Internet of things data compression system and method
OwnerDunling Liinventor
Life of this patent
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Abstract
A disclosure for lossless data compression can include receiving a data block by a processor, performing, by the processor, a sparse transform extraction on the data block, selecting, by the processor, a transform matrix for the data block, modeling, by the processor, the selected transform matrix for the data block, selecting, by the processor, a transform coefficient model for the data block, modeling, by the processor, the selected transform coefficient model for the data block, compressing, by the processor, the data in the data block using the selected transform matrix and the selected transform coefficient model.
Claims28
A method for lossless data compression, comprising:
- receiving a data block of one-dimensional data from sensor devices or gauges by a processor;
- performing, by the processor, a sparse transform of extraction on the data block, wherein the sparse transform is defined as Y=AX, wherein A is defined as an nxM sparse transfer matrix, n is a number of elements in a subset, and M is the number of elements in an original set;
- selecting, by the processor, a transform matrix for the data block;
- modeling, by the processor, the selected transform matrix for the data block;
- selecting, by the processor, a transform coefficient model for the data block;
- modeling, by the processor, the selected transform coefficient model for the data block;
- and
- compressing, by the processor, the data in the data block using the selected transform matrix and the selected transform coefficient model.
A lossless data compression system, comprising:
- a gateway comprising a processor that can receive a stream of one-dimensional data from sensor devices or gauges,
- wherein the processor is configured to compress data using a multichannel data compression encoder by:
- receiving a data block;
- performing a sparse transform extraction on the data block, wherein the sparse transform is defined as Y=AX, wherein A is defined as an nxM sparse transfer matrix, n is a number of elements in a subset, and M is the number of elements in an original set;
- selecting a transform matrix for the data block;
- modeling the selected transform matrix for the data block;
- selecting a transform coefficient model for the data block;
- modeling the selected transform coefficient model for the data block;
- and
- compressing the data in the data block using the selected transform matrix and the selected transform coefficient model.
Dependencies are inferred from the claim text.