional features (such as pattern location) to be easily integrated into the models. The learners have the capability to account for the temporal information through the recursive partitioning method. The second contribution focuses on the comprehensibility of the models. A new representation is integrated with local feature importance measures from tree-based ensembles, to diagnose and interpret time intervals that are important to the model. Multivariate time series (MTS) are especially challenging because the input consists of a collection of TS and both features within TS and interactions between TS can be important to models. Another contribution uses a different representation to produce computationally efficient strategies that learn a symbolic representation for MTS. Relationships between the multiple TS, nominal and missing values are handled with tree-based learners. Applications such as speech recognition, medical diagnosis and gesture recognition are used to illustrate the methods. Experimental results show that the TS representations and methods provide better results than competitive methods on a comprehensive collection of benchmark datasets. Moreover, the proposed approaches naturally provide solutions to similarity analysis, predictive pattern discovery and feature selection. / Dissertation/Thesis / Ph.D. Industrial Engineering 2012

Handreichung zur Ontologie ListDB Onto als wichtiger Baustein für die Interoperabilität zwischen verschiedenen Videodaten in der Verkehrsforschung.

Os codificadores preditivos de voz por analise-mediante-síntese vem sendo amplamente aplicados em telefonia móvel celular e em telecomunicações sigilosas. A predição linear do sinal de voz e as técnicas de análise-mediante-síntese são apresentadas de forma a relacionar algumas características perceptivas da audição humana as técnicas e parâmetros usados no processamento de sinais. Esta classe de codificadores e descrita no contexto do codificador preditivo excitado por códigos. Estruturas especiais do codificador tais como livros de códigos adaptativos, esparsos e definidos por base vetorial são abordadas bem como melhoramentos de processamento tais quais as buscas com ortogonalidade. Propõe-se um novo codificador, o codificador preditivo linear com excitação decomposta em vetores singulares, que complementa uma representação recentemente anunciada da excitação da voz com buscas em livros de códigos adaptativos. Os resultados de um estudo de codificadores principais desta classe são apresentados. A analise comparativa baseia-se em medidas objetivas temporais e espectrais. Um estudo suplementar de seleção espectral das características da excitação e de quantização do conjunto completo de parâmetros do codificador proposto revelou resultados interessantes sobre a representação espectral adaptativa e sobre a sensibilidade a quantização das características da excitação. / Analysis-by-synthesis linear predictive speech coders are widely applied in mobile and secure telecommunications. Linear prediction of speech signals and analysis-by-synthesis techniques are presented so that some perceptual features of human hearing may be related to signal processing techniques and parameters. The basic operation of this class of coders is described in the framework of the code-excited predictive coder. Special coder structures such as adaptive, sparse and vector-basis codebooks are introduced as well as processing enhancements such as orthogonal searches. A recently introduced representation of voice excitation is complemented by adaptive codebook searches to give rise to the new proposed coder, the singular-vector-decomposed excitation linear predictive coder. The sults of a study of some important coders in this class is present. The coders are compared on the basis of waveform and spectral objective distortion measures. A further study of spectral selection of excitation features, and quantization of the whole set of parameters is performed on the proposed coder. Some interesting results are described concerning the adaptive spectral representation and the sensitivity to quantization of the excitation features.

