Abstract: An embodiment of the present disclosure a system and method for extracting items of information, such as wine from a wine list of a commercial establishment.

Abstract: Described is a decoder suitable for use with any communication or storage system. The described decoder has a modular decoder hardware architecture capable of implementing a noise guessing process and due to its dependency only on noise, the decoder design is independent of any encoder, thus making it a universal decoder. Hence, the decoder architecture described herein is agnostic to any coding scheme.

Abstract: Described are concepts, systems, devices and methods that enhance decoding performance of channels subject to correlated noise. The concepts, systems, devices and methods can be used with any combination of codes, code-rates and decoding techniques. In embodiments, a continuous realization of effective noise is estimated from a lead channel by subtracting its decoded output from its received signal. This estimate is then used to improve the accuracy of decoding of an otherwise orthogonal channel that is experiencing correlated effective noise. In this approach, channels aid each other through the post-decoding provision of estimates of effective noise. In some embodiments, the lead channel is not pre-determined, but is chosen dynamically based on which of a plurality of decoders completes first, or using soft information including an estimate of effective noise that is least energetic or most likely to have occurred.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Abstract: The present application concerns an iterative bit-flipping decoding method using symbol or bit reliabilities, which is a variation of GRAND decoding and is denoted by ordered reliability bits GRAND (ORBGRAND). It comprises receiving a plurality of demodulated symbols from a noisy transmission channel; and receiving for the plurality of demodulated symbols, information indicating a ranked order of reliability of at least the most unreliable information contained within the plurality of demodulated symbols. A sequence of putative noise patterns from a most likely pattern of noise affecting the plurality of symbols through one or more successively less likely noise patterns is provided.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Abstract: Described are concepts, systems, devices and methods that enhance decoding performance of channels subject to correlated noise. The concepts, systems, devices and methods can be used with any combination of codes, code-rates and decoding techniques. In embodiments, a continuous realization of effective noise is estimated from a lead channel by subtracting its decoded output from its received signal. This estimate is then used to improve the accuracy of decoding of an otherwise orthogonal channel that is experiencing correlated effective noise. In this approach, channels aid each other through the post-decoding provision of estimates of effective noise. In some embodiments, the lead channel is not pre-determined, but is chosen dynamically based on which of a plurality of decoders completes first, or using soft information including an estimate of effective noise that is least energetic or most likely to have occurred.

Abstract: The present application concerns an iterative bit-flipping decoding method using symbol or bit reliabilities, which is a variation of GRAND decoding and is denoted by ordered reliability bits GRAND (ORBGRAND). It comprises receiving a plurality of demodulated symbols from a noisy transmission channel; and receiving for the plurality of demodulated symbols, information indicating a ranked order of reliability of at least the most unreliable information contained within the plurality of demodulated symbols. A sequence of putative noise patterns from a most likely pattern of noise affecting the plurality of symbols through one or more successively less likely noise patterns is provided.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Abstract: Described are concepts, systems, devices and methods that enhance decoding performance of channels subject to correlated noise. The concepts, systems, devices and methods can be used with any combination of codes, code-rates and decoding techniques. In embodiments, a continuous realization of effective noise is estimated from a lead channel by subtracting its decoded output from its received signal. This estimate is then used to improve the accuracy of decoding of an otherwise orthogonal channel that is experiencing correlated effective noise. In this approach, channels aid each other through the post-decoding provision of estimates of effective noise. In some embodiments, the lead channel is not pre-determined, but is chosen dynamically based on which of a plurality of decoders completes first, or using soft information including an estimate of effective noise that is least energetic or most likely to have occurred.

Abstract: Systems and methods of the present disclosure enable automated analyses of a biological sample using a processing system by receiving signal profiles of each allele of a set of cells in the sample. A set of allele vectors are determined based on a mapping of the magnitude of the measurement of each signal profile at each locus to an index location. A set of cell vectors is generated by concatenating each allele vector of each cell. A cluster model is utilized to generate clusters of the signal profiles based on the set of cell vectors to represent contributors. A first likelihood of a target contributor matching a contributor and a second likelihood of the target contributor not matching any contributor are determined by comparing the target signal profile to each cluster. A likelihood ratio is determined from a ratio of the first likelihood and the second likelihood.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Abstract: Described is a decoder suitable for use with any communication or storage system. The described decoder has a modular decoder hardware architecture capable of implementing a noise guessing process and due to its dependency only on noise, the decoder design is independent of any encoder, thus making it a universal decoder. Hence, the decoder architecture described herein is agnostic to any coding scheme.

Abstract: Described are concepts, systems, devices and methods that enhance decoding performance of channels subject to correlated noise. The concepts, systems, devices and methods can be used with any combination of codes, code-rates and decoding techniques. In embodiments, a continuous realization of effective noise is estimated from a lead channel by subtracting its decoded output from its received signal. This estimate is then used to improve the accuracy of decoding of an otherwise orthogonal channel that is experiencing correlated effective noise. In this approach, channels aid each other through the post-decoding provision of estimates of effective noise. In some embodiments, the lead channel is not pre-determined, but is chosen dynamically based on which of a plurality of decoders completes first, or using soft information including an estimate of effective noise that is least energetic or most likely to have occurred.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. In various embodiments, soft information is used to generate a symbol mask that identifies the collection of symbols that are suspected to differ from the channel input, and only these are subject to guessing. This decoder embodies or approximates maximum likelihood (optionally with soft information) decoding for any code. In some embodiments, the decoder tests abounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. In various embodiments, soft information is used to generate a symbol mask that identifies the collection of symbols that are suspected to differ from the channel input, and only these are subject to guessing. This decoder embodies or approximates maximum likelihood (optionally with soft information) decoding for any code. In some embodiments, the decoder tests abounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Abstract: Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.