Example methods, apparatus, and articles of manufacture to capture and compress telematics data are disclosed herein. An example computer-implemented method, executed by a processor, to represent telematics data includes identifying, with the processor, a physical intersection of roads, identifying, with the processor, virtual lines crossing the roads, assigning, with the processor, ordinals to the virtual lines, representing, with the processor, a physical traversal through the physical intersection captured in first telematics data by a pair of the ordinals, and storing the pair of the ordinals in second compressed telematics data.

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.

An apparatus of neural network model decompression includes processing circuitry. The processing circuitry can be configured to receive, from a bitstream of a compressed neural network representation, one or more first syntax elements associated with a 3-dimensional coding unit (CU3D) partitioned from a 3-dimensional coding tree unit (CTU3D). The first CTU3D can be partitioned from a tensor in a neural network. The one or more first syntax elements can indicate that the CU3D is partitioned based on a 3D pyramid structure that includes multiple depths. Each depth corresponds to one or more nodes. Each node has a node value. Second syntax elements corresponding to the node values of the nodes in the 3D pyramid structure can be received from the bitstream in a breadth-first scan order for scanning the nodes in the 3D pyramid structure. Model parameters of the tensor can be reconstructed based on the received second syntax elements.

A method includes generating a plurality of parity blocks from a plurality of lines of data blocks. The plurality of lines of data blocks are stored in data sections of memory of a cluster of computing devices of the computing system by distributing storage of individual data blocks of the plurality of lines of data blocks among unique data sections of the cluster of computing devices. The plurality of parity blocks are stored in parity sections of memory of the cluster of computing devices by distributing storage of parity blocks of the plurality of parity blocks among unique parity sections of the cluster of computing devices.

Examples described herein relate to a manner of determining a number of bits to encode compression data. Some examples include: compressing pixel data of a region of pixels in a frame; determining a number of bits associated with at least two partitions; utilizing the determined number of bits to encode residual values generated from the compressing the pixel data; and storing the encoded residual values. In some examples, the at least two partitions comprise a first partition and a second partition. Some examples include: encoding residuals in the first partition using a number of bits associated with the first partition and encoding residuals in the second partition using a number of bits associated with the second partition. Some examples include: determining a distribution of bins of residuals, wherein each different bin represents a number of bits used to encode a residual value and determining a midpoint of a total number of residuals as a bin that stores a residual that is approximately 50 percentile of the total number of residuals of the distribution.

A method of encoding input data includes dividing the input data into a plurality of data packets, an input packet of the plurality of data packets including a plurality of digits in a first base system, base-converting the input packet from the first base system to generate a base-converted packet including a plurality of converted digits in a second base system, the second base system having a base value lower than that of the first base system, and incrementing the converted digits to generate a coded packet for transmission through a communication channel.

Technology is disclosed for selectively compressing data based on similarity of pages within the data that is to be compressed. At least one corresponding hash value is generated for each one of multiple candidate pages to be compressed. In response to the hash values generated for the candidate pages, the technology selects a set of similar candidate pages from the candidate pages. The set of similar candidate pages are a subset of the candidate pages that includes less than all the candidate pages. The set of similar candidate pages are compressed as a single unit, separately from one or more other ones of the candidate pages that were not selected to be included in the set of similar candidate pages.

Example methods, apparatus, and articles of manufacture to compress telematics data are disclosed herein. An example computer-implemented method includes identifying, using one or more processors, a portion of recorded telematics data representing a physical transversal of a physical intersection of two or more road segments, wherein each road segment has an assigned unique ordinal value; identifying, using one or more processors, a first road segment on which the physical transversal entered the intersection; identifying, using one or more processors, a second road segment on which the physical transversal exited the intersection; identifying, using one or more processors, a pair of ordinal values including a first ordinal value assigned to the first road segment, and a second ordinal value assigned to the second road segment; and storing the pair of ordinal values instead of the portion of the recorded telematics data in a compressed representation of the recorded telematics data.

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.

Systems and methods for processing online data are disclosed. One such method includes receiving a plurality of data points in a time-series at a short term storage. The method also includes calculating at least one approximation coefficient based on the plurality of data points using a wavelet transform, including calculating a highest level approximation coefficient, and calculating estimated value based on the highest level approximation coefficient. The method further includes calculating differences between the estimated value and the plurality of data points of the short term storage, and determining whether a maximum difference among the calculated differences is less than a predetermined threshold. The method further includes, based on the maximum difference being greater than or equal to the predetermined threshold, storing the oldest data point of the short term storage in a long term storage.