Recent Publications

Journal Publications

Conference Papers

Books

Book Chapters

Journal Publications

• Y. Seo, B. Moraffah, L. M. Kaplan and A. Papandreou-Suppappola, “Dependent Bayesian nonparametric learning for tracking in unknown time-varying environments with merged measurements,” IEEE Transactions on Signal Processing, to be submitted, 2026
• A. Eastman and A. Papandreou-Suppappola, “Real time reactivity reinforcement learning with agile sensing for tracking in unknown spectrum sharing environments,” IEEE Transactions on Signal Processing, to be submitted 2026.
• N. Velez-Cruz and A. Papandreou-Suppappola, “Bayesian learning of nonlinear gene regulatory networks with
  switching architectures”, Frontiers in Signal Processing, vol. 4, 2024 doi.org/10.3389/frsip.2024.1323538
• P. Weber and A. Papandreou-Supppappola, “Analog isolated multilevel quantizer for voltage sensing while main-
  taining galvanic isolation,” arXiv:2402.18758 [eess.SP], February 2024
• B. Moraffah and A. Papandreou-Suppappola, “Multiple object tracking with dynamic dependencies using Bayesian nonparametric modeling,” Sensors, vol. 22, January 2022 , doi: 10.3390/s22010388
• B. Moraffah and A. Papandreou-Suppappola, “Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking,” arXiv:2004.10798 [cs.LG], April 2020.
• J. Zhou, A. Papandreou-Suppappola, and C.Chakrabarti, “Parallel Gibbs sampler for wavelet-based Bayesian compressive sensing with high reconstruction accuracy,” Journal of Signal Processing Systems, to appear 2020.
• F. Solis and Antonia Papandreou-Suppappola, “Power dissipation and surface charge in EEG: application to eigenvalue structure of integral operators,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 5, pp. 1232-1242, May 2020.
• J. A. Northrop and A. Papandreou-Suppappola, “Computationally efficient estimation of compound K-
  distributed sea clutter in thermal noise and its application to sea echo reflectivity observations,” IEEE Transactions on Aerospace and Electronic Systems, vol. 56, pp. 2340-2350, June 2020
• J. S. Kota and A. Papandreou-Suppappola, ” Joint design of transmit waveforms for object tracking in coexisting multimodal sensing systems,” Sensors, Special Issue Multiple Object Tracking: Making Sense of the Sensors, Sensors vol. 19, issue 8, https://doi.org/10.3390/s19081753, doi:10.3390/s19081753, 2019.
• S. Chakraborty, A. Banerjee, S. K. S. Gupta, P. R. Christensen, and A. Papandreou-Suppappola, “Estimation of dynamic land cover parameters for change detection in MODIS time-series, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 6, pp. 1769-1776, June 2018.
• M. Zhou, J. J. Zhang, A. Papandreou-Suppappola, “Multiple target tracking in urban environments,” IEEE Transactions in Signal Processing, vol. 64, pp. 1270-1279, March 2016.
• A. ElMoslimany, M. Zhou, T. M. Duman, A. Papandreou-Suppappola, “An underwater acoustic communications scheme exploiting biological sounds,” Wireless Communications and Mobile Computing, vol. 16, pp. 2194-2211, October 2016.
• S. P. Ebenezer and A. Papandreou-Suppappola, “Low RCS target tracking in estimated rapidly-varying sea clutter using a Kronecker product approximation algorithm,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, pp. 1639-1649, December 2015.
• S. P. Ebenezer and A.Papandreou-Suppappola, “Generalized recursive track-before-detect with proposal partitioning for tracking varying number of multiple targets in low SNR,” IEEE Transactions on Signal Processing, vol. 64, pp. 2819-2834, June 2016.
• B. O’Donnell, A. Maurer, A. Papandreou-Suppappola, P. Stafford, “Time-frequency analysis of peptide microarray data: Application to brain cancer immunosignatures,” Cancer Informatics, vol. 14, pp. 219-233, June 2015.
• M. Banavar, J. J. Zhang, B. Chakraborty, H. Kwon, Y. Li, H. Jiang, A. Spanias, C. Tepedelenlioglu, C. Chakrabarti, A. Papandreou-Suppappola, “An overview of recent advances on distributed and agile sensing algorithms and implementation,” Digital Signal Processing, pp. 1-14, vol. 39, April 2015.
• D. Chakraborty, N. Kovvali, A. Papandreou-Suppappola, and A. Chattopadhyay, “An adaptive learning damage estimation method for structural health monitoring,” Journal of Intelligent Material, Systems and Structures, (1045389X14522531, pp. 1-9, first published on April 14, 2014) pp. 125–143, vol. 26, January 2015.
• S. Edla, N. Kovvali, and A. Papandreou-Suppappola, “Electrocardiogram signal modeling with adaptive parameter estimation and cardiac classification using sequential Bayesian methods,” IEEE Transactions on Signal Processing, pp. 2667–2680, vol. 62, no. 10, May 2014.
• M. Seaver, A. Chattopadhyay, A. Papandreou-Suppappola, S. B. Kim, N. Kovvali, C. R. Farrar, M. H. Triplett and M. M. Derriso, “Workshop on transitioning structural health monitoring technology to military platforms,” Journal of Intelligent Material, Systems and Structures, vol. 24, no. 17, pp. 2063-2073, November 2013.
• L. Miao, J. J. Zhang, C. Chakrabarti, and A. Papandreou-Suppappola, “Efficient Bayesian tracking of multiple sources of neural activity: Algorithms and real-time FPGA implementation,” IEEE Transactions on Signal Processing, vol. 61, pp. 633-647, February 2013.
• I. Kyriakides, D. Morrell and A. Papandreou-Suppappola, “Adaptive highly localized waveform design for multiple target tracking,” EURASIP Journal on Advances in Signal Processing, vol. 180, pp. 1-17, August 2012.
• L. Miao, S. Michael, N. Kovvali, C. Chakrabarti, and A. Papandreou-Suppappola, “Multi-source neural activity estimation and sensor scheduling: Algorithms and hardware implementation,” Journal of Signal Processing Systems, vol. 70, pp. 145-162, February 2013.
• L. Miao, J. J. Zhang, C. Chakrabarti, A. Papandreou-Suppappola, “Algorithm and parallel implementation of particle filtering and its use in waveform-agile sensing,” Journal of Signal Processing Systems, vol. 65, pp. 211-227, November 2011. (invited).
• L. Ravichandran, A. Papandreou-Suppappola, Z. Lacroix, A. Spanias, and C. Legendre, “Waveform Mapping and Time-Frequency Processing of DNA and Protein Sequences,” IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4210-4224, September 2011.
• N. F. Josso, J. J. Zhang, A. Papandreou-Suppappola, C. Ioana, T. M. Duman, “Nonstationary system analysis methods for underwater communications,” EURASIP Journal on Advances in Signal Processing, Special Issue on: Recent Advances in Theory and Methods for Nonstationary Signal Analysis, vol. 11, article ID 807472, 14 pages, 2011.

