Wavelet based recognition for pulsar signals

A signal from a pulsar can be decomposed into a set of features. This set is a unique signature for a given pulsar. It can be used to decide whether a pulsar is newly discovered or not. Features can be constructed from coefficients of a wavelet decomposition. Two types of wavelet based pulsar features are proposed. The energy based features reflect the multiscale distribution of the energy of coefficients. The singularity based features first classify the signals into a class with one peak and a class with two peaks by exploring the number of the straight wavelet modulus maxima lines perpendicular to the abscissa, and then implement further classification according to the features of skewness and kurtosis. Experimental results show that the wavelet based features can gain comparatively better performance over the shape parameter based features not only in the clustering and classification, but also in the error rates of the recognition tasks. (C) 2015 Elsevier B.V. All rights reserved.

[1]  L. M. Sarro,et al.  Automated supervised classification of variable stars - I. Methodology , 2007, 0711.0703.

[2]  Raj Aggarwal,et al.  A wavelet transform based decision making logic method for discrimination between internal faults and inrush currents in power transformers , 2000 .

[3]  Osvaldo A. Rosso,et al.  Brain electrical activity analysis using wavelet-based informational tools , 2002 .

[4]  Gerald Kaiser,et al.  A Friendly Guide to Wavelets , 1994 .

[5]  Patrick P C Tsui,et al.  Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification. , 2006, Ultrasonics.

[6]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook , 2002 .

[7]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[8]  Arturo Garcia-Perez,et al.  Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation , 2008, IEEE Transactions on Industrial Electronics.

[9]  J. Starck,et al.  Polarized wavelets and curvelets on the sphere , 2009 .

[10]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Fredrick A. Jenet,et al.  A Wide-Bandwidth Digital Recording System for Radio Pulsar Astronomy , 1997 .

[12]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[13]  Jien-Chen Chen,et al.  Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines , 2006 .

[14]  M. Vencelj,et al.  Pulse shape classification in liquid scintillators using the fuzzy c-means algorithm , 2010 .

[15]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[16]  G. A. Ruiz,et al.  Analysis of CMB maps with 2D wavelets , 1999 .

[17]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[18]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing - The Sparse Way, 3rd Edition , 2008 .

[19]  Peter J. Rousseeuw,et al.  Fuzzy clustering using scatter matrices , 1996 .

[20]  L. J. Liivamägi,et al.  Wavelet analysis of the cosmic web formation , 2010, 1012.3550.

[21]  Fanrang Kong,et al.  Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis , 2013 .

[22]  Sami K. Solanki,et al.  Noise reduction in astronomical spectra using wavelet packets , 1997 .

[23]  I. Moortel,et al.  Application of wavelet analysis to transversal coronal loop oscillations , 2002 .

[24]  Jing Lin,et al.  Fault feature separation using wavelet-ICA filter , 2005 .

[25]  Jean-Luc Starck,et al.  Weak lensing mass reconstruction using wavelets , 2005, astro-ph/0503373.

[26]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[27]  Sunil Kumar,et al.  Wavelets in Control Engineering , 2012 .

[28]  P. P. Vaidyanathan,et al.  Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial , 1990, Proc. IEEE.

[29]  S. Garner,et al.  Wavelet Analysis of Sodium Iodide Spectra , 2006, IEEE Transactions on Nuclear Science.

[30]  P. Vaidyanathan.  Multirate Systems And Filter Banks , 1992 .

[31]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  J.-L. Starck,et al.  Spherical 3D isotropic wavelets , 2012 .

[33]  D. Backer.  Pulsar average wave forms and hollow-cone beam models , 1976 .

[34]  A. Habibi,et al.  Introduction to wavelets , 1995, Proceedings of MILCOM '95.

[35]  G. P. Srivastava,et al.  Characterisation of metal loss defects from magnetic flux leakage signals with discrete wavelet transform , 2000 .

[36]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[37]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[38]  Gholamreza Akbarizadeh,et al.  A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[40]  R. Bracewell.  The Fourier Transform and Its Applications , 1966 .

[41]  Jie Gao,et al.  An Efficient Method to Process the Quantized Acoustoelectric Current: Wavelet Transform , 2011, IEEE Transactions on Instrumentation and Measurement.

[42]  P. Tse,et al.  Machine fault diagnosis through an effective exact wavelet analysis , 2004 .

[43]  Bor-Chen Kuo,et al.  A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[44]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[45]  M. Bailes,et al.  DSPSR: Digital Signal Processing Software for Pulsar Astronomy , 2010, Publications of the Astronomical Society of Australia.