Time-frequency division network for high-bandwidth fiber optic communications

1995 ◽  
Author(s):  
Tim E. Olson ◽  
Dennis M. Healy, Jr. ◽  
Ulf L. Oesterberg ◽  
Reese T. Prosser
Keyword(s):  
Fiber Optic ◽  
Frequency Division ◽  
Fiber Optic Communications ◽  
Time Frequency ◽  
High Bandwidth

High-Temperature, High-Bandwidth Fiber Optic Pressure and Temperature Sensors for Gas Turbine Applications

2004 ◽  
Author(s):  
Robert Fielder ◽  
Matthew Palmer ◽  
Wing Ng ◽  
Matthew Davis ◽  
Aditya Ringshia
Keyword(s):  
High Temperature ◽  
Gas Turbine ◽  
Fiber Optic ◽  
Temperature Sensors ◽  
High Bandwidth

Balanced photoreceivers for analog and digital fiber optic communications (Invited Paper)

2005 ◽  
Author(s):  
Abhay Joshi ◽  
Xinde Wang ◽  
Dan Mohr ◽  
Don Becker ◽  
Christoph Wree
Keyword(s):  
Fiber Optic ◽  
Fiber Optic Communications

Comparison of optical back propagation schemes for fiber-optic communications

2013 ◽  
Vol 19 (1) ◽  
pp. 4-9 ◽  
Author(s):  
Mahdi Malekiha ◽  
Dong Yang ◽  
Shiva Kumar
Keyword(s):  
Fiber Optic ◽  
Back Propagation ◽  
Fiber Optic Communications

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

Sign in / Sign up

Export Citation Format

Share Document