Joakim T. A. Waldemark

IMPLEMENTATION OF A PULSE COUPLED NEURAL NETWORK IN FPGA

The Pulse Coupled neural network, PCNN, is a biologically inspired neural net and it can be used in various image analysis applications, e.g. time-critical applications in the field of image pre-processing like segmentation, filtering, etc. a VHDL implementation of the PCNN targeting FPGA was undertaken and the results presented here. The implementation contains many interesting features. By pipelining the PCNN structure a very high throughput of 55 million neuron iterations per second could be achieved. By making the coefficients re-configurable during operation, a complete recognition system could be implemented on one, or maybe two, chip(s). Reconsidering the ranges and resolutions of the constants may save a lot of hardware, since the higher resolution requires larger multipliers, adders, memories etc.

Detection of apnea using a short-window FFT technique and an artificial neural network

Sleep apnea is characterized by frequent prolonged interruptions of breathing during sleep. This syndrome causes severe sleep disorders and is often responsible for development of other diseases such as heart problems, high blood pressure and daytime fatigue, etc. After diagnosis, sleep apnea is often successfully treated by applying positive air pressure (CPAP) to the mouth and nose. Although effective, the (CPAP) equipment takes up a lot of space and the connected mask causes a lot of inconvenience for the patients. This raised interest in developing new techniques for treatment of sleep apnea syndrome. Several studies have indicated that electrical stimulation of the hypoglossal nerve and muscle in the tongue may be a useful method for treating patients with severe sleep apnea. In order to be able to successfully prevent the occurrence of apnea it is necessary to have some technique for early and fast on-line detection or prediction of the apnea events. This paper suggests using measurements of respiratory airflow (mouth temperature). The signal processing for this task includes the use of a short window FFT technique and uses an artificial back propagation neural net to model or predict the occurrence of apneas. The results show that early detection of respiratory interruption is possible and that the delay time for this is small.

Image analysis for airborne reconnaissance and missile applications

This paper describes how the pulse coupled neural network (PCNN) can be used in various image analysis applications. We especially focus on two time-critical applications, in particular, airborne reconnaissance and missile navigation. Today, biologically inspired sensor analysis systems such as the PCNN can be used in many different applications related to these two major applications. New ideas are shown on how to use PCNN in combination with other image processing transforms, e.g. the Radon transform and foveation point detection to solve image interpretation and missile navigation problems. This includes solving tasks such as image segmentation, object detection and target identification. Finally, a VHDL implementation of the PCNN targeting FPGA is presented.

Neural networks and PCA for determining region of interest in sensory data preprocessing

Principal component analysis (PCA) and artificial neural networks are used to investigate electronic gas sensor responses for various alcohol chemicals. PCA is used to identify and visualize the best features to use for classification as well as for detecting outliers. A regular feed forward back propagation neural network (FBP) was used for the actual classification due to the fact that FBP determines better the non-linear borders of the various region of interest involved in the classification. Furthermore, we consider the tradeoff between classification speed and accuracy.

Generic VHDL implementation of a PCNN with loadable coefficients

This paper presents a general VHDL implementation of a Pulse Coupled Neural Network. The VHDL implementation is targeted for FPGA but can also be used with advantage for ASIC implementations. This particular case deals with images of the size 128 X 128 pixels coming at a rate of 60 images per second, each image iterated by the PCNN 70 times, i.e. a real time image processing system. Thanks to the generality, this suggested solution can easily be transformed into, e.g., a solution with images sized 32 X 32 pixels, coming at a speed of 960 images per second, assuming the same iteration length. The hardware requirement and problems are analyzed and solutions are proposed. Some problems that are dealt with are: the huge amount of data produced, the high throughput (i.e. the rate of new data produced) and the loading of coefficients during runtime.

Pulse-coupled neural network implementation in FPGA

Pulse Coupled Neural Networks (PCNN) are biologically inspired neural networks, mainly based on studies of the visual cortex of small mammals. The PCNN is very well suited as a pre- processor for image processing, particularly in connection with object isolation, edge detection and segmentation. Several implementations of PCNN on von Neumann computers, as well as on special parallel processing hardware devices (e.g. SIMD), exist. However, these implementations are not as flexible as required for many applications. Here we present an implementation in Field Programmable Gate Arrays (FPGA) together with a performance analysis. The FPGA hardware implementation may be considered a platform for further, extended implementations and easily expanded into various applications. The latter may include advanced on-line image analysis with close to real-time performance.

HUGIN: a small satellite trying to be intelligent

Micro and nano-satellites are important tools to explore and test new ideas and various new devices for space missions without spending extreme amounts of money. The actual launch cost per kilogram payload on a micro or nano-satellite can be as high or even higher than ordinary satellites but the turn around time and quick responses are extremely important. The HUGIN project is a nano-satellite (less than 10 kg) explicitly designed to test magnetic coils and adaptive artificial neural network (ANN) algorithms for attitude control purposes. A small PC video camera is also included and if the control function is successful then also tests of adaptive image processing using other ANN and biologically inspired methods will be performed.

Hadron energy reconstruction by combined calorimetry using a neural network

The energy event reconstruction for non-leptonic decays of the Z in the DELPHI experiment is a complicated task. The energy resolution is dependent considerably from the neutral hadrons energy measurement in the electromagnetic and hadron calorimeter of the detector.

Pulse-coupled neural networks for cruise missile guidance and mission planning

Mission planning and missile navigation control are important tasks to solve when dealing with cruise missiles. A large variation of solutions has been used all the way back to world war II and the German VI missile. Today, biologically inspired sensor analysis systems such as, e.g. pulsed coupled neural networks (PCNN), can be used in many different applications related to these two major tasks, mission planing and missile navigation. This paper discusses generally the cruise missile related problems and gives example on how they are being solved. New ideas as shown on how to use PCNN in combination with other image processing transforms, e.g. the radon transform, to solve the planning and navigation problems. This includes solving tasks such as image segmentation, target identification and maze navigation.