Abstract: A process for simulating an input sequence partitions the input sequence into a set of substrings and a tail. The substrings preferably have lengths that are not all equal. An output sequence then includes the substrings in a random order.

Abstract: Embedding a watermark includes identifying watermark locations in the data stream, partitioning the identified watermark locations into watermark location blocks each to be embedded with a watermark value portion from the watermark value and altering a predetermined maximum number of data stream values to embed the watermark value portion and introduce no more than a predetermined level of distortion according to a code table. Extracting the watermark bit-sequence includes identifying watermark locations in the data stream, partitioning the identified watermark locations into watermark location blocks each expected to be embedded with a watermark value portion from the watermark value and generating the watermark value by matching each data stream value to a watermark value portion using a code table.

Abstract: Denoising such as discrete universal denoising (DUDE) that scans a noisy signal in an attempt to characterize probabilities of finding symbol values in a particular context in a clean signal can perform a rough denoising on the noisy signal and identify contexts from a roughly denoised signal. The rough denoising improves estimation of the statistical properties of the clean signal by reducing the false differentiation of contexts that noise can otherwise create. Statistical information regarding occurrences of symbols in the noisy signal and corresponding contexts in the roughly denoised signal can then be used to denoise the noisy signal. The specifics of the rough denoising can be chosen based on knowledge of the noise or of the clean data. Alternatively, the DUDE can be used in an iterative fashion where the denoised signal produced from a prior iteration provides the contexts for the next iteration.

Abstract: Systems and methods are disclosed for denoising for a finite input, general output channel. In one aspect, a system is provided for processing a noisy signal formed by a noise-introducing channel in response to an error correction coded input signal, the noisy signal having symbols of a general alphabet. The system comprises a denoiser and an error correction decoder. The denoiser generates reliability information corresponding to metasymbols in the noisy signal based on an estimate of the distribution of metasymbols in the input signal and upon symbol transition probabilities of symbols in the input signal being altered in a quantized signal. A portion of each metasymbol provides a context for a symbol of the metasymbol. The quantized signal includes symbols of a finite alphabet and is formed by quantizing the noisy signal. The error correction decoder performs error correction decoding on noisy signal using the reliability information generated by the denoiser.

Abstract: Various embodiments of the present invention provide a compression method and system that compresses received data by first denoising the data and then losslessly compressing the denoised data. Denoising removes high entropy features of the data to produce lower entropy, denoised data that can be efficiently compressed by a lossless compression technique. One embodiment of the invention is a universal lossy compression method obtained by cascading a denoising technique with a universal lossless compression method. Alternative embodiments include methods obtained by cascading a denoising technique with one or more lossy or lossless compression methods.

Abstract: In various embodiments of the present invention, a noisy signal denoiser is tuned and optimized by selecting denoiser parameters that provide relatively highly compressible denoiser output. When the original signal can be compared to the output of a denoiser, the denoiser can be accurately tuned and adjusted in order to produce a denoised signal that resembles as closely as possible the clear signal originally transmitted through a noise-introducing channel. However, when the clear signal is not available, as in many communications applications, other methods are needed. By adjusting the parameters to provide a denoised signal that is globally or locally maximally compressible, the denoiser can be optimized despite inaccessibility of the original, clear signal.

Abstract: In various embodiments of the present invention, a context-based denoiser is applied to each noisy-image symbol embedded within a context to determine a replacement symbol for the noisy-signal symbol. The context-based denoiser includes a context-modeling component that efficiently generates context classes and symbol-prediction classes, assigns individual contexts to context classes and symbol-prediction classes, collects symbol-occurrence statistics related to the generated context classes and symbol-prediction classes, and, optionally, generates noisy-symbol predictions.

Abstract: An image is compressed by selectively performing at least one of palettization and interframe coding on certain regions of the image. The regions are adaptively determined.

Abstract: An apparatus for operating on a received signal that includes a noise-free signal that has been corrupted by a channel is disclosed. A memory stores a channel corruption function specifying the probability that a symbol having a value I was converted to a symbol having a value J by the channel, and a degradation function measuring the signal degradation that occurs if a symbol having the value I is replaced by symbol having a value J. The controller parses one of the received signal or the processed signal into phrases, and replaces one of the symbol having a value I in a context of that symbol in the received signal with a symbol having a value J if the replacement would reduce the estimated overall signal degradation in the processed signal. The context of a symbol depends on the phrase associated with the symbol.

Abstract: Use of Generalized Context Trees to assign a unique state from a finite set to any string is provided. The method optionally refines the generalized context tree into a refined generalized context tree having a finite state machine (FSM) property. Refining occurs whenever the generalized context tree does not have the finite state machine property. Alternately, a method for constructing a representation of a source usable within an FSM is provided, comprising evaluating a node comprising a suffix tail and verifying the suffix tail is included in the representation, and inserting at least one node to the representation when the suffix tail is not in the representation.

