ISBN-13: 9783639036053 / Angielski / Miękka / 2008 / 100 str.
The only independent variable in electronic computing is time. Optical computing on the other hand, has inherently two degrees of freedom, the two variables that define a point in a plane. Optical systems always process information in parallel. Such a simple optical element as a lens is capable of performing such a difficult task as a Fourier transform. Therefore, when it comes to pattern classification, optical computing is an attractive option. The Phase Only Filter has been showed to be a powerful tool for tracking objects in a two-dimensional plane. In this research, a special tracking technique is developed to overcome weaknesses of the POF under noisy circumstances. The POF is generally implemented in the 2-D plane. However, the POF has neither been trained as a pattern classifier for one-dimensional data, nor as a multi-dimensional data classifier. Methods are developed in this work to apply the POF to multi-dimensional pattern classification. Moreover, POF equivalent neural network techniques are devised and implemented for pattern classification. Two level neural network is developed for the case of multi-class classification, and a method of training is developed."
The only independent variable in electronic computing is time. Optical computing on the other hand, has inherently two degrees of freedom, the two variables that define a point in a plane. Optical systems always process information in parallel. Such a simple optical element as a lens is capable of performing such a difficult task as a Fourier transform. Therefore, when it comes to pattern classification, optical computing is an attractive option. The Phase Only Filter has been showed to be a powerful tool for tracking objects in a two-dimensional plane. In this research, a special tracking technique is developed to overcome weaknesses of the POF under noisy circumstances. The POF is generally implemented in the 2-D plane. However, the POF has neither been trained as a pattern classifier for one-dimensional data, nor as a multi-dimensional data classifier. Methods are developed in this work to apply the POF to multi-dimensional pattern classification. Moreover, POF equivalent neural network techniques are devised and implemented for pattern classification. Two level neural network is developed for the case of multi-class classification, and a method of training is developed.