Many recent research results have concluded that the multiple-input multiple-output (MIMO) wireless communication architecture is a promising approach to achieve high bandwidth efficiencies. MIMO wireless channels can be simply defined as a link for which both the transmitting and receiving ends are equipped with multiple antenna elements. This advanced communication technology has the potential to resolve the bottleneck in traffic capacity for future wireless networks. Applying MIMO techniques to mobile communication systems, the problem of channel fading between the transmitters and receivers, which results in received signal strength fluctuations, is inevitable. The time-varying nature of the mobile channel affects various aspects of receiver design. This thesis provides some analytical methodologies to investigate the variation of MIMO eigenmodes. Although the scope is largely focussed on the temporal variation in this thesis, our results are also extended to frequency variation. Accurate analytical approximations for the level crossing rate (LCR) and average fade duration (AFD) of the MIMO eigenmodes in an independent, identically distributed (i.i.d.) flat-fading channel are derived. Furthermore, since several channel metrics (such as the total power gain, eigenvalue spread, capacity and Demmel condition number) are all related to the eigenmodes, we also derive their LCRs and AFDs using a similar approach. The effectiveness of our method lies in the fact that the eigenvalues and corresponding channel metrics can be well approximated by gamma or Gaussian variables. Our results provide a comprehensive, closed-form analysis for the temporal behavior of MIMO channel metrics that is simple, robust and rapid to compute. An alternative simplified formula for the LCR for MIMO eigenmodes is also presented with applications to different types of autocorrelation functions (ACF). Our analysis has been verified via Monte Carlo computer simulations. The joint probability density function (PDF) for the eigenvalues of a complex Wishart matrix and a perturbed version of it are also derived in this thesis. The latter version can be used to model channel estimation errors and variations over time or frequency. Using this PDF, the probabilities of adaptation error (PAE) due to feedback delay in some adaptive MIMO schemes are evaluated. In particular, finite state Markov chains (FSMC) have been used to model rate-feedback system and dual-mode antenna selection schemes. The PDF is also applied to investigate MIMO systems that merge singular value decomposition (SVD)-based transceiver structure and adaptive modulation. A FSMC is constructed to investigate the modulation state entering rates (MSER), the average stay duration (ASD), and the effects of feedback delay on the accuracy of modulation state selection in mobile radio systems. The system performance of SVD-based transceivers is closely related to the quality of the channel information at both ends of the link. Hence, we examine the effect of feedback time delay, which causes the transmitter to use outdated channel information in time-varying fading channels. In this thesis, we derive an analytical expression for the instantaneous signal to interference plus noise ratio (SINR) of eigenmode transmission with a feedback time delay. Moreover, this expression implies some novel metrics that gauge the system performance sensitivity to time-variations of the steering vectors (eigenvectors of the channel correlation matrix) at the transmitter. Finally, the fluctuation of the channel in the frequency domain is of interest. This is motivated by adaptive orthogonal frequency division multiplexing (OFDM) systems where the signalling parameters per subcarriers are assigned in accordance with some channel quality metrics. A Gaussian distribution has been suggested to approximate the number of subcarriers using certain signalling modes (such as outage/transmission and diversity/multiplexing), as well as the total data rates, per OFDM realization. Additionally, closed-form LCRs for the channel gains (including the individual eigenmode gains) over frequency are also derived for both single-input single-output (SISO) and MIMO-OFDM systems. The corresponding results for the average fade bandwidth (AFB) follow trivially, These results may be useful for system design, for example by calculating the feedback overheads based on subcarrier aggregation.

Judgements of and sensitivity to style are ubiquitous. People become sensitive to the structural regularities of complex or “polymorphous” categories through exposure to individual examples, which allows them respond to new items that are of the same style as those previously experienced. This thesis investigates whether a dimension reduction mechanism could account for how people learn about the structure of complex categories. That is, whether through experience, people extract the primary dimensions of variation in a category and use these to analyse and categorise subsequent instances. We used Singular Value Decomposition (SVD) as the method of dimension reduction, which yields the main dimensions of variation of pixel-based stimuli (eigenvectors). We then tested whether a simple autoassociative network could learn to distinguish paintings by Picasso and Braque which were reconstructed from only these primary dimensions of variation. The network could correctly classify the stimuli, and its performance was optimal with reconstructions based on just the first few eigenvectors. Then we reconstructed the paintings using either just the first 10 (early reconstructions) or all 1,894 eigenvectors (full reconstructions), and asked human participants to categorise the images. We found that people could categorise the images with either the early or full reconstructions. Therefore, people could learn to distinguish category membership based on the reduced set of dimensions obtained from SVD. This suggests that a dimension reduction mechanism analogous to SVD may be operating when people learn about the structure and regularities in complex categories.

