Publications by Topic

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Topics

Non-convex Penalties for Sparse Least Squares

  1. Sparse regularization via convex analysis.
    (doi) (pdf) (software) (Code Ocean, Matlab) (Code Ocean, Python)
    I. Selesnick.
    IEEE Transactions on Signal Processing, 65(17):4481-4494, September 2017.
  2. Sparse signal approximation via non-separable regularization.
    (doi) (pdf)
    I. Selesnick and M. Farshchian.
    IEEE Transactions on Signal Processing, 65(10):2561-2575, May 2017
  3. Sparsity amplified.
    (pdf) (slides) (software) (doi)
    I. Selesnick.
    IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), March 2017.
  4. Total variation denoising via the Moreau envelope.
    (pdf) (software) (doi) (arXiv)
    I. Selesnick.
    IEEE Signal Processing Letters, 24(2):216-220, February 2017.
  5. Nonconvex nonsmooth optimization via convex-nonconvex majorization-minimization.
    (doi) (read online)
    A. Lanza, S. Morigi, I. Selesnick, and F. Sgallari.
    Numerische Mathematik, vol. 136, no. 2, pp. 343-381, 2017.
  6. Enhanced sparsity by non-separable regularization.
    (IEEE) (arXiv) (pdf) (software)
    I. W. Selesnick and I. Bayram.
    IEEE Transactions on Signal Processing, 64(9):2298-2313, May 2016
  7. Enhanced low-rank matrix approximation.
    (IEEE) (arXiv) (pdf) (software)
    A. Parekh and I. W. Selesnick.
    IEEE Signal Processing Letters, 23(4):493-497, April 2016
  8. Convex fused lasso denoising with non-convex regularization and its use for pulse detection.
    (doi) (arXiv)
    A. Parekh and I. W. Selesnick.
    IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 12 Dec. 2015.
  9. Convex denoising using non-convex tight frame regularization.
    (IEEE) (arXiv)
    A. Parekh and I. W. Selesnick.
    IEEE Signal Processing Letters, 22(10):1786-1790, October 2015.
  10. Artifact-free wavelet denoising: non-convex sparse regularization, convex optimization.
    (Preprint pdf) (IEEE) (software) (Code Ocean)
    Y. Ding and I. W. Selesnick.
    IEEE Signal Processing Letters, 22(9):1364-1368, September 2015.
  11. Convex 1-D total variation denoising with non-convex regularization.
    (pdf) (IEEE) (software)
    I. W. Selesnick, A. Parekh, and I. Bayram.
    IEEE Signal Processing Letters, 22(2):141-144, February 2015.
  12. Group-sparse signal denoising: non-convex regularization, convex optimization
    (pdf) (doi) (Code Ocean) (more)
    P.-Y. Chen and I. W. Selesnick.
    IEEE Trans. on Signal Processing, 62(13):3464-3478, July 1, 2014.
  13. Sparse signal estimation by maximally sparse convex optimization (pdf) (arXiv).
    I. W. Selesnick and I. Bayram.
    IEEE Trans. Signal Processing, 62(5):1078-1092, March 2014.

Applications of Sparse Signal Processing

Imaging

  1. Efficient and robust image restoration using multiple-feature L2-relaxed sparse analysis priors.
    (IEEE) (software)
    J. Portilla, A. Tristan-Vega, and I. W. Selesnick.
    IEEE Transactions on Image Processing, 24(12):5046-5059, December 2015.
  2. Three dimensional data-driven multiscale atomic representation of optical coherence tomography.
    (IEEE)
    R. Kafieh, H. Rabbani, and I. Selesnick.
    IEEE Transactions on Medical Imaging, 34(5):1042-1062, May 2015.

Machine fault monitoring

  1. Repetitive transients extraction algorithm for detecting bearing faults.
    (doi)
    W. He, Y. Ding, Y. Zi, and I. W. Selesnick.
    Mechanical Systems and Signal Processing. Volume 84, Part A, 1 February 2017, pages 227-244.
  2. Detection of faults in rotating machinery using periodic time-frequency sparsity.
    (doi) (arXiv)
    Y. Ding, W. He, B. Chen, Y. Zi, and I. W. Selesnick.
    Journal of Sound and Vibration. Available online 21 July 2016.
  3. Sparsity-based algorithm for detecting faults in rotating machines.
    (doi) (arXiv)
    W. He, Y. Ding, Y. Zi, and I. W. Selesnick.
    Mechanical Systems and Signal Processing. Volumes 72-73, pages 46-64, May 2016.

Biomedical signal analysis

  1. Convex fused lasso denoising with non-convex regularization and its use for pulse detection.
    (doi) (arXiv)
    A. Parekh and I. W. Selesnick.
    IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 12 Dec. 2015.
  2. Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization.
    (Science Direct) (Preprint pdf)
    A. Parekh, I. W. Selesnick, D. M. Rapoport, and I. Ayappa.
    Journal of Neuroscience Methods, volume 251, pages 37-46, 15 August 2015.
  3. Sleep spindle detection using time-frequency sparsity (pdf).
    A. Parekh, I. W. Selesnick, D. M. Rapoport, I. Ayappa.
    IEEE Signal Processing in Medicine and Biology Symposium (SPMB), December 2014.
  4. ECG enhancement and QRS detection based on sparse derivatives (pdf).
    X. Ning and I. W. Selesnick.
    Biomedical Signal Processing and Control. vol. 8, no. 6, pp 713-723, November 2013.

Baseline estimation / Peak fitting

  1. Chromatogram baseline estimation and denoising using sparsity (BEADS)
    (Elsevier) (pdf preprint)
    X. Ning, I. W. Selesnick, and L. Duval
    Chemometrics and Intelligent Laboratory Systems, 139:156-167, 15 December 2014.
    Software: BEADS_toolbox.zip.

Arifact reduction

  1. Sparsity-based correction of exponential artifacts.
    (doi) (pdf)
    Y. Ding and I. W. Selesnick.
    Signal Processing. 120:236-238, March 2016.
  2. Transient artifact reduction algorithm (TARA) based on sparse optimization.
    I. W. Selesnick, H. L. Graber, Y. Ding, T. Zhang, and R. L. Barbour.
    IEEE Trans. on Signal Processing. 62(24):6596-6611, 15 December 2014.

Radar

  1. Mitigation of wind turbine clutter for weather radar by signal separation.
    (IEEE) (pdf)
    F. Uysal, I. Selesnick, and B. M. Isom.
    IEEE Transactions on Geoscience and Remote Sensing. 54(5):2925-2934, May 2016.
  2. Dynamic clutter mitigation using sparse optimization (pdf) (IEEE)
    F. Uysal, I. Selesnick, U. Pillai, and B. Himed.
    IEEE Aerospace and Electronic Systems Magazine, 29(7):37-49, July 2014.
  3. Doppler-streak attenuation via oscillatory-plus-transient decomposition of IQ data. (pdf).
    I. W. Selesnick, K. Y. Li, S. U. Pilla, and B. Himed.
    Int. Conf. Radar Sys., Oct. 2012.
    Video presentation by B. Himed

Q-Factor-based Signal decomposition

  1. Resonance-based signal decomposition: a new sparsity-enabled signal analysis method.
    I. W. Selesnick.
    Signal Processing, 2010, doi:10.1016/j.sigpro.2010.10.018
  2. Sparse signal representations using the tunable Q-factor wavelet transform.
    I. W. Selesnick. In Proc. SPIE 8138 (Wavelets and Sparsity XIV), August 2011.
  3. Wavelet transform with tunable Q-factor (project page).
    I. W. Selesnick.
    IEEE Trans. on Signal Processing. 59(8):3560-3575, August 2011.
  4. A new sparsity-enabled signal separation method based on signal resonance.
    I. W. Selesnick.
    In Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), March 2010. Presentation slides.
  5. Oscillatory + transient signal decomposition using overcomplete rational-dilation wavelet transforms.
    I. W. Selesnick and I. Bayram.
    In Proceedings of SPIE, volume 7446 (Wavelets XIII), August 2-4, 2009.

Wavelets

  1. Artifact-free wavelet denoising: non-convex sparse regularization, convex optimization.
    (Preprint pdf) (IEEE) (software) (Code Ocean)
    Y. Ding and I. W. Selesnick.
    IEEE Signal Processing Letters, 22(9):1364-1368, September 2015.
  2. Sparse signal representations using the tunable Q-factor wavelet transform.
    I. W. Selesnick. In Proc. SPIE 8138 (Wavelets and Sparsity XIV), August 2011.
  3. Wavelet transform with tunable Q-factor (project page).
    I. W. Selesnick.
    IEEE Trans. on Signal Processing. 59(8):3560-3575, August 2011.
  4. Emerging applications of wavelets: A review.
    A. N. Akansu and W. A. Serdijn and I. W. Selesnick.
    Physical Communication. 2009. doi:10.1016/j.phycom.2009.07.001
  5. Signal restoration with overcomplete wavelet transforms: comparison of analysis and synthesis priors.
    I. W. Selesnick and M. A. T. Figueiredo.
    In Proceedings of SPIE, volume 7446 (Wavelets XIII), August 2-4, 2009.
  6. Frequency-domain design of overcomplete rational-dilation wavelet transforms.
    I. Bayram and I. W. Selesnick.
    IEEE Trans. on Signal Processing. 57(8):2957-2972, August 2009.
  7. Overcomplete discrete wavelet transforms with rational dilation factors.
    I. Bayram and I. W. Selesnick.
    IEEE Trans. on Signal Processing. 57(1):131-145, January 2009.
  8. On the frame bounds of iterated filter banks.
    I. Bayram and I. W. Selesnick.
    Applied and Computational Harmonic Analysis. 27(2):255-262, September 2009.
  9. On the dual-tree complex wavelet packet and M-band transforms.
    I. Bayram and I. W. Selesnick.
    IEEE Trans. on Signal Processing, 56(6):2298-2310, June 2008. Software (zip file).
  10. Optimization of symmetric self-Hilbertian filters for the dual-tree complex wavelet transform.
    B. Dumitrescu, I. Bayram, and I. W. Selesnick.
    IEEE Signal Processing Letters, 15:146-149, January 1, 2008.
  11. Wavelets, a modern tool for signal processing.
    I. W. Selesnick. Physics Today. 60(10):78-79, October 2007. Expanded version.
  12. Video coding using 3-D dual-tree wavelet transform.
    B. Wang, Y. Wang, I. Selesnick, and A. Vetro.
    EURASIP Journal on Image and Video Processing. Volume 2007 (2007), Article ID 42761.
  13. A higher-density discrete wavelet transform.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 54(8):3039-3048, August 2006.
  14. The dual-tree complex wavelet transform - A coherent framework for multiscale signal and image processing.
    I. W. Selesnick, R. G. Baraniuk, and N. Kingsbury.
    IEEE Signal Processing Magazine, 22(6):123-151, November 2005.
  15. Symmetric nearly shift-invariant tight frame wavelets.
    A. F. Abdelnour and I. W. Selesnick.
    IEEE Trans. on Signal Processing, 53(1):231-239, January 2005.
  16. Symmetric nearly orthogonal and orthgonal nearly symmetric wavelets.
    A. F. Abdelnour and I. W. Selesnick.
    The Arabian Journal for Science and Engineering, vol. 29, num. 2C, pp:3-16, December 2004.
  17. Symmetric wavelet tight frames with two generators.
    I. W. Selesnick and A. F Abdelnour.
    Applied and Computational Harmonic Analysis, 17(2):211-225, 2004. Includes MATLAB software toolbox.
  18. The double-density dual-tree DWT.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 52(5):1304-1314, May 2004.
  19. Grobner bases and wavelet design.
    J. Lebrun and I. Selesnick.
    Journal of Symbolic Computing, 37(2):227-259, February, 2004. (Invited paper in special issue on Computer Algebra and Signal Processing).
  20. Complex wavelet transforms with allpass filters.
    F. C. Fernandes, I. W. Selesnick, R. L. C. van Spaendonck, and C. S. Burrus.
    Signal Processing, 83(5):1689-1706, 2003.
  21. The design of approximate Hilbert transform pairs of wavelet bases.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 50(5):1144-1152, May 2002.
  22. Hilbert transform pairs of wavelet bases.
    I. W. Selesnick.
    IEEE Signal Processing Letters, 8(6):170-173, June 2001.
  23. The Double Density DWT.
    I. W. Selesnick. In A. Petrosian and F. G. Meyer, editors, Wavelets in Signal and Image Analysis: From Theory to Practice. Kluwer, 2001.
  24. Smooth wavelet tight frames with zero moments.
    I. W. Selesnick.
    Applied and Computational Harmonic Analysis, 10(2):163-181, March 2001.
  25. Balanced multiwavelet bases based on symmetric FIR filters.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 48(1):184-191, January 2000.
  26. Interpolating multiwavelet bases and the sampling theorem.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 47(6):1615-1621, June 1999.
  27. The Slantlet transform.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 47(5):1304-1313, May 1999.
  28. Balanced GHM-like multiscaling functions.
    I. W. Selesnick.
    IEEE Signal Processing Letters, 6(5):111-112, May 1999.
  29. Multiwavelet bases with extra approximation properties.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 46(11):2998-12909, November 1998.
  30. Explicit formulas for orthogonal IIR wavelets.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 46(4):1138-1141, April 1998.

Denoising

  1. Sparsity-assisted signal smoothing (revisited).
    (pdf) (slides) (doi) (software) (Code Ocean)
    I. Selesnick.
    IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), March 2017.
  2. Total variation denoising via the Moreau envelope.
    (pdf) (software) (doi) (arXiv)
    I. Selesnick.
    IEEE Signal Processing Letters, 24(2):216-220, February 2017.
  3. Generalized total variation: tying the knots.
    (IEEE) (Preprint pdf) Software: GTVknots_software.zip
    I. W. Selesnick.
    IEEE Signal Processing Letters, 22(11):2009-2013, November 2015.
  4. Artifact-free wavelet denoising: non-convex sparse regularization, convex optimization.
    (Preprint pdf) (IEEE) (software) (Code Ocean)
    Y. Ding and I. W. Selesnick.
    IEEE Signal Processing Letters, 22(9):1364-1368, September 2015.
  5. Sparsity-assisted signal smoothing (SASS) (project page).
    I. W. Selesnick.
    Excursions in Harmonic Analysis, Volume 4. R. Balan et al., editors, Springer-Birkhauser., 2015.
  6. Three dimensional data-driven multiscale atomic representation of optical coherence tomography.
    (IEEE)
    R. Kafieh, H. Rabbani, and I. Selesnick.
    IEEE Transactions on Medical Imaging, 34(5):1042-1062, May 2015.
  7. Convex 1-D total variation denoising with non-convex regularization.
    (pdf) (IEEE) (software)
    I. W. Selesnick, A. Parekh, and I. Bayram.
    IEEE Signal Processing Letters, 22(2):141-144, February 2015.
  8. Group-sparse signal denoising: non-convex regularization, convex optimization
    (pdf) (doi) (Code Ocean) (more)
    P.-Y. Chen and I. W. Selesnick.
    IEEE Trans. on Signal Processing, 62(13):3464-3478, July 1, 2014.
  9. Simultaneous low-pass filtering and total variation denoising (project page).
    I. W. Selesnick, H. L. Graber, D. S. Pfeil, and R. L. Barbour.
    IEEE Trans. Signal Processing, 62(5):1109-1124, March 2014.
  10. Translation-invariant shrinkage/thresholding of group sparse signals (project page).
    P.-Y. Chen and I. W. Selesnick.
    Signal Processing. vol. 94, pp 476-489, January 2014.
  11. Total variation denoising with overlapping group sparsity (project page).
    I. W. Selesnick and P.-Y. Chen.
    IEEE Int. Conf. Acoust., Speech, Signal Proc. (ICASSP), May 2013.
  12. Polynomial smoothing of time series with additive step discontinuities (project page).
    I. W. Selesnick, S. Arnold, and V. R. Dantham.
    IEEE Trans. Signal Processing, 60(12):6305-6318, Dec. 2012.
  13. The estimation of spherically-contoured Laplace random vectors in additive white Gaussian noise.
    I. W. Selesnick.
    IEEE Trans. on Signal Processing, 56(8):3482-3496, August 2008.
  14. Bayesian estimation of Bessel K form random vectors in AWGN.
    P. Khazron and I. W. Selesnick.
    IEEE Signal Processing Letters, 15:261-264, February 8, 2008.
  15. An elliptically contoured exponential mixture model for wavelet based image denoising.
    F. Shi and I. W. Selesnick.
    Applied and Computational Harmonic Analysis. 23(1):131-151, July 2007.
  16. Optimization of dynamic measurement of receptor kinetics by wavelet denoising.
    N. M. Alpert, A. Reilhac, T. C. Chio, I. Selesnick.
    NeuroImage, 30(2):444-51, April 2006.
  17. Bivariate shrinkage with local variance estimation.
    L. Sendur and I. W. Selesnick.
    IEEE Signal Processing Letters, 9(12):438-441, December 2002.
  18. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency.
    L. Sendur and I. W. Selesnick.
    IEEE Trans. on Signal Processing. 50(11):2744-2756, November 2002.

Digital Filters

  1. Maximally flat lowpass digital differentiators.
    I. W. Selesnick.
    IEEE Trans. on Circuits and Systems II. 49(3):219-223, March 2002.
  2. Low-pass filters realizable as all-pass sums: design via a new flat delay filter.
    I. W. Selesnick.
    IEEE Trans. on Circuits and Systems II, 46(1):40-50, January 1999.
  3. A modified algorithm for constrained least square design of multiband FIR filters without specified transition bands.
    I. W. Selesnick, M. Lang, and C. S. Burrus.
    IEEE Trans. on Signal Processing, 46(2):497-501, February 1998.
  4. Generalized digital Butterworth filter design.
    I. W. Selesnick and C. S. Burrus.
    IEEE Trans. on Signal Processing, 46(6):1688-1694, June 1998.
  5. Maximally flat low-pass FIR filters with reduced delay.
    I. W. Selesnick and C. S. Burrus.
    IEEE Trans. on Circuits and Systems II, 45(1):53-68, January 1998.
  6. Exchange algorithms that complement the Parks-McClellan algorithm for linear phase FIR filter design.
    I. W. Selesnick and C. S. Burrus.
    IEEE Trans. on Circuits and Systems II, 44(2):137-142, February 1997.
  7. Constrained least square design of FIR filters without specified transition bands.
    I. W. Selesnick, M. Lang, and C. S. Burrus.
    IEEE Trans. on Signal Processing, 44(8):1879-1892, August 1996.
  8. Constrained least square design of 2D FIR filters.
    M. Lang, I. W. Selesnick, and C. S. Burrus.
    IEEE Trans. on Signal Processing, 44(5):1234-1241, May 1996.
  9. Iterative reweighted least-squares design of FIR filters.
    C. S. Burrus, J. A. Barreto, and I. W. Selesnick.
    IEEE Trans. on Signal Processing, 42:2926-2936, November 1994.

More

  1. A parametric model for saccadic eye movement.
    (pdf) (doi) (software) (Code Ocean, Matlab) (Code Ocean, Python)
    W. Dai, I. Selesnick, J.-R. Rizzo, J. Rucker, and T. Hudson.
    IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 3 Dec. 2016.
  2. A diagonally-oriented DCT-like 2D block transform.
    I. W. Selesnick and O. G. Guleryuz.
    In Proc. SPIE 8138 (Wavelets and Sparsity XIV), August 2011.
  3. A subband adaptive iterative shrinkage/thresholding algorithm.
    I. Bayram and I. W. Selesnick.
    IEEE Trans. on Signal Processing. 58(3):1131-1143, March 2010.
  4. Solving the optimal PWM problem for single-phase inverters.
    D. Czarkowski, D. Chudnovsky, G. Chudnovsky, and I. W. Selesnick.
    IEEE Trans. on Circuits and Systems II, 49(4):465-475, April, 2002.
  5. Automatic generation of prime length FFT programs.
    I. W. Selesnick and C. S. Burrus.
    IEEE Trans. on Signal Processing, 44(1):14-24, January 1996.