Abstract: One embodiment of the present invention is a sensor comprising one or more sensing devices, data-transmission components that transmit sensor data to a receiving component, and a processing component. The processing component executes routines to record sensing-device output as data for transmission to the receiving entity and to control the data-transmission components to transmit the data to the receiving entity. The processing component executes one or more compressing routines to compress data prior to transmission, when data compression is estimated to result in a lower power cost than transmitting uncompressed data, and controlling the data-transmission components to transmit data without compressing the data when data compression is estimated to result in a higher power cost than transmitting uncompressed data.

Abstract: A joint image compression system (200, 300) and method (100) compress a target image and a reference image under a selected transform to produce a compressed difference image. The joint image compression system includes a computer readable media (210) and a computer program (220) stored on the computer readable media. The computer program includes instructions that implement selecting (110) a transform from among a plurality of transforms that includes a subset determined projective (SDP) transform, where the selected transform minimizes a cumulative mapping error (CME) for corresponding feature points in each of a target image (302) and a reference image (304). The instructions further implement applying (120) the selected transform to one of the target image and the reference image; forming (130) a difference image under the selected transform and compressing (140) the difference image to produce a compressed difference image.

Abstract: A first node receives aggregated compressed data and unaggregated data from a second node in a wireless multi-hop network. The first node compresses its own collected data based on the received unaggregated data. The first node aggregates its own compressed data with the aggregated compressed data received from the second node. The first node forwards an unaggregated version of its own collected data along with aggregated compressed data to a next hop in the wireless multi-hop network.

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 relate to a discrete denoiser that replaces symbols in a received, noisy signal with replacement symbols in order to produce a recovered signal less distorted with respect to an originally transmitted, clean signal than the received, noisy signal. Certain, initially developed discrete denoisers employ an analysis of the number of occurrences of metasymbols within the received, noisy signal in order to select symbols for replacement, and to select the replacement symbols for the symbols that are replaced. Denoisers that represent examples of the present invention use blended counts that are combinations of the occurrences of metasymbol families within a noisy signal to determine the symbols to be replaced and the replacement symbols corresponding to them.

Abstract: Certain examples of the present invention are directed to an image-recording device. The image-recording device includes an imaging component that records an image of an environment, a projection component that projects, into the environment, an (n,d) reliable M*-sequence of symbols, and a distance component. The distance component identifies j consecutive symbols reflected back to the imaging component from a surface in the environment, where j?n, detects and corrects a misidentified symbol within the j consecutive symbols based on the minimum distance t of the (n,d) reliable M*-sequence, determines a first position of the j consecutive symbols with respect to the image, determines a second position of the j consecutive symbols in the M*-sequence of symbols, and determines, from the first and second position, a distance t from the surface to the imaging component.

Abstract: An approximate enumerative coding method (100, 200) and apparatus (300) employ a cardinality-approximating (C-A) lower bound in mapping a message M to a 2-dimensional (2-D) codeword array that satisfies a 2-D constraint. The method (100) includes encoding the message M as a codeword array X using an encoder apparatus. The encoding determines entries in a codeword array X using the C-A lower bound. The C-A lower bound is a function of several terms, namely a memory term k, a cardinality of a set of sequences satisfying a horizontal constraint, a columnar extension probability of the 2-D constraint, and a non-negative constant that is a function of the columnar extension probability. The apparatus (300) includes an encoder processor (310), memory (320) and a computer program (330) stored in the memory (320) and executed by the encoder processor (310).

Abstract: One embodiment of the present invention is a sensor comprising one or more sensing devices, data-transmission components that transmit sensor data to a receiving component, and a processing component. The processing component executes routines to record sensing-device output as data for transmission to the receiving entity and to control the data-transmission components to transmit the data to the receiving entity. The processing component executes one or more compressing routines to compress data prior to transmission, when data compression is estimated to result in a lower power cost than transmitting uncompressed data, and controlling the data-transmission components to transmit data without compressing the data when data compression is estimated to result in a higher power cost than transmitting uncompressed data.

Abstract: Method and system embodiments of the present invention are directed to encoding information in ways that are compatible with constraints associated with electrical-resistance-based memories and useful in other, similarly constrained applications, and to decoding the encoded information. One embodiment of the present invention encodes k information bits and writes the encoded k information bits to an electronic memory, the method comprising systematically encoding the k information bits to produce a vector codeword, with additional parity bits so that the codeword is resilient to bit-transition errors that may occur during storage of the codeword in, and retrieval of the codeword from, the electronic memory, ensuring that the codeword does not violate a weight constraint, and writing the codeword to the electronic memory.

