Falkonry Inc.

Cmmb Vision USA Inc.

Mohammad H Firooz

Hui Liu

Nikunj R Mehta

Sumit Roy

Greg Olsen

Peter Nicholas Pritchard

A method for determining specific conditions occurring on industrial equipment based upon received signal data from sensors attached to the industrial equipment is provided. Using a server computer system, signal data is received and aggregated into feature vectors. Feature vectors represent a set of signal data over a particular range of time. The feature vectors are clustered into subsets of feature vectors based upon attributes the feature vectors. One or more sample episodes are received, where a sample episode includes sample feature vectors and specific classification labels assigned to the sample feature vectors. A signal data model is created that includes the associated feature vectors, clusters, and assigned classification labels. The signal data model is used to assign classification labels to newly received signal data using the mapping information for the existing feature vectors, existing clusters and associated classification labels to determine the specific conditions occurring on the industrial equipment.

Machine learning of physical conditions based on abstract relations and sparse labels

Embodiments of systems and methods for efficient broadcasting via random linear packet combining are described. A plurality of data packets are received from a data source according to embodiments. The plurality of data packets is divided into a plurality of data blocks, and bits associated with the plurality of data blocks are multiplied by a set of coefficients to generate a plurality of product values. An encoded data packet having a plurality of encoded data blocks may also be generated by linearly combining the plurality of product values for respective data blocks of each of the plurality of data packets into corresponding encoded data blocks of the encoded data packet. Because each encoded data packet includes information about a complete set of data packets, rather than just a subset, less broadcast redundancy may be required.

Efficient broadcasting via random linear packet combining

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San Jose, CA, United States of America

Mohammad H Firooz