Singlemode-Multimode-Singlemode Fiber-Optic Interferometer Signal Demodulation Using MUSIC Algorithm and Machine Learning

The paper is aimed at improving the efficiency of signal processing for intermode fiber-optic interferometers. To do so, we propose to use the MUSIC algorithm. It is shown that the use of traditional methods for estimating the number of signal components leads to poor operation of the MUSIC algorithm when applied to intermode interference signals. The possibility of using machine learning to estimate the number of signal components was investigated. The advantage of the proposed signal processing for demodulating the signals of an intermode interferometer over the Fourier transform has been experimentally demonstrated on the examples of simultaneous strain and curvature measurement, as well as pulse-wave sensing. The results can be also applied for processing signals of other optical-fiber sensors and multi-component signals of a different nature, for example, optical coherence tomography and radar signals

Singlemode-Multimode-Singlemode Fiber-Optic Interferometer Signal Demodulation Using MUSIC Algorithm and Machine Learning

The paper is aimed at improving the efficiency of signal processing for intermode fiber-optic interferometers. To do so, we propose to use the MUSIC algorithm. It is shown that the use of traditional methods for estimating the number of signal components leads to poor operation of the MUSIC algorithm when applied to intermode interference signals. The possibility of using machine learning to estimate the number of signal components was investigated. The advantage of the proposed signal processing for demodulating the signals of an intermode interferometer over the Fourier transform has been experimentally demonstrated on the examples of simultaneous strain and curvature measurement, as well as pulse-wave sensing. The results can be also applied for processing signals of other optical-fiber sensors and multi-component signals of a different nature, for example, optical coherence tomography and radar signals

Continuous Hue-Based Self-Calibration of a Smartphone Spectrometer Applied to Optical Fiber Fabry-Perot Sensor Interrogation

Smartphone-based optical spectrometers allow the development of a new generation of portable and cost-effective optical sensing solutions that can be easily integrated into sensor networks. However, most commonly the spectral calibration relies on the external reference light sources which have known narrow spectral lines. Such calibration must be repeated each time the fiber and diffraction grating holders are removed from the smartphone and reattached. Moreover, the spectrometer wavelength scale can drift during the measurement because of the smartphone temperature fluctuations. The present work reports on a novel spectral self-calibration approach, based on the correspondence between the light wavelength and the hue features of the spectrum measured using a color RGB camera. These features are caused by the nonuniformity of camera RGB filters' responses and their finite overlap, which is a typical situation for RGB cameras. Thus, the wavelength scale should be externally calibrated only once for each smartphone spectrometer and can further be continuously verified and corrected using the proposed self-calibration approach. An ability of the plug-and play operation and the temperature drift elimination of the smartphone spectrometer was experimentally demonstrated. Conducted experiments involved interrogation of optical fiber Fabry-Perot interferometric sensor and demonstrated a nanometer-level optical path difference resolution