Åge Eide

Feature extraction from photographic images using a hybrid neural network

A new method for extracting features from photographic images has been developed. The input image is through a pulse coupled neural network transformed to a set of signatures, well suited for classification by unsupervised neural networks. A strategy using multiple self-organizing feature maps in a hierarchical manner is developed. With this approach, using a certain degree of supervision, an acceptable classification is obtained when applied to test images. The method is applied to license plate recognition.

Method for star identification using neural networks

Identification of star constellations with an onboard star tracker provides the highest precision of all attitude determination techniques for spacecraft. A method for identification of star constellations inspired by neural network (NNW) techniques is presented. It compares feature vectors derived from histograms of distances to multiple stars around the unknown star. The NNW method appears most robust with respect to position noise and would require a smaller database than conventional methods, especially for small fields of view. The neural network method is quite slow when performed on a sequential (serial) processor, but would provide very high speed if implemented in special hardware. Such hardware solutions could also yield lower low weight and low power consumption, both important features for small satellites.

Filtering data from a drift-chamber detector using neural networks

Abstract This paper describes the implementation of an analog neural network chip (Intel 80170NX ETANN) to filter a multi-signal read-out from flash-ADCs used in a liquid argon time projection chamber (ICARUS). The training and subsequent testing of the network is discussed in some detail. A network consisting of multiple sub-networks is proposed to perform signal filtering, determination of peak position and track finding.

Implementing the dynamic decay adjustment algorithm in a CNAPS parallel computer system

Abstract This paper presents the implementation of the Dynamic Decay Adjustment (DDA) algorithm in a CNAPS parallel computer system having 128 processing nodes. The DDA algorithm has several inherent advantages, and the implementation of it in the CNAPS system is shown to perform very well. The DDA implementation is first tested with noisy character patterns to demonstrate its general inherent noise resistance. A more realistic application test involving the identification of Higgs events is then presented. Using the momenta and transverse momenta of the four leading particles from the H 0 → Z 0 Z 0 → μ + μ − μ + μ − decay following gg and W + W − fusion, it is possible to obtain a good indentification of these events as well as good rejection of the background.

Treatment of obstructive sleep apnea syndrome by monitoring patients airflow signals

The breathing patterns from sleeping persons suffering from sleep apnea have been measured. A method based on the neural network-like O-algorithm has been applied to capture the onset of sleep apnea. This method is suggested as an indicator for early on-line detection of obstructions in the upper airway. Results from the system tested with airflow signals recorded from five patients during sleep indicate acceptable performance and treatment for developing apnea is possible.

Audio signal identification via pattern capture and template matching

Abstract This research reports on a system able to classify different signals containing auditive information based on capture of small signal segments present in specific types of sound. After using a Haar wavelet transform at the preprocessing stage, a neural network known as the O-algorithm compares segments from candidate audio signals against predefined templates stored in the network. The classification performance is tested with three different applications.

Artificial neural networks as measuring devices

Abstract A neural network, yielding full control of the results of the uncertainties in the data set, is developed. The network may be used as a tool in order to calculate physical parameters, as well as a “traditional” neural network for pattern recognition.

Wavelets and signal processing

We briefly review the use of the wavelet and wavelet packet transforms. We describe their application to signal processing as devices for feature extraction and reduction of data for neural networks, in particular their implementation in hardware for signal identification.

Implementing the new Zero Instruction Set Computer (ZISC036) from IBM for a Higgs search

Abstract Implementation of the new IBM Zero Instruction Set Computer (ZISC036) on a PC/ISA-bus card as well as on a VME-card is reported. The ZISC circuit has 36 processing elements of a type similar to that of Radial Basis Function (RBF) neurons. It is a highly parallel and cascadable building block with on-chip learning capability, and is well suited for pattern recognition, signal processing, etc. Results of a test on identification of simulated Higgs events are given.

Investigation of a VLSI neural network chip as part of a secondary vertex trigger

Abstract An analog VLSI neural network chip (ETANN) has been trained to detect secondary vertices in simulated data for a fixed target heavy flavour production experiment. The detector response and associative memory track finding were modelled by a simulation, but the vertex detection was performed in hardware by the neural network chip and requires only a few microseconds per event. The chip correctly tags 30% of the heavy flavour events while rejecting 99% of the background, and is thus well adapted for secondary vertex triggering applications. A general purpose VME module for interfacing the ETANN to experiments, equipped with ADC/DAC circuits and a 68070 CPU, is also presented.