A decoding circuit and a chip are disclosed. The decoding circuit includes, connected in a sequence, a charge/discharge unit, a capacitor and a conversion unit. The charge/discharge unit is able to charge and discharge the capacitor, and a ratio of a total time required to transfer any amount of charge into the capacitor to a total time required to transfer the same amount of charge from the capacitor is a predetermined value. The conversion unit is configured to output a third level when a voltage on the capacitor exceeds a predetermined voltage and to otherwise output a fourth level. This arrangement alleviates the computational burden of an MCU, eliminates any adverse effect of noise in a transmitted signal, allows an extended effective transmission distance when using an HBS protocol and is self-adaptive to signals transmitted at different clock rates, thus solving the problems with the prior art including heavy MCU computational burden, a tradeoff between error correction and transmission distance and insufficient adaptiveness to signals transmitted at different clock rates.

Provided is a non-transitory storage medium that stores a computer readable program for a computer of a medical information processing device configured to output and/or input medical information, the program causing the computer to perform acquiring locale information in environment information of the medical information processing device and presuming a character encoding of the medical information based on the locale information acquired in the acquiring.

Provided is a non-transitory storage medium that stores a computer readable program for a computer of a medical information processing device configured to output and/or input medical information, the program causing the computer to perform acquiring locale information in environment information of the medical information processing device and presuming a character encoding of the medical information based on the locale information acquired in the acquiring.

A disclosure for lossless data compression can include receiving a data block by a processor, performing, by the processor, a sparse transform extraction on the data block, selecting, by the processor, a transform matrix for the data block, modeling, by the processor, the selected transform matrix for the data block, selecting, by the processor, a transform coefficient model for the data block, modeling, by the processor, the selected transform coefficient model for the data block, compressing, by the processor, the data in the data block using the selected transform matrix and the selected transform coefficient model.

A method of compressing weights of a neural network includes compressing a weight set including the weights of a the neural network, determining modified weight sets by changing at least one of the weights, calculating compression efficiency values for the determined modified weight sets based on a result of compressing the weight set and results of compressing the determined modified weight sets, determining a target weight of the weights satisfying a compression efficiency condition among the weights based on the calculated compression efficiency values, and determining a final compression result by compressing the weights based on a result of replacing the determined target weight.

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.

A decompression system has a plurality of decompression devices in an array or chain layout for decompressing respective compressed data values of a compressed data block. A first decompression device is connected to a next decompression device, and a last decompression device is connected to a preceding decompression device. The first decompression device decompresses a compressed data value and reduces the compressed data block by extracting a codeword of the compressed data value and removing the compressed data value from the compressed data block, retrieving a decompressed data value out of the extracted codeword, and passing the reduced compressed data block to the next decompression device. The last decompression device receives a reduced compressed data block from the preceding decompression device and decompresses another compressed data value by extracting a codeword of the other compressed data value, and retrieving another decompressed data value out of the extracted codeword. Elected for publication; FIG. 8.

Techniques and apparatus for parallel data compression are described. An apparatus to provide parallel data compression may include at least one memory and logic for a compression component, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to provide at least one data input sequence to a plurality of compression components, determine compression information for the plurality of compression components, and perform a compression process on the at least one data input sequence via the plurality of compression components to generate at least one data output sequence, the plurality of compression components to perform the compression process in parallel based on the compression information.

A method for executing computer programs in a trusted execution environment of a device is disclosed. The method includes retrieving a genomic differentiation object corresponding to a computer program that comprises a set of encoded executable instructions. The method further includes modifying the genomic differentiation object based on genomic regulation instructions (GRI) to obtain a modified genomic differentiation object, wherein the GRI were used to encode the set of encoded executable instructions of the computer program. The method includes obtaining a first instruction that is to be executed from the first set of encoded executable instructions of the computer program; obtaining a first sequence from first metadata associated with the first encoded instruction; generating a genomic engagement factor (GEF) based on the first sequence and the modified genomic differentiation object; decoding the first encoded instruction using the GEF to obtain a first decoded instruction; and executing the first decoded instruction.

A method for executing computer programs in a trusted execution environment of a device is disclosed. The method includes retrieving a genomic differentiation object corresponding to a computer program that comprises a set of encoded executable instructions. The method further includes modifying the genomic differentiation object based on genomic regulation instructions (GRI) to obtain a modified genomic differentiation object, wherein the GRI were used to encode the set of encoded executable instructions of the computer program. The method includes obtaining a first instruction that is to be executed from the first set of encoded executable instructions of the computer program; obtaining a first sequence from first metadata associated with the first encoded instruction; generating a genomic engagement factor (GEF) based on the first sequence and the modified genomic differentiation object; decoding the first encoded instruction using the GEF to obtain a first decoded instruction; and executing the first decoded instruction.