Methods and systems for providing dynamic dictionary-based compression and decompression are described herein. A computing device may receive, during a currently running session with a client device, a plurality of messages. The computing device may determine, based on the plurality of messages, one or more frames. The computing device may determine, based on the one or more frames, data samples. The computing device may compress the one or more frames based on a compression dictionary. The computing device may train, during the currently running session, the compression dictionary based on the determined data samples, to create a new compression dictionary. The computing device may determine, during the currently running session and based on receiving additional messages, one or more additional frames. In addition, the computing device may compress the one or more additional frames based on the new compression dictionary.

Methods and systems for providing dynamic dictionary-based compression and decompression are described herein. A computing device may receive, during a currently running session with a client device, a plurality of messages. The computing device may determine, based on the plurality of messages, one or more frames. The computing device may determine, based on the one or more frames, data samples. The computing device may compress the one or more frames based on a compression dictionary. The computing device may train, during the currently running session, the compression dictionary based on the determined data samples, to create a new compression dictionary. The computing device may determine, during the currently running session and based on receiving additional messages, one or more additional frames. In addition, the computing device may compress the one or more additional frames based on the new compression dictionary.

The computation load of computation using a neural network can be lowered. A computation apparatus has a prediction device, an encoder, and a decoder, and encodes and decodes data by using a probability density distribution. Of a learning process and a compression process, at least the compression process can be executed. By performing learning by using a neural network created by the learning process, a probability distribution table that causes a parameter and a symbol value probability distribution to correspond to each other can be used. In the compression process, the prediction device calculates the parameter from input data, and the encoder compresses the input data by using the symbol value probability distribution on the basis of the calculated parameter and the probability distribution table.

A computer: acquires, from a compression dictionary that associates each of codes having a length according to a frequency of appearance of a set of a word and a word meaning of the word with that set, the set of any word and that word meaning, and one of the codes associated with the set of the any word and that word meaning; selects, from among a plurality of fixed-length codes stored in the memory with a same length in association with the set of the word and that word meaning, one of the fixed-length codes associated with the set of the any word and that word meaning; generates a conversion dictionary that associates the selected one of the fixed-length codes with the acquired one of the codes; and specifies, by the conversion dictionary, the individual fixed-length codes associated with each of the codes contained in compressed data.

A data processing platform, method, and program product perform compression and decompression of a set of data items. Suffix data and a prefix are selected for each respective data item in the set of data items based on data content of the respective data item. The set of data items is sorted based on the prefixes. The prefixes are encoded by querying multiple encoding tables to create a code word containing compressed information representing values of all prefixes for the set of data items. The code word and suffix data for each of the data items are stored in memory. The code word is decompressed to recover the prefixes. The recovered prefixes are paired with their respective suffix data.

A memory system includes a storage device and a memory controller. The memory controller includes an encoder and a decoder. The encoder includes a first code table updating section configured to update the encoding code table and an encoding flow controlling section configured to control input to the first code table updating section by using a first data amount indicating a data amount of the input symbol. The first data amount is calculated based on the input symbol. The decoder includes a second code table updating section configured to update the decoding code table and a decoding flow controlling section configured to control input to the second code table updating section by using a second data amount indicating a data amount of the output symbol. The second data amount is calculated based on the output symbol in the same way as the calculation of the first data amount.

A pre-shared compression dictionary is received. The pre-shared compression dictionary was generated based on an analysis of sample data for use in compression of other data. A compressed version of a batch of machine-generated data is received. The batch of machine-generated data has been compressed at least in part using the pre-shared compression dictionary and a batch-specific compression dictionary. The received compressed batch is uncompressed using the batch-specific compression dictionary to determine an intermediate version. The intermediate version is uncompressed using the pre-shared compression dictionary to determine an uncompressed version of the batch of machine-generated data.

A computerized method and apparatus compresses symbolic information, such as text. Symbolic information is compressed by recursively identifying pairs of symbols (e.g., pairs of words or characters) and replacing each pair with a respective replacement symbol. The number of times each symbol pair appears in the uncompressed text is counted, and pairs are only replaced if they appear more than a threshold number of times. In recursive passes, each replaced pair can include a previously substituted replacement symbol. The method and apparatus can achieve high compression especially for large datasets. Metadata, such as the number of times each pair appears, generated during compression of the documents can be used to analyze the documents and find similarities between two documents.

A radar detection method that comprises receiving a signal from each of one or more receive antennas, each received signal including a plurality of chirps, each received signal corresponding to a transmit signal reflected by a reflector; obtaining a range signal by downmixing each received signal with a reference chirp signal; performing a frequency transform on the range signal to obtain, for each chirp in the plurality of chirps, a set of range coefficients; compressing each set of range coefficients to obtain a plurality of compressed coefficient sets, wherein each compressed coefficient set corresponds to a set of range coefficients; storing each compressed coefficient set in a buffer; decompressing each compressed coefficient set to obtain buffered sets of range coefficients; processing the buffered sets of range coefficients to determine a range to a reflector of the transmit signal; and reporting the range to an electronic control unit.

A vehicle log transmission device includes: a vehicle log obtainer that obtains the vehicle log; a vehicle state extractor that extracts a vehicle state from the obtained vehicle log; difference generation log storage that stores a list of difference generation logs, each being a vehicle log for each of vehicle states shared between the vehicle log transmission device and a vehicle log analysis server; a difference generation log selector that selects a difference generation log from the list in accordance with the extracted vehicle state; a difference log generator that generates a difference log based on the obtained vehicle log and the selected difference generation log; and a difference log transmitter that transmits, to the vehicle log analysis server, the generated difference log and a difference generation log identifier corresponding to the selected difference generation log.