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
Topological photonics, leveraging concepts from condensed matter physics, offers transformative potential in the design of robust optical systems. This study investigates the integration of topologically protected edge states into plasmonic nanostructures for enhanced optical sensing. We propose a toy model comprising two chains of metallic filaments forming a one-dimensional plasmonic crystal with diatomic-like unit cells, positioned on a waveguide. The system exhibits edge states localized at the boundaries and a central defect, supported by the Su-Schrieffer-Heeger (SSH) model. These edge states, characterized by significant electric field enhancement and topological robustness, are shown to overcome key limitations in traditional plasmonic sensors, including sensitivity to noise and fabrication inconsistencies. Through coupled mode theory, we demonstrate the potential for strong coupling between plasmonic and guided optical modes, offering pathways for improved interferometric sensing schemes. This work highlights the applicability of topological photonics in advancing optical sensors.

Abstract
This study explores the application of machine learning algorithms to optimize the geometry of the plasmonic layer in a surface plasmon resonance photonic crystal fiber sensor. By leveraging the simplicity of linear regression ( LR) alongside the advanced predictive capabilities of the gradient boosted regression (GBR) algorithm, the proposed approach enables accurate prediction and optimization of the plasmonic layer's configuration to achieve a desired spectral response. The integration of LR and GBR with computational simulations yielded impressive results, with an R-2 exceeding 0.97 across all analyzed variables. Moreover, the predictive accuracy demonstrated a remarkably low margin of error, epsilon < 10(-15). This combination of methods provides a robust and efficient pathway for optimizing sensor design, ensuring enhanced performance and reliability in practical applications.

Abstract
In our study, we conducted a thorough analysis of the spectral characteristics of a D-shaped surface plasmon resonance (SPR) photonic crystal fiber (PCF) refractive index sensor, incorporating a full width at half maximum (FWHM) analysis. We explored four distinct plasmonic materials—silver (Ag), gold (Au), Ga-doped zinc oxide (GZO), and an Ag-nanowire metamaterial—to understand their impact on sensor performance. Our investigation encompassed a comprehensive theoretical modeling and analysis, aiming to unravel the intricate relationship between material composition, sensor geometry, and spectral response. By scrutinizing the sensing properties offered by each material, we laid the groundwork for designing multiplasmonic resonance sensors. Our findings provide valuable insights into how different materials can be harnessed to tailor SPR sensing platforms for diverse applications and environmental conditions, fostering the development of advanced and adaptable detection systems. This research not only advances our understanding of the fundamental principles governing SPR sensor performance but also underscores the potential for leveraging varied plasmonic materials to engineer bespoke sensing solutions optimized for specific requirements and performance metrics. © 2025 SBMO/SBMag.

Abstract
The spectral response of a SPR (surface plasmon resonance) sensor depends on the engineering of the conducting layer. In this paper, we analyze theoretically the spectra of a D-shaped SPR PCF (photonic crystal fiber) refractive index sensor considering four different plasmonic materials: Ag, Au, Ga-doped zinc oxide (GZO) and an Ag-nanowire metamaterial. The sensing properties provided by each material and how they form the bases to design multiplasmonic resonance sensors are the focus of our discussion.