IPTV is a growing form of distribution for TV and media. Reports show that the market will grow from the current 20-30 million subscribers to almost 100 million 2012. IPTV extends the traditional TV viewing with new services like renting movies from your TV. It could also be seen as a bridge between the traditional broadcast approach and the new on demand approach the users are used to from internet. Since there are many actors in the IPTV market that all deliver the same basic functionality, companies must deliver better products that separate them from the competitors. This can be done either through doing things better than the others and/or delivering functionality that others can’t deliver. This thesis project presents the development of a prototype system for serving user generated content in the IPTV middleware Dreamgallery. The developed prototype is a fully working system that includes (1) a fully automated system for transcoding, of video content. (2) A web portal presented with solutions for problems related to user content uploading and administration. (3) Seamless integration with the Dreamgallery middleware and end user GUI, with two different ways of viewing content. One way for easy exploration of new content and a second more structured way of browsing the content. A study of three open source encoding softwares is also presented. The three encoders were subjects to tests of: speed, agility (file format support) and how well they handle files with corrupted data.

Background: The usage of the Force-Velocity relationship for individualizing training regimes for athletes has increased in popularity. This can be done through measurements of muscle mechanical properties and creating individual force-velocity profiles. To do this, one must use valid and reliable test equipment. These types of equipment are often expensive and impractical, which limits the usage to a small population with the right financial means. Therefore, the purpose of this study was to examine the concurrent validity of the inertial sensor Vmaxpro for measuring muscle mechanical properties. Method: 52 male ice-hockey players (age:17.9 ± 2.2 years, body weight: 77.7 ± 10.6 kg, height: 180.3 ± 6.2 cm) participated in this study and performed two jumps each on four different loading conditions (unloaded, 25, 50 and 75% of BW). The jumps were recorded simultaneously with an inertial sensor and a linear transducer. Three different variables were analyzed: peak velocity (pV), average velocity (avgV) and average power (avgP). Pearson’s correlation coefficient (r), linear regression analysis, Bland-Altman analysis, and standard error of estimate (SEE) was used to examine the concurrent validity. Results: The results showed a strong correlation, agreement and small SEE for pV: r=0.98, bias = -0.12, SEE = 0.08, for avgV: r=0.98 bias = 0.01, SEE = 0.04 and for avgP: r = 0.97, bias = 30.94, SEE = 73.47. Practical application: The results from the present study indicate that the Vmaxpro can be used for assessing muscle mechanical properties. Furthermore, since the Vmaxpro is both cheap and portable, it can potentially expand the usage of test equipment to clubs and associations with limited budgets.

Aim of this thesis is to design and construct odometric sensors for a mobile robot with Ackermann steering Bender 2 and to design a mathematical model which would evaluate the the trajectory of the robot using measured data of these sensors. The first part summarizes theoretical knowledge, while the second, the practical part, describes the design of the front axle, the design and the operating software of the front encoders and the odometric models. The last part deals with the processing and evaluation of the measured data.

The diploma thesis focuses on the electronics design of a hobby CNC milling machine. There are analysed and described the individual electronic parts and components related to this problematic. Three electronics suggestions are made, these suggestions are compared. Then, one of these variants is selected and implemented. Next, is created a relevant PLC program to control the CNC milling machine with visualization for easy control.

Diploma thesis is focused on real-time 4K video encoding using NVENC technology. First chapter describes the most used video codecs H.264 and HEVC. There is an explanation of the principle of graphic cards and their programmable units. Analysis of the solution of open source Video Codec SDK is also part of the thesis. The main focus of the thesis is an implementation of an application which can handle 4K video encoding from multiple cameras in real time. Performance and qualitative tests were performed for application. Results of these tests were analyzed and discussed.

This Diploma thesis aims to explain the data coding using turbo codes. These codes belong to the group of error correction codes. We can reach the high efficiency using these codes. The first part describes process of encoding and decoding. There are describes parts of encoder and decoder. Principle of encoding and decoding demonstrate a simple example. The end of this part contains description of two most frequently used decoding algorithms (SOVA and MAP). The second part contains description of computer program that was made for using as teaching aid. This program was created in Matlab GUI. This program enables to browse error correction process step by step. This program contains graphic interface with many options and display results. In the third part is described program created in Matlab Simulink that was implemented into the TMS320C6713 kit and there is description of measuring procedure. For verification of efficiency of turbo codes was measured any parameters. Some of these parameters are: number of decoding iterations, generating polynoms and using of puncturing. The last part contains measured value and result evaluation.

In this diploma thesis an algorithm for the evaluation of picture quality of H.264-coded video sequences is introduced and applied. As a measure of picture quality objective metric the peak signal to noise ratio (PSNR) is used. While the computation of the PSNR usually requires a reference signal and compares it to the distorted video sequence, this algorithm is able to evaluate PSNR following the coded transform coefficients. Thus, no reference signal is needed.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19138号 / 情博第584号 / 新制||情||102(附属図書館) / 32089 / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 佐藤 高史, 教授 小野寺 秀俊, 教授 髙木 直史 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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[pt] Esta dissertação propõe e avalia estratégias baseadas nos Stacked Supervised Auto-encoders (SSAE) para representação de imagens faciais em aplicações de vídeo vigilância. O estudo foca na identificação de faces a partir de uma amostra por pessoa na galeria (single sample per person - SSPP). Variações em termos de pose, expressão facial, iluminação e oclusão são abordadas de duas formas. Primeiro, o SSAE extrai atributos das imagens de faces que são robustos contra tais variações. Segundo, exploram-se as múltiplas amostras que podem ser coletadas nas sequências de vídeo de uma pessoa (multiple samples per person probe - MSPPP). Os métodos propostos foram avaliados e comparados usando os bancos de vídeos Honda/UCSD e VIDTIMIT. Adicionalmente, foi estudada a influência de parâmetros relacionados com a arquitetura do SSAE utilizando o banco de imagens estáticas Extended Yale B. Os resultados demonstraram que as estratégias que exploram as MSPPP em combinação com o SSAE podem superar o desempenho de outros métodos SSPP, como os Padrões Binários Locais (LBP), para reconhecimento de faces em vídeos. / [en] This work proposes and evaluates strategies based on Stacked Supervised Auto-encoders (SSAE) for face representation in video surveillance applications. The study focuses on the identification task with a single sample per person (SSPP) in the gallery. Variations in terms of pose, facial expression, illumination and occlusion are approached in two ways. First, the SSAE extracts features from face images, which are robust to such variations. Second, multiple samples per persons probes (MSPPP) that can be extracted from video sequences are exploited to improve recognition accuracy. The proposed methods were compared upon Honda/UCSD and VIDTIMIT video datasets. Additionally, the influence of the parameters related to SSAE architecture was studied using the Extended Yale B dataset. The experimental results demonstrated that strategies combining SSAE and MSPPP are able to outperform other SSPP methods, such as local binary patterns, in face recognition from video.