KEYWORDS: Binary data, Neural networks, Telecommunications, Optical communications, Thermography, Remote sensing, Data storage, Distortion, Light sources, Signal to noise ratio
Thresholding techniques that account for noise are essential for the efficiency and accuracy of an optical
communication or optical data storage system. Various types of noise in the system can result in error. To
recover the data from the noisy signal, the error must be corrected by a fast and accurate signal processing
algorithm. By considering the crosstalk effect of the neighboring channels, we have devised a multi-level
thresholding method to set the threshold values based on the neighboring channel values. We compare the
binary characterization performance of a neural network and the local multi-level adaptive thresholding method
for decoding noisy transmission images. We show that the multi-thresholding implementation results in an
average of 57.42% less binary characterization errors than the artificial neural network across twenty unique
mixed noise optical conditions.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.