153 results about "Noisy data" patented technology

Noise reduction in a one- or two-dimensional capacitive sensor array is achieved by rejecting and re-acquiring noisy signals. The sensor array is formed of crossed X and Y lines for drive and sense functions respectively, each of the X lines being driven in turn to acquire a full frame of data from the sensor array. A controller actuates the X lines in turn and, for each X line, charge is transferred to charge measurement capacitors connected to respective ones of the Y lines. The controller measures a signal value from a first one of the measurement capacitors, and then tests if that Y signal value has a magnitude lying within an acceptable range. If not, the measurement capacitors are all reset without their signal values being measured, and the controller re-drives the same X line to initiate another charge transfer into the measurement capacitors for that X line. It is then attempted once again to acquire Y signal values for that X line. Noisy signal values are thus rejected, and the sensor re-acquires substitute signal values. This is distinct from the approach of acquiring data and then reprocessing it to remove or suppress noisy data as in the prior art.

A method of image processing using kernel regression is provided. An image gradient is estimated from original data that is analyzed for local structures by computing a scaling parameter, a rotation parameter and an elongation parameter using singular value decomposition on local gradients of the estimated gradients locally to provide steering matrices. A steering kernel regression having steering matrices is applied to the original data to provide a reconstructed image and new image gradients. The new gradients are analyzed using singular value decomposition to provide new steering matrices. The steering kernel regression with the new steering matrices is applied to the noisy data to provide a new reconstructed image and further new gradients. The last two steps are repeated up to ten iterations to denoise the original noisy data and improve the local image structure.

An automatic peak selection method for multidimensional data that selects peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data is described. Such data are characterized by non-normally distributed noise that varies in different dimensions. The method computes local noise thresholds for each one-dimensional component of the data. Each point has a local noise threshold applied to it for each dimension of the data set, and a point is selected as a candidate peak only if its value exceeds all of the applied local noise thresholds. Contiguous candidate peaks are clustered into actual peaks. The method is preferably implemented as part of a high-throughput platform for analyzing complex biological mixtures.

Methods and apparatus for estimating and correcting carrier frequency offsets in a bust multi-tone receiver are described. Course and fine carrier frequency estimates are generated from the signal's preamble. Decision directed carrier frequency offset estimates are then generated from the signal field and data fields of the multi-tone signal. Frequency error estimates are generated for each tone of the signal and combined using a weighted average to generate the frequency error estimate used to perform the correction operation. Error estimates corresponding to noisy data tones are weighted less then estimates corresponding to less noisy data tones. In cases of low SNR frequency error estimates corresponding to pilots are weighted by an extra amount as compared to error estimates corresponding to tones used to transmit data symbols. During times of high SNR error estimates corresponding to pilot tones are weighted in the same manner as error estimates corresponding to data tones.

A method and apparatus for ensuring a level of privacy for answering a convolution query on data stored in a database is provided. The method and apparatus includes the activities of determining (402) the level of privacy associated with at least a portion of the data stored in the database and receiving (404) query data, from a querier, for use in performing a convolution over the data stored in the database. The database is searched (406) for data related to the received query data and the data that corresponds to the received query data is retrieved (408) from the database. An amount of noise based on the determined privacy level is generated (410) and added (412) to the retrieved data to create noisy data which is then communicated (414) to the querier.

Example embodiments of systems and methods for network pattern matching provide the ability to match hidden networks from noisy data sources using probabilistic multi-attribute graph matching analysis. The algorithms may map roles and patterns to observed entities. The outcome is a set of plausible network models. The pattern-matching methodology of these systems and methods may enable the solution of three challenges associated with social network analysis, namely network size and complexity, uncertain and incomplete data, and dynamic network structure.

This invention maps possibly noisy digital input from any of a number of different hardware or software sources such as keyboards, automatic speech recognition systems, cell phones, smart phones or the web onto an interpretation consisting of an action and one or more physical objects, such as robots, machinery, vehicles, etc. or digital objects such as data files, tables and databases. Tables and lists of (i) homonyms and misrecognitions, (ii) thematic relation patterns, and (iii) lexicons are used to generate alternative forms of the input which are scored to determine the best interpretation of the noisy input. The actions may be executed internally or output to any device which contains a digital component such as, but not limited to, a computer, a robot, a cell phone, a smart phone or the web. This invention may be implemented on sequential and parallel compute engines and systems.

Lossless and lossy codes are combined for data compression. In one embodiment, the most significant bits of each value are losslessly coded along with a lossy version of the original data. Upon decompression, the lossless reduced-precision values establish absolute bounds for the lossy code. Another embodiment losslessly codes the leading bits while trailing bits undergo lossy coding. Upon decompression, the two codes are summed. The method preserves edges and other sharp transitions for superior lossy compression. Additionally, the method enables description-length inference using noisy data.

Spatial data sequences are extracted in a frequency-space domain at selected frequencies in selected data windows in seismic data. A signal model and residuals are iteratively constructed for each extracted spatial data sequence. Each data sample in each extracted spatial data sequence is assessed to determine if the data sample is noisy. Each noisy data sample in each data sequence is replaced by a corresponding signal model value. The earth's subsurface is imaged using the noise-attenuated seismic data.

