This disclosure provides methods, devices, and systems for data compression. The present implementations more specifically relate to encoding techniques for compressing probability tables used for entropy coding. In some aspects, an entropy encoder may encode a probability table so that one or more contexts are represented by fewer bits than would otherwise be needed to represent the frequency of each symbol as a proportion of the total frequency of all symbols associated with such contexts. For example, if a given row of the probability table (prior to encoding) includes a number (M) of entries each having a binary value represented by a number (K) of bits, the same row of entries may be represented by fewer than M*K bits in the encoded probability table.

Embodiments of this application disclose example data compression methods and example base stations. One example method includes obtaining, by a base station, identification information of at least one of a data compression device or a data decompression device. A dictionary can then be generated based on the identification information. The dictionary can then be sent to the data compression device and the data decompression device to enable the data compression device and the data decompression device to perform data compression and data transmission based on the dictionary.

A system and method for data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.

Embodiments of the present disclosure are directed to forwarding a large amount of data in a machine-to-machine (M2M) system, and a method for operating a first device may include: transmitting, to a second device, a request message for bulk data transfer; and receiving, from the second device, an acknowledgment message for notifying completion of the bulk data transfer, wherein the request message may include at least one of information for identifying data to be included in bulk data, information on a target common service entity (CSE) in a target platform that receives the bulk data, information for indicating processing of source data after the bulk data transfer, and information for accessing the target CSE.

As disclosed herein, a computer implemented method for reading a record on a storage device includes reading, by a processor, the record from the storage device, and generating, by a processor, a header corresponding to the record. The method further includes adding, by a processor, header information corresponding to the metadata to the header, and providing, by a processor, the header and the record to a host. Also disclosed herein, a computer implemented method for writing a record on a storage device includes receiving, by a processor, a header and the record from a host to be written to the storage device, and determining, by a processor, metadata included in header information corresponding to the header. The method further includes determining, by a processor, a write behavior specified by the metadata, and writing, by a processor, the metadata and the record to the storage device according to the write behavior.

A system for encoding and decoding data-tokens. In some examples, the system may be configured to encode and decode integers. In other cases, the system may be configured to encode and decode symbols or bytes of data.

In various embodiments, a system comprising a network interface, a processor, and a non-transient memory medium operatively coupled to the processor is disclosed. The memory medium is configured to store a plurality of instructions configured to program the processor to receive a digital bit stream, transform the digital bit stream to an encoded digital bit stream. The encoded digital bit stream comprises at least one of a gateway channel, a composite channel, or a data channel and any combination thereof, and provides the encoded digital bit stream to the network interface for transmission. A non-transitory computer-readable memory medium and a computer-implemented method are disclosed.

A system and method for data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.

A system and method for encoding personal health monitor data using a plurality of encoding libraries. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction or on some other criteria for a given portion of the data. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but provides substantial additional security in that multiple decoding libraries must be used to decode the data. In some embodiments, each portion of data may further be encoded using different sourceblock sizes, providing further security enhancements as decoding requires multiple decoding libraries and knowledge of the sourceblock size used for each portion of the data. In some embodiments, encoding libraries may be randomly or pseudo-randomly rotated to provide additional security.

This disclosure provides methods, devices, and systems for data compression. The present implementations more specifically relate to encoding techniques for compressing probability tables used for entropy coding. In some aspects, an entropy encoder may encode a probability table so that one or more contexts are represented by fewer bits than would otherwise be needed to represent the frequency of each symbol as a proportion of the total frequency of all symbols associated with such contexts. For example, if a given row of the probability table (prior to encoding) includes a number (M) of entries each having a binary value represented by a number (K) of bits, the same row of entries may be represented by fewer than M*K bits in the encoded probability table.