Abstract:
A method for spectral interpretation in absorption spectroscopy uses a nonlinear spectral fitting algorithm for interpretation of spectral features in complex absorption spectra. The algorithm combines two spectral modeling techniques for generating spectral models to be used in the curve fitting process: a line-shape model and a basis-set model. The selected models for all gas components are additively combined using a least squares minimization, allowing for quantification of multiple species simultaneously.
Abstract:
An image capturing device (202) can include a sensor array (210), a lens (230) positioned at a first distance from an intermediate image (235), and apolychromat (220) positioned at a second distance from the lens (230). The poly chro mat (220) can diffract the intermediate image (235) according to a transform function (207) to produce a dispersed sensor image (215) onto the sensor array (210). The dispersed sensor image (215) can represent a spatial code of the intermediate image (235).
Abstract:
Die Erfindung befasst sich mit der Spektrometrie von infrarotem, sichtbarem und ultraviolettem Licht. Vorgeschlagen werden einfach herzustellende und kostengünstige Spektrometer, die dadurch gekennzeichnet sind, dass die Einzelkanäle von Vielkanalanalysatoren spektrale Empfindlichkeiten nach allgemeinen spektralen Funktionen aufweisen, wobei unter Verwendung einer allgemeinen spektralen Funktion der Verzicht auf ausschliessliche Nutzung monochromatischer Funktionen verstanden werden soll. Dadurch wird ermöglicht, dass die Geräte wesentlich einfacher herzustellen sind und darüber hinaus über eine Vielfalt von spektralen Funktionen verfügen, die auch für spezielle anwenderdefinierte Messaufgaben herangezogen werden können.
Abstract:
Provided are a hyperspectral image sensor and a hyperspectral imaging system including the hyperspectral image sensor. The hyperspectral image sensor includes a light-receiving sensor that is pixelated, and a plurality of metasurfaces that are arranged in front of the light-receiving sensor apart from each other in a stacking direction, and each have an array of meta-atoms. At least one of the plurality of metasurfaces is a random metasurface in which the meta-atoms are disorderly arranged, and a speckle pattern is formed on a sensing surface of the light-receiving sensor by the plurality of metasurfaces.
Abstract:
Systems, devices, and methods of analyzing an interfered peak of a sample spectrum is disclosed. The sample spectrum may be generated using a detector of an optical spectrometer. The interfered peak may be produced by a plurality of spectral peaks of different wavelengths. The method may include generating interfered curve parameters representative of the peak shape of each spectral emission in the interfered peak based at least in part on a model of expected curve parameters for the optical spectrometer and a location of the interfered peak on the detector of the optical spectrometer; fitting a plurality of curves to the interfered peak, each curve corresponding to one of the plurality of spectral emissions of different wavelengths forming the interfered peak, wherein each curve is fitted using the interfered curve parameters provided by the model of expected peak parameters; and outputting the plurality of curves for further analysis.
Abstract:
An image capturing device (202) can include a sensor array (210), a lens (230) positioned at a first distance from an intermediate image (235), and apolychromat (220) positioned at a second distance from the lens (230). The polychromat (220) can diffract the intermediate image (235) according to a transform function (207) to produce a dispersed sensor image (215) onto the sensor array (210). The dispersed sensor image (215) can represent a spatial code of the intermediate image (235).
Abstract:
A system for determining a spectrum includes an interface and a processor. The interface is configured to receive a sample set of intensity data for an array of spatial locations and a set of spectral configurations. The processor is configured to determine a region of interest using the sample set of intensity data and determine a spectral peak for the region of interest.
Abstract:
A method of performing color calibration of a multispectral image sensor (MIS) includes obtaining test measurement data of at least one color chart that is measured by a test MIS under at least one lighting environment, obtaining reference measurement data of the at least one color chart that is measured by a reference MIS under the at least one lighting environment, the reference MIS being calibrated in advance, and generating, based on the test measurement data and the reference measurement data, at least one transformation model configured to transform measurements between the test MIS and the reference MIS.
Abstract:
In some implementations, a device may receive spectroscopic data associated with a dynamic process. The device may generate a principal component analysis (PCA) model based on a first block of spectra from the spectroscopic data. The device may project a second block of spectra from the spectroscopic data to the PCA model generated based on the first block of spectra. The device may determine a value of a metric associated with the second block based on projecting the second block of spectra to the PCA model. The device may determine whether the dynamic process has reached an end point based on the value of the metric associated with the second block.
Abstract:
In some implementations, a device may receive spectroscopic data associated with a dynamic process. The device may generate a principal component analysis (PCA) model based on a first block of spectra from the spectroscopic data. The device may project a second block of spectra from the spectroscopic data to the PCA model generated based on the first block of spectra. The device may determine a value of a metric associated with the second block based on projecting the second block of spectra to the PCA model. The device may determine whether the dynamic process has reached an end point based on the value of the metric associated with the second block.