The invention discloses a microblog topic detection and hotspot evaluation method based on semantic expansion, belongs to the field of text information processing, and particularly relates to microblog noisy data filtering and a microblog topic detection and topic hotspot evaluation method and system based on semantic expansion. The method comprises the steps of firstly giving out a microblog noisy data filtering method for filtering microblogs with low information content, then supplementing effective semantic information in microblog comments into microblog semanteme so as to improve the detection effect of microblog topics, finally carrying out microblog topic hotspot evaluation, and thus obtaining hot topics.

A method of anomaly detection applicable to telecommunications or retail fraud or software vulnerabilities uses inductive logic programming to develop anomaly characterization rules from relevant background knowledge and a training data set, which includes positive anomaly samples of data covered by rules. Data samples include 1 or 0 indicating association or otherwise with anomalies. An anomaly is detected by a rule having condition set which the anomaly fu,lfils. Rules are developed by addition of conditions and unification of variables, and are filtered to remove duplicates, equivalents, symmetric rules and unnecessary conditions. Overfitting of noisy data is avoided by an encoding cost criterion. Termination of rule construction involves criteria of rule length, absence of negative examples, rule significance and accuracy, and absence of recent refinement. Iteration of rule construction involves selecting rules with unterminated construction, selecting rule refinements associated with high accuracies, and iterating a rule refinement, filtering and evaluation procedure to identify any refined rule usable to test data. Rule development may use first order logic or Higher Order logic.

The invention discloses the training and identification method and system of a bidirectional neural network model used for processing a noisy voice. The method comprises the followings steps of acquiring simulation noisy data and real noisy data; calculating the time-frequency hidden value label of the simulation noisy data, setting the label of the simulation noisy data to be a training target ina bidirectional neural network, and inputting the simulation noisy data after preset processing into the bidirectional neural network so as to carry out training; using a clustering mode to estimatethe soft time-frequency hidden value label of the real noisy data, setting the soft label of the real noisy data in the bidirectional neural network to be the training target, and inputting the real noisy data after preset processing into the bidirectional neural network so as to carry out training; and outputting the neural network parameters of the trained bidirectional neural network. In the invention, through introducing the real and non-simulated training data, the neural network model is trained. On one hand, a training data size is increased; on the other hand, the mismatching of the simulation data and the real data is reduced.

Disclosed is a method for evaluating seismic data, the method including identifying seismic data sets which are homogeneous; and identifying noise in the data sets by at least one of performing a macro analysis of the data sets to identify noise components; performing a correlation analysis of the data sets to identify correlating noise data; performing a polarization analysis to identify a direction of noise; and performing a spectral analysis to identify frequencies associated with noise. An associated system and computer program product are provided.

Method for completely specifying orientation of electromagnetic receivers dropped to the ocean bottom in an electromagnetic survey. Survey data are selected, rejecting noisy data with long offsets and data where the receiver has saturated with short offsets (61). A model is developed comprising three independent receiver orientation angles completely specifying the receiver orientation in three dimensions, and an earth resistivity model including a water layer and possibly an air layer (62). Maxwell's equations, applied to the model and the selected data, are then inverted to determine the receiver orientations (63).

The invention discloses a monitoring data compression method applicable to an operation and maintenance automation system. The method includes the following steps that 1, data preprocessing is conducted, wherein smoothing is conducted on original data to reduce interference with an SDT algorithm from noisy data; 2, preliminary data compression is conducted, wherein a control algorithm is adopted to compress data preliminarily; 3, further data compression is conducted, wherein the SDT algorithm is adopted for further compression; 4, SDT compression precision parameter matching is conducted, wherein after each time of data compression, compression precision parameters are matched with the characteristics of data fluctuation changes according to the data fluctuation changes, and compression precision parameters generated after matching are obtained; 5, adaptive adjustment of the SDT compression precision parameters is conducted, wherein the step 2, the step 3 and the step 4 are repeated according to the compression precision parameters generated after matching, and then the compression precision parameters are adaptively adjusted; 6, the step 2, the step 3 and the step 5 are repeated until the compression precision parameters are completely matched with the characteristics of the data fluctuation changes, the optimal compression precision parameters are obtained, and then the step 2 and the step 3 are repeated to complete data compression.

A method and system for recognizing (and/or predicting) failures of sensors used in monitoring gas turbines applies a sparse coding process to collected sensor readings and defines the L-1 norm residuals from the sparse coding process as indicative of a potential sensor problem. Further evaluation of the group of residual sensor readings is perform to categorize the group and determine if there are significant outliers (“abnormal data”), which would be considered as more likely associated with a faulty sensor than noisy data. A time component is introduced into the evaluation that compares a current abnormal result with a set of prior results and making the faulty sensor determination if a significant number of prior readings also have an abnormal value. By taking the time component into consideration, the number of false positives is reduced.