Philippe A. Robert

A novel method for Brillouin gain spectrum measurements in optical fibers is presented. It is based on the pump and probe technique with the specificity to use a single laser source together with an external modulator to generate the interacting lightwaves. The high accuracy and inherent stability of the technique makes it suitable for calibration and reference measurements. Different fibers with different refractive index profiles have been tested and characterized. The problem of the evolution of the polarization of the interacting waves is addressed in the article and a polarization insensitive determination of the actual Brillouin gain coefficient is made possible through two successive measurements with different polarizations. The effects of strain and temperature on the Brillouin gain spectrum are also fully characterized.

A novel configuration of a distributed fiber sensor by Brillouin gain analysis has been developed for temperature and strain monitoring. It uses a single laser source, and the required light signals are all generated with an electro-optic modulator, resulting in high stability and excellent reliability of the measuring setup. Measurement of the induced strain in a wound fiber is presented as a demonstration of the system performance.

A theoretical model of wavelength modulation spectroscopy that uses a laser diode on a Lorentzian absorption line is presented. This theory describes the general case of a current-modulated semiconductor laser, for which a combined intensity and frequency modulation with an arbitrary phase shift occurs. On the basis of this model, the effect of several modulation parameters on the detected signals is evaluated. Experimental signals measured on an absorption line of CO2 by use of a 2-microm distributed-feedback laser are also presented and validate this analysis. These experimental results agree with the calculated signals, confirming the relevance of the model.

motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.