A method and system are provided for model-based analysis of samples of interest and management of sample classification. Predetermined modeled data is provided including data indicative of K models for respective K measurement schemes based on a predetermined function having a spectral line shape, data indicative of M characteristic vectors of M predetermined group to which different samples relate, and data indicative of a common vector of weights for the M groups. A data processor utilizes the data and operates to apply model-based processing to measured spectral data of a sample of interest using the predetermined modeled data, and generate classification data indicative of relation of the specific sample of interest to one of the M predetermined groups.

It is disclosed a method, an apparatus, a computing device, and a computer-readable storage medium for identifying a signal. The method includes demodulating a modulated signal to generate a transmission signal, transmitting the transmission signal, receiving an echo signal generated by a reflection of the transmission signal, demodulating the echo signal to obtain demodulated information, identifying the demodulated information by using a target network model to obtain an identification result of the echo signal, and outputting the identification result to a graphical user interface for display.

An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.

A system and method for detecting human intruders while rejecting/ignoring an occupant's registered pet. An object detection system is configured to detect an object that is present in a monitored area and generate a signal output relative to the type of object. A signature processor is configured to receive the generated signal output and produce an object signature, and compare a threshold signature to the object signature, wherein the threshold signature is generated using a photograph of a reserved object, and wherein the object detection system rejects the detected object when the object signature is determined to be similar to the threshold signatures.

An object recognition system using an ultrasonic sensor is proposed, the system including an amplifier amplifying an output of the ultrasonic sensor; an analog-to-digital converter converting an output of the amplifier into a digital signal; and a feature point extractor extracting feature points using an output of the analog-to-digital converter; and an object recognizer recognizing the object by using the feature points extracted from the feature point extractor.

Described is a system for decoding spiking reservoirs even when the spiking reservoir has continuous synaptic plasticity. The system uses a set of training patterns to train a neural network having a spiking reservoir comprised of spiking neurons. A test pattern duration d is estimated for a set of test patterns P, and each test pattern is presented to the spiking reservoir for a duration of d/P seconds. Output spikes from the spiking reservoir are generated via readout neurons. The output spikes are measured and the measurements are used to compute firing rate codes, each firing rate code corresponding to a test pattern in the set of test patterns P. The firing rate codes are used to decode performance of the neural network by computing a discriminability index (DI) to discriminate between test patterns in the set of test patterns P.

Systems, methods, and kits are disclosed for collection, labeling and analyzing biological samples containing nucleic acid in conjunction with collecting at least one ridge and valley signature of an individual. Such devices and methods are used in forensic, human identification, access control and screening technologies to rapidly process an individual's identity or determine the identity of an individual.

Systems and methods for identifying and analyzing neuropsychological flow patterns, include creating a knowledge base of neuropsychological flow patterns. The knowledge base is formed by obtaining signals from multiple research groups for particular behavioral processes, localizing sources of activity participating in the particular behavioral processes, identifying sets of patterns of brain activity for the behavioral processes and neuropsychologically analyzing the localized sources and the identified patterns for each of the research groups. The neuropsychological analysis includes identifying all possible pathways for the identified sets of patterns, ranking the possible pathways based on likelihood for the particular behavioral process and reducing the number of ranked possible pathways based on additional constraints. A system for comparison of obtained signals from an individual to the created knowledge base is provided. These obtained signals are then used to further update the existing knowledge base.

A sensor device is coupled to a mechanical machine. The sensor device detects vibrations of the mechanical machine and transmits the vibration data to a remote processing device. The vibration data may be compressed prior to transmission. The remote processing device receives the data and generates a reconstructed version of the vibration data. The remote processing device includes a machine learning model trained to examine vibration data and to identify a motion pattern associated with an error condition. The machine learning model is applied to the reconstructed vibration data and detects an occurrence of an error condition in the mechanical machine. An alert indicating that an error condition has been detected is transmitted to a human operator. The human operator verifies the status of the mechanical machine and confirms that an error condition has occurred. In response to receipt of the confirmation, the machine learning model is further trained on training data updated to include the vibration data generated by the mechanical machine.

In one aspect, the present disclosure related to a method for modifying a beacon light source for use in a light-based positioning system. In some embodiments, the method includes selecting a modulation scheme for the light source, determining a duty cycle for the light source based on the modulation scheme, the duty cycle having a proportion of time the light source is in an on state and a corresponding proportion of time the light source is in an off state, the proportion of time the light source is in an off state resulting in reduced luminosity of the light source, and supplying additional power to the light source to compensate for the reduced luminosity of the light source by the duty cycle.