A method for material classification of an object. The method contains the steps of measuring, by a first light sensor, an intensity of a first light signal from an object; measuring, by a second light sensor, an intensity of a second light signal from the object; inputting features related to the intensity of the first light signal and features related to the intensity of the second light signal into a neural network; calculating by the neural network, for each of a plurality of possible classes of material, a probability of the object containing the material of the class; and determining a material of the object based on one of the plurality of possible classes of material that has a highest probability. Embodiments of the invention provide an automatic solution of material classification based on neural network, which achieves a high successful recognition rate of the material.

An apparatus for obtaining image data and functional data from a biological sample, the apparatus including: an interferometer configured to acquire interferometric information at a plurality of time points along an imaging plane for which at least one axis of the plane is at least partially along a depth or axial dimension that is based on radiations provided from a reference interfered with by the biological sample; and a processor configured to receive the interferometric information from the interferometer and configured to: process the interferometric information to generate an image of the biological sample along the imaging plane; determine frequency information based on the plurality of time points of the interferometric information, the frequency information reflecting temporal modulations induced by dynamic functions of the biological sample; generate a spatial map of the frequency information, and the spatial map of the frequency information indicating the dynamic functions of the biological sample.

Optical data is collected from an optical sensor of a dual wavelength, and in order to detect the fire from the collected optical data, an average value of a first wavelength, an average value of a second wavelength, and a ratio of the average values of the two wavelengths are calculated, and an amount of change of a slope of the ratio is used to detect the fire and determine the fire occurrence time. From the determined fire occurrence time, fire features are extracted from the optical data in real time according to defined rules to configure a data set. The data set may be used for learning and inference techniques to identify a fire or non-fire, a fire source, a combustion material, and the like.

Instrument control and data acquisition in advanced analytic systems that utilize optical pulses for sample analysis are described. Clocking signals for data acquisition, data processing, communication, and/or other data handling functionalities can be derived from an on-board pulsed optical source, such as a passively mode-locked laser. The derived clocking signals can operate in combination with one or more clocking signals from a stable oscillator, so that instrument operation and data handling can tolerate interruptions in operation of the pulsed optical source.

In a process for producing a resin powder, a physical property of a water absorbent resin powder is predicted from a near-infrared absorption spectrum. A predicting apparatus (100) includes: a measurement data obtaining section (11) which obtains near-infrared measurement data: and a predicting section (13) which inputs, into a prediction model, at least any one selected from the group consisting of the near-infrared measurement data and one or more pieces of processed data which have been generated on the basis of the near-infrared measurement data and outputs prediction information concerning a physical property of a resin powder.

A main object of the present technology is to provide a technique for automatically proposing a better combination of fluorochrome-labeled antibodies.

The present technology provides an information processing apparatus including a processing unit that generates a combination list of fluorophores for a biomolecule on the basis of an expression level category obtained by classifying a plurality of biomolecules used for analysis of a sample on the basis of an expression level in the sample, a brightness category obtained by classifying a plurality of fluorophores usable for analysis of the sample on the basis of brightness, correlation information between the plurality of fluorophores, and expression relationship information of the plurality of biomolecules, in which the processing unit selects a fluorophore to be assigned to the biomolecule in the combination list from fluorophores belonging to a brightness category associated with an expression level category to which the biomolecule belongs.

A method for measuring a characteristic of a thin film is disclosed. The method includes a) obtaining a measured spectrum from a target region on the substrate by using a spectroscopic ellipsometer, b) obtaining a physical model capable of obtaining an estimated parameter value related to the characteristic of the thin film through regression analysis of the measured spectrum, c) obtaining a machine learning model capable of obtaining a reference parameter value related to the characteristic of the thin film by using the measured spectrum, and d) obtaining an integrated model which uses an integrated error function capable of considering both of a first error function and a second error function, and obtaining an optimum parameter value through regression analysis of the integrated model.

A non-contact system for the sensing of pH includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network a pH of a fluid.

A metrology system may receive a model for measuring one or more selected attributes of a target including features distributed in a selected pattern based on regression of spectroscopic scatterometry data from a scatterometry tool for a range of wavelengths. The metrology system may further generate a weighting function for the model to de-emphasize portions of the spectroscopic scatterometry data associated with wavelengths at which light captured by the scatterometry tool when measuring the target is predicted to include undesired diffraction orders. The metrology system may further direct the spectroscopic scatterometry tool to generate scatterometry data of one or more measurement targets including fabricated features distributed in the selected pattern. The metrology system may further measure the selected attributes for the one or more measurement targets based on regression of the scatterometry data of the one or more measurement targets to the model weighted by the weighting function.

A data processing method for detection of deterioration of semiconductor process kits includes the following steps: acquiring a plurality of Raman spectra data of a semiconductor process kit and performing a plurality calculating processes on the Raman spectra data to obtain a first deterioration state determining parameter indicating the aging degree of the entire semiconductor process kit and a second deterioration state determining parameter indicating the degree of variation of the internal molecular structure of the semiconductor process kit.