Abstract
In this paper we describe the implementation of a secure key distribution system based on an ultra-long fiber laser with a bi-directional erbium doped fiber amplifier. The resilience of the system was tested against passive attacks from an eavesdropper, having been observed a similarity in spectrum for both secure configurations of the system.

Abstract
This study presents the dependence of strain sensitivity on cavity length in conventional Fabry-Perot (F-P) sensors. A high number of F-P sensors were required and to ensure their reproducibility, a manufacturing process was developed to obtain similar sensors but with different types of lengths. A hollow-core silica tube was used to fabricate several F-P cavities by fusion splicing it between two sections of SMF28 fiber. The fabricated F-P has a varying length ranging from 15 to 2500 mu m. The cavities were measured under a microscope and the reflected spectrum was acquired for each one. Strain measurements were performed for a maximum strain of 1000 mu epsilon. The strain sensitivity showed a highly linear correlation with increment lambda(FSR). Small length variations for short cavities heavily affect the FSR value. The smallest and longest cavities present sensitivities of 8.71 and 2.68 pm/mu epsilon, respectively. Thermal characterization for low- and high-temperature regimes was also performed and is constant for tested sensors.

Abstract
This work addresses the fabrication of straight silica-core liquid-cladding suspended waveguides inside a microfluidic channel through fs-laser micromachining. These structures enable the reconfiguration of the waveguide's mode profile and enhance the evanescent interaction between light and analyte. Further, their geometry resembles a tapered optical fiber with the added advantage of being monolithically integrated within a microfluidic platform. The fabrication process includes an additional post-processing thermal treatment responsible for smoothening the waveguide surface and reshaping it into a circular cross-section. Suspended waveguides with a minimum core diameter of 3.8 mu m were fabricated. Their insertion losses can be tuned and are mainly affected by mode mismatch between the coupling and suspended waveguides. The transmission spectrum was studied and it was numerically confirmed that it consists of interference between the guided LP01 mode and uncoupled light and of modal interference between the LP01 and LP02 modes.

Abstract
Extreme learning machines (ELMs) are a versatile machine learning technique that can be seamlessly implemented with optical systems. In short, they can be described as a network of hidden neurons with random fixed weights and biases, that generate a complex behaviour in response to an input. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding about their optical implementations. This work makes use of an optical complex media to implement an ELM and introduce an ab-initio theoretical framework to support the experimental implementation. We validate the proposed framework, in particular, by exploring the correlation between the rank of the outputs, H, and its generalization capability, thus shedding new light into the inner workings of optical ELMs and opening paths towards future technological implementations of similar principles.

Abstract
Reservoir computing is a versatile approach for implementing physically Recurrent Neural networks which take advantage of a reservoir, consisting of a set of interconnected neurons with temporal dynamics, whose weights and biases are fixed and do not need to be optimized. Instead, the training takes place only at the output layer towards a specific task. One important requirement for these systems to work is nonlinearity, which in optical setups is usually obtained via the saturation of the detection device. In this work, we explore a distinct approach using a photorefractive crystal as the source of the nonlinearity in the reservoir. Furthermore, by leveraging on the time response of the photorefractive media, one can also have the temporal interaction required for such architecture. If we space out in time the propagation of different states, the temporal interaction is lost, and the system can work as an extreme learning machine. This corresponds to a physical implementation of a Feed-Forward Neural Network with a single hidden layer and fixed random weights and biases. Some preliminary results are presented and discussed. © Published under licence by IOP Publishing Ltd.

Abstract
Light propagating in nonlinear optical materials opens the possibility to emulate quantum fluids of light with accessible tabletop experiments by taking advantage of the hydrodynamical interpretation. In this context, various optical materials have been studied in recent years, with nematic liquid crystals appearing as one of the most promising ones due to their controllable properties. Indeed, the application of an external electric field can tune their nonlocal response, and this mechanism may be useful for producing fluids of light and developing optical analogues. In this work, we extend the applicability of nematic liquid crystal to support optical analogues and study the possibility of emulating turbulent phenomena by using two fluids of light. These fluids interact with each other through the nonlinearity of the medium and generate instabilities that will lead to turbulent regimes. We also explore the possibility of exciting turbulent regimes through the decay of dark soliton stripes. The preliminary results are presented. © 2022, Universidade do Porto - Faculdade de Engenharia. All rights reserved.