UIB-2001-01 DNA-based parallel computation of simple arithmetic

Autoren: Hubert Hug, Rainer Schuler

We propose a model for representing and manipulating binary numbers on a DNA-Chip which allows parallel execution of simple arithmetic. As an example we describe how addition of binary numbers can be done. In this example the number of steps is independent of the size (bits) of the numbers, however the time for some biochemical reactions is still large, and will increase with the size of the sequences to be assembled.


UIB-2001-02 3-D Visual Object Classification with Hierarchical Radial Basis Function Networks

Autoren: Friedhelm Schwenker, Hans A. Kestler, Günther Palm

In this chapter we present a 3-D visual object recognition system for an autonomous mobile robot. This object recognition system performs the following three tasks: Object localisation in the camera images, feature extraction, and classification of the extracted feature vectors with hierarchical radial basis function (RBF) networks.


UIB-2001-03 RBF network classification of ECGs as a potential marker for sudden cardiac death

Autoren: Hans A. Kestler, Friedhelm Schwenker, Günther Palm

Non-invasive risk assessment after myocardial infarction is a major but still unresolved goal in clinical cardiology. Various parameters such as ventricular late potentials, T-wave alternans, and repetitive ventricular extrasystoles have been shown to indicate an increased risk of sudden cardiac death. However, the practical use of these arrhythmic markers into clinical decision making remains difficult. In this chapter we will describe two approaches of risk stratification with RBF networks using high--fidelity ECG recordings. Based on these high--fidelity recordings different aspects of conduction defects are exemplarily investigated. The first utilizes established features derived from signal averaged QRS complexes (heartbeats) and the second investigation centers on capturing morphology changes within the QRS complex.


UIB-2001-04 Classification of Bioacoustic Time Series Utilizing Pulse Detection, Time and Frequency Features and Data Fusion

Autoren: Christian Dietrich, Klaus Riede, Friedhelm Schwenker, Günther Palm

Classifying the sounds of species is a fundamental challenge in the study of animal vocalizations. Most of these studies are based on manual inspection and labeling of sound spectra, which relies on agreement between human experts. In this study recorded songs of crickets (Grylloidea) from Thailand and Ecuador are analysed and classified automatically. For this, the locations of pulses are determined and different features from the time and frequency domain are extracted automatically from the time series. For the categorization of the sound patterns these different features are combined through data fusion, temporal fusion and decision fusion. Local features and global features of the sound patterns are distinguished. For the classification a fuzzy-k-nearest-neighbour classifier is used. This classifier scheme exhibits a large similarity to artificial neural networks, in particular to radial basis function neural networks. We present classification results for a data set of 28 different species.