Bio-signal processing using a novel lock-in detection technique for the portable bio-optical systems

2008 ◽  
Author(s):  
In-Il Jung ◽  
Doo Gun Kim ◽  
Woon-Kyung Choi ◽  
Do-Gyun Kim ◽  
Young-Wan Choi
Keyword(s):  
Signal Processing ◽  
Detection Technique ◽  
Optical Systems ◽  
Lock In

The Application of Signal Processing Techniques in Improving Spectrometer Detection Limit

2011 ◽  
Vol 148-149 ◽  
pp. 1045-1050
Author(s):  
Zhi Ying Wu ◽  
Yi Zhang ◽  
Zuo Yuan Shen
Keyword(s):  
Signal Processing ◽  
Detection Limit ◽  
High Sensitivity ◽  
Processing Technique ◽  
Laser Source ◽  
Sampled Data ◽  
High Concentration ◽  
Signal Processing Technique ◽  
Processing Techniques ◽  
Lock In

Improving spectrometer detection limit using statistical principle and signal-processing technique are described simply in this work. In the detection of gas photoacoustic(PA) signal, accurate partitions of the sampled data affect on the detection limit to some extent although Lock-in amplification technique with high SNR and microphone sensor with high sensitivity have been used. A model and the resulting algorithm are proposed from PA-signal samples. The techniques are validated at ppb level on PA spectrometer for NH3 breath detection in high concentration of CO2 and H2O based on tunable erbium-doped fiber laser (TEDFL) or for multicomponent trace gas detection based on waveguide CO2 laser or on other laser source.

All-Optical Signal Processing for UltraHigh Speed Optical Systems and Networks

2012 ◽  
Vol 30 (24) ◽  
pp. 3760-3770 ◽  
Author(s):  
Lianshan Yan ◽  
Alan E. Willner ◽  
Xiaoxia Wu ◽  
Anlin Yi ◽  
Antonella Bogoni ◽  
...  
Keyword(s):  
Signal Processing ◽  
Optical Signal Processing ◽  
Optical Signal ◽  
Optical Systems ◽  
All Optical

scholarly journals Silicon photonics devices for integrated analog signal processing and sampling

Nanophotonics ◽  
2014 ◽  
Vol 3 (4-5) ◽  
pp. 313-327 ◽  
Author(s):  
Steven Spector ◽  
Cheryl Sorace-Agaskar
Keyword(s):  
Signal Processing ◽  
Silicon Photonics ◽  
High Speed ◽  
Analog Signal Processing ◽  
Photonic Devices ◽  
Analog Signal ◽  
Optical Systems ◽  
Analog To Digital ◽  
And Performance ◽  
Analog Systems

AbstractSilicon photonics offers the possibility of a reduction in size weight and power for many optical systems, and could open up the ability to build optical systems with complexities that would otherwise be impossible to achieve. Silicon photonics is an emerging technology that has already been inserted into commercial communication products. This technology has also been applied to analog signal processing applications. MIT Lincoln Laboratory in collaboration with groups at MIT has developed a toolkit of silicon photonic devices with a focus on the needs of analog systems. This toolkit includes low-loss waveguides, a high-speed modulator, ring resonator based filter bank, and all-silicon photodiodes. The components are integrated together for a hybrid photonic and electronic analog-to-digital converter. The development and performance of these devices will be discussed. Additionally, the linear performance of these devices, which is important for analog systems, is also investigated.

scholarly journals Effective Flicker Detection Technique Using Artificial Neural Network For Video

10.29007/xwg4 ◽  
2018 ◽  
Author(s):  
Ripal Patel ◽  
Shubha Pandey ◽  
Chirag Patel ◽  
Robinson Paul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signal Processing ◽  
Accurate Result ◽  
Threshold Value ◽  
Detection Technique ◽  
Frame Difference ◽  
Network Concept ◽  
Consecutive Frame ◽  
Computational Part

Flicker is the most common and an intolerable blemish present in signal processing world that leads to distortion in the transmitted frame of a video string. To dodge such misinterpretations a technique for detection of the flickering frame in a video is depicted in this research. Earlier methods were based on removing flicker by calculating the threshold of the consecutive frame difference and then finding the flickering frame. The proposed method in this research includes finding flickering frame using neural network concept. Therefore, the advantage of the practice disclosed here is that it removes the tedious calculation part of the threshold value and thereby the computational part becomes easier with added accurate result.

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