Abstract: In various embodiments of the present invention, optimal or near-optimal multidirectional context sets for a particular data-and/or-signal analysis or processing task are determined by selecting a maximum context size, generating a set of leaf nodes corresponding to those maximally sized contexts that occur in the data or signal to be processed or analyzed, and then building up and concurrently pruning, level by level, a multidirectional optimal context tree constructing one of potentially many optimal or near-optimal context trees in which leaf nodes represent the context of a near-optimal or optimal context set that may contain contexts of different sizes and geometries. Pruning is carried out using a problem-domain-related weighting function applicable to nodes and subtrees within the context tree. In one described embodiment, a bi-directional context tree suitable for a signal denoising application is constructed using, as the weighting function, an estimated loss function.

Abstract: A distinguished node is dynamically selected from a subset of nodes in a wireless network. Data samples from the subset of nodes are received in view of the distinguished node status. At least one estimate is generated from the data samples and the data samples are compressed conditioned on the estimate.

Abstract: A first node receives aggregated compressed data and unaggregated data from a second node in a wireless multi-hop network. The first node compresses its own collected data based on the received unaggregated data. The first node aggregates its own compressed data with the aggregated compressed data received from the second node. The first node forwards an unaggregated version of its own collected data along with aggregated compressed data to a next hop in the wireless multi-hop network.

Abstract: A first node in a wireless network transmits a periodic pilot signal to a second node. The second node receives the periodic pilot signal and retransmits the signal back to the first node. The retransmitted pilot signal includes a phase adjustment in view of an internal processing delay at the second node. The phase adjustment involves matching a phase of the retransmitted pilot signal to a phase of the received pilot signal. The first node measures a roundtrip delay of the pilot signal and the distance between the nodes is computed based at least on the measured roundtrip delay.

Abstract: Various embodiments of the present invention relate to a discrete denoiser that replaces symbols in a received, noisy signal with replacement symbols in order to produce a recovered signal less distorted with respect to an originally transmitted, clean signal than the received, noisy signal. Certain, initially developed discrete denoisers employ an analysis of the number of occurrences of metasymbols within the received, noisy signal in order to select symbols for replacement, and to select the replacement symbols for the symbols that are replaced. Denoisers that represent examples of the present invention use blended counts that are combinations of the occurrences of metasymbol families within a noisy signal to determine the symbols to be replaced and the replacement symbols corresponding to them.

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: A method and apparatus for processing a received digital signal that has been corrupted by a channel is disclosed. The method includes storing the received digital signal and receiving a partially corrected sequence of symbols that includes an output of a preliminary denoising system operating on the received digital signal. Information specifying a signal degradation function that measures the signal degradation that occurs if a symbol having the value I is replaced by a symbol having the value J is utilized to generate a processed digital signal by replacing each symbol having a value I in a context of that symbol in the received digital signal with a symbol having a value J if replacement reduces a measure of overall signal degradation in the processed digital signal relative to the received digital signal as measured by the degradation function and the partially corrected sequence of symbols.

Abstract: Embodiments of the present invention provide context-class-based universal denoising of noisy images and other noise-corrupted data sets. Prediction-error statistics for each prediction class, relative to a prefiltered image, are collected to estimate a bias for each prediction class, and prediction-error statistics for each conditioning class, relative to a prefiltered image, are accumulated based on the difference between predicted values and corresponding prefiltered-image symbols. The prediction-error statistics are accumulated using computed prediction-error-statistics vectors, with inversion of a prediction-error vector generated from each prediction prior to accumulation in a prediction-error-statistics vector. Conditional probability distributions are computed for individual contexts, which allow for computing a clean-image-estimated, value for each noisy-image value by minimizing a computed distortion over a range of possible estimated-clean-image symbols.

Abstract: Embodiments of the present invention are directed to various enhanced discrete-universal denoisers that have been developed to denoise images and other one-dimensional, two-dimensional or higher-dimensional data sets in which the frequency of occurrence of individual contexts may be too low to gather efficient statistical data or context-based symbol prediction. In these denoisers, image quality, signal-to-noise ratios, or other measures of the effectiveness of denoising that would be expected to increase monotonically over a series of iterations may decrease, due to assumptions underlying the discrete-universal-denoising method losing validity. Embodiments of the present invention apply context-class-based statistics and statistical analysis to determine, on a per-context-class basis, when to at least temporarily terminate denoising iterations on each conditioning class.

Abstract: One embodiment of the present invention is directed to an adaptive context-based predictor that predicts a value {circumflex over (x)} from a context, stored in an electronic memory, corresponding to a noisy-dataset symbol zi of a noisy dataset corrupted with noise modeled as being introduced by a noise-introducing channel. The adaptive context-based predictor is adapted according to one or more parameters that specify adaptive context-based-predictor operation, at least one of which functionally depends, or partially functionally depends, on a level of noise represented by the noise-introducing channel. The adaptive context-based predictor computes a number of intermediate values from the context, computes the predicted value {circumflex over (x)} from the intermediate values, and stores the predicted value {circumflex over (x)} in the electronic memory.

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.