Patent classifications
H03M7/3088
Compression device and decompression device
According to one embodiment, an interleaving unit divides a symbol string into first and second symbols. A first coding unit converts the first symbols to first codewords. A first packet generating unit generates first packets including the first codewords. A first request generating unit generates first packet requests including sizes of variable length packets. A second coding unit converts the second symbols to second codewords. A second packet generating unit generates second packets including the second codewords. A second request generating unit generates second packet requests including sizes of variable length packets. A multiplexer outputs a compressed stream including the first and second variable length packets cut out from the first and second packets.
Communication Method And Apparatus
Example communication methods and apparatus are described. One example method includes obtaining a first compression dictionary by a compression node. The compression node compresses a first data packet by using a first compression dictionary, and sends a first compressed data packet to the decompression node. The compression node compresses a second data packet by using the first compression dictionary, and sends a second compressed data packet to the decompression node.
PERMUTATION-BASED CODING FOR DATA STORAGE AND DATA TRANSMISSION
Methods of encoding and decoding data are described wherein the encoding method comprises: receiving a data file and dividing the data file or data stream into one or more data blocks, each data block having a predetermined size N and comprising a sequence of data units, e.g. byte values; and, iteratively encoding the data file into a data key based on a first permutation function and a first dictionary of permutation indices, preferably the encoded data file having a total size that is equal to or smaller than the original data file and preferably the data key having a size that is equal to or smaller than size of a data block. Iteratively encoding the data file comprises one or more encoding iterations, wherein each encoding iteration includes: determining a first permutation index defining a permutation to generate the first input data block from a first ordered data block, the generating including providing at least the first input data block to an input of the first permutation function, and the first ordered data block being obtainable by ordering the first input data block; determining a first permutation dictionary index representing a location in the first dictionary in which the first permutation index is stored; generating a first frequency data block defining the number of occurrences for each potential data value in the input data block, preferably determining the number of occurrences for each potential data value in the input data block and ordering the determined occurrences in a sequence of values in a hierarchical order, e.g. increasing or decreasing order of the data value; processing the frequency data block; and determining an encoded data block, the encoded data block comprising the first permutation dictionary index and the processed frequency data block. The encoding method further comprises outputting the data key comprising the one or more encoded data blocks and, optionally, iteration information.
System and method for reducing physiological data size
The present disclosure pertains to systems and methods for encoding and/or decoding brain activity signals for data reduction. In a non-limiting embodiment, first user data associated with a first sleep session of a user is received. The first user data is determined to include at least a first instance of a first sleep feature being of a first data size. A first value representing the first instance during a first temporal interval is determined. First encoding data representing the first value is determine, the first encoding data being of a second data size that is less than the first data size. Second user data is generated by encoding the first user data using the first encoding data to represent the first instance in the second user data, and the second user data is stored.
Streaming-friendly technology for detection of data
A method by a network device for detecting data in a data stream. The method includes receiving the data stream, where the data stream includes a sequence of original characters, generating a sequence of type-mapped characters corresponding to the sequence of original characters, converging each of two or more consecutive occurrences of a first character in the sequence of type-mapped characters into a single occurrence of the first character, searching for occurrences of one or more predefined sequences of characters in the sequence of type-mapped characters, and responsive to finding an occurrence of any of the one or more predefined sequences of characters, extracting a sequence of characters in the sequence of original characters corresponding to the occurrence of the predefined sequence of characters found in the sequence of type-mapped characters.
Encoding method and encoding apparatus
An encoding apparatus includes a memory and a processor configured to acquire text data, specify a first dynamic dictionary among a plurality of dynamic dictionaries based on attribute information of a first word included in the text data, register the first word in association with a first dynamic code in the first dynamic dictionary, and encode the first word into the first dynamic code.
SYSTEM AND METHOD TO USE DICTIONARIES IN LZ4 BLOCK FORMAT COMPRESSION
An information handling system for compressing data includes a data storage device and a processor. The data storage device stores a dictionary and an uncompressed data block. The processor prepends the dictionary to the uncompressed data block, determines, from the uncompressed data block, a literal data string and a match data string where the match data string is a matching entry of the dictionary, and compresses the uncompressed data block into a compressed data block that includes the literal data string and an offset pointer that points to the matching entry.
BUFFER CONTROL METHOD AND USER EQUIPMENT
A buffer control method, a UE, and a non-volatile computer-readable storage medium are provided. The buffer control method for the UE includes: transmitting, by the UE, first check information to a network side device; receiving, by the UE, first buffer resetting information from the network side device; releasing or emptying a current compression buffer of the UE in accordance with content in the first buffer resetting information; and when the UE receives a dictionary activating or enabling indication: storing, by the UE, a dictionary into the compression buffer; compressing, by the UE through UDC, an uncompressed data packet to obtain a compressed data packet; and transmitting, by the UE, the compressed data packet, wherein the compression buffer is continuously updated by the UE in accordance with the uncompressed data packet.
COMPRESSION, SEARCHING, AND DECOMPRESSION OF LOG MESSAGES
Log messages are compressed, searched, and decompressed. A dictionary is used to store non-numeric expressions found in log messages. Both numeric and non-numeric expressions found in log messages are represented by placeholders in a string of log “type” information. Another dictionary is used to store the log type information. A compressed log message contains a key to the log-type dictionary and a sequence of values that are keys to the non-numeric dictionary and/or numeric values. Searching may be performed by parsing a search query into subqueries that target the dictionaries and/or content of the compressed log messages. A dictionary may reference segments that contain a number of log messages, so that all log message need not be considered for some searches.
Memory preserving parse tree based compression with entropy coding
A method, computer program product, and system includes a processor obtaining data including values and generating a value conversion dictionary by applying a parse tree based compression algorithm to the data, where the value conversion dictionary includes dictionary entries that represent the values. The processor obtains a distribution of the values and estimates a likelihood for each based on the distribution. The processor generates a code word to represent each value, a size of each code word is inversely proportional to the likelihood of the word. The processor assigns a rank to each code word, the rank for each represents the likelihood of the value represented by the code word; and based on the rank associated with each code word, the processor reorders each dictionary entry in the value conversion dictionary to associate each dictionary entry with an equivalent rank, the reordered value conversion dictionary comprises an architected dictionary.