A method is provided for identifying or authenticating an object. The method includes vibrating the object at a plurality of frequencies. The vibrations from the object are sensed at each of the plurality of frequencies using an accelerometer. A vibration profile of the object is generated using the sensed vibrations. The generated vibration profile is then compared to a stored vibration profile. It is determined if the generated vibration profile matches the stored vibration profile. A match indicates that the object has been identified or authenticated. In another embodiment, an object capable of implementing the method is provided. In another embodiment, the object may include a replaceable accessary. In this case, the initial and generated vibration profiles may be created with the replacement accessary attached to the object. A match of the generated and initial vibration profiles indicates that the replaceable accessary is authentic.

A computer-implemented clustering method is disclosed for image segmentation, social network analysis, computational biology, market research, search engine and other applications. At the heart of the method is a point-set kernel that measures the similarity between a data point and a set of data points. The method has a procedure that employs the point-set kernel to expand from a seed point to a cluster; and finally identifies all clusters in the given dataset. Applying the method for image segmentation, it identifies several segments in the image, where points in each segment have high similarity: but points in one segment have low similarity with respect to other segments. The method is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering methods are either efficient or effective; and even efficient ones have difficulty dealing with large scale datasets without massive parallelization.

A user-selected group of data points is received. Weighted distances between further data points with the user-selected group of data points are computed, the weighted distances computed based on respective weights assigned to dimensions of data points. Density-based grouping of the further data points is performed based on the computed weighted distances, the density-based grouping producing cohorts of data points. A graphical visualization is generated including pixels representing the user-selected group of data points and the cohorts of data points. The graphical visualization provides a temporal-based visualized identification of the cohorts with the user selected group of data points.

An analysis device includes an analysis unit configured to receive scattered light, transmitted light, fluorescence, or electromagnetic waves from an observed object located in a light irradiation region light-irradiated from a light source and analyze the observed object on the basis of a signal extracted on the basis of a time axis of an electrical signal output from a light-receiving unit configured to convert the received light or electromagnetic waves into the electrical signal.

Methods, apparatus, systems and articles of manufacture are disclosed for scalable architectures for reference signature matching and updating. An example method for scalable architectures for reference signature matching and updating includes accessing site signatures to be compared to reference signatures from a first group of media sources. Determining if a first reference node is an owner of a first one of the site signatures. Comparing a neighborhood of site signatures including the first site signature to reference signatures in a first subset of reference signatures when the first reference node is the owner of the first site signature, the first subset of references signatures stored in a first memory partition associated with the first reference node. Not comparing site signature to reference signatures when the first reference node is not the owner of the first one of the site signatures.

The power consumption of a control device or a data processing system is reduced. Safety is enhanced. An electronic device is operated in a simple way. A control device includes an arithmetic circuit, an input unit, and a power management unit. The input unit includes a sensor element. The power management unit has a function of controlling supply and shutdown of power to the arithmetic circuit. The power management unit has a function of supplying power to the arithmetic circuit in response to a detection signal output from the sensor element. The sensor element includes one or more selected from an acceleration sensor, an angular velocity sensor, and a magnetic sensor. The arithmetic circuit includes a register. The register includes a first circuit and a second circuit. The register has a function of storing, in the second circuit, first data stored in the first circuit in a period during which the power management unit supplies power to the arithmetic circuit and retaining the first data, in a period during which the power management unit stops power supply to the arithmetic circuit. The arithmetic circuit has a function of generating second data with use of signal data output from the sensor element and the first data.

The coordinate system fixing unit uses the displacement of an object under measurement between photographed images in chronological order to generate fixed-coordinate chronological images. The displacement calculation unit uses the fixed-coordinate chronological images to calculate a two-dimensional spatial distribution of the displacement of the surface of the object under measurement. The displacement difference calculation unit calculates a two-dimensional displacement difference distribution by removing an error component from the two-dimensional spatial distribution. The depth movement amount calculation unit calculates a depth movement amount from the two-dimensional displacement difference distribution. The displacement separation unit calculates in-plane displacement from the two-dimensional displacement difference distribution. The determination unit uses the in-plane displacement and/or the depth movement amount to determine whether there is an abnormality in the object under measurement.

A system and method are provided for monitoring subject behavior during sleep onset. In some aspects, a system includes one or more sensors configured to acquire behavioral data from a subject using input provided during sleep onset. The system also includes a processor programmed to at least assemble a time-series of behavioral responses using the behavioral data acquired using the one or more sensors, and estimate an instantaneous probability of response using the time-series of behavioral responses. The processor is also programmed to generate a statistical model of wakefulness using the instantaneous probability of response, and estimate, using the model, a probability indicative of a degree to which the subject is awake at each point in time during the sleep onset process. The processor is further configured to generate a report indicative of sleep onset in the subject. The system also includes an output for displaying the report.

An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.

One or more embodiments described herein relate to predicting, using adaptive artificial intelligence techniques, typical and aberrant physiological reactions of a patient to psychiatric counseling. Treatment plans can be determined and calculated based on previously-gathered demographic and/or biometric data, and/or modifications to treatment plans can be determined and/or implemented based on emergent recognition of reaction types, such as reclassifying reactions that would previously have been deemed typical as aberrant (or vice versa).