Os codificadores preditivos de voz por analise-mediante-síntese vem sendo amplamente aplicados em telefonia móvel celular e em telecomunicações sigilosas. A predição linear do sinal de voz e as técnicas de análise-mediante-síntese são apresentadas de forma a relacionar algumas características perceptivas da audição humana as técnicas e parâmetros usados no processamento de sinais. Esta classe de codificadores e descrita no contexto do codificador preditivo excitado por códigos. Estruturas especiais do codificador tais como livros de códigos adaptativos, esparsos e definidos por base vetorial são abordadas bem como melhoramentos de processamento tais quais as buscas com ortogonalidade. Propõe-se um novo codificador, o codificador preditivo linear com excitação decomposta em vetores singulares, que complementa uma representação recentemente anunciada da excitação da voz com buscas em livros de códigos adaptativos. Os resultados de um estudo de codificadores principais desta classe são apresentados. A analise comparativa baseia-se em medidas objetivas temporais e espectrais. Um estudo suplementar de seleção espectral das características da excitação e de quantização do conjunto completo de parâmetros do codificador proposto revelou resultados interessantes sobre a representação espectral adaptativa e sobre a sensibilidade a quantização das características da excitação. / Analysis-by-synthesis linear predictive speech coders are widely applied in mobile and secure telecommunications. Linear prediction of speech signals and analysis-by-synthesis techniques are presented so that some perceptual features of human hearing may be related to signal processing techniques and parameters. The basic operation of this class of coders is described in the framework of the code-excited predictive coder. Special coder structures such as adaptive, sparse and vector-basis codebooks are introduced as well as processing enhancements such as orthogonal searches. A recently introduced representation of voice excitation is complemented by adaptive codebook searches to give rise to the new proposed coder, the singular-vector-decomposed excitation linear predictive coder. The sults of a study of some important coders in this class is present. The coders are compared on the basis of waveform and spectral objective distortion measures. A further study of spectral selection of excitation features, and quantization of the whole set of parameters is performed on the proposed coder. Some interesting results are described concerning the adaptive spectral representation and the sensitivity to quantization of the excitation features.

A COMPARATIVE EVALUATION OF FOREGROUND / BACKGROUND SEGMENTATION ALGORITHMS Pakyurek, Muhammet M.Sc., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. G&ouml / zde Bozdagi Akar September 2012, 77 pages Foreground Background segmentation is a process which separates the stationary objects from the moving objects on the scene. It plays significant role in computer vision applications. In this study, several background foreground segmentation algorithms are analyzed by changing their critical parameters individually to see the sensitivity of the algorithms to some difficulties in background segmentation applications. These difficulties are illumination level, view angles of camera, noise level, and range of the objects. This study is mainly comprised of two parts. In the first part, some well-known algorithms based on pixel difference, probability, and codebook are explained and implemented by providing implementation details. The second part includes the evaluation of the performances of the algorithms which is based on the comparison v between the foreground background regions indicated by the algorithms and ground truth. Therefore, some metrics including precision, recall and f-measures are defined at first. Then, the data set videos having different scenarios are run for each algorithm to compare the performances. Finally, the performances of each algorithm along with optimal values of their parameters are given based on f measure.

The coding and feedback inaccuracies of the channel state information (CSI) in limited feedback multiple-input multiple-output (MIMO) wireless systems can severely impact the achievable data rate and reliability. The CSI is mathematically represented as a Grassmann manifold or manifold of unitary matrices. These are non-Euclidean spaces with special constraints that makes efficient and high fidelity coding especially challenging. In addition, the CSI inaccuracies may occur due to digital representation, time variation, and delayed feedback of the CSI. To overcome these inaccuracies, the manifold structure of the CSI can be exploited. The objective of this dissertation is to develop a new signal processing techniques on the manifolds to harvest the benefits of MIMO wireless systems. First, this dissertation presents the Kerdock codebook design to represent the CSI on the Grassmann manifold. The CSI inaccuracy due to digital representation is addressed by the finite alphabet structure of the Kerdock codebook. In addition, systematic codebook construction is identified which reduces the resource requirement in MIMO wireless systems. Distance properties on the Grassmann manifold are derived showing the applicability of the Kerdock codebook to beam-forming and spatial multiplexing systems. Next, manifold-constrained algorithms to predict and encode the CSI with high fidelity are presented. Two prominent manifolds are considered; the Grassmann manifold and the manifold of unitary matrices. The Grassmann manifold is a class of manifold used to represent the CSI in MIMO wireless systems using specific transmission strategies. The manifold of unitary matrices appears as a collection of all spatial information available in the MIMO wireless systems independent of specific transmission strategies. On these manifolds, signal processing building blocks such as differencing and prediction are derived. Using the proposed signal processing tools on the manifold, this dissertation addresses the CSI coding accuracy, tracking of the CSI under time variation, and compensation techniques for delayed CSI feedback. Applications of the proposed algorithms in single-user and multiuser systems show that most of the spatial benefits of MIMO wireless systems can be harvested. / text

