In this paper, we present a method to optimize Multi-Channel Free Space Optical Communication for statically aligned transmitter-receiver pairs. Pattern recognition algorithms are employed to minimize crosstalk between pixels, reducing the need for channel redundancy. Digitization is accomplished through comparison with several look up tables which are generated during alignment. Mathematical modeling has been performed to simulate the optical misalignment. A multistage automated alignment system can be developed based on the models. Simulation of the in plane and out-of-plane translation and rotation shows that this method builds a foundation of an effective self-healing precision optical alignment system.
We have designed optical processing architecture and algorithms utilizing the DMD as the input and filter Spatial Light Modulators (SLM). Detailed system analysis will be depicted. Experimental demonstration, for the first time, showing that a complex-valued spatial filtered can be successfully written on the DMDSLM using a Computer Generated Hologram (CGH)  encoding technique will also be provided. The high-resolution, high-bandwidth provided by the DMD and its potential low cost due to mass production will enable its vast defense and civil application.
In this paper, we present a method for reducing the computation time of Automated Target Recognition (ATR) algorithms through the utilization of the parallel computation on Graphics Processing Units (GPUs). A selected multistage ATR algorithm is refounded to encourage efficient execution on the GPU. Such refounding includes parallel reimplementations of optical correlation, Feature Extraction, Classification and Correlation using NVIDIA's CUDA programming model. This method is shown to significantly reduce computation time of the selected ATR algorithms allowing the potential for further complexity and real-time applications.
Automatic pattern recognition algorithms are implemented to correct distortion and remove noise from the optical
medium in the multi-channel optical communication systems. The post-processing involves filtering and correlation to
search for accurate location of every optical data element. Localized thresholding and neural network training methods
are used to accurately digitize the analog optical images into digital data pages. The goal is to minimize the bit-errorrate
(BER) in the optical data transmission and receiving process. Theoretical analysis and experimental tests have been
carried out to demonstrate the improved optical data retrieval accuracy.
We present the development of advanced automatic target recognition (ATR) algorithms for the hair
follicles identification in digital skin images to accurately direct the laser beam to remove the hair. The ATR system
first performs a wavelet filtering to enhance the contrast of the hair features in the image. The system then extracts
the unique features of the targets and sends the features to an Adaboost based classifier for training and recognition
operations. The ATR system automatically classifies the hair, moles, or other skin lesion and provides the accurate
coordinates of the intended hair follicle locations. The coordinates can be used to guide a scanning laser to focus
energy only on the hair follicles. The intended benefit would be to protect the skin from unwanted laser exposure
and to provide more effective skin therapy.