Self-normalized density map (SNDM) for counting microbiological objects.

The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U[Formula: see text]-Net. Two statistical methods for deep neural networks are utilized: the bootstrap and the Monte Carlo (MC) dropout.

Multiple intermodal-vectorial four-wave mixing bands generated by selective excitation of orthogonally polarized LP and LP modes in a birefringent fiber.

This study investigates the nonlinear frequency conversions between the six polarization modes of a two-mode birefringent fiber. The aim is to demonstrate that the selective excitation of different combinations of linearly polarized spatial modes at the pump wavelength initiates distinct intermodal-vectorial four-wave mixing processes. In particular, this study shows that exciting two orthogonally polarized LP and LP modes can lead to the simultaneous generation of up to three pairs of different spatial modes of orthogonal polarizations at different wavelengths.

Generation of microbial colonies dataset with deep learning style transfer.

We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion. The developed generator employs traditional computer vision algorithms together with a neural style transfer method for data augmentation. We show that the method is able to synthesize a dataset of realistic looking images that can be used to train a neural network model capable of localising, segmenting, and classifying five different microbial species.

Deep learning-based waste detection in natural and urban environments.

Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data.