Ultrafast temporal pattern recognition through a persistent spectral hole-burning filter

Mingzhen Tian; Ivan Lorgere; Jean-Pierre Galaup; Jean-Louis Le Gouet

doi:10.1117/12.308881

22 May 1998 Ultrafast temporal pattern recognition through a persistent spectral hole-burning filter

Mingzhen Tian, Ivan Lorgere, Jean-Pierre Galaup, Jean-Louis Le Gouet

Proceedings Volume 3490, Optics in Computing '98; (1998) https://doi.org/10.1117/12.308881
Event: Optics in Computing '98, 1998, Bruges, Belgium

Abstract

With present electronic equipment it is difficult to perform the temporal analysis of optical signals in the picosecond range. One usually resorts to sampling techniques that do not apply to single-event signals. Pattern recognition is an alternative way to perform signal analysis. This technique is well known in the context of the spatial analysis of two-dimension scenes. It relies on the comparison of an input object with a series of filter patterns, that have been stored in a bank. It is restricted to the recognition of one pattern among those that are contained in the bank but it applies to single events and it can be very fast since it relies on the cross correlation processing capabilities of non-linear optical materials. We adapt this procedure to the temporal domain.

Citation Download Citation

Mingzhen Tian, Ivan Lorgere, Jean-Pierre Galaup, and Jean-Louis Le Gouet "Ultrafast temporal pattern recognition through a persistent spectral hole-burning filter", Proc. SPIE 3490, Optics in Computing '98, (22 May 1998); https://doi.org/10.1117/12.308881

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available