Fast, efficient, and robust compression-based methods for detecting boundaries in arbitrary datasets, including sequences (1D datasets), are desired. The methods, each employing three simple algorithms, approximate the information distance between two adjacent sliding windows within a dataset. One of the algorithms calculates an initial ordered list of subsequences; while a second algorithm updates the ordered list of subsequences by dropping a first entry and appending a last entry rather than calculating completely new ordered lists with each iteration. Large values in the distance metric are indicative of boundary locations. A smoothed z-score or a wavelet-based algorithm may then be used to locate peaks in the distance metric, thereby identifying boundary locations. An adaptive version of the method employs a collection of window sizes and corresponding weighting functions, making it more amenable to real datasets with unknown, complex, and changing structures.

The present invention discloses a DAC method having signal calibration mechanism. A first conversion circuit generates a first analog signal according to an input digital signal. A second conversion circuit generates a second analog signal according to the input digital signal and a pseudo-noise digital signal. An echo transmission circuit processes a signal on an echo path to generate an echo signal. A first and a second calibration circuits generate a first and a second calibration signals. A calibration parameter calculation circuit performs calculation according to a difference between the echo signal and a sum of the first and the second calibration signals and related path information to generate a first and a second offsets. The first and the second calibration circuits converge first and second response coefficients and update a first and a second codeword offset tables according to the first and the second offsets.

A non-uniform sampling pADC is disclosed. The pADC may include an optical pulse source configured to generate uniform optic pulses. The pADC may include a non-uniform sampling system. The non-uniform sampling system may include an inter-pulse timing modulation sub-system configured to convert the uniform optic pulses into non-uniform optic pulses. The non-uniform sampling system may include a timing control sub-system configured to control the timing of the optical pulse source. The pADC may include an optical modulator configured to modulate the non-uniform optical pulses. The pADC may include a photodetector configured to convert the modulated non-uniform optic pulses into electronic pulses. The pADC may include a pulse capture assembly configured to capture a pulse amplitude of the electronic pulses and generate sampled radio frequency output pulses. The pADC may include a quantizer configured to quantize the sampled radio frequency output pulses and generate digital radio frequency output signals.

Methods, systems, and apparatus, including computer-readable storage media for hardware compression and decompression. A system can include a decompressor device coupled to a memory device and a processor. The decompressor device can be configured to receive, from the memory device, compressed data that has been compressed using an entropy encoding, process the compressed data using the entropy encoding to generate uncompressed data, and send the uncompressed data to the processor. The system can also include a compressor device configured to generate, from uncompressed data, a probability distribution of codewords, generate a code table from the probability distribution, and compress incoming data using the generated code table.

The generation of symbol-encoded data from digital data, as part of the compression of the digital data into a compressed digital data, can be performed with reference to multiple alternative alphabets. A selection of a specific alphabet is made based on the digital data being compressed, the compression parameters, or combinations thereof. Information indicative of the selected alphabet is encoded into one or more headers of the resulting compressed digital data. A single alphabet can be selected for all of a set of digital data being compressed, or multiple different alphabets can be selected, with different ones of the multiple different alphabets being utilized to compress different portions of the digital data. Additionally, rather than explicitly specifying a specific selected alphabet, the header information can comprise information from which a same alphabet can be independently selected heuristically by both the compressor and the corresponding decompressor.

Aspects of the disclosure provide for methods and systems for Sub-codebook based Trellis Coded Quantization for CSI Feedback. An aspect of the disclosure provides method executed by a receiver. The method includes receiving a signal from a transmitter, via a communication channel between the receiver and the transmitter. The method further includes estimating parameters associated with the channel based on the received signal. The method further includes obtaining phase information from the estimated parameters. The method further includes applying a trellis coded quantization (TCQ) scheme to the obtained phase information by mapping each sub-codebook index of a set of sub-codebook indices to output bits of each trellis branch making the distance between sub-codebooks maximally equal. The method further includes transmitting a channel state information (CSI) measurement feedback to the transmitter, the CSI measurement feedback based on the TCQ scheme and comprising one or more of: a beginning state, input bits to the TCQ scheme, and a sub-codebook index.

An apparatus for shifting a digital signal having a first sample rate by a shift time to provide a shifted signal having a second sample rate is provided. The apparatus includes a sample rate converter configured to provide a value of an interpolated signal at a compensated sample time as a sample of the shifted signal, the interpolated signal being based on the digital signal. The sample rate converter is configured to modify a time interval between a sample time of the digital signal and the compensated sample time based on the shift time.

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 for verifying a material data chain (MDC) that is maintained by a creator is disclosed. The method includes receiving an unverified portion of the MDC from the creator including a set of consecutive material data blocks (MDBs). Each respective MDB includes respective material data, respective metadata, and a creator verification value. The method includes modifying a genomic differentiation object assigned to the verification cohort based on first genomic regulation instructions (GRI) that were used by the creator to generate the creator verification value. For each MDB in the unverified portion, the method includes determining a verifier verification value based on the MDB, a preceding MDB in the MDC, and a genomic engagement factor (GEF) determined with respect to the MDB. The GEF corresponding to an MDB is determined by extracting a sequence from the metadata of a MDB and mapping the sequence into the modified genomic differentiation object.

A method for verifying a material data chain (MDC) that is maintained by a creator is disclosed. The method includes receiving an unverified portion of the MDC from the creator including a set of consecutive material data blocks (MDBs). Each respective MDB includes respective material data, respective metadata, and a creator verification value. The method includes modifying a genomic differentiation object assigned to the verification cohort based on first genomic regulation instructions (GRI) that were used by the creator to generate the creator verification value. For each MDB in the unverified portion, the method includes determining a verifier verification value based on the MDB, a preceding MDB in the MDC, and a genomic engagement factor (GEF) determined with respect to the MDB. The GEF corresponding to an MDB is determined by extracting a sequence from the metadata of a MDB and mapping the sequence into the modified genomic differentiation object.