Viavi Solutions Inc.

Changmeng Hsiung

Lan Sun

Christopher G Pederson

Nada A O'Brien

Marc K Von Gunten

Curtis Ross Hruska

Charles Andrew Hulse

Peng Zou

Heinz W Siesler

Michael Klimek

Edward Gooding

A device may receive information identifying results of a spectroscopic measurement performed on an unknown sample. The device may determine a decision boundary for a quantification model based on a configurable parameter, such that a first plurality of training set samples of the quantification model is within the decision boundary and a second plurality of training set samples of the quantification model is not within the decision boundary. The device may determine a distance metric for the spectroscopic measurement performed on the unknown sample relative to the decision boundary. The device may determine a plurality of distance metrics for the second plurality of training set samples of the quantification model relative to the decision boundary. The device may provide information indicating whether the spectroscopic measurement performed on the unknown sample corresponds to the quantification model.

Reduced false positive identification for spectroscopic quantification

A device may receive spectroscopic data associated with a dynamic process. The device may identify a pseudo steady state end point based on the spectroscopic data. The pseudo steady state end point may indicate an end of a pseudo steady state associated with the dynamic process. The device may identify a reference block and a test block based on the pseudo steady state end point, and may generate a raw detection signal associated with the reference block and a raw detection signal associated with the test block. The device may generate an averaged statistical detection signal based on the raw detection signal associated with the reference block and the raw detection signal associated with the test block, and may determine whether the dynamic process has reached a steady state based on the averaged statistical detection signal.

Dynamic process end point detection

A device may obtain a master beta coefficient of a master calibration model associated with a master instrument. The master beta coefficient may be at a grid of a target instrument. The device may perform constrained optimization of an objective function, in accordance with a set of constraints, in order to determine a pair of transferred beta coefficients. The constrained optimization may be performed based on an initial pair of transferred beta coefficients, the master beta coefficient, and spectra associated with a scouting set. The device may determine, based on the pair of transferred beta coefficients, a transferred beta coefficient. The device may determine a final transferred beta coefficient based on a set of transferred beta coefficients including the transferred beta coefficient. The final transferred beta coefficient may be associated with generating a transferred calibration model, corresponding to the master calibration model, for use by the target instrument.

Focusing linear model correction and linear model correction for multivariate calibration model maintenance

A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

Health tracking device

A device may receive information identifying results of a set of spectroscopic measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the results of the set of spectroscopic measurements, wherein the classification model includes at least one class relating to a material of interest for a spectroscopic determination, and wherein the classification model includes a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement. The device may receive information identifying a particular result of a particular spectroscopic measurement of an unknown sample. The device may determine whether the unknown sample is included in the no-match class using the classification model. The device may provide output indicating whether the unknown sample is included in the no-match class.

Reduced false positive identification for spectroscopic classification

A device may receive information identifying results of a spectroscopic measurement of an unknown sample. The device may perform a first classification of the unknown sample based on the results of the spectroscopic measurement and a global classification model. The device may generate a local classification model based on the first classification. The device may perform a second classification of the unknown sample based on the results of the spectroscopic measurement and the local classification model. The device may provide information identifying a class associated with the unknown sample based on performing the second classification.

Identification using spectroscopy

A device may receive a classification model generated based on a set of spectroscopic measurements performed by a first spectrometer. The device may store the classification model in a data structure. The device may receive a spectroscopic measurement of an unknown sample from a second spectrometer. The device may obtain the classification model from the data structure. The device may classify the unknown sample into a Kosher or non-Kosher group or a Halal or non-Halal group based on the spectroscopic measurement and the classification model. The device may provide information identifying the unknown sample based on the classifying of the unknown sample.

Spectroscopic classification of conformance with dietary restrictions

A device may obtain raw heartbeat data associated with a plurality of wavelength channels. The device may generate, based on a feature vector transformation, a plurality of feature vectors, each corresponding to a respective one of the plurality of wavelength channels. The device may identify a set of selected feature vectors, from the plurality of feature vectors, based on a plurality of squares of correlation coefficients, each associated with a respective pair of the plurality of feature vectors. The device may generate, based on a principal component analysis, an average feature vector of the set of selected feature vectors. The device may determine initial heartbeat cycle data based on the average feature vector. The device may correct heartbeat cycle gaps in the initial heartbeat cycle data in order to determine final heartbeat cycle data.

Autonomous full spectrum biometric monitoring

A device may obtain a master calibration set, associated with a master calibration model of a master instrument, that includes spectra, associated with a set of samples, generated by the master instrument. The device may identify a selected set of master calibrants based on the master calibration set. The device may obtain a selected set of target calibrants that includes spectra, associated with the subset of the set of samples, generated by the target instrument. The device may create a transfer set based on the selected set of master calibrants and the selected set of target calibrants. The device may create a target calibration set, corresponding to the master calibration set, based on the transfer set. The device may generate, using an optimization technique associated with the transfer set and a support vector regression modeling technique, a transferred calibration model, for the target instrument, based on the target calibration set.

Transfer of a calibration model using a sparse transfer set

A device may receive training spectral data associated with a manufacturing process that transitions from an unsteady state to a steady state. The device may generate, based on the training spectral data, a plurality of iterations of a support vector machine (SVM) classification model. The device may determine, based on the plurality of iterations of the SVM classification model, a plurality of predicted transition times associated with the manufacturing process. A predicted transition time, of the plurality of predicted transition times, may identify a time, during the manufacturing process, that a corresponding iteration of the SVM classification model predicts that the manufacturing process transitioned from the unsteady state to the steady state. The device may generate, based on the plurality of predicted transition times, a final SVM classification model associated with determining whether the manufacturing process has reached the steady state.

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Redwood City, CA, United States of America

Changmeng Hsiung