Judgements of and sensitivity to style are ubiquitous. People become sensitive to the structural regularities of complex or “polymorphous” categories through exposure to individual examples, which allows them respond to new items that are of the same style as those previously experienced. This thesis investigates whether a dimension reduction mechanism could account for how people learn about the structure of complex categories. That is, whether through experience, people extract the primary dimensions of variation in a category and use these to analyse and categorise subsequent instances. We used Singular Value Decomposition (SVD) as the method of dimension reduction, which yields the main dimensions of variation of pixel-based stimuli (eigenvectors). We then tested whether a simple autoassociative network could learn to distinguish paintings by Picasso and Braque which were reconstructed from only these primary dimensions of variation. The network could correctly classify the stimuli, and its performance was optimal with reconstructions based on just the first few eigenvectors. Then we reconstructed the paintings using either just the first 10 (early reconstructions) or all 1,894 eigenvectors (full reconstructions), and asked human participants to categorise the images. We found that people could categorise the images with either the early or full reconstructions. Therefore, people could learn to distinguish category membership based on the reduced set of dimensions obtained from SVD. This suggests that a dimension reduction mechanism analogous to SVD may be operating when people learn about the structure and regularities in complex categories.

The first part of thesis focuses on theoretical introduction to the methods of text mining -- Information retrieval, classification and clustering. LSA method is presented as an advanced model for representing textual data. Furthermore, the work describes source data and methods for their preprocessing and preparation used to enhance the effectiveness of text mining methods. For each chosen text mining method there are defined evaluation metrics and used already existing, or newly implemented, programs are presented. The results of experiments comparing the effects of different preprocessing type and use of different models of the source data are then demonstrated and discussed in the conclusion.

This thesis investigates how recommendation systems has been used and can be used with the help of different machine learning algorithms. Algorithms used and presented are decision tree, random forest and singular-value decomposition(SVD). Together with Tingstad, we have tried to implement the SVD function on their recommendation engine in order to enhance the recommendation given. A trivial presentation on how the algorithms work. General information about machine learning and how we tried to implement it with Tingstad’s data. Implementations with Netflix’s and Movielens open-source dataset was done, estimated with RMSE and MAE.

Com o avanço da internet, novos tipos de negócios surgiram. Por exemplo, o sistema de anúncios online: produtores de sites e diversos outros conteúdos podem dedicar em uma parte qualquer de sua página um espaço para a impressão de anúncios de diversas lojas em troca de um valor oferecido pelo anunciante. É neste contexto que este trabalho se insere. O objetivo principal é o desenvolvimento de algoritmos que preveem a probabilidade que um dado usuário tem de se interessar e clicar em um anúncio a que está sendo exposto. Este problema é conhecido como predição de CTR (do inglês, \"Click-Through Rate\") ou taxa de conversão. Utiliza-se para isto uma abordagem baseada em regressão logística integrada a técnicas de fatoração de matriz que preveem, através da obtenção de fatores latentes do problema, a probabilidade de conversão para um anúncio impresso em dado site. Além disto, testes considerando uma estratégia dinâmica (em função do tempo) são apresentados indicando que o desempenho previamente obtido pode melhorar ainda mais. De acordo com o conhecimento do autor, esta é a primeira vez que este procedimento é relatado na literatura. / With the popularization of the internet, new types of business are emerging. An example is the online marketing system: publishers can dedicate in any given space of theirs websites a place to the printing of banners from different stores in exchange for a fee paid by the advertiser. It\'s in this context that this work takes place. Its main goal will be the development of algorithms that forecasts the probability that a given user will get interested in the ad he or she is seeing and click it. This problem is also known as CTR Prediction Task. To do so, a logistic regression approach is used combined with matrix factorization techniques that predict, through latent factor models, the probability that the click will occur. On top of that, several tests are conducted utilizing a dynamic approach (varying in function of time) revealing that the performance can increase even higher. According to the authors knowledge, this is the first time this test is conducted on the literature of CTR prediction.

Equality between women and men is one ongoing question. Even though women represent half of the world's population she is barely visible in any media. Global, national and lokal studies shows that the man is, and has been, medially over-represented for a very long time. But equality is not just about the quantitative space, but also the general presentation. Therefore, this study examines gender representation and stereotypes in the Swedish childrens papers Kamratposten and SvD Junior. By a quantitative content analysis two coding schemes have been constructed based on Yvonne Hirdmans theory about the gender order, Stuart Halls representation theory and Jesper Strömbäcks theoretical summary of the media logic. By analysing both text and pictures this study have been able to answer its three issues. The result is later presented with both diagrams and descriptive text where it shown how the outcome goes against the previous researches and this study is by that in some cases unexpected. In summary both Kamratposten and SvD Junior contains about the same percentages girls/woman and boys/men. This sort of equality is not common in earlier studies. However, the stereotypes is a very common occurrence in both papers which also have been proved to be the most prominent features in other Swedish media. For example, girls/women are quoted primarily within softer subjects, such as family, feelings and relationships. Instead, boys/men are quoted within harder subjects like sport, games and science. The total result of this study thereby shows that the papers is very much identical in every sense.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第17106号 / 工博第3617号 / 新制||工||1549(附属図書館) / 29836 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 杉浦 邦征, 教授 白土 博通, 教授 河野 広隆 / 学位規則第4条第1項該当

