Disclosed herein are methods and systems for detection and discrimination of optical signals from a densely packed substrate. These have broad applications for biomolecule detection near or below the diffraction limit of optical systems, including in improving the efficiency and accuracy of polynucleotide sequencing applications.

The present invention discloses a method for authentication of animal species origin of leather products, which includes the following steps: Step 1: Model establishment: (1) Collect leather samples from different animal species origins, set mass spectrometric parameters, cut the surface of leather samples using a preheated electric soldering iron, and detect the resulting sample ions using the mass spectrometer; (2) Create a multivariate statistical model based on principal component analysis and linear discriminant analysis of rapid evaporative ionization mass spectrometric data and evaluate the model with cross-validation tests; Step 2: Analysis of real leather samples: detect and authenticate the identity of real leather samples based on the multivariate statistical model. The authentication method disclosed in present invention is a rapid analytical method that requires no sample pretreatment and can identify the animal species origin of leather products rapidly and accurately.

This disclosure relates to diagnosing a nature and magnitude of faults within electrical and mechanical systems.

A system and method for processing human related data to make personalized and context aware decisions with distributed machine learning at an edge and a cloud is disclosed. A nearest edge computing device receives first, second and third sensed signals from first, second and third sensory devices, determines when the first, second and third sensed signals exceed corresponding thresholds, correlates pairs of the sensed signals to generate multiple correlation patterns, determines a lag time between the first sensed signal exceeding the first threshold and the second sensed signal exceeding the second threshold, provides each of the multiple correlation patterns and the lag time as inputs to multiple long short term memory (LSTM) neural networks, controls the multiple LSTM neural networks to provide outputs, and maps the patient to a stage of a medical condition based at least in part on the multiple correlation patterns and the lag time.

Methods, apparatus, and processor-readable storage media for automatic identification of resources in contention in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and multiple candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each candidate time series; for each similarity measurement, assigning weights to the candidate time series based on similarity to the primary time series relative to the other candidate time series; generating, for each candidate time series, a similarity score based on the weights assigned across the similarity measurements; identifying, based on the similarity scores, at least one of the candidate time series as representative of at least one resource in contention with respect to latency data represented by the primary time series; and outputting identification of the identified candidate time series for use in automated actions.

A system and method for processing human related data to make personalized and context aware decisions with distributed machine learning at an edge and a cloud is disclosed. A nearest edge computing device receives first, second and third sensed signals from first, second and third sensory devices, determines when the first, second and third sensed signals exceed corresponding thresholds, correlates pairs of the sensed signals to generate multiple correlation patterns, determines a lag time between the first sensed signal exceeding the first threshold and the second sensed signal exceeding the second threshold, provides each of the multiple correlation patterns and the lag time as inputs to multiple long short term memory (LSTM) neural networks, controls the multiple LSTM neural networks to provide outputs, and maps the patient to a stage of a medical condition based at least in part on the multiple correlation patterns and the lag time.

Detecting a counterfeit status of a target device by: selecting a set of frequencies that best reflect load dynamics or other information content of a reference device while undergoing a power test sequence; obtaining target electromagnetic interference (EMI) signals emitted by the target device while undergoing the same power test sequence; creating a sequence of target kiviat plots from the amplitude of the target EMI signals at each of the set of frequencies at observations over the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint to a reference kiviat tube EMI fingerprint for the reference device undergoing the power test sequence to determine whether the target device and the reference device are of the same type; and generating a signal to indicate a counterfeit status based at least in part on the results of the comparison.

Disclosed herein are methods and systems for detection and discrimination of optical signals from a densely packed substrate. There have broad applications for biomolecule detection near or below the diffraction limit of optical systems, including in improving the efficiency and accuracy or polynucleotide sequencing applications.

A method for in-ova fertilisation determination and gender determination on a closed egg. The aim is to specify a method for the in-ovo fertilisation determination and gender determination on a closed egg. This aim is achieved by a method in which a closed egg is positioned, candled and/or illuminated, next an image of the closed egg is recorded, then the captured data are evaluated and the position of the cardiovascular system located in the egg is calculated. A detection unit is adjusted via the calculated position of the cardiovascular system by means of a positioning unit and subsequently the blood is stimulated, then the blood-specific and blood-foreign absorption spectra are detected and selected, the fertilisation is ascertained and then the spectra containing blood-foreign information are compensated by a compensation method and the spectra are classified for sex determination.

The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.