Foreground segmentation is a common first step in tracking and surveillance applications.  The purpose of foreground segmentation is to provide later stages of image processing with an indication of where interesting data can be found.  This thesis is an investigation of how foreground segmentation can be performed in two contexts: as a pre-step to trajectory tracking and as a pre-step in indoor surveillance applications. Three methods are selected and detailed: a single Gaussian method, a Gaussian mixture model method, and a codebook method.  Experiments are then performed on typical input video using the methods.  It is concluded that the Gaussian mixture model produces the output which yields the best trajectories when used as input to the trajectory tracker.  An extension is proposed to the Gaussian mixture model which reduces shadow, improving the performance of foreground segmentation in the surveillance context.

This master thesis describes the practical usage of AMR-WB (Adaptive Multi Rate - Wide Band) codec and its implementation on a digital signal processor which is integrated in functional voice communication system Siemens HiPath 4000. The first part is focused on the complete codec description, especially on an encoder and decoder. The second part partly describes signal processors and then is followed by the practical part of the implementation which is solved from the preliminary activities up to the optimalization of the final functional solution.

Práce představuje novou adaptabilní metodu pro on-line vyhledávání videa v reálném čase pomocí vizuálních slovníků. Nová metoda se zaměřuje na nízkou výpočetní náročnost a přesnost vyhledání při on-line použití. Metoda vychází z technik využitých u statických vizuálních slovníků. Tyto běžné techniky jsou upraveny tak, aby byly schopné se adaptovat na proměnlivá data. Postupy, které toto u nové metody řeší, jsou - dynamická inverzní frekvence dokumentů, adaptabilní vizuální slovník a proměnlivý invertovaný index. Navržený postup byl vyhodnocen na úloze vyhledávání videa a prezentované výsledky ukazují, jaké vlastnosti má adaptabilní metoda ve srovnání se statickým přístupem. Nová adaptabilní metoda je založena na konceptu plovoucího okna, který definuje, jakým způsobem se vybírají data pro adaptaci a ke zpracování. Společně s konceptem je definován i matematický aparát, který umožňuje vyhodnotit, jak koncept nejlépe využít pro různé metody zpracování videa. Praktické využití adaptabilní metody je konkrétně u systémů pro zpracování videa, kde se očekává změna v charakteru vizuálních dat nebo tam, kde není předem známo, jakého charakteru vizuální data budou.

Due to its significant advantage in spectral efficiency, multiple-antenna communication technology will undoubtedly be a major component in future wireless system implementations. However, the full exploitation of this technology also requires perfect feedback of channel state information (CSI) to the transmitter-- something that is not practically feasible. This motivates the study of limited feedback systems, where CSI feedback is rate limited. This thesis focuses on the optimal design of limited feedback systems for three types of communication channels: the relay channel, the single-user point-to-point channel, and the multiuser broadcast channel. For the relay channel, we prove the efficiency of the Grassmannian codebooks as the source and relay beamforming codebooks, and propose a method for CSI exchange between the relay and the destination when global CSI is not available at destination. For the single-user point-to-point channel, we study the joint power control and beamforming problem and address the channel magnitude and direction quantization codebook design problem. It is shown that uniform quantization of the channel magnitude (in dB scale) is asymptotically optimal regardless of the channel distribution. The analysis further derives the optimal split of feedback bandwidth between the magnitude and direction quantization codebooks. For the multiuser broadcast channel, we first prove the sufficiency of a product magnitude-direction quantization codebook for managing the multiuser interference. We then derive the optimal split of feedback bandwidth across the users and their magnitude and direction codebooks. The optimization results reveal an inherent structural difference between the single-user and multiuser quantization codebooks: a multiuser codebook should have a finer direction quantization resolution as compared to a single-user codebook. It is further shown that the users expecting higher rates and requiring more reliable communication should provide a finer quantization of their CSI. Finally, we determine the minimum required total feedback rate based on users' quality-of-service constraints and derive the scaling of the system performance with the total feedback rate.