Paul E. Keller

Overview of electronic nose algorithms

The electronic nose is a natural match for physiologically motivated odor analysis. Both the olfactory system and the electronic nose consist of an array of chemical sensing elements and a pattern recognition system. This paper reviews different approaches to chemical data analysis (i.e., chemometrics) found in both commercial and experimental electronic nose systems. The electronic nose algorithms discussed include those based on statistical methods, standard artificial neural network approaches, and those based on advanced biological models of the olfactory system.

Pulse-coupled neural networks for medical image analysis

Pulse-coupled neural networks (PCNNs) have recently become fashionable for image processing. This paper discusses some of the advantages and disadvantages of PCNNs for performing image segmentation in the realm of medical diagnostics. PCNNs were tested with magnetic resonance imagery (MRI) of the brian and abdominal region and nuclear scintigraphic imagery of the lungs (V/Q scans). Our preliminary results show that PCNNs do well at contrast enhancement. They also do well at image segmentation when each segment is approximately uniform in intensity. However, there are limits to what PCNNs can do. For example, when intensity significantly varies across a single segment, that segment does not properly separate from other objects. Another problem with the PCNN is properly setting the various parameters so that a uniform response is achieved over a set of imagery. Sometimes, a set of parameters that properly segment objects in one image fail on a similar image.

Mimicking biology: applications of cognitive systems to electronic noses

The electronic nose draws its inspiration from biology. Both the electronic nose and the biological olfactory system consist of an array of chemical sensing elements and a pattern recognition system. This paper reviews the basic concepts of electronic noses and their relationship to biological olfaction. Different approaches to chemical data analysis including statistical methods, standard artificial neural network approaches, and those based on advanced biological models of the olfaction are described. Finally, a prototype system is reviewed.

Physiologically inspired pattern recognition for electronic noses

The electronic noise is a natural match for physiologically motivated chemical data analysis. Both the olfactory system and the electronic nose consist of an array of chemical sensing elements and a pattern recognition system. Physiologically motivated approaches to automated chemical analysis with electronic noses are discussed in this paper. Also, applications of electronic noses to environmental sensing, food processing, and medicine are referenced. The quantity and complexity of the data collected by sensor arrays can make conventional chemical analysis of data in an automated fashion difficult. One approach to odor or volatile compound identification is to build an array of sensors, where each sensor in the array is designed to respond to a specific chemical. With this approach, the number of unique sensors must be at least as great as the number of chemicals being monitored. It is both expensive and difficult to build highly selective chemical sensors. An alternative approach is to use sensors that have a broader response and rely on advanced information processing to discriminate between different chemicals. This latter approach was inspired by biological olfactory systems and is the approach incorporated in electronic noses to reduce the requirements on both the number and the selectivity of the sensors.

Segmentation of medical imagery with pulse-coupled neural networks

This paper discusses some of the advantages and disadvantages of pulse-coupled neural networks (PCNNs) for performing image segmentation in the realm of medical diagnostics. PCNNs were tested with magnetic resonance imagery (MRI) of the brain and abdominal region and nuclear scintigraphic ventilation/perfusion imagery of the lungs (V/Q scans). Our preliminary results show that PCNNs do well at contrast enhancement. They also do well at image segmentation when each segment is approximately uniform in intensity. However, there are limits to what PCNNs can do. For example, when intensity significantly varies across a single segment, that segment does not properly separate from other objects. Another difficulty is finding the optimum parameter values so that a uniform response is achieved over a set of imagery. Also, a set of parameters that properly segments objects in one image is sometimes unsuccessful on a similar image.

Neural-network-based data analysis for chemical sensor arrays

Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. In this paper, we examine the effectiveness of using artificial neural networks for real-time data analysis of a sensor array. Analyzing the sensor data in parallel may allow for rapid identification of contaminants in the field without requiring highly selective individual sensors. We use a prototype sensor array which consists of nine tin-oxide Taguchi-type sensors, a temperature sensor, and a humidity sensor. We illustrate that by using neural network based analysis of the sensor data, the selectivity of the sensor array may be significantly improved, especially when some (or all) of the sensors are not highly selective.