O presente trabalho apresenta um benchmark para avaliar o desempenho de uma plataforma embarcada WandBoard Quad no processamento de imagens, considerando o uso da sua GPU Vivante GC2000 na execução de rotinas usando OpenGL ES 2.0. Para esse fim, foi tomado por base a execução de filtros de imagem em CPU e GPU. Os filtros são as aplicações mais comumente utilizadas em processamento de imagens, que por sua vez operam por meio de convoluções, técnica esta que faz uso de sucessivas multiplicações matriciais, o que justifica um alto custo computacional dos algoritmos de filtros de imagem em processamento de imagens. Dessa forma, o emprego da GPU em sistemas embarcados é uma interessante alternativa que torna viável a realização de processamento de imagem nestes sistemas, pois além de fazer uso de um recurso presente em uma grande gama de dispositivos presentes no mercado, é capaz de acelerar a execução de algoritmos de processamento de imagem, que por sua vez são a base para aplicações de visão computacional tais como reconhecimento facial, reconhecimento de gestos, dentre outras. Tais aplicações tornam-se cada vez mais requisitadas em um cenário de uso e consumo em aplicações modernas de sistemas embarcados. Para embasar esse objetivo foram realizados estudos comparativos de desempenho entre sistemas e entre bibliotecas capazes de auxiliar no aproveitamento de recursos de processadores multicore. Para comprovar o potencial do assunto abordado e fundamentar a proposta do presente trabalho, foi realizado um benchmark na forma de uma sequência de testes, tendo como alvo uma aplicação modelo que executa o algoritmo do Filtro de Sobel sobre um fluxo de imagens capturadas de uma webcam. A aplicação foi executada diretamente na CPU e também na GPU embarcada. Como resultado, a execução em GPU por meio de OpenGL ES 2.0 alcançou desempenho quase 10 vezes maior com relação à execução em CPU, e considerando tempos de readback, obteve ganho de desempenho total de até 4 vezes. / This work presents a benchmark for evaluating the performance of an embedded WandBoard Quad platform in image processing, considering the use of its GPU Vivante GC2000 in executing routines using OpenGL ES 2.0. To this goal, it has relied upon the execution of image filters in CPU and GPU. The filters are the most commonly applications used in image processing, which in turn operate through convolutions, a technique which makes use of successive matrix multiplications, which justifies a high computational cost of image filters algorithms for image processing. Thus, the use of the GPU for embedded systems is an interesting alternative that makes it feasible to image processing performing in these systems, as well as make use of a present feature in a wide range of devices on the market, it is able to accelerate image processing algorithms, which in turn are the basis for computer vision applications such as facial recognition, gesture recognition, among others. Such applications become increasingly required in a consumption and usage scenario in modern applications of embedded systems. To support this goal were carried out a comparative studies of performance between systems and between libraries capable of assisting in the use of multicore processors resources. To prove the potential of the subject matter and explain the purpose of this study, it was performed a benchmark in the form of a sequence of tests, targeting a model application that runs Sobel filter algorithm on a stream of images captured from a webcam. The application was performed directly on the embbedded CPU and GPU. As a result, running on GPU via OpenGL ES 2.0 performance achieved nearly 10 times higher with respect to the running CPU, and considering readback times, achieved total performance gain of up to 4 times.

This thesis aims generally at getting positives and negatives of popular operating system Android running on development kit Tiny6410 which is a part of FriendlyARM family. The operating system and development kit are designed to be used in user multimedia embedded applications with communication features. The goal of this thesis is to take a look on this framework and to measure main parameters of the operating system, specifically to measure switching context time and time for allocation and operation of memory block. Next goal is to create user application which will control and monitor connected oscilloscope DSO-2150 by Hantek via USB interface. This application will also demonstrate positives and negatives of all programming enviroments, which are available, for user application development with consideration of their optimal use.

This work presents a design and implementation of graphic user interfaces for board games. The objects of the investigation were various types of board games and graphic effects for virtual board game scene. We have developed a user interface design for different games. In the following sections the OpenGL ES library and its application to the Android platform are characterized. We explored the possibility of implementation in the native code. We have developed a system for plug-ins that allows implementing game logic in scripting language and defining the virtual scene for board game in XML language.

Tato práce se zaměřuje na technologii Cave Automatic Virtual Environment a konkrétně pak na využití principů rozšířené reality v tomto prostředí. Dává si za cíl odpovědět na otázku, zdali je možné použít v prostředí CAVE existující frameworky pro rozšířenou realitu, konkrétně ty, pracující na platformě iOS. Hlavní důraz je kladen na rozpoznávání markerů v tomto prostředí a na zvýšení přesnosti jejich rozpoznání. Práce odpovídá na množství otázek z této oblasti, jako jaké markery je vhodné použít, jaké jsou omezení a největší obtíže. Výstupem je demonstrační aplikace, pracující na platformě iOS, která v je prostředí CAVE otestovaná a plně použitelná. Tato aplikace by měla vylepšit uživatelský vjem z prostředí CAVE tím, že mu poskytne dodatečné informace a také základní možnosti interakce se zobrazenými objekty.