Engineering topics
Publications and source records attributed to Hanan, Jay C..
A method, apparatus, and system provide the ability for storing holograms at high speed. A single laser diode emits a collimated laser beam to both write to and read from a photorefractice crystal. One or more liquid crystal beam steering spatial light modulators (BSSLMs) steer a reference beam, split from the collimated laser beam, at high speed to the photorefractive crystal.
A method, apparatus, and system provide the ability for storing holograms at high speed. A single laser diode emits a collimated laser beam to both write to and read from a photorefractice crystal. One or more liquid crystal beam steering spatial light modulators (BSSLMs) or Micro-Electro-Mechanical Systems (MEMS) mirrors steer a reference beam, split from the collimated laser beam, at high speed to the photorefractive crystal.
In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.
At the Optical Computing Lab in the Jet Propulsion Laboratory (JPL) a binary holographic data storage system was designed and tested with methods of recording and retrieving the binary information. Levels of error correction were introduced to the system including pixel averaging, thresholding, and parity checks. Errors were artificially introduced into the binary holographic data storage system and were monitored as a function of the defect area fraction, which showed a strong influence on data integrity. Average area fractions exceeding one quarter of the bit area caused unrecoverable errors. Efficient use of the available data density was discussed. .
Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.