Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis
Christos Karapanagiotis, Aleksander Wosniok, Konstantin Hicke, Katerina Krebber
Abstract
To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.
Topics & Concepts
Robustness (evolution)Convolutional neural networkComputer scienceTime domainFrequency domainDomain analysisSignal processingBrillouin zoneBrillouin scatteringArtificial neural networkNoise (video)Time–frequency analysisArtificial intelligencePattern recognition (psychology)Electronic engineeringOpticsOptical fiberTelecommunicationsEngineeringDigital signal processingPhysicsComputer visionComputer hardwareImage (mathematics)Software constructionProgramming languageRadarBiochemistrySoftware systemChemistrySoftwareGeneAdvanced Fiber Optic SensorsPhotonic and Optical DevicesMechanical and Optical Resonators