Certain examples relate to emulating a spectral measurement device in a color measurement apparatus. In these examples, a primary spectral measurement device measures a first spectral characteristic of a rendered color output. A predictive model, parametrized by parameter values, is applied to the measurement from the primary spectral measurement device to determine a predicted measurement of a second spectral characteristic of the rendered color output which would be measured by an ancillary spectral measurement device. Parameter values are generated by training the predictive model with data from the primary spectral measurement device and the ancillary spectral measurement device.

A CubeSat compatible spectrometer including a slit having a first length and first width; a diffraction grating; and a two dimensional focal plane array electromagnetically coupled to the diffraction grating. The 2D focal plane array includes an array of pixels including a plurality of sets of pixels. Diffraction of electromagnetic radiation transmitted through the slit by the diffraction grating forms a plurality of beams, each of the beams comprising a different one of the bands of the wavelengths in the electromagnetic radiation, and each of the beams transmitted onto a different one of the sets of the pixels.

Various embodiments disclosed herein describe a divided-aperture infrared spectral imaging (DAISI) system that is adapted to acquire multiple IR images of a scene with a single-shot (also referred to as a snapshot). The plurality of acquired images having different wavelength compositions that are obtained generally simultaneously. The system includes at least two optical channels that are spatially and spectrally different from one another. Each of the at least two optical channels are configured to transfer IR radiation incident on the optical system towards an optical FPA unit comprising at least two detector arrays disposed in the focal plane of two corresponding focusing lenses. The system further comprises at least one temperature reference source or surface that is used to dynamically calibrate the two detector arrays and compensate for a temperature difference between the two detector arrays.

A spectrometry method used with an apparatus including a spectrometry section, a spectroscopic controller, a spectroscopic image generator, and a display section, the method including generating a teaching-purpose spectroscopic image, generating and displaying a teaching-purpose visualized image, identifying a first teaching area in the teaching-purpose visualized image and generating a first teaching-purpose spectrum, displaying a first icon based on the display color of the first teaching area, accepting teacher data that teaches chromaticity in correspondence with the first icon, generating a conversion rule based on the relationship between the spectrum and the teacher data, generating a measurement-purpose spectrum, and calculating chromaticity based on the conversion rule.

ultraviolet (UV) based imaging method for determining protein concentrations of unknown protein samples based on automated multi-wavelength calibration. In various embodiments, a processor receives each of a standard set of wavelength data and an unknown set of wavelength data as recorded by a detector. Each standard set of wavelength data and unknown set of wavelength data defines a series of absorbance-to-wavelength value pairs across a first range of wavelengths selected from a range of a single-wavelength light beams of a UV spectra. The processor generates a multi-wavelength calibration model based on each of a first series of first absorbance-to-wavelength value pairs of the standard set of wavelength data. The processor implements the multi-wavelength calibration model to determine, for each unknown protein sample of the given unknown protein samples, a plurality of protein concentration values.

A two-layer hybrid solid wedged etalon was fabricated and combined with a traditional imager to make a compact computational spectrometer. The hybrid wedge was made of Nb.sub.2O.sub.5 and Infrasil 302 and was designed to operate from 0.4-2.4 μm. Initial demonstrations used a CMOS imager and operated from 0.4-0.9 μm with spectral resolutions<30 cm.sup.−1 from single snapshots. The computational spectrometer operates similarly to a spatial Fourier Transform infrared (FTIR) spectrometer with spectral reconstruction using a non-negative least squares fitting algorithm based on analytically computed wavelength response vectors determined from extracted physical thicknesses across the entire two-dimensional wedge. This computational technique resulted in performance and spectral resolutions exceeding those that could be achieved from Fourier techniques. With an additional imaging lenses and translational scanning, the system can be converted into a hyperspectral imager.

The present disclosure relates to a method for calibrating an optical sensor, an optical sensor and a related electronic device. The method includes: acquiring a plurality of spectral detection values of ambient light collected by the optical sensor; acquiring a plurality of first parameter detection values of the ambient light and the corresponding plurality of second parameter detection values according to the plurality of spectral detection values, a type of the first parameter detection values being different from a type of the second parameter detection values; determining at least one effective detection value from the plurality of first parameter detection values according to the plurality of second parameter detection values; and calibrating the optical sensor according to the at least one effective detection value.

The disclosed embodiments include thin film multivariate optical element and detector combinations, thin film optical detectors, and downhole optical computing systems. In one embodiment, a thin film multivariate optical element and detector combination includes at least one layer of multivariate optical element having patterns that manipulate at least one spectrum of optical signals. The thin film multivariate optical element and detector combination also includes at least one layer of detector film that converts optical signals into electrical signals. The thin film optical detector further includes a substrate. The at least one layer of multivariate optical element and the at least one layer of detector film are deposited on the substrate.

There are provided a learning apparatus, an operation method of the learning apparatus, a non-transitory computer readable recording medium storing an operation program of the learning apparatus, and an operating apparatus capable of further improving accuracy of prediction of a quality of a product by a machine learning model in a case where learning is performed by inputting, as learning input data, multi-dimensional physical-property relevance data, which is derived from multi-dimensional physical-property data of the product, to the machine learning model. In the learning apparatus, a first processor is configured to extract a high-contribution item from the plurality of items of the multi-dimensional physical-property relevance data by using the temporary machine learning model; and selectively input the multi-dimensional physical-property relevance data of the high-contribution item to the machine learning model, perform learning, and output the machine learning model as a learned model to be provided for actual operation.

Provided is a meat inspection apparatus including a light source configured to emit a plurality of inspection lights to a plurality of regions of meat, respectively, a light detector configured to generate a plurality of emission spectrum signals based on measuring a plurality of fluorescences emitted from the plurality of regions, and a processor configured to receive the plurality of emission spectrum signals from the light detector, generate hyperspectral images of the meat based on the plurality of emission spectrum signals, and obtain a state of the meat based on the hyperspectral images, wherein the processor is further configured to obtain the state of the meat for a plurality of sub-regions in each of the plurality of regions.