A method performed by an encoder of a base station system, for handling a data stream for transmission over a transmission connection between a remote unit and a base unit of the base station system, the remote unit being arranged to transmit wireless signals to, and receive from, mobile stations. The method comprises quantizing a plurality of IQ samples, converting the quantized plurality of IQ samples to IQ predictions, calculating per sample a difference between the quantized plurality of IQ samples and the IQ predictions in order to create IQ prediction errors. The method further comprises quantizing the IQ predictions or the IQ prediction errors, entropy encoding the IQ prediction errors and sending the entropy encoded IQ prediction errors over the transmission connection to a decoder of the base station system. The method can be performed by a decoder.

A storage system comprises a plurality of enclosures and a storage controller. Each enclosure comprises at least one processing device and a plurality of drives configured in accordance with a redundant array of independent disks (RAID) arrangement. The storage controller obtains data pages associated with an input-output request, provides the data pages to a processing device of a given enclosure, and issues a command to the processing device to perform at least one operation based at least in part on the data pages. The processing device of the given enclosure receives the data pages from the storage controller, generates compressed data pages based at least in part on the received data pages, stores one or more of the compressed data pages on the plurality of drives according to the RAID arrangement and returns information associated with the storage of the compressed data pages to the storage controller.

A method for protecting data in a data memory against an undetected change, wherein a functional variable x is encoded via a value, an input constant, an input signature and a timestamp D into a coded variable, where the functional variable is normalized relative to a base to form the integer value from the functional variable.

Block compression schemes used for image compression are susceptible to generating image blocks having redundant bit sets (i.e. a redundant bit combination), where one of the bit sets in the block is not meaningfully different from the other bit set in the block. As a result, one of the bit sets will be meaningless to a decompression scheme used to decompress the image and thus will not contribute to improving a quality of the decompressed image. The present disclosure provides a technique to exploit redundant bit combinations in a compressed representation of an image, including to exploit more than just the simple case of bit sets that are identical. Exploiting a redundant bit combination will allow an otherwise meaningless bit set to be used for some other discriminating purpose, which can allow for a higher image quality after decompression.

A data compression method includes: storing data to be written into a first address and a second address into a data buffer in response to a data write request to the first address and the second address of a memory module from a host; according to a relationship between the first address and the second address, selecting a compression scheme from pre-configured compression schemes, and attempting to compress the data to be written into the first address and the second address into compressed data that can be stored into either the first address or the second address by using a pre-defined compression method, if the attempt to compress successes, storing the compressed data into the first address or the second address of the memory module, and identifying the compressed data by using redundant ECC bits to form first identification information.

An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.

An abnormality detection device according to an embodiment includes a detector, a remover, and a learner. The detector detects first abnormal data in detection target data which is an abnormality detection target by inputting the detection target data to a first autoencoder which performed learning based on first learning target data which is a learning target. The remover removes data associated with the first abnormal data from the first learning target data to generate second learning target data by inputting the first learning target data to a second autoencoder which performed learning based on the first abnormal data detected by the detector. The learner causes the first autoencoder to perform learning based on the second learning target data generated by the remover.

Embodiments described herein provide a system comprising a storage unit, a control module, a compression module, and a communication module. During operation, the storage unit can store a piece of data. The control module determines whether data stored in the storage unit has triggered a storage operation in a distributed storage system. The compression module then compresses the piece of data by encoding the piece of data using fewer bits than the bits of the piece of data. Subsequently, the communication module sends the compressed piece of data to a plurality of storage nodes in the distributed storage system for persistent storage.

A method for data compression includes acquiring compensation data, comparing the compensation data with preset base value data to obtain a compensation data deviation, and performing an encoding compression on the compensation data deviation to obtain compressed data.

A lossless data compressor prevents normalization overruns on-the-fly as symbol occurrence counts are rounded to generate symbol frequencies, allowing an encoding table generator to generate encoding table entries without waiting for the symbol frequency table to finish filling. Rounding errors are accumulated as symbols are normalized and compensated for by reducing a symbol frequency when the symbol frequency is at least 2 and the accumulated errors have exceeded a threshold. The symbol frequency is also reduced when the number of remaining states in the encoding table is insufficient for a number of remaining unprocessed symbols and states for a current encoding table entry. Since error compensation occurs as symbols are being normalized, encoding table generation is not forced to wait for all symbols in the block to be processed, reducing latency. Three pipeline stages can operate on three input blocks: symbol counting, normalization/error compensation/encoding table generation, and data encoding.