New Algorithms for General Sensors or How to Improve Electronic Noses

Sammanfattning: This thesis consists of three parts. The first part describes some new algorithms that we have invented for use in the field of sensor technology. Sensor technology is evolving rapidly and new sensors such as electronic noses and tongues have emerged on the market. Most of these sensors are non-specific and needs to be trained before real life usage. We have developed algorithms to ease the training of these sensors. The first algorithm is a superior algorithm (called ODP) for supervised feature extraction. This algorithm outperforms PCA and LDA. It consists of powerful pre-processing - to avoid statistical problems - as well as a method for minimizing the classification error in the variable reduced space. ODP is protected by a patent application. The second new algorithm (called GBP) is used for variable reduction when data consists of sensory panel judgments of samples as either "good" or "bad". GBP is protected by a patent application. The third algorithm part consists of some algorithms for measuring the information content in multi-cluster data thereby facilitating objective statements about the performance of sensors and algorithms.In the second part of the thesis we try to model the response of the electronic tongue through a first and a second order model. The second order model has five parameters and shows very good fit with experimental data. It may in the future help to compress tongue data as well as doing noise rejection.The third part consists of one old work that I did on hybrid control systems and is poorly related the rest of the work in this thesis.