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.

A network authentication system can be configured for sampling a plurality of signal samples from a device on a network, providing the plurality of signal samples to a first machine-learned model that is configured to determine a device fingerprint based at least in part on the plurality of signal samples, and providing the device fingerprint to a second machine-learned model that is configured to classify the device based at least in part on the device fingerprint.

A distribution of response times of a storage system can be estimated for a proposed workload using a trained learning process. Collections of information about operational characteristics of multiple storage systems are obtained, in which each collection includes parameters describing the configuration of the storage system that was used to create the collection, workload characteristics describing features of the workload that the storage system processed, and storage system response times. For each collection, workload characteristics are aggregated, and the storage system response information is used to train a probabilistic mixture model. The aggregated workload information, storage system characteristics, and probabilistic mixture model parameters of the collections form training examples that are used to train the learning process. Once trained, the learning process is used to provide a distribution of response times that would be expected from a storage system having a proposed configuration when processing a proposed workload.

Input sound spectra are acquired. The input sound spectra include sound spectra corresponding to multiple sound sources. Predicted sound spectra are isolated from the input sound spectra by performing spectrum isolation processing on the input sound spectra. Updated input sound spectra are acquired by removing the predicted sound spectra from the input sound spectra. Next isolated predicted sound spectra continue to be acquired through the updated input sound spectra, until the updated input sound spectra include no sound spectrum.

In an example, the present invention provides a method for processing rf backscattered signals. The method includes generating a plurality of rf signals numbered from 1 to N, where N is an integer greater than 1, from, respectively, a plurality of rf sources numbered from 1 to N. In an example, each of the rf sources is an antenna. In an example, the method includes transferring the plurality of rf signals to a predetermined region of space. The method includes receiving a stream of back scattered signals derived from each of the the rf signals numbered from 1 to N from the predetermined space, each stream of back scattered signals being one of a plurality of backscattered signals numbered 1 to N corresponding, respectively, to the plurality of rf sources numbered from 1 to N. The method includes processing each stream of the backscattered signals, using a digital signal processor, at a predetermined time to normalize the stream of backscattered signals to form a normalized signal corresponding to the stream of the backscattered signals and outputting a plurality of normalized signals numbered from 1 to N corresponding, respectively, to the plurality of back scattered signals, numbered from 1 to N.

A method, computer system, and computer program product are provided for detecting drift in predictive models for network devices and traffic. A plurality of streams of time-series telemetry data are obtained, the time-series telemetry data generated by network devices of a data network. The plurality of streams are analyzed to identify a subset of streams, wherein each stream of the subset of streams includes telemetry data that is substantially empirically distributed. The subset of streams of time-series data are analyzed to identify a change point. In response to identifying the change point, additional time-series data is obtained from one or more streams of the plurality of streams of time-series telemetry data. A predictive model is trained using the additional time-series data to update the predictive model and provide a trained predictive model.

A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One or more leads of the plurality of leads that are derivable from a combination of other leads of the plurality of leads are identified, and a portion of the electrocardiogram trace data does not include electrocardiogram trace data of the one or more leads. The portion of the electrocardiogram data is provided to a trained machine learning model, to evaluate the portion of the electrocardiogram trace data with respect to one or more cardiac disease states. A risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time is generated by the trained machine learning model based on the evaluation.

Multi-level hierarchical routing matrices for pattern-recognition processors are provided. One such routing matrix may include one or more programmable and/or non-programmable connections in and between levels of the matrix. The connections may couple routing lines to feature cells, groups, rows, blocks, or any other arrangement of components of the pattern-recognition processor.

A method for testing a wireless tag by a testing unit. The method comprises: transmitting, by a first antenna, a prescribed patten that is recognizable by a tag to put the tag into a testing mode; transmitting a trigger signal to a tag from a second antenna, the trigger signal being adapted to cause a tag to at least respond with a prescribed signal when the tag is good; waiting up to a prescribed amount of time after transmission of the trigger signal for a response to the trigger signal from a tag that is within range of the second antenna; when a valid response is received from the tag within the prescribed amount of time, designating the tag as having passed the test; and when a valid response is not received from the tag within the prescribed amount of time, designating the tag as having failed the test.

Detecting sequences of computer-executed operations, including training a BLSTM to determine forward and backward probabilities of encountering each computer-executed operations within a training set of consecutive computer-executed operations in forward and backward execution directions of the operations, and identifying reference sequences of operations within the training set where for each given one of the sequences the forward probability of encountering a first computer-executed operation in the given sequence is below a predefined lower threshold, the forward probability of encountering a last computer-executed operation in the given sequence is above a predefined upper threshold, the backward probability of encountering the last computer-executed operation in the given sequence is below the predefined lower threshold, and the backward probability of encountering the first computer-executed operation in the given sequence is above the predefined upper threshold, and where the predefined lower threshold is below the predefined upper threshold.