A method and apparatus for the quantitative determination of an individual's risk for sudden cardiac death (SCD) is described. Risk stratification is accomplished (and may have a sensitivity and specificity of greater than about 90%) by determining the presence in any individual being tested for SCD risk of sequences identified herein to correlate quantitatively with SCD risk. Both the number of such sequences present and their alignment scores (similarity) with the SCD risk sequence ensemble are used to calculate quantitative SCD risk.

A contactless biometrics monitoring system includes: a first device to transmit a modulated signal into an environment, the modulated signal being modulated to amplify one or more biometric patterns of a user located within the environment; and a second device to receive a reflection of the modulated signal off the user located within the environment. The reflection includes a vibration component, and the vibration component indicates biometric information of the user corresponding to the one or more biometric patterns amplified by the modulated signal.

A data recognition method includes: extracting a feature map from input data based on a feature extraction layer of a data recognition model; pooling component vectors from the feature map based on a pooling layer of the data recognition model; and generating an embedding vector by recombining the component vectors based on a combination layer of the data recognition model.

Disclosed herein is a method and system a system that enables users to simply tap their smartphone or other electronic device to an object to discover and rapidly utilize contextual functionality. As described herein, the system and method provide for recognition of physical contact with uninstrumented objects, and summons object-specific interfaces.

Techniques allow a computer to responsively search for graph shapes similar to a user-selected graph shape much faster. Data can be pre-processed and stored as vectors, along with an index. The index can be used to find similar vectors that represent graph shapes similar to a user-selected shape in a computationally efficient manner. Vectors of multiple resolutions can be used to anticipate different sizes of a graph that a user can select, and comparisons can be repeated and refined. When a satisfactorily small number of candidate vectors are determined, more computationally intensive distance calculations can be performed on data reconstructed from the vectors.

Methods and systems provide for using echo measurements, to detect cable faults in cables of vehicular communication networks.

A system and method for processing satellite imagery with high-frequency revisit to monitor vegetation using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a vegetation monitoring server 106. The server receives a first set of satellite images and a second set of satellite images from one or more satellites 102A-B. The vegetation monitoring server 106 adjusts the first set of satellite images according to a spectral band of the second set of satellite images using pre-processing techniques. The deep learning model 108 generates a reconstructed image from a cloudy image of the first set of satellite images by replacing the cloudy and shadow pixels with the same pixels of the same image in third set of satellite images obtained from the satellite 102C. The vegetation monitoring server 106 generates observation data from the reconstructed image to provide vegetation monitoring to a geographical area.

An artificial intelligence-based interference recognition method for an electrocardiogram, comprising: cutting and sampling heart beat data of a first data amount, and inputting the heart beat data to be recognized that is obtained by cutting and sampling into an interference recognition binary classification model for interference recognition; in a sequence of the heart beat data, performing signal anomaly determination on a heart beat data segment where an inter-beat interval is greater than or equal to a preset interval determination threshold value, so as to determine whether the heart beat data segment is an abnormal signal; if the heart beat data segment is not an abnormal signal, determining a starting data point and an ending data point of sliding sampling in the heart beat data segment according to a set time with a preset time width, and performing sliding sampling on the data segment from the starting data point until the ending data point so as to obtain multiple sampling data segments; and using each sampling data segment as heart beat data to be recognized for interference recognition.

The invention provides a visualized time sequence pattern matching method based on Hough transformation, and relates to the technical field of data visualization analysis. The method comprises the steps of: firstly, judging whether historical data to be matched is one-dimensional time sequence data or multi-dimensional time sequence data, and if the historical data to be matched is the multi-dimensional time sequence data, performing normalization processing; performing time sequence selection: selecting a time sequence to be matched from the historical data in a time window pattern, and eliminating the selected time sequence from the historical data; converting a time sequence image in original coordinates to Hough space through the Hough transformation, and judging the similarity matching situation of the time sequence through a voting mechanism; and finally, screening the finally-matched results according to the voting results.

An information processing device 100 of the present invention includes an analysis unit 121 and an encoding unit 122. The analysis unit 121 extracts a partial time-series data set obtained by dividing a time-series data set that is a set of time-series data including a plurality of elements at given time intervals, and calculates correlation data representing a correlation between elements of time-series data included in the partial time-series data set. The encoding unit 122 generates coded data based on the time-series data of the partial time-series data set and the correlation data.