This paper discusses the application of MOEM technology to adaptive optics. An experiment is described in which a micromachined mirror array is used in a closed loop adaptive optic demonstration. An interferometer wavefront sensor is used for wavefront sensing. Parallel analog electronics are used for the wavefront reconstruction. Parallel operational amplifiers are used to drive the micromirrors. The actuators utilize a novel silicon design developed by SY Technology, Inc. The actuators have a measured frequency response of 15kHz, and a maximum usable stroke of 4 microns. The entire adaptive optic demonstration has a bandwidth exceeding 10kHz. Measured performance is described. The experiments conducted are designed to explore the feasibility of creating a single chip adaptive optic system, also described in this paper. This chip would combine all on a single VLSI chip aspects of a complete adaptive optics system, wavefront sensing, wavefront reconstruction, and wavefront correction. The wavefront sensing would be accomplished with a novel compact shearing interferometer design. The analog refractive and diffractive micro optics will be fabricated using a new single step analog mask technology. The reconstruction circuit would use an analog resistive grid solver. The resistive grid would be fabricated in polysilicon. The drive circuits and micromirror actuators would use standard CMOS silicon fabrication methods.
The real-time recurrent learning (RTRL) algorithm was modified and applied to the task of function prediction. This recurrent neural network was modified to include both a variable learning rate, and a linear output combined with sigmoidal hidden units. The simple learning rate modification allows faster network convergence while avoiding most cases of catastrophic divergence. In addition, a linear output combined with hidden sigmoidal units enables the network to predict unbounded functions. The modified recurrent network was then used to simulate a linear system (second order Butterworth filter). In addition, the recurrent network was applied to two specific applications: predicting 3-D head position in time, and voice data reconstruction. The accuracy at which the network predicted the pilot's head position was compared to the best linear statistical prediction algorithm. The application of the network to the reconstruction of voice data showed the recurrent network's ability to learn temporally encoded sequences, and make decisions as to whether or not a speech signal sample was considered a fricative or a voiced portion of speech.
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