The present invention relates to a method of associating at least one state in a plurality of states to a new value output by a drifting sensor, the method comprising: /a/ receiving a signal from the sensor, said signal comprising a plurality of values; /b/clustering the values of said signal into a number of clusters equal to the number of the plurality of states, each cluster being associated with a respective state in the plurality of states; /c/ for the new value of the signal, associating at least one state in said plurality of states or a probability rating representing the probability to be associated with one state in said plurality of states for said new value of the signal, the associated state or the associated probability rating being determined based on at least distances (d.sub.H, d.sub.L) of said new value of the signal to respective clusters.

A hardware implementable lossless data compression decompression algorithm is disclosed, where the input data string is described in term of consecutive groups of alternating same type bits, where one of these groups of same type bits is defined as a preferred group with the other groups having either lower or higher number of same type bits, where the data string is partitioned into variable length processing strings where the variable length is determined by the occurrence of the preferred group or of a determined number of bits consisting of groups of lower number of same type bits, where these variable length processing strings are processed function of the configuration and content of each processing string only, where consecutive processing strings are additionally processed based on their content only, where processing is performed in a loop until a certain target performance is achieved, where processing is done without any data analysis, and where no negative compression gain is achieved for any content of an input string.

A computing device of a database system includes a plurality of processing modules, a computing device operating system, and an application specific operating system. The application specific operating system includes at least one custom instruction set that configures operation of a configurable set of processing modules of the plurality of processing modules based on generating, for each processing module of the configurable set of processing modules, a corresponding configuration signal indicating a selected instruction set of either the computing device operating system or the application specific operating system. Each processing module of the configurable set of processing modules operates in accordance with the selected instruction set based on the corresponding configuration signal.

Aspects of the disclosure provide methods and apparatuses for neural network model compression/decompression. In some examples, an apparatus for neural network model decompression includes receiving circuitry and processing circuitry. The processing circuitry decodes, from a bitstream corresponding to a representation of a neural network, at least a syntax element to be applied to multiple blocks in the neural network. Then, the processing circuitry reconstructs, from the bitstream, weight coefficients in the blocks based on the syntax element.

Techniques of data compression involve ordering the points of a point cloud according to distance along a space-filling curve. Advantageously, a space-filling curve has the property that points close in distance along the curve are close together in Euclidean space. Thus, differences between points ordered by distance along such a curve, e.g., a Hilbert curve, will be close. When the curve is fractal, i.e., self-similar at all levels, the differences will be small even when the points are very unevenly clustered throughout the point cloud. Such small differences will provide greatly improved compression to the resulting delta-encoded set of points.

A method for compressing flow data, including: constructing multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

The current document is directed to a multi-stage metric-data compression method and subsystem for compressing metric data collected and stored within distributed computing systems to facilitate computer-system management and administration. In a described implementation, metric data is partitioned into constant metric data, low-variability metric data, and high-variability metric data. High-variability metric data is compressed by identifying a set of basis metrics, or independent metrics, with respect to which a remaining set of dependent metrics can be expressed using coefficient multipliers. The high-variability metric data can then be stored as a set of independent metrics and set of coefficients, along with a small amount of additional data.

The present disclosure relates to an information processing apparatus and an information processing method that are capable of distributing higher-quality G-PCC streams. When G-PCC streams obtained by encoding Point Cloud data according to G-PCC are generated, there are generated spatial positional information indicating spatial positions of respective pieces of partial Point Cloud data that represent individual parts into which the Point Cloud data is segmented, and grouping information that groups the partial Point Cloud data. The present technology is applicable to a generating apparatus that generates G-PCC streams, for example.

A data input sub-system of a large scale application specific computing system receives a data set that includes a plurality of records, each with a plurality of data fields, and divides the data set into a plurality of data segments. The data input sub-system further restructures records of data segments based on a key field of the plurality of data fields to produce restructured data segments and generates storage instructions for storing the restructured data segments. A data storage and processing sub-system of the computing system interprets the storage instructions to determine resources to engage and stores the restructured data segments using engaged resources. A query and results sub-system of the computing system generates an initial query plan based on a data processing request, optimizes the initial query plan to produce an optimized query plan, and sends the optimized query plan to the data storage and processing sub-system for execution.

A method for encoding or decoding at least one image, an image being split into blocks of elements. The method includes, for at least one block: splitting the block into at least two areas; and processing at least one of the areas. The processing includes scanning the elements of the area according to a predetermined scanning order, and for at least one scanned element, called a current element: selecting at least one predictor element previously encoded or decoded according to a prediction function; and predicting the current element: from the at least one predictor element, if the at least one predictor element belongs to the area; or from at least one replacement value, otherwise.