Certain aspects of the present disclosure provide techniques for measurement encoding and decoding using neural networks to compress and decompress measurement data. One example method generally includes: generating, via each of a plurality of neural network encoders operating on measurement data, a compressed measurement based on a respective portion of the measurement data, wherein each of the neural network encoders is based on the same neural network model; generating at least one message indicative of the measurement data based on the compressed measurements; and transmitting the at least one message.

According to one embodiment, an interleaving unit divides a symbol string into first and second symbols. A first coding unit converts the first symbols to first codewords. A first packet generating unit generates first packets including the first codewords. A first request generating unit generates first packet requests including sizes of variable length packets. A second coding unit converts the second symbols to second codewords. A second packet generating unit generates second packets including the second codewords. A second request generating unit generates second packet requests including sizes of variable length packets. A multiplexer outputs a compressed stream including the first and second variable length packets cut out from the first and second packets.

A system is provided for electronic data compression by automated time-dependent compression algorithm. In particular, the system may track instances in which a particular dataset is used, copied, or accessed over time. For certain datasets (e.g., datasets that have not been accessed for a threshold amount of time), the system may use a time-based compression algorithm that progressively removes the least significant bits of such datasets as time passes. The compression of the datasets may continue until the system detects that further compression would cause the dataset to be unreadable or unrecoverable. In this way, the system may minimize the computing resources allocated to storing datasets that are not frequently accessed.

An example method may include obtaining multiple symbols each associated with a different state in a quadrature amplitude modulation scheme used for transmission of a data signal. The method may further include determining a sequence of symbols of the multiple symbols for a first portion of the data signal. The determining may include selecting an index value from multiple index values for the first portion of the data signal. The determining may also include obtaining energy states of multiple sequences of the symbols using the energy levels of the symbols. The determining may further include obtaining a relationship between the energy levels of the symbols, the energy states of the multiple sequences of the symbols, and the multiple index values. The determining may further include selecting the sequence of symbols based on the relationship and the index value for the first portion of the data signal.

A method for processing image data and a system thereof are provided. The method is operated in the system including an encoding system and a decoding system. In the decoding system, multiple image data packages are received from the encoding system. The image data packages include multiple encoded data that are formed by encoding the pixels of an image and the pixels are beforehand rearranged according to an arrangement order. The arrangement order is exemplarily made based on the quantity of encoding circuits of the encoding system. In the decoding system, the encoded data received from the encoding system are sequentially stored in a memory according to the arrangement order. The decoding circuits start to decode the encoded data from an initial code synchronously for enhancing decoding performance. The method can be applied to decoding of high resolution images. The image is reproduced after the decoding process.

A method for text compression comprises recognizing a prefix string of one or more text characters preceding a target string of a plurality of text characters to be compressed. The prefix string is provided to a natural language generation (NLG) model configured to output one or more predicted continuations each having an associated rank. If the one or more predicted continuations include a matching predicted continuation relative to the next one or more text characters of the target string, the next one or more text characters are compressed as an NLG-type compressed representation. If no predicted continuations match the next one or more text characters of the target string, a longest matching entry in a compression dictionary is identified. The next one or more text characters of the target string are compressed as a dictionary-type compressed representation that includes the dictionary index value of the longest matching entry.

Systems and methods are provided for loop escape analysis in executing computer instructions. In one embodiment, a method comprises instructions performed by at least one computer process. The method comprises receiving a set of executable computer instructions stored on a storage medium (e.g., by reading the instructions from a tangible, non-transitory storage medium). The method further comprises analyzing the computer instructions to determine a loop, analyzing the computer instructions to determine at least one new variable in the loop, and storing, in a data structure, at least one of an operation related to the variable or a value related to the variable. The method further comprises determining whether to compress the data structure upon reaching the end of the loop, and, based on the determination, compressing the data structure. Systems and computer-readable media are also provided.

A method for encoding a sequence of records, each record of said sequence of records comprising a plurality of different fields, said different fields being identical for each record of said sequence of records, said method comprising selecting an encoding algorithm for each field of said plurality of fields such that said each field is associated with a selected encoding algorithm; encoding data of said each field using said selected encoding algorithm to determine encoded field data for said each field for said each record; and for said each record, interleaving said encoded field data for said each field to produce an encoded sequence of said records wherein said encoded field data are interleaved for said each record.

A framework and the associated method, schema and design for processing digital data, whether random or not, through encoding and decoding losslessly and correctly for purposes including the purposes of encryption/decryption or compression/decompression or both. There is no assumption of the digital information to be processed before processing. An universal coder is invented and now pigeonhole meets blackhole.

A method for execution by a dispersed storage and task (DST) client module includes issuing a read threshold number of read slice requests are issued to storage units of the set of storage units. One or more encoded slices of a selected read threshold number of encoded slices are received. When a next encoded data slice of a decode threshold number of encoded data slices is received within a response timeframe, outputting of the next encoded data slice is initiated. When the next encoded data slice is not received within the response timeframe, receiving of another decode threshold number of encoded slices of the set of encoded slices is facilitated. The other decode threshold number of encoded slices are decoded to produce recovered encoded data slices, where the recovered encoded data slices includes at least a recovered next encoded data. Outputting of the recovered next encoded data slice is initiated.