Abstract
The use of graphene oxide (GO) as a saturable absorber for short pulses generation in an Erbium-doped fiber laser was studied and demonstrated. The saturable absorber consisted of a thin GO film, with a high concentration of monolayer GO flakes, spray-coated on the end face of a ferrule-connected fiber. By including the saturable absorber in the laser cavity and controlling the intra-cavity polarization, the generation of shortpulsed light was achieved under mode-locking and Q-switching operations. Under mode-locking operation, it was observed a pulse train with a fundamental repetition rate of 1.48 MHz, with a working wavelength centered at 1564.4 nm. In the Q-switch operation, a pulse train with a 12.7 kHz repetition rate and a 14.3 µs pulse duration was attained for a 230-mA pump current. Further investigation showed a linear dependence of the repetition rate with the pump power, attaining frequencies between 12.7 and 14.4 kHz.

Abstract
Using surface resonance (SPR) as a sensitivity enhancer, this work describes the development of a transmissive multimode optical fiber sensor with a gold (Au) thin film that measures glucose concentration. The fiber's cladding was initially removed, and an Au layer was then sputtered onto its surface to simultaneously excite SPR and reflect light, making the SPR sensor extremely sensitive to changes in the environment's refractive index. A range of glucose concentrations, from 0.0001 to 0.5000 g/ml, were tested on the sensor. A maximum sensitivity of 161.302 nm/(g/mL) was attained for the lowest glucose concentration, while the highest concentration yielded a sensitivity of 312.000 nm/(g/mL). The proposed sensor's compact size, high sensitivity, good stability and practicality make it a promising candidate for a range of applications, including detecting diabetes.

Abstract
Abstract The development of computing paradigms alternative to von Neumann architectures has recently fueled significant progress in novel all-optical processing solutions. In this work, we investigate how the coherence properties can be exploited for computing by expanding information onto a higher-dimensional space in the photonic extreme learning machine framework. A theoretical framework is provided based on the transmission matrix formalism, mapping the input plane onto the output camera plane, resulting in the establishment of the connection with complex extreme learning machines and derivation of upper bounds for the hidden space dimensionality as well as the form of the activation functions. Experiments using free-space propagation through a diffusive medium, performed in low-dimensional input space regimes, validate the model and the proposed estimator for the dimensionality. Overall, the framework presented and the findings enclosed have the potential to foster further research in a multitude of directions, from the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.

Abstract

Abstract
Speckle-based fiber optic sensors are well-known to offer high sensitivity but are strongly limited on the in terrogation side by low camera frame rates and dynamic range. To address this limitation, we present a novel interrogation framework that explores event-based vision to achieve high throughput, high bandwidth, and low-latency speckle analysis of a multimode optical fiber sensor. In addition, leveraging an optimized decomposition of the raw event streams through multi-point calibration and machine learning optimization, our approach also proves capable of isolating simultaneous deformations applied at distinct points. The experimental results vali date the methodology by separating the signals of four piezoelectric actuators over a 400 Hz-20 kHz range with minimal crosstalk applied over varying distances from 3 cm to 75 cm. Overall, these results establish event-driven speckle interrogation as a new versatile platform for real-time, multi-point acoustic sensing and pave the way for its application in complex and unstructured environments in future works.

Abstract
Speckle-based fiber optic sensors are well-known to offer high sensitivity but are strongly limited on the interrogation side by low camera frame rates and dynamic range. To address this limitation, we present a novel interrogation framework that explores event-based vision to achieve high throughput, high bandwidth, and low-latency speckle analysis of a multimode optical fiber sensor. In addition, leveraging an optimized decomposition of the raw event streams through multi-point calibration and machine learning optimization, our approach also proves capable of isolating simultaneous deformations applied at distinct points. The experimental results validate the methodology by separating the signals of four piezoelectric actuators over a 400Hz - 20kHz range with minimal crosstalk applied over varying distances from 3cm to 75cm. Overall, these results establish event-driven speckle interrogation as a new versatile platform for real-time, multi-point acoustic sensing and pave the way for its application in complex and unstructured environments in future works.