Abstract: Disclosed herein are systems and devices for use in determining a level of a trait in an agriculture or food product sample. Further disclosed is a portable spectrometer system coupled with chemometric analysis methods to determine a level of a trait in an agriculture or food product sample, the portable spectrometer system comprising: a spectrometer; a sample stage adjacent the spectrometer; a motor coupled to the sample stage; and a system housing enclosing the motor and the spectrometer; wherein rotation of the motor rotates the sample stage, and wherein the motor is controllable in response to spectroscopy requirements.

Abstract: A method of digital measuring the color of fabrics based on digital camera, includes: making plain fabric samples; obtaining ground-truth color of plain fabrics using a spectrophotometer; capturing a raw format digital image of the plain fabrics using the digital camera and extracting raw camera responses of the plain fabrics; capturing a raw format digital image of a target fabric and extracting the raw camera responses of a ROI in the target fabric; calculating a Euclidean distance and a similarity coefficient between the raw camera responses of the ROI in the target fabric and the plain fabrics; normalizing the Euclidean distance and the similarity coefficient; calculating a weighting coefficient of each color data of the plain fabrics based on the normalized Euclidean distance and similarity coefficient; weighting every color data of plain fabrics with a corresponding weighting coefficient; and summing the weighted color data of the plain fabrics.

Abstract: Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.

Abstract: Disclosed herein are systems and devices for use in determining a level of a trait in an agriculture or food product sample. Further disclosed is a portable spectrometer system coupled with chemometric analysis methods to determine a level of a trait in an agriculture or food product sample, the portable spectrometer system comprising: a spectrometer; a sample stage adjacent the spectrometer; a motor coupled to the sample stage; and a system housing enclosing the motor and the spectrometer; wherein rotation of the motor rotates the sample stage, and wherein the motor is controllable in response to spectroscopy requirements.

Abstract: A semiconductor device includes a substrate, a buffer layer disposed on the substrate, a barrier layer disposed on the buffer layer, a source, a drain, and a gate stack. The source, the drain, and the gate stack are disposed on the barrier layer. The gate stack includes a first epitaxial layer on the barrier layer, a second epitaxial layer on the first epitaxial layer, and a third epitaxial layer on the second epitaxial layer. The semiconductor device further includes a gate disposed on the gate stack.

Abstract: Big data health service method and system based on remote fundus screening are provided. The method includes steps of: acquiring information to be analyzed sent by remote terminal agency; pre-interpreting information to be analyzed, and judging whether information to be analyzed is qualified; extracting characteristic data from information to be analyzed if it is qualified, and forming structured quantitative index; sorting and analyzing characteristic data and quantitative index according to knowledge calculation model to obtain analysis conclusion; and storing information to be analyzed, characteristic data, quantitative index, and analysis conclusion into pre-designed database.

Abstract: The present invention relates to fundus image processing field, especially a preprocessing method for quantitative analysis of fundus image, and storage device. A preprocessing method for quantitative analysis of fundus image, wherein the step includes, acquiring a to-be-processed fundus image; performing optic disk positioning on the to-be-processed fundus image; performing macular fovea positioning on the to-be-processed image; and calculating a quantization parameter of a distance between a center of the macular fovea and a bitamporal edge of the optic disk. Through this method, the data obtained is converted from absolute representation to relative representation, and through normalization, a meaningful and comparable quantification is formed between people, between different people, and even between inspection results of different instruments. analyze data. It ensures that fundus images from different sources can form meaningful and comparable quantitative indicators.

Abstract: A quality control method for remote fundus screening includes acquiring a fundus image of an examinee through a fundus camera by terminal application institution and transmitting the fundus image as well as personal information and inquiry data which are carried by the examinee to a remote interpretation and consultation center. The remote interpretation consultation center determines whether the fundus image, personal information, and consultation data are qualified; if the fundus image, personal information, and consultation data are unqualified, the remote interpretation consultation center terminates the interpretation.

Abstract: Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.

Abstract: Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.

Abstract: Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.