Abstract: Methodologies and corresponding systems for a Local adaptive fusion regression (LAFR) process are able to search a large library of spectral measurement for a linear calibration (training) set, which is spectrally matrix matched to a target sample spectrum, and also tightly bracketed about an “unknown” prediction property (analyte) for the target sample. Using a matched calibration set, the likelihood of an accurate prediction by the selected calibration set is greatly enhanced. The LAFR process integrates multiple spectral similarity information with contextual considerations between source analyte contents, model, and analyte predictions. LAFR facilitates onsite chemical analysis such as with a handheld spectrometer, dedicated in-line process analyzers and benchtop instruments. LAFR is based on a Beer’s law like linear relationship where a calibration model (mathematical relationship) is made that linearly relates the analyte amount, e.g., concentration, to the measured spectral responses.

Abstract: Methods for identifying marks in a defaced metal surface by use of computer-implemented processing of images obtained according to a thermal lock-in imaging technique are described. Methods include processing phase images and/or amplitude images according to principal component analysis of a concatenated input matrix and development of a score image for each principal component determined by the analysis. Score images or extracted features of score images (e.g., Zernike moments) are compared to images/features in a reference data library and based upon the comparison, the defaced mark can be identified.

Abstract: Methods for identifying marks in a defaced metal surface by use of computer-implemented processing of images obtained according to a thermal lock-in imaging technique are described. Methods include processing phase images and/or amplitude images according to principal component analysis of a concatenated input matrix and development of a score image for each principal component determined by the analysis. Score images or extracted features of score images (e.g., Zernike moments) are compared to images/features in a reference data library and based upon the comparison, the defaced mark can be identified.