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In scientific research and industrial applications, selecting the most accurate model to describe a relationship between input parameters and target characteristics of experiments is crucial.
Silicon nitride fiber is a critical material mainly used in the aerospace industry but has many applications. This fiber is prized for its properties, as it is transparent to electromagnetic radiation but allows signals to go through it.
This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.
This invention introduces a system for microscopy called pan-sharpening, enabling the generation of images with both full-spatial and full-spectral resolution without needing to capture the entire dataset, significantly reducing data acquisition time.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.
This technology introduces an advanced machine learning approach for enhancing chemical imaging by correlating data from two mass spectrometry imaging (MSI) techniques.
The vast majority of energy conversion technologies and industrial processes depend on heat exchangers for transferring heat between fluids.
Aromas play a significant role in the quality and safety of food, beverages, and even manufactured products. The ability to detect and interpret these aromas accurately can enhance product safety and consumer satisfaction.