A nucleic acid sequence measuring apparatus (1) measures a target (TG) having a specific nucleic acid sequence included in a sample. The nucleic acid sequence measuring apparatus (1) includes a detector (12) configured to detect fluorescence emitted from a nucleic acid sequence measuring device (DV) which emits fluorescence due to an addition of the target (TG), and a calculator (25) configured to measure the target based on a difference between a first amount of light indicating an amount of fluorescent light emitted from a predefined measurement region of the nucleic acid sequence measuring device (DV) at a first time point before or immediately after an addition of the sample to the nucleic acid sequence measuring device (DV) and a second amount of light indicating an amount of fluorescent light emitted from the measurement region at a second time point after a predefined time has elapsed from the addition of the sample to the nucleic acid sequence measuring device (DV), based on a detection result of the detector (12).

Machine learning analysis for classifying agglutination of fluid samples. A method includes scanning a unique scannable code printed on a test card, wherein the test card comprises a negative control fluid sample, a positive control fluid sample, and a test fluid sample. The method includes capturing an image of the test card and providing the image of the test card to a machine learning algorithm configured to assess agglutination of the test fluid sample based on the image. The method includes receiving from the machine learning algorithm one or more of a qualitative analysis or a quantitative analysis of the agglutination of the test fluid sample.

Aspects of the present disclosure include reconfigurable integrated circuits for characterizing particles of a sample in a flow stream. Reconfigurable integrated circuits according to certain embodiments are programmed to calculate parameters of a particle in a flow stream from detected light; compare the calculated parameters of the particle with parameters of one or more particle classifications; classify the particle based on the comparison between the parameters of the particle classifications and the calculated parameters of the particle; and adjust one or more parameters of the particle classifications based on the calculated parameters of the particle. Methods for characterizing particles in a flow stream with the subject integrated circuits are also described. Systems and integrated circuit devices programmed for practicing the subject methods, such as on a flow cytometer, are also provided.

A system includes a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer and a cloud server including a server processor. The server processor receives the spectrometer data generated by the spectrometer from the processor, analyzes the spectrometer data, identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample and provides to the processor data representative of a graphical display, which includes an indication of whether or not the scanned sample includes the one or more unique characteristics of the spectrometer data.

The embodiment of the present application relates to the field of substance ingredient detection, for example, relates to a substance ingredient detection method and apparatus, and a detection device. The method includes: obtaining spectral information of a substance to be detected; and matching the spectral information with a pre-obtained prediction model based on a machine learning algorithm to obtain the ingredients of the substance to be detected. In the embodiment of the present application, the spectral information of the substance to be detected is obtained, and then the spectral information is matched with the prediction model based on the machine learning algorithm to obtain the prediction result of the ingredients of the substance to be detected. In the embodiment of the present application, the machine learning algorithm is combined with spectral recognition, the traditional algorithm is abandoned, the recognition speed is improved, and the substance detection efficiency is greatly improved.

Disclosed herein is an electronic apparatus and method capable of identifying a state of an object. The electronic apparatus includes a light-emitting diode array configured to transmit light beams having different wavelengths, a photodiode array configured to receive the light beams, a display, and a processor configured to control the light-emitting diode array to transmit the light beams having the different wavelengths toward an object, identify a state of the object based on intensities reflected on the object according to the light beams having the different wavelengths that are received by the photodiode array, and display information about the state of the object on the display.

A device for optically identifying surfaces, in particular for optically identifying structured and/or pictorial surfaces, spaces and/or e.g. paintings or sculptures is simple to use independently of the location. For this purpose, the device includes a housing in which light-emitting and light-receiving elements are arranged, and the device also includes a first portion having at least one lens, a portion that follows the first portion in the longitudinal direction and has a screen, and an adjoining handle portion.

The invention specifies an apparatus for evaluating sensor measured values (1.1), having: —a sensor (1), wherein a model function that is suitable for a least squares regression and definable by a parameter vector is provided for evaluating the sensor measured values (1.1) of the sensor (1), wherein at least one parameter of the parameter vector forms a sensor output signal (3), and —a computing and evaluation unit (2) that has a neural network (2.1), which estimates the parameter vector on the basis of actually ascertained sensor measured values (1.1), and a least squares regression module (2.2), wherein the neural network (2.1) is trained with parameter vectors and the associated sensor measured values, and that is set up: .sup.∘—to use the trained neural network (2.1) to ascertain at least one parameter estimate vector for sensor measured values (1.1) measured using the sensor (1) as an input variable for the least squares regression module (2.2), .sup.∘—if a convergence criterion is satisfied for the performance of the least squares regression, to terminate the least squares regression and .sup.∘—to output the at least one parameter of the most recently ascertained parameter vector as sensor output signal (3). An associated automated method for evaluating sensor measured values and a use of the apparatus are likewise specified.

Systems, devices and methods for calibrating or increasing the accuracy of light sensing devices. The methods can include calibrating a light sensing device with a calibration source that is adapted to mimic at least one representative spectrum.

A system for infrared analysis of a target surface region of a subject includes a reservoir containing a medium at a predetermined temperature and a conduit defining a channel for transmitting the medium from the reservoir to the target surface region. The conduit may have a first end that is attached to an outlet of the reservoir and a second end that is flexibly conformable to a shape corresponding to a perimeter of the target surface region. The system may further include an infrared camera(s) operable to capture infrared image data of the target surface region and one or more processors operable to produce a representation of the captured infrared image data at a plurality of timings relative to the transmission of the medium from the reservoir to the target surface region. Adjunctive reflective surfaces may ensure that IR signals from target geometric surfaces can be captured for analysis.