Very Small Information Systems/Spring 2006
Information about the course in summer semester 2006.
Students, please add your group/project here under a main heading. If the content gets too large you can move it to a sub-page. See the example section below.
Project Page[edit | edit source]
What goes to your project page?
- Short projects description
- Draft version and final paper
Group A - Intelligent Humidity and heating control[edit | edit source]
Jonas Kortsen, Niels Walhgreen, Per Chr. Møller and Christian Almskou
Wooden houses are known to have problems with low temperatures and extremes of relative humidity. It is known that wooden houses, and in particular summerhouses, that are not occupied all year round, need to be monitored to make sure that humidity and temperature does not exceed certain levels. Therefore our systems primary goal is to help avoid woodwork damages caused by humidity. This is solved through monitoring and adjustment of the indoors climate. This is done through the use of intelligent humidity and temperature control, and remote monitoring by use of a built in webserver. In addition, the system is capable of logging data about its surrounding conditions, enabling improvement of the enclosed algorithms. To support the control of humidity, we have developed a range of hardware devices providing the necessary inputs and enabling the actual environment control.
Group B - Intelligent Home Control: Activity-based Intelligent Heat Regulation[edit | edit source]
Simon Andreassen, Thomas Lerche, Claus Egholm Nielsen and Morten Milbak.
Abstract: This project concerns the use of the JOP controller board in the effort of reducing the energy consumption used on heating in a regular residence. By means of a set of motion sensors, data is collected about activity in the residence. The intention is to predict periods during the day with inactivity in specific parts of the residence with the aim to reduce the power consumed within these periods. In order to accomplish this prediction, machine learning methods have been applied. To gain information of the activity level we have focused on the use of infrared motion sensors connected directly to the JOP controller board. Behavioral patterns are identified which is used to predict future events of activity. This prediction has enabled our system to successfully control the heating in a house without any manual user interaction.
Group C - Barcode recognition under resource limitations[edit | edit source]
Morten Okholm, Troels Nielsen and Søren Krabbe.
Research has been conducted on recognising barcodes from images and a few commercial services has been developed. This document outlines a project that will look into the resource requirements for such recognition and try to take it one step further by experimenting with real time filtering of nonbarcode images from a stream of images, still under heavy resource limitations. This will allow barcode scanning on small camera devices to work more similar to traditional barcode scanners, where the scanner registers when a barcode is in view to be scanned with no user interaction. The role of JOP in this project is to act as a representative for small devices, meaning that a wishful outcome of the project is guidelines and recommendations for implementing the concept in 'real life' small devices like cell phones.
Group D - Voice recognition[edit | edit source]
Christian B. Olsen, Jacob H. Svalastoga, Michael J. Moltke and Thomas S. Olesen
It has always been an issue in different situations to be able to identify an individual. Today when more and more interactions take place electronically it becomes increasingly relevant to be able to identify an individual in this way. The ultimate way to do this is by using biometrics. In this paper we will concentrate on the specific field in biometrics called speaker recognition, identifying a person from the sound of their voice. We will extract the easily found amplitude values per timeperiod from a normal voicesample soundwave. With the Fast Fourier Transform theorem we move from the time domain to the frequency domain, and by using the Nearest Neighbour data mining algorithm we are finally able to distinquish the voice of one individual from another.
Group E - JOP Stock Monitor[edit | edit source]
Nikolaj Ostri and Christian Christiansen
The scope of this paper is to develop software for data mining on real-time stock rates to support buying decisions for stock traders. The application is intended to run on a mobile device like a mobile phone or PDA.
Trading stocks has become something every person with a computer can do. Unfortunately trading stocks can be very risky and especially short-term trading (less than one year)is close to 50/50 betting.
The concrete implementation of the stock monitor will first happen on a normal PC and several experiments will be run to decide which algorithm is most accurate and able to deliver the best return on investment.
Given this is an application which is intended to be run on a very small processor we will after deciding on algorithm implement a prototype on a FPGA board.