Optical Neural Network Classifier Architectures

We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and classification of high-dimensional data for Air Force Hostile Target Identification (HTI). This architecture utilizes a grayscale-input radial basis function neural network based on a previously demonstrated binary-input version. The greyscale-input capability broadens the range of applications for the classifier by allowing it to handle 8 bit input data. We characterized a key component of this system, a variable phase retarder, and found that the phase uniformity changed less than 7% with applied voltage. An optical wavelet transform preprocessor is also discussed. The preprocessor produces a reduced feature set of multiwavelet images to improve training times and discrimination capability of the neural network. The design uses a joint transform correlator (JTC) to provide cross correlations of multiple input images. We present experimental results for a JTC which used four input images generated with a spatial light modulator. We then propose using wavelet functions as input images to perform a multiwavelet feature extraction. The results from the retarder characterization and optical wavelet transform work were to be used in a software simulation of the neural network system to determine its feasibility. However, this work remains unfinished as this project was canceled due to budget cuts. Prepared in collaboration with Florida Inst. of Tech., Melbourne, FL.