This report is a master thesis, written by an engineering physics and electrical engineering student at Luleå University of Technology.The desires of this project was to remove dislocations from wrapped phase maps using sparse reconstructive methods. Dislocations is an error that can appear in phase maps due to improper filtering or inadequate sampling. Dislocations makes it impossible to correctly unwrap the phasemap.The report contains a mathematical description of a sparse reconstructive method. The sparse reconstructive method is based on KSVDbox which was created by R. Rubinstein and is free for download and use. The KSVDbox is a MATLAB implementation of a dictionary learning algorithm called K-SVD with Orthogonal Matching Pursuit and a sparse reconstructive algorithm. A guide for adapting the toolbox for inpainting is included, with a couple of examples on natural images which supports the suggested adaptation. For experimental purposes a set of simulated wrapped phase maps with and without disloca-tions were created. These simulated phase maps are based on work by P. Picart. The MATLAB implementation that was used to generate these test images can be found in the appendix of this report such that they can easily be generated by anyone who has the interest to do so. Finally the report leads to an outline of five different experiments that was designed to test the KSVDbox for the processing of dislocations. Each one of these experiments uses a different dictionary. These experiments are due to inpainting with, 1. A dictionary based on Discrete Cosine Transform. 2. An adaptive dictionary, where the dictionary learning algorithm has been shown what thearea in the phase map that was damaged by dislocations should look like. 3. An adaptive dictionary, where the dictionary learning algorithm has been allowed to trainon the phase map that with damages. This is done such that areas with dislocations areignored. 4. An adaptive dictionary, where training is done on a separate image that has been designedto contain general phase patterns. 5. An adaptive dictionary, that results from concatenating the dictionaries used in experiment 3 and 4. The first three experiments are complimented with experiments done on a natural image for comparison purposes.The results show that sparse reconstructive methods, when using the scheme used in this work, is unsuitable for processing of dislocations in phase maps. This is most likely because the reconstructive method has difficulties in acquiring a high contrast reconstruction and there is nothing in the algorithm that causes the inpainting from any direction to match with the inpainting from other directions.

Ingeniero Civil Eléctrico / La identificación de husos sigma se realiza manualmente por expertos en la medicina del sueño. El proceso consiste en inspeccionar el electroencefalograma (EEG) de los registros polisomnográficos y marcar los intervalos en los que se observan los patrones. Este proceso es bastante tedioso y complicado, especialmente considerando que se buscan patrones de onda que no suelen durar más de algunos segundos en registros de aproximadamente 8 horas. Para aliviar el trabajo de los expertos se han desarrollado sistemas automáticos de detección de husos sigma capaces de identificar estos patrones en el EEG. En esta memoria se propone un nuevo método de detección automático de husos sigma en que se entrenan las formas de onda de un diccionario, usando un algoritmo de aprendizaje supervisado, para que éstas sean representativas de los husos sigma. Posteriormente, se utiliza un modelo de descomposición de señal para descomponer la señal de un canal del EEG en un número finito de componentes representados por la convolución entre las formas de onda del diccionario aprendido y un conjunto de trenes de pulsos que indican los intervalos de la señal donde se identifican patrones de onda semejantes a las formas de onda del diccionario aprendido. Los intervalos de la señal que son descompuestos por el modelo de descomposición, son consideradas como las detecciones del método, debido a que estos intervalos presentan una alta correlacción con las formas de onda representativas de los husos sigma que componen el diccionario aprendido. En el desarrollo de este método se utilizó un único registro polisom- nográfico de un niño de 10 años, con el cual se formaron los conjuntos de entrenamiento y de prueba usando fragmentos del registro en la etapa de sueño N2. El método obtuvo resultados preliminares satisfactorios que verifican su capacidad para detector husos sigma en la etapa de sueño N2 de un registro polisomnográfico, con una tasa de verdaderos positivos promedio de 85,080 % y una tasa de falsos positivos promedio de 14,995 %. El método de detección de husos sigma propuesto ofrece una metodología novedosa que no utiliza los usuales métodos espectrales para analizar el EEG. Además con este proceso se obtiene un diccionario con formas de onda representativas de los husos sigma que se puede utilizar para estudiar y caracterizar los husos sigma detectados por el método.