The present invention concerns a method of sampling a test signal. The method comprises: acquiring training signals sampled at a plurality of sampling locations; running an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling the test signal at the sampling locations represented by the n indices.

Implementations of the disclosure provide logarithm and anti-logarithm operations on a hardware processor based on linear piecewise approximation. An example processor includes a piece wise linear log approximation circuit that receives an input of a floating-point number comprising a sign, an exponent and a mantissa. The piece wise linear log approximation circuit approximates a fractional portion of a fixed point number using a linear approximation of the mantissa of the floating-point number. The piece wise linear log approximation circuit also derives an integer from the exponent.

A method and a system for transmitting data are provided. In a source apparatus, original data is divided into a plurality of source segments, a similarity calculation is performed for each of the source segments to obtain a similarity set, and the similarity set is transmitted to a target apparatus. In the target apparatus, whether a target segment corresponding to the source segment exists in the target apparatus is determined according to the similarity set to obtain a comparison result, and the comparison result is transmitted to the source apparatus. In the source apparatus, after the original data is dehydrated according to the comparison result to obtain dehydration data, the dehydration data is transmitted to the target apparatus. In the target apparatus, the dehydration data is rehydrated to the original data.

Disclosed are a VCD vector compression method and device based on circuit toggle behaviors. The method comprises: converting a VCD format file into a current matrix model, wherein three dimensions of the current matrix model are one time dimension and two spatial dimensions; performing preliminary screening based on an overall toggle feature: dividing the current matrix into several time segments in accordance with an equal interval in the time dimension, and performing screening according to the overall toggle feature, and forming a preliminary screened current distribution matrix by the screened time segments; performing fine screening based on region toggle features: performing further screening according to local toggle features, and forming a fine screened current distribution matrix by the screened time segments; and re-outputting the fine screened current distribution matrix as a VCD format file after vector compression.

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 binary data compression/decompression method is disclosed, where any input binary data string (IFDS) is uniquely and reversibly compressed/decompressed without any data loss by first uniquely formatting and fully describing the IFDS using a set of binary constructs, followed by creating complex structures from custom combinations of the binary constructs that occur within the IFDS content, wherein the choice of the custom combinations depend on the IFDS content therefore creating IFDS content variations and distributions from an expected nominal base reflecting the actual content of the IFDS, followed by uniquely processing these variations and distributions using several schemes, each bringing a unique compression feature, and wherein once this processing completes, another repeating compression cycle can be applied until the desired compressed file size or a file floor size limit is reached, size below which the disclosed compression has limitations.

Compressing files is disclosed. An input file to be compressed is first aligned. Aligning the file includes splitting the file into sequences that can be aligned. The result is a compression matrix, where each row of the matrix corresponds to part of the file. The compression matrix may also serve as a warm start if additional compression is desired. Compression may be performed in stages, where an initial compression matrix is generated in a first stage using larger letter sizes for alignment and then a second compression stage is performed using smaller letter sizes. A consensus sequence id determined from the compression matrix. Using the consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The compressed file includes the pointer pairs and the consensus sequence.

A database system includes a plurality of computing devices. Each computing device includes a plurality of processing modules, a computing device operating system, and an application specific operating system. The computing device operating system includes a computing device operating system file system management instruction set. The application specific operating system includes at least one custom file system management instruction set operable to configure operation of a configurable set of processing modules of the plurality of processing modules based on generating a corresponding file system management configuration signal for each processing module of the configurable set of processing modules indicating a selected file system management instruction set of the computing device operating system or the application specific operating system.

Disclosed is a computer configured to create graph data having a vertex corresponding to a single index, an edge that links a pair of the vertices having a correlation, and an edge weight as a value of the element from the correlation matrix data having correlation values between a plurality of indices as elements, in which the correlation matrix data is acquired from the storage unit, elements of a spanning tree formed by linking vertices corresponding to indices included in the acquired correlation matrix data and an element having a value equal to or greater than a predetermined threshold value are detected, and the graph data is created on the basis of the detected elements.

Adaptively compressing an input string (10) comprising a sequence of symbols in order to create a plurality of segment dictionaries D.sub.m, with the steps of: generating a lookup map (110); generating a key value segment S.sub.m,n; searching the lookup map for each symbol received in the input string (120, 130); upon detecting a symbol is not stored in the lookup map, adding the symbol by storing the symbol at a next sequential key index in the lookup map lookup map (135) and assigning a next sequential key value entry to the symbol and adding this key value to the key value segment S.sub.m,n (150); upon detecting the symbol is stored in the lookup map, adding the corresponding key value assigned to this symbol to the next sequential entry of the key value segment S.sub.m,n (150); wherein a new key value segment S.sub.m,n+1 of the lookup map is generated if the number of different symbols equals the number of available key values k=2.sup.n for the opened/current key value segment S.sub.m,n (141, 142), and where-in the lookup map is converted into a segment dictionary D.sub.m if the maximal key value size k.sub.nmax=2.sup.nmax is reached (132, 133, 134), with n being any positive integral number 1 to nmax, nmax denoting the maximal bit size, and m being any positive integral number denoting the consecutive numbering of segment dictionaries D.sub.m.