Mid-infrared dual comb spectroscopy with asynchronous optical parametric oscillators

Dual-comb spectroscopy (DCS) is a novel approach that uses asynchronous broadband coherent sources to achieve Fourier-transform-like spectroscopy but with no moving parts and at kHz acquisition rates. To date, fully resolved and accurate dual-comb spectrometers have been demonstrated in the near-infrared and applied to broadband spectroscopy for precise measurement of molecular centerlines, spectral lidar, and greenhouse gases from the near- to mid-IR. This thesis describes DCS with asynchronous optical parametric oscillators and explores their applications in rapid, high-resolution broadband spectroscopy in the mid-infared. Initially a dual-comb spectrometer was designed with two identical optical parametric oscillators (OPOs) pumped by two identical Yb:fibre lasers and its stability performance was characterized measuring relative intensity noise. First experiments were accomplished by using free-running independent MgO:PPLN based OPOs with a repetition-rate difference of 500 Hz, achieving resolutions of 0.2 cm-1 across a wavelength range 3.1 to 3.5 μm; an absolute wavelength calibration technique was employed to allow registration and averaging of consecutively acquired dual-comb spectra. Then experiments were repeated with a dual-comb source for the spectral fingerprint region based on a pair of entirely free-running OPOs, each pumped by a 1-µm femtosecond laser and utilizing the new gain medium orientation-patterned gallium phosphide (OPGaP) to produce broadband idler pulses tunable from 6–8 µm. Methane absorption spectroscopy in the deep infrared region was demonstrated with the same wavelength calibration approach for both dual-comb spectrometers, leading to a high quality and low-noise absorbance measurement with spectral coverage simultaneously spanning the methane P, Q and R branches in good agreement with the Hitran database

Joint 1D and 2D Neural Networks for Automatic Modulation Recognition

The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O\u27Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these architectures and integrated the models to perform joint detection and classification. To our knowledge, the present research is the first to study and successfully combine a lD ResNet classifier and Yolo v3 object detector to fully automate the process of AMR for parameter estimation, pulse extraction and waveform classification for non-cooperative scenarios. The overall performance of the joint detector/ classifier is 90 at 10 dB signal to noise ratio for 24 digital and analog modulations