Dossier: Advances in Signal Processing and Image Analysis for Physico-Chemical, Analytical Chemistry and Chemical Sensing
Open Access
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 69, Number 2, March-April 2014
Dossier: Advances in Signal Processing and Image Analysis for Physico-Chemical, Analytical Chemistry and Chemical Sensing
Page(s) 229 - 244
Published online 28 March 2013
  • Cavanagh J., Fairbrother W.J., Palmer A.G., Skelton N.J., Rance M. (2006) Protein NMR Spectroscopy: Principles and Practice, Academic Press, San Diego, CA.
  • Bax A. (1985) A simple description of two-dimensional NMR spectroscopy, Bull. Magn. Reson. 7, 4, 167-183.
  • Bax A., Lerner L. (1986) Two-dimensional nuclear magnetic resonance spectroscopy, Science 232, 960-967. [CrossRef] [PubMed]
  • Canet D. (1996) Nuclear magnetic resonance spectroscopy. Concepts and methods, John Wiley & Sons Ltd, West Sussex, England.
  • Marshall I., Bruce S.D., Higinbotham J., MacLullich A., Wardlaw J.M., Ferguson K.J., Seckl J. (2000) Choice of spectroscopic lineshape model affects metabolite peak areas and area ratios, Magn. Reson. Med. 44, 646-649. [CrossRef] [PubMed]
  • Suvichakorn A., Antoine J.P. (2008) Analyzing NMR spectra with the Morlet wavelet, Proc. 16th European Signal Process. Conf. EUSIPCO 2008, Lausanne, Suisse, 25-29 Aug.
  • Bartha R., Drost D.J., Menon R.S., Williamson P.C. (2000) Spectroscopic lineshape correction by QUECC: Combined QUALITY deconvolution and eddy current correction, Magn. Reson. Med. 44, 641-645. [CrossRef] [PubMed]
  • Marple S.L. (1987) Digital spectral analysis with applications, Prentice Hall, Englewood Cliffs.
  • Kay S.M. (1988) Modern spectral estimation. Theory and application, Prentice Hall, Englewood Cliffs.
  • Matlengiewicz M., Henzel N., Czachowska D., Schmit-Quilès F., Nicole D., Lauer J.C. (1994) Computer aided analysis of 13C NMR spectra of multicomponent mixtures: 3. Analysis of individual components of a heavy gasoline from liquefaction of Polish coal, Fuel 73, 6, 843-850. ISSN 0016-2361. [CrossRef]
  • Bresler Y., Macovski A. (1986) Exact maximum likelihood parameter estimation of superimposed exponential signals in noise, IEEE Trans. Acoust. Speech Signal Process. 34, 5, 1081-1089. [CrossRef]
  • Rubtsov D.V., Griffin J.L. (2007) Time-domain Bayesian detection and estimation of noisy damped sinusoidal signals applied to NMR spectroscopy, J. Magn. Reson. 188, 367-379. [CrossRef] [PubMed]
  • Kumaresan R., Tufts D.W. (1982) Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise, IEEE Trans. Acoust. Speech Signal Process. 30, 833-840. [CrossRef]
  • Kung R., Arun K.S., Bhaskar Rao D.V. (1983) State-space and singular value decomposition-based approximation methods for the harmonic retrieval problem, J. Opt. Soc. Am. 73, 12, 1799-1811. [CrossRef]
  • Hua Y., Sarkar T.K. (1990) Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise, IEEE Trans. Acoust. Speech Signal Process. 38, 5, 814-824. [CrossRef] [MathSciNet]
  • Barkhuijsen H., de Beer R., Bovée W.M.M.J., van Ormondt D. (1985) Retrieval of frequencies, amplitudes, damping factors, and phases from time-domain signals using a linear least-squares procedure, J. Magn. Reson. 63, 465-481.
  • Barkhuijsen H., de Beer R., van Ormondt D. (1987) Improved algorithm for noniterative time domain model fitting to exponentially damped magnetic resonance signals, J. Magn. Reson. 73, 553-557.
  • Hoch J.C., Stern A.S. (1996) NMR data processing, Wiley-Liss, New York.
