In one embodiment, an apparatus comprises a first compression engine to receive a first compressed data block from a second compression engine that is to generate the first compressed data block by compressing a first plurality of repeated instances of data that each have a length greater than or equal to a first length. The first compression engine is further to compress a second plurality of repeated instances of data of the first compressed data block that each have a length greater than or equal to a second length, the second length being shorter than the first length, wherein each compressed repeated instance of the first and second pluralities of repeated instances comprises a location and length of a data instance that is repeated. The apparatus further comprises a memory buffer to store the compressed first and second plurality of repeated instances of data.

A system and method for qualitative analysis of time progressive signals, comprising: a qualitative signal analysis module, comprising at least a processor, a memory, and a long term storage device; and an output processor module comprising at least a processor a memory and a network interface has been devised. The qualitative signal analysis module retrieves signal data over time and applies pre-programmed protocols to compare multiple aspects of the signal data to derive meaningful data. The output processor module encodes data generated by the qualitative signal analysis module for use in subsequent analytical steps such as further manipulation, classification or long term storage.

A method, article comprising machine-readable instructions and apparatus that processes data systems for encoding, decoding, pattern recognition/matching and data generation is disclosed. State subsets of a data system are identified for the efficient processing of data based, at least in part, on the data system's systemic characteristics.

A front-end unit that operates within a C-RAN architecture to perform the functions of cellular signal processing and resource selection between an RRU and the BBU pool network is described. The front-end unit supports flexible load migration and CoMP (coordinated multipoint) in the CRAN BBU while also reducing data transmission within the BBU pool network or between the BBU pool network and the RRU.

One embodiment of the present invention provides a system for facilitating compression of a Cascading Style Sheet (CSS) file. During operation, the system stores the CSS file in a local storage device and identifies a merge parameter in the CSS file. The merge parameter can be one of: a repeated selector, a repeated property in a selector, and a same value of a property in different selectors. The system then determines whether a merge condition is satisfied for the merge parameter and merges the merge parameter in the CSS file to obtain a compressed merge parameter. The merge condition indicates whether merging of the merge parameter is consistent with original style in the CSS file.

The present disclosure provides computer implemented methods and systems for analysing datasets, such as large data sets output from nucleic acid sequencing technologies. In particular, the present disclosure provides for data analysis comprising computing the BWT of a collection of strings in an incremental, character by character, manner. The present disclosure also provides compression boosting strategies resulting in a BWT of a reordered collection of data that is more compressible by second stage compression methods compared to non-reordered computational analysis.

There is a method of compressing image data comprising a set of image values each representing a position in image-value space so as to define an occupied region thereof. The method comprises selectively applying a series of compression transforms to subsets of the image data items to generate a transformed set of image data items occupying a compacted region of value space. The method further comprises identifying a set of one or more reference data items that quantizes the compacted region in value space. For each image data item in the set of image data items, a sequence of decompression transforms from a fixed set of decompression transforms is identified that generates an approximation of that image data item when applied to a selected one of the one or more reference data items. Each image data item in the set of image data items is encoded as a representation of the identified sequence of decompression transforms for that image data item. The encoded image data items, set of reference data items and the fixed set of decompression transforms are stored as compressed image data.

A method of compressing image data comprising a set of image values each representing a position in image-value space so as to define an occupied region thereof. The method comprises selectively applying a series of compression transforms to subsets of the image data items to generate a transformed set of image data items occupying a compacted region of value space. The method further comprises identifying a set of one or more reference data items that quantizes the compacted region in value space. For each image data item in the set of image data items, a sequence of decompression transforms from a fixed set of decompression transforms is identified that generates an approximation of that image data item when applied to a selected one of the one or more reference data items. Each image data item in the set of image data items is encoded as a representation of the identified sequence of decompression transforms for that image data item. The encoded image data items, set of reference data items and the fixed set of decompression transforms are stored as compressed image data.

A method of compressing digital signal data obtained from a signal is described. The method includes: receiving digital signal data associated with a signal and/or generating digital signal data based on a signal; transforming the digital signal data into a transform domain, thereby generating transformed digital signal data; determining at least one characteristic parameter based on the transformed digital signal data by an artificial intelligence circuit; detecting and/or classifying at least one wanted signal portion based on the at least one characteristic parameter by the artificial intelligence circuit; and storing only a subset of the digital signal data that is associated with the at least one wanted signal portion. Further, a signal compressor circuit for compressing digital signal data obtained from a signal and a computer program are described.

There is provided an apparatus comprising means for training a neural network, wherein the training comprises applying a loss function configured to increase sparsity of a weight tensor of the neural network and to cause a plurality of non-zero elements of the weight tensor to be substantially equal to each other; and means for entropy coding the weight tensor to obtain a compressed neural network.