Search results for "Noisy data"

showing 3 items of 3 documents

Algebraic parameter estimation of a multi-sinusoidal waveform signal from noisy data

2013

International audience; In this paper, we apply an algebraic method to estimate the amplitudes, phases and frequencies of a biased and noisy sum of complex exponential sinusoidal signals. Let us stress that the obtained estimates are integrals of the noisy measured signal: these integrals act as time-varying filters. Compared to usual approaches, our algebraic method provides a more robust estimation of these parameters within a fraction of the signal's period. We provide some computer simulations to demonstrate the efficiency of our method.

0209 industrial biotechnology[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingSignalsymbols.namesake020901 industrial engineering & automation[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingControl theory[INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering[ INFO.INFO-AU ] Computer Science [cs]/Automatic Control Engineering0202 electrical engineering electronic engineering information engineeringFraction (mathematics)Algebraic numberNoisy data[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingMathematicsEstimation theory020206 networking & telecommunicationsAmplitudeSinusoidal waveformEuler's formulasymbols[INFO.INFO-AU] Computer Science [cs]/Automatic Control EngineeringAlgorithm[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Application of clustering techniques to electron-diffraction data: determination of unit-cell parameters.

2012

A new approach to determining the unit-cell vectors from single-crystal diffraction data based on clustering analysis is proposed. The method uses the density-based clustering algorithm DBSCAN. Unit-cell determination through the clustering procedure is particularly useful for limited tilt sequences and noisy data, and therefore is optimal for single-crystal electron-diffraction automated diffraction tomography (ADT) data. The unit-cell determination of various materials from ADT data as well as single-crystal X-ray data is demonstrated.

DiffractionDBSCANbusiness.industryComputer sciencePhysics::OpticsPattern recognitionDiffraction tomographyOpticsElectron diffractionStructural BiologyArtificial intelligencebusinessCluster analysisNoisy dataActa crystallographica. Section A, Foundations of crystallography
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Hybrid recommendation methods in complex networks

2015

We propose here two new recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three relevant data sets, and we compare their performance with several recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow to attain an improvement of performances of up to 20\% with respect to existing non-parametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a …

Statistics and ProbabilityNormalization (statistics)Social and Information Networks (cs.SI)FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceNonparametric statisticsFOS: Physical sciencesComputer Science - Social and Information NetworksCondensed Matter PhysicPhysics and Society (physics.soc-ph)Complex networkRecommender systemcomputer.software_genreComputer Science - Information RetrievalBipartite graphConvex combinationData miningNoisy datacomputerInformation Retrieval (cs.IR)Statistical and Nonlinear Physic
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