154 results about "Noisy data" patented technology

An implantable medical device (IMD) capable of monitoring physiologic data, distinguishing relatively noisy and noise free physiologic data, and recording noisy and relatively noise free segments of physiologic data in separate memory registers of a limited memory for retrieval and analysis at a later time. Preferably the physiologic data comprises the sampled EGM of the heart detected from sense electrode pairs that are implanted in the patient at sites where extraneous electrical noise, e.g., electromyographic signals, are also capable of being detected. The sense electrode pairs can constitute one or both sense electrodes located on or adjacent to the atrial and / or ventricular heart chambers and coupled to the IMD by a lead body or sense electrode pairs that are located remotely from the heart, e.g. at a subcutaneous implantation site of the IMD. A plurality of noisy EGM episode data registers store a corresponding plurality of noisy EGM episode data sets on a FIFO basis and another plurality of noise free EGM episode data registers to store a corresponding plurality of relatively noise free EGM episode data sets on a FIFO basis. Any form of discrimination of noisy data from relatively noise free data can be employed at the time of recording, but because the stored EGM episode data sets are subsequently viewed and analyzed by a physician, discrimination with absolute certainty is not required, and the physician can alter the detection criteria to fine tune it.

Methods, systems and computer program products for simplifying complex data stream problems involving feature extraction from noisy data. Exemplary embodiments include a method for processing a data stream, including applying multiple operators to the data stream, wherein an operation by each of the multiple operators includes retrieving the next chunk for each of set of input parameters, performing digital processing operations on a respective next chunk, producing sets of output parameters and adding data to one or more internal data stores, each internal data store acting as a data stream source.

Systems and methods are presented that enable logical reasoning even in the presence of noisy (inconsistent) data. The knowledge base is processed in order to make it consistent and is also compiled. This processing includes checking and correcting spelling, removing stopwords, performing, grouping words of similar and related meaning, and compacting the knowledge base. A robot can use the processed knowledge base to perform many different types of tasks, such as answering a query, determining a course of action that is designed to achieve a particular goal, and determining its own location.

A method for monitoring engine performance includes sampling exhaust gas temperature associated with a turbine engine over an interval of operational time of the turbine engine. The method further includes applying a first test to identify statistical outliers on the sampled exhaust gas temperature data and removing identified statistical outliers from the sampled exhaust gas temperature data. Subsequently, the method includes applying a second test to identify step changes in slope of the exhaust gas temperature data and dividing the interval of operational time into one or more segments based upon the identified step changes. Finally, the method includes determining a slope for each segment and combining the segments to obtain a rate of performance deterioration of the turbine engine.

The invention discloses a method for establishing a mapping knowledge domain based on a book catalogue. The method comprises the steps that a catalogue page in a digitized book is extracted, the lengths of items in the catalogue are differentiated, and part-of-speech tagging is conducted on the long items through a natural language processing tool, so that part-of-speech arrays are obtained, and candidate nodes are extracted according to rules of conjunctions, punctuations and parts of speech; the long items and the short items are authenticated in the Baidu encyclopedia and the Hudong encyclopedia, a leader-member relation and parallel relations are formed through a catalogue structure and serve as a framework of the mapping knowledge domain, the strong and weak parallel relations are differentiated and serve as increments respectively, and the leader-member relation is supplemented with the strong and weak parallel relations; according to a noisy data excavating algorithm with suffixes serving as a base, nodes are selected from the items which do not pass the authentication of the encyclopedias and the mapping knowledge domain is supplemented with the selected nodes; finally, the weights of relations in the supplemented mapping knowledge domain are calculated and ranked, so that noise is removed through screening. Compared with an existing mapping knowledge domain, the mapping knowledge domain established through the method is richer in node, better in expandability and higher in accuracy.

A method of speech recognition with compensation is provided by modifying HMM models trained on clean speech with cepstral mean normalization. For all speech utterances the MFCC vector is calculated for the clean database. This mean MFCC vector is added to the original models. An estimate of the background noise is determined for a given speech utterance. The model mean vectors adapted to the noise are determined. The mean vector of the noisy data over the noisy speech space is determined and this is removed from model mean vectors adapted to noise to get the target model.

A method for tracking a sinusoidal electromagnetic signal in noisy data using a Kalman filter or other tracking algorithm. The method is useful for controlled source electromagnetic surveying where longer source-receiver offsets can cause the source signal to decay significantly and be difficult to retrieve from magnetotelluric or other electromagnetic background.

The invention discloses a food image automatic classification method based on convolutional neural networks. The food image automatic classification method comprises steps that 1) food image data is crawled by a network crawler from Internet, and food images having correct labels are artificially screened out and filed in an Initial Data data set; 2) the network crawler is used to search a lot of food image data of the target classification in a mainstream search engine and an image sharing website, and at the same time, a step 4) is executed regularly; 4) a Food CNN network is used for data screening, and the data is divided into a Crawl Data and a Noisy Data; 5) the expanded data Crawl Data is used to update the Food CNN network; 6) whether the Noisy Data data size is reasonable is determined, and whether the crawling is continued is determined; 7) the crawling is stopped, and a Food Final CNN is trained. A correction rate is high.

A noisy data alignment algorithm for determining cycle count offsets for noisy pairs of n monitoring devices. A direct cycle count offset matrix is determined based upon the highest correlation coefficients produced by correlating frequency variation data from each device pair Dij. For each direct cycle count offset Mij, indirect cycle count offsets are calculated as a function of at least Mk, where k≠i≠j, to produce indirect cycle count offsets. The statistical mode of these indirect offsets is compared with the corresponding Mij in the matrix. When they differ, Mij in the direct matrix is adjusted to be equal to the statistical mode. All indirect cycle count offsets for all other unique device pairs, Mij, are calculated to iterate to a single solution in which all indirect cycle count offsets are equal to the corresponding direct cycle count offset. An optional verification algorithm is also provided.

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 can efficiently select peaks from very noisy data such as two-dimensional liquid chromatography-mass spectrometry (LC-MS) data. 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.