A method to automatically optimize waveform captures from an electrical system includes capturing at least one energy-related waveform using at least one Intelligent Electronic Device (IED) in the electrical system. The at least one captured energy-related waveform is analyzed to determine if the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis and/or other use. In response to determining the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis, and/or use, the at least one captured energy-related waveform may be compressed using at least one compression technique to generate at least one compressed energy-related waveform. One or more actions may be taken based on or using the at least one compressed energy-related waveform.

Systems, methods, and software are disclosed herein for compressing audio data. In an implementation, sampled values of an audio signal have a dynamic range. A division of the dynamic range, into at least a lower range and an upper range, is identified based on a fixed mapping of a lower portion of the sampled values to a subset of quanta in a set of quanta having a depth less than a depth of the sampled values. Then an adaptive mapping of an upper portion of the sampled values to a remaining subset of quanta in the set of quanta is also identified, based at least on a dimension of the upper range. The fixed mapping is used to encode the lower portion of the sampled values based, while the adaptive mapping is used to encode the upper portion of the sampled values based on the adaptive mapping.

Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.

A data communication acquires a map image, determines high- and low-risk areas in the map, determines whether to transmit data related to the high- or low-risk areas, detects objects around the system, determines a position in the map image for each of the objects detected, determines whether the objects belongs to the high- or low-risk areas, determines a data compression ratio for each of the objects detected, compresses data related to each of the objects, compresses data related to each of the objects belonging to the high-risk area when data related to the high-risk area is determined to be transmitted, compresses data related to each of the objects belonging to the low-risk area when data related to the low-risk area is determined to be transmitted, receives reply data replied in association with the compression data transmitted, and makes a decision in accordance with the reply data.

A compute device to manage workflow to disaggregated computing resources is provided. The compute device comprises a compute engine receive a workload processing request, the workload processing request defined by at least one request parameter, determine at least one accelerator device capable of processing a workload in accordance with the at least one request parameter, transmit a workload to the at least one accelerator device, receive a work product produced by the at least one accelerator device from the workload, and provide the work product to an application.

Techniques and apparatuses to decompress data that has been stack compressed is described. Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.

A computer-implemented method for improving compression of predictive models includes generating an unlabeled simulated data set by expanding an initial data set, and generating a labeled data set by predicting the unlabeled, simulated data set using a complex model to output a plurality of labels. The method also includes training a relatively simple neural network using the labeled data set.

A method for compressing a behavior event and a computer device therefor are provided. The method for compressing the behavior event includes generating, by a processor of the computer, an event block on the basis of an event target, when the behavior event occurs, updating, by the processor, input/output (I/O) information while the behavior event occurs to the event block, and storing, by the processor, the event block, when the behavior event is ended.

Techniques for data compression for efficient network management are described herein. In one example, group(s) of bytes are formed from among input bytes to be compressed. The groups are formed by including bytes having at least a certain number (e.g., three) zero-valued most significant bits (MSBs). A byte of input data having several zero-valued MSBs may be in several groups. A group having the largest product (number of bytes in the group times number of zero-valued MSBs in all bytes in the group) may be selected. A compressed-bytes array may be formed with data of the selected group of bytes, wherein the number of zero-valued MSBs originally present in all of the bytes of the group of bytes has been removed (to compress the array). An uncompressed-bytes array may be formed with bytes of the input bytes of data not in the selected group of bytes. An address-bit array may be formed to indicate the array in which data associated with each of the input bytes of data is stored.

A train-linking lossless data compressor examines a block of data and uses a same coder to generate a same code when all data values in the input block are identical. When the input data is not all the same value, then a Gaussian coder, a Laplace coder, and a delta coder are activated in parallel. The three compressed code lengths are compared and the smallest code length is output as the compressed code when it is smaller than a copy code length. The copy code is a tag followed by copying all the data in the input block. When the smallest of the three compressed code lengths is larger than the copy code length, the file is not compressible, and the copy code is output. No frequency table is required so latency is low. The delta coder subtracts data values from an average value of the last data block.