The role of phase-polarization interactions in the laser processing of polarization-sensitive materials.

Taking into account phase-polarization interactions is crucial for the formation of spatially structured laser beams. The effects that arise in this context can lead to the modulation of individual field components and the transformation of the overall light field. In this study, we investigate the impact of phase and polarization distributions with radial dependencies in polar coordinates on the longitudinal component of laser beams passing through a transmissive spatial light modulator (SLM) based on twisted nematic liquid crystals.

HyperKAN: Kolmogorov-Arnold Networks Make Hyperspectral Image Classifiers Smarter.

In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov-Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images.

Roadmap on computational methods in optical imaging and holography [invited].

Unlabelled: Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging.

A perspective on the artificial intelligence's transformative role in advancing diffractive optics.

Artificial intelligence (AI) is transforming diffractive optics development through its advanced capabilities in design optimization, pattern generation, fabrication enhancement, performance forecasting, and customization. Utilizing AI algorithms like machine learning, generative models, and transformers, researchers can analyze extensive datasets to refine the design of diffractive optical elements (DOEs) tailored to specific applications and performance requirements. AI-driven pattern generation methods enable the creation of intricate and efficient optical structures that manipulate light with exceptional precision.