  • Koehl P. (1999) Linear prediction spectral analysis of NMR data, Prog. NMR Spectr. 34, 257-299. [CrossRef]
  • Van Huffel S., Chen H., Decaniere C., Van Hecke P. (1994) Algorithm for time-domain NMR data fitting based on total least squares, J. Magn. Reson. Ser. A 110, 228-237. [CrossRef]
  • Poullet J.B., Sima D.M., Van Huffel S. (2008) MRS signal quantitation: A review of time- and frequency-domain methods, J. Magn. Reson. 195, 134-144. [CrossRef] [PubMed]
  • Clark M.P., Scharf L.L. (1994) Two-dimensional modal analysis based on maximum likelihood, IEEE Trans. Signal Process. 42, 6, 1443-1452. [CrossRef]
  • Li Y., Razavilar J., Ray K.J. (1998) A high-resolution technique for multidimensional NMR spectroscopy, IEEE Trans. Biomed. Eng. 45, 1, 78-86. [CrossRef] [PubMed]
  • Sacchini J.J., Steedly W.M., Moses R.L. (1993) Two-dimensional Prony modeling and parameter estimation, IEEE Trans. Signal Process. 41, 11, 3127-3137. [CrossRef]
  • Hua Y. (1992) Estimating two-dimensional frequencies by matrix enhancement and Matrix Pencil, IEEE Trans. Signal Process. 40, 9, 2267-2280. [CrossRef]
  • Liu X., Sidiropoulos N. (2002) On constant modulus multidimensional harmonic retrieval, Proc. IEEE ICASSP 2002, Orlando, Florida, 13-17 May, Vol. 3, pp. 2977-2980.
  • Sidiropoulos N.D. (2001) A new 2-D harmonic retrieval algorithm, Proc. 39th Allerton Conf. Comm. Control Computing, Urbana-Champaign, October.
  • Rouquette S., Najim M. (2001) Estimation of frequencies and damping factors by two-dimensional ESPRIT type methods, IEEE Trans. Signal Process. 49, 1, 237-245. [CrossRef]
  • Wax M., Kailath T. (1985) Detection of signals by information theoretic criteria, IEEE Trans. Acoust. Speech Signal Process. 33, 2, 387-392. [CrossRef] [MathSciNet]
  • Sandgren N., Stoica P., Frigo F.J. (2006) Area selective signal parameter estimation for two-dimensional MR spectroscopy data, J. Magn. Reson. 183, 50-59. [CrossRef] [PubMed]
  • Silverstein S.D., Engeler W.E., Tardif J.A. (1991) Parallel architectures for multirate superresolution spectrum analyzers, IEEE Trans. Circ. Syst. 38, 4, 449-453. [CrossRef]
  • Steedly W.M., Ying C.-H.J., Moses R.L. (1994) A modified TLS-Prony method using data decimation, IEEE Trans. Signal Process. 42, 9, 2292-2303. [CrossRef]
  • Tkacenko A., Vaidyanathan P.P. (2001) The role of filter banks in sinusoidal frequency estimation, J. Franklin Inst. 338, 5, 517-547. [CrossRef] [MathSciNet]
  • Zoltowski M.D., Kautz G.M., Silverstein S.D. (1993) Beamspace Root-MUSIC, IEEE Trans. Signal Process. 41, 1, 344-364. [CrossRef]
  • Tang J., Norris J.R. (1988) LP-ZOOM, a linear prediction method for local spectral analysis of NMR signals, J. Magn. Reson. 79, 190-196.
  • Mandelshtam V.A. (2001) FDM: the filter diagonalization method for data processing in NMR experiments, Prog. NMR Spectr. 38, 159-196. [CrossRef]
  • Rao S., Pearlman W. (1996) Analysis of linear prediction, coding, and spectral estimation from subbands, IEEE Trans. Inf. Theory 42, 4, 1160-1178. [CrossRef]
  • Stoica P., Nordsjö A.E. (1997) Subspace-based frequency estimation in the presence of moving-average noise using decimation, Signal Process. 63, 211-220. [CrossRef]
  • Djermoune E.-H., Tomczak M., Mutzenhardt P. (2004) An adaptive subband decomposition approach for automatic analysis of NMR data, J. Magn. Reson. 169, 1, 73-84. [CrossRef] [PubMed]
  • Dologlou I., Van Huffel S., van Ormondt D. (1998) Frequency-selective MRS data quantification with frequency prior knowledge, J. Magn. Reson. 130, 2, 238-243. [CrossRef] [PubMed]
  • Mandelshtam V.A., Taylor H.S., Shaka A.J. (1998) Application of the filter diagonalization method to one- and two-dimensional NMR spectra, J. Magn. Reson. 133, 304-312. [CrossRef] [PubMed]