Conference Papers

• A. Eastman and A. Papandreou-Suppappola, “Action selection methods to explore spectrum sharing environments using double Q-learning-while-tracking,” Asilomar Conference on Signals, Systems and Computers, 2025
• A. Eastman, Y. Seo and A. Papandreou-Suppappola, “Dynamic adaptive inference with reinforcement learning in time-varying future generation sensing environments,” Asilomar Conference on Signals, Systems and Computers, 2025
• Y. Seo, L. M. Kaplan and A. Papandreou-Suppappola, “Tracking multiple objects with dynamic dependencies and merged measurements,” Asilomar Conference on Signals, Systems and Computers, 2025
• P. Zhang, C. Chakrabarti and A. Papandreou-Suppappola, “Feature-based learning for computationally efficient estimation in high-frequency time-varying spectrum-sharing environments,” Asilomar Conference on Signals, Systems and Computers, 2025
• Y. Zhang, B. Moraffah and A. Papandreou-Suppappola, “Interference mitigation in spectrum sharing environments using time-frequency processing and feature clustering,” Asilomar Conference on Signals, Systems and Computers, pp. 514-518, 2022.
• N. Velez-Cruz, B. Moraffah and A. Papandreou-Suppappola, “Switching Langevin dynamics for gene regulatory networks,” Asilomar Conference on Signals, Systems and Computers, pp. 1316-1320, 2022.
• N. Velez-Cruz, B. Moraffah and A. Papandreou-Suppappola, “Sequential Bayesian inference using stochastic models of gene regulatory networks,” Asilomar Conference on Signals, Systems and Computers, pp. 568-572, 2021.
• J. Ikram, A. Chattopadhyay and A. Papandreou-Suppappola, “Synchrosqueezing transform matched to nonlinear group delay for mode estimation of ultrasonic guided waves,” Asilomar Conference on Signals, Systems and Computers, pp. 558-562, 2021.
• J. Ikram, A. Chattopadhyay and A. Papandreou-Suppappola, “Unsupervised mode extraction and group velocity estimation for ultrasonic guided waves propagating in dispersive material,” Asilomar Conference on Signals, Systems and Computers, pp. 343-347, 2020.
• B. Moraffah, C. Brito, B. Venkatesh and A. Papandreou-Suppappola “Use of hierarchical Dirichlet processes to integrate dependent observations from multiple disparate sensