Abstract: A system for identifying tags comprises an imaging sensor and a processor. The imaging sensor acquires image(s) of tag(s) from light reflected from the tag(s) on a tagged item. The processor is configured to: receive the image(s) and a library of tag types; using the image(s), determine feature metrics using a machine learning algorithm and is based on an image processing, manipulation, and/or correction; using the feature metrics and the library of tag types, determine a tag type of the tag(s) in the image(s) based on a local maxima determination, a bounding box generation, a tag candidate patch extraction, a tag candidate segmentation, a tag candidate feature metric determination, and/or a comparison to a model; determine a confidence level of the tag type; and in response to the confidence level being above a threshold level, provide the tag type determined.

Abstract: A system for typing biological cells includes a tunable Fabry-Perot etalon, and imaging sensor, and a processor. The imaging sensor acquires one or more images of one or more biological cells from light transmitted through the tunable Fabry-Perot etalon. Each image represents signal associated with one or more wavelengths transmitted through the tunable Fabry-Perot etalon. The processor is configured to determine a type of each of the one or more biological cells. Determining the type uses a machine learning algorithm and is based at least in part on one or more of an image segmentation, a patch extraction, a feature extraction, a feature compression, a deep feature extraction, a feature fusion, a feature classification, and a prediction map reconstruction.

Abstract: A system includes a tunable Fabry-Perot etalon, a detector, and a processor. The tunable Fabry-Perot etalon has a settable gap. The detector measures light intensity transmitted through the tunable Fabry-Perot etalon. The processor is configured to determine the calibrated spectral measurement, wherein the calibrated spectral measurement is based at least in part on a measurement set of detected light intensities for a selected set of settable gaps and a reconstruction matrix. The reconstruction matrix is based at least in part on calibration measurements using one or more field material targets, prior stored full calibrations for each of the one or more field material targets, and the selected set of settable gaps.

Abstract: A system includes a tunable Fabry-Perot etalon, a detector, and a processor. The tunable Fabry-Perot etalon has a settable gap. The detector measures light intensity transmitted through the tunable Fabry-Perot etalon. The processor is configured to determine the calibrated spectral measurement, wherein the calibrated spectral measurement is based at least in part on a measurement set of detected light intensities for a selected set of settable gaps and a reconstruction matrix. The reconstruction matrix is based at least in part on calibration measurements using one or more field material targets, prior stored full calibrations for each of the one or more field material targets, and the selected set of settable gaps.

Abstract: A system for typing biological cells includes a tunable Fabry-Perot etalon, and imaging sensor, and a processor. The imaging sensor acquires one or more images of one or more biological cells from light transmitted through the tunable Fabry-Perot etalon. Each image represents signal associated with one or more wavelengths transmitted through the tunable Fabry-Perot etalon. The processor is configured to determine a type of each of the one or more biological cells. Determining the type uses a machine learning algorithm and is based at least in part on one or more of an image segmentation, a patch extraction, a feature extraction, a feature compression, a deep feature extraction, a feature fusion, a feature classification, and a prediction map reconstruction.