  • Romano R., Motta A., Camassa S., Pagano C., Santini M.T., Indovina P.L. (2002) A new time-domain frequency-selective quantification algorithm, J. Magn. Reson. 155, 2, 226-235. [CrossRef] [PubMed]
  • Stoica P., Sandgren N., Selén Y., Vanhamme L., Van Huffel S. (2003) Frequency-domain method based on the singular value decomposition for frequency-selective NMR spectroscopy, J. Magn. Reson. 165, 1, 80-88. [CrossRef] [PubMed]
  • Tomczak M., Djermoune E.-H. (2002) A subband ARMA modeling approach to high-resolution NMR spectroscopy, J. Magn. Reson. 158, 86-98. [CrossRef]
  • Vanhamme L., Sundin T., Van Hecke P., Van Huffel S., Pintelon R. (2000) Frequency-selective quantification of biomedical magnetic resonance spectroscopy data, J. Magn. Reson. 143, 1, 1-16. [CrossRef] [PubMed]
  • Sandgren N., Selén Y., Stoica P., Li J. (2004) Parametric methods for frequency-selective MR spectroscopy, J. Magn. Reson. 168, 259-272. [CrossRef] [PubMed]
  • Coifman R.R., Wickerhauser M.V. (1992) Entropy-based algorithms for best basis selection, IEEE Trans. Inf. Theory 38, 2, 713-718. [CrossRef]
  • Donoho D.L., Johnstone I.M. (1994) Ideal denoising in an orthonormal basis chosen from a library of bases. Technical Report 461, Dept. of Statistics, Stanford University, Sept.
  • Meyer F.G., Averbuch A., Strömberg J.-O. (2000) Fast adaptive wavelet packet image compression. IEEE Trans. Image Process. 9, 5, 792-800. [CrossRef] [PubMed]
  • Moulin P. (1996) Signal estimation using adapted tree-structured bases and the MDL principle, IEEE Int. Symp. Time-Frequency and Time-Scale Analysis, Paris, 18-21 June, pp. 141-143.
  • Mainardi L.T., Origgi D., Lucia P., Scotti G., Cerutti S. (2002) A wavelet packets decomposition algorithm for quantification of in vivo 1H-MRS parameters, Med. Eng. Phys. 24, 201-208. [CrossRef] [PubMed]
  • van den Bran den Lambrecht C., Karrakchou M. (1995) Wavelet packet-based high-resolution spectral estimation, Signal Process. 47, 135-144. [CrossRef]
  • Tomczak M., Djermoune E.-H., Mutzenhardt P. (2007) High-resolution MR spectroscopy via adaptive sub-band decomposition, Castleman B.C. (ed.), New Research on Magnetic Resonance Imaging, Novascience Publishers, Chap. 9, pp. 241-289.
  • Priestley M.B. (1989) Spectral analysis and time series, Academic Press, San Diego, CA.
  • Drouiche K. (2000) A new test for whiteness, IEEE Trans. Signal Process. 48, 7, 1864-1871. [CrossRef] [MathSciNet]
  • Djermoune E.-H. (2003) Estimation des paramètres de sinusoïdes amorties par décomposition en sous-bandes adaptative. Application à la spectroscopie RMN, PhD thesis, Université Henri Poincaré, Nancy 1, France.
  • Djermoune E.-H., Tomczak M. (2004) An adapted filterbank for frequency estimation, Proc. 12th European Signal Image Process. Conf. EUSIPCO 2004, Vienna, Austria, 6-10 Sept., pp. 2171-2174.
  • Djermoune E.-H., Brie D., Tomczak M. (2009) A subband algorithm for estimating the parameters of two-dimensional exponential signals, Proc. European Signal Process. Conf., EUSIPCO 2004, Glasgow, Scotland, 25-28 Aug.
  • Djermoune E.-H., Tomczak M. (2009) Perturbation analysis of subspace-based methods in estimating a damped complex exponential, IEEE Trans. Signal Process. 57, 11, 4558-4563. [CrossRef] [MathSciNet]
  • Reddy V.U., Biradar L.S. (1993) SVD-based information theoretic criteria for detection of the number of damped/undamped sinusoids and their performance analysis, IEEE Trans. Signal Process. 41, 2872-2881. [CrossRef]
  • Denoyer L.K., Dodd J.G. (1991) Maximum likelihood deconvolution for spectroscopy and chromatography, Amer. Lab. 23, 19-22.
  • Jacques L., Duval L., Chaux C., Peyré G. (2011) A panorama on multiscale geometric representations, intertwining spatial, directional and frequency selectivity, Signal Process. 91, 12, 2699-2730. [CrossRef]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.