Gallery — Project / Lab Report

This document serves both as a template and a guideline for your report. You can replace headings and body text with your content. Before you start writing, however, we recommend that you carefully consider the instructions in this document.

In deze handleiding worden twee technieken beschreven voor visuele cryptografie. Bij de eerste techniek worden twee transparanten met schijnbaar willekeurige patronen van zwarte blokjes over elkaar geschoven om een geheime afbeelding tevoorschijn te laten komen. De tweede techniek gebruikt twee afbeeldingen in grijstinten die transparant over elkaar geschoven worden om een geheime afbeelding op te roepen. De enige voorkennis die nodig is om deze technieken te kunnen uitvoeren, is het gebruik van een rekenblad (hier: Excel) en van een fotobewerkingsprogramma.

Chicago-style for­mat­ting for Theses and Dissertations based on Kate L. Tura­bian's "A Man­ual for Writ­ers of Re­search Papers, Th­e­ses, and Dis­ser­ta­tions: Chicago Style for Stu­dents and Re­searchers," 9th edi­tion. More information: http://www.ctan.org/pkg/turabian-formatting

Esta es una plantilla utilizada para la presentación de trabajos prácticos, cuenta con elementos predefinidos y distintas secciones. Originalmente fue diseñana para la cátedra de Simulación de Ingeniería en Sistemas pero se puede utilizar para cualquier otra cátedra

MATLAB final report for MATH 210-01

This report documents the program and the outcomes of Dagstuhl Seminar TBD "Seminar Sample".

This paper is a report of an assignment of a course Computational Intelligence. The main goal of the assignment is to apply computational intelligence techniques in a practical setting - building a controller for a race car in The Open Racing Car Simulator (TORCS) using artificial neural network and evolving that network with evolutionary algorithm techniques. Keywords: computational intelligence, car controller, artificial neural network

A thesis/report template.

Gesture recognition and its implementation that support Human Computer systems are becoming very popular mode of interaction now a days. It allows to interfacing the man machine commutative information flow naturally. Vision based gesture recognition has the potential that can provide intuitive and effective interaction between man and machine. However there are not adequate tools and techniques that support for developing, detecting or executing these tasks. In this paper we will implement a prototype that facilitates recording data during building some action based activities captured by the Kinect sensor. We analyze those recorded clips and visualize the user interactions by recognition the gestures objects based on depth, IR and skeletal data. Kinect tools include an analysis feature, a time-line-based approach that manually or automatically can mark the recording sequences of clips. We will implement both discrete and continuous gestures by using AdaBoast machine learning approach to detect hands activities. Our result suggest that the learning mechanism can achieve more than 98% of confidence level of given gestures. Keywords: Gesture recognition, Kinect, HCI, Machine learning, AdaBoost, Computer vision