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NEURAL NETWORK DEMODULATORS FOR BRAGG OPTICAL SENSORS

詳細技術說明

None

*Abstract

Optical sensors are important in many applications, particularly in structural and environmental monitoring. There are two main types of optical sensors: Bragg and Fabry-Perot. Each of these sensors has advantages and disadvantages. Bragg sensors are advantageous because their reflection spectrum has a center wavelength that shifts linearly with applied strain, while the wavelength of a Fabry-Perot sensor shifts non-linearly. However, optical sensors require demodulators in order to function properly. Currently, demodulators for Fabry-Perot sensors are less expensive than demodulators for Bragg sensors. Because of this, Bragg sensors are used seldom in comparison to Fabry-Perot sensors. Researchers at Missouri University of Science and Technology (formerly University of Missouri-Rolla) have developed neural network demodulators for Bragg sensors, which cost significantly less to manufacture and use. These demodulators use standard detectors and optical components, while the neural network approach accurately measures the shift in Bragg center wavelength and absolute strain. Overall, implementation of the neural network demodulator design results in lower instrumentation costs, reduced instrument sizes, and real-time processing. Key Terms:-Optical sensor-Bragg sensor -Fabry-Perot sensor-Demodulator -Neural Network Demodulation Technology Features: -Neural Network Demodulation -Standard detectors-Standard optical components Technology Benefits:-Reduced instrumentation costs -Reduced instrumentation sizes-Real-time processing -Enhanced optical sensor

*Principal Investigation

Name: Rohit Dua, Assistant Professor

Department:

Name: Steve Watkins

Department:

Name: Donald Wunsch

Department:

主要類別

電子

細分類別

計算機，通信和消費電子產品 /小工具

其他

國家/地區

美國