Abstract: A denoising process statistically processes a series of frames of a motion picture to construct respective data structures for the frames. Each data structure indicates for each of multiple contexts, occurrences of symbols that have the same context and are in the corresponding one of the frames. The data structures for multiple frames are combined to construct an enhanced data structure for one of the frames, and symbols in that frame are replaced with values determined using the enhanced data structure.

Abstract: Embedding a watermark includes receiving a datastream of repeated values, determining if an end of run mode permits embedding a first or second binary value, transmitting a shortened version of the datastream when the next bit is a first binary value, appending to the datastream a redundant codeword indicating when the next bit is a first binary value and transmitting an unshortened version of the repeated data values to indicate the next bit is a second binary value and appending a conventional codeword from the codespace indicating the end of run mode and second binary value. Extracting a watermark bit-sequence includes determining if an end of run mode could have embedded either binary value, indicating a first binary value when the end of run mode uses a redundant codeword from the codespace and indicating a second binary value when the end of run mode uses a conventional codeword.

Abstract: In various embodiments of the present invention, a binary mask corresponding to a noisy symbol sequence is produced to indicate which of the symbols in the noisy symbol sequence has potentially been modified, or altered, by a noisy channel. DUDE, DUDE-CTI, and other denoising methods are modified to employ the bit mask in order to avoid the computational overhead and potential errors incurred in attempting to denoise symbols that are not likely to have been altered by the noisy channel.

Abstract: One embodiment of the present invention provides a demultiplexer implemented as a nanowire crossbar or a hybrid nanowire/microscale-signal-line crossbar with resistor-like nanowire junctions. The demultiplexer of one embodiment provides demultiplexing of signals input on k microscale address lines to 2k or fewer nanowires, employing supplemental, internal address lines to map 2k nanowire addresses to a larger, internal, n-bit address space, where n>k. A second demultiplexer embodiment of the present invention provides demultiplexing of signals input on n microscale address lines to 2k nanowires, with n>k, using 2k, well-distributed, n-bit external addresses to access the 2k nanowires.

Abstract: A denoising process models a noisy signal using classes and subclasses of symbol contexts. The process generates class count vectors having components that combine occurrence counts for different symbols in different contexts. Biases determined separately for each subclass and a fixed predictor indicate which symbol occurrence counts for different context are combined in the same component of a class count vector. For impulse noise, the bias for a subclass can be the average error that results when the fixed predictor predicts non-noisy symbols found in contexts of the context subclass. Denoising of impulse noise can select replacement symbols without matrix multiplication or a channel matrix inverse by evaluating distributions that result from subtracting error probabilities from probability vectors associated with respective contexts. Probability mass can be moved from adjacent components of the probability vector to assure that subtraction of the error probabilities leaves non-negative results.

Abstract: An image is compressed by selectively performing at least one of palettization and interframe coding on certain regions of the image. The regions are adaptively determined.

Abstract: A discrete, universal denoising method is applied to a noisy signal for which the source alphabet is typically large. The method exploits a priori information regarding expected characteristics of the signal. In particular, using characteristics of a continuous tone image such as continuity and small-scale symmetry allows definition of context classes containing large numbers of image contexts having similar statistical characteristics. Use of the context classes allows extraction of more reliable indications of the characteristic of a clean signal.

Abstract: A method of and system for generating reliability information for a noisy signal received through a noise-introducing channel. In one embodiment, symbol-transition probabilities are determined for the noise-introducing channel. Occurrences of metasymbols in the noisy signal are counted, each metasymbol providing a context for a symbol of the metasymbol. For each metasymbol occurring in the noisy signal, reliability information for each possible value of the symbol of the metasymbol is determined, the reliability information representing a probability that the value in the original signal corresponding to the symbol of the metasymbol assumed each of the possible values. In another embodiment, error correction coding may be performed by adding redundant data to an original signal prior to transmission by the noise-introducing channel and performing error correction decoding after transmission.

Abstract: A method for encoding and decoding a sequence is provided. The method comprises searching a set of candidate trees varying in size for a tree T having a plurality of states. Tree T provides a structure that relatively minimizes code length of the sequence from among all the candidate trees. The method further comprises encoding data conditioned on the tree T, which may be a generalized context tree (GCT), using a sequential probability assignment conditioned on the states of the tree T. This encoding may use finite state machine (FSM) closure of the tree. Also provided are methods for decoding an encoded binary string when the encoded string includes a full tree or generalized context tree, as well as decoding an encoded string using incomplete FSM closure, incremental FSM, and suffix tree construction concepts.

Abstract: A memory card comprising an magnetic random access memory (MRAM) array that comprises a plurality of magnetic memory cells and a controller coupled to the MRAM array. The controller is configured to communicate with a host device, and the controller is configured perform an error correction function associated with at least one of the plurality of magnetic memory cells.