Thomas Lindblad

Inherent features of wavelets and pulse coupled networks

Biologically inspired image/signal processing like the pulse coupled neural network (PCNN) and the wavelet (packet) transforms are described. The two methods are applied to two-dimensional data in order to demonstrate the features of each method, pinpoint differences as well as similarities. The inherent properties (with respect to filtering, segmentation, etc.) of the two approaches with respect to detectors for physics experiments as well as remote sensing are discussed.

Hardware Implementation of 1D Wavelet Transform on an FPGA for Infrasound Signal Classification

Infrasound is a low frequency acoustic phenomenon that typically ranges from 0.01 to 20 Hz. The data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, i.e., the Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important for real-time signal classification. Performing wavelet decomposition on the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of discrete wavelet transforms (DWT) for real-time infrasound data processing; our approach uses only two FIR filters, a high-pass and a low-pass filter. A compact implementation was realized with pipelining techniques and multiple use of generalized building blocks. The design was described in VHDL and the FPGA implementation and simulation were performed on the QUARTUS II platform.

Survey of neural network hardware

We survey the currently available neural network hardware, including VLSI chips (digital, analog, and hybrid), PC accelerator cards, and multi-board neurocomputers. We concentrate on commercial hardware, but also include a few prototypes of special interest. As examples of applications, some systems developed for high energy physics experiments that use this hardware are presented.

Patterns from the sky: satellite image analysis using pulse coupled neural networks for pre-processing, segmentation and edge detection

In this work we attempt to distinguish land from water in satellite images, specifically images taken by the FORTE satellite. First, we successfully approximate areas hidden by stationary artefacts in the image. We then segment regions of land from water. Finally, we determine the boundaries of the surrounding landmasses.

Automatic design of pulse coupled neurons for image segmentation

Automatic Design of Algorithms through Evolution (ADATE) is a program synthesis system that creates recursive programs in a functional language with automatic invention of recursive help functions and self-adaptive optimization of numerical values. We implement a neuron in a pulse coupled neural network (PCNN) as a recursive function in the ADATE language and then use ADATE to automatically evolve better PCNN neurons for image segmentation. Our technique is generally applicable for automatic improvement of most image processing algorithms and neural computing methods. It may be used either to generally improve a given implementation or to tailor that implementation to a specific problem, which with respect to image segmentation for example can be road following for autonomous vehicles or infrared image segmentation for heat seeking missiles that are to distinguish the heat source of the target from flares.

Different volatile signals emitted by human ovarian carcinoma and healthy tissue

UNLABELLED Many cancers are detected at a late stage resulting in high mortality rates. Thus, it is essential to develop inexpensive and simple methods for early diagnosis. Detection of different malignancies using canine scent, as well as other technical methods, has been reported in peer-reviewed journals, indicating that this may represent a new diagnostic tool for malignancies. AIM This study aims to test the detection of different volatile organic compound signals emitted by ovarian carcinoma and normal tissues. MATERIALS & METHODS A previously tested electronic nose is used in the pilot study to analyze human grade 3 seropapillary ovarian carcinoma samples. The recorded signals were compared with healthy human Fallopian tube specimens. A variety of algorithms were tested and confusion matrices compared. In parallel, an external validation study was performed using the same type and grade of human ovarian carcinomas with healthy myometrium (first part) and postmenopausal ovarium (second part) specimens as controls. Both sample types were obtained from individuals who did not participate in the pilot study. RESULTS Method sensitivity was 100% (15 of 15) in the pilot study. The first part of the validation study demonstrated that 84.8% of cancer tissues (sensitivity: 84.8%) and 88.6% of the control samples (specificity: 88.6%) were correctly classified. In the second part the JRip algorithm correctly classified 75% of cancer tissues (sensitivity: 75%) and 80% of the control ovarian tissues (specificity: 80%). Collating results gives a sensitivity of 84.4%, whereas overall specificity was 86.8%. CONCLUSION Although based on a limited number of samples, our results strongly suggest that specific volatile organic compound signals emitted by ovarian carcinomas may be used for early diagnosis of the disease.

Implementation of pulse-coupled neural networks in a CNAPS environment

Pulse coupled neural networks (PCNNs) are biologically inspired algorithms very well suited for image/signal preprocessing. While several analog implementations are proposed we suggest a digital implementation in an existing environment, the connected network of adapted processors system (CNAPS). The reason for this is two fold. First, CNAPS is a commercially available chip which has been used for several neural-network implementations. Second, the PCNN is, in almost all applications, a very efficient component of a system requiring subsequent and additional processing. This may include gating, Fourier transforms, neural classifiers, data mining, etc, with or without feedback to the PCNN.

Parameter adaptation in a simplified pulse-coupled neural network

In a general purpose pulse coupled neural network (PCNN) algorithm the following parameters are used: 2 weight matrices, 3 time constants, 3 normalization factors and 2 further parameters. In a given application, one has to determine the near optimal parameter set to achieve the desired goal. Here a simplified PCNN is described which contains a parameter fitting part, in the least squares sense. Given input and a desired output image, the program is able to determine the optimal value of a selected PCNN parameter. This method can be extended to more general PCNN algorithms, because partial derivatives are not required for the fitting. Only the sum of squares of the differences is used.

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.

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.