A spectroscopic metrology system includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating including two or more layers, the model including geometric parameters indicative of a geometry of a test layer of the multilayer grating and dispersion parameters indicative of a dispersion of the test layer. The controller further receives a spectroscopic signal of a fabricated multilayer grating corresponding to the modeled multilayer grating from the spectroscopic metrology tool. The controller further determines values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. The controller further predicts a bandgap of the test layer of the fabricated multilayer grating based on the determined values of the one or more parameters of the test layer of the fabricated structure.

Provided is an optical system which may acquire a hyperspectral image by acquiring a spectral image of an object to be measured, which includes, to collect spectral data and train the neural network, an image forming part forming an image from an object to be measured and transmitting collimated light, a slit moving to scan the incident image and passing and outputting a part of the formed image, and a first optical part obtaining spectral data by splitting light of the image received through the slit by wavelength. Also, the system includes, to decompose overlapped spectral data and to infer hyperspectral image data through the trained neural network, an image forming part forming an image from an object to be measured and transmitting collimated light, and a first optical part obtaining spectral data by splitting light of the received image by wavelength.

A method for determining a quality condition of an agricultural product comprises: receiving a received light at a light detector, the received light comprising reflected, scattered, refracted, and/or deflected light from the agricultural product; transmitting the received light to a spectrometer; producing agricultural product (AP) spectral data of the received light using the spectrometer; with a computer in electrical communication with the spectrometer, comparing the AP spectral data to reference spectral data to determine whether the agricultural product has the quality condition, the reference spectral data corresponding to known quality conditions of the agricultural product; and with the computer, generating an output signal corresponding to the quality condition of the agricultural product.

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.

The conformal spectral library training method (CSLTM) of the disclosure allows sets of voltages for an optical filter to be calculated by way of a direct calculation without processing large amounts of spectral information, which significantly increases the speed of processing spectral information.

A system includes a first spectral modulator, a second spectral modulator, a light guide optically, a photodetector, and an electronic control device. The first spectral modulator receives sample light, and modulates the sample light according to a first spectral response pattern to produce first modulated light. The second spectral modulator receives the first modulated light from the first spectral modulator via the light guide, modulates the first modulated light according to a second spectral response pattern to produce second modulated light, and transmits the second modulated light to the photodetector. The photodetector measures an intensity of the second modulated light incident on the photodetector, and generates one or more signals corresponding to the intensity of the second modulated light. The electronic control device determines a spectral distribution of the sample light based on the one or more signals.

A system for computational imaging spectroscopy to provide compact and lightweight design, as well as large field of view of an object to be captured. The system includes imaging components, and computational device. The imaging components includes lens assembly, a fixed or variable-diameter aperture, spectral filter array and imaging sensor. The lens assembly provides wide angle of view, image-side telecentricity, and further may correct for longitudinal chromatic aberrations. The lens assembly may not provide correction of lateral chromatic aberrations. Furthermore, the lens assembly provides image-space telecentricity so as to chief rays are incident perpendicular to image sensor. The lens assembly may produce different chromatic aberrations pattern for each wavelength within the spectral range of interest. The pass-band nanofilter array is configured to filter a plurality of specific bands of light reflected from the imaged object and further produces a plurality of spatio-spectral samples of the imaged object projected onto the photosensitive pixels of imaging sensor. The computational device reconstructs complete spectral cube within the spectral range of interest, and further enables the computation of object reflectance at each pixel of the captured image from the plurality of spatio-spectral samples registered by the imaging sensor.

An object recognition apparatus includes a first spectrometer configured to obtain a first type of spectrum data from light scattered, emitted, or reflected from an object; a second spectrometer configured to obtain a second type of spectrum data from the light scattered, emitted, or reflected from the object, the second type of spectrum data being different from the first type of spectrum data; an image sensor configured to obtain image data of the object; and a processor configured to identify the object using data obtained from at least two from among the first spectrometer, the second spectrometer, and the image sensor and using at least two pattern recognition algorithms.

A means to automate, using fuzzy logic, the classification of spectral power distributions of optical radiation for lighting systems, and more particularly horticultural lighting systems, is presented. After inputting the spectral power distribution of optical radiation from one or more light sources, radial basis function weights for the spectral power distribution are determined and fuzzified preparatory to fuzzy logic classification. Fuzzy if-then rules are then applied, and an aggregate of the rule votes from the fuzzy if-then rules applied is used to classify the spectral power distribution. The system utilizes a spectral sensor, a fuzzifier module, a fuzzy rule database, fuzzy rule engine, an output fuzzifier module, and a means of displaying the spectral power distribution classification.

Systems and methods here may be used for automated capturing and analyzing spectrometer data of multiple sample gemstones on a stage, including mapping digital camera image data of samples, applying a Raman Probe to a first sample gemstone under evaluation on the stage, receiving spectrometer data of the sample gemstone from the probe, automatically moving the stage to a second sample, using the image data, and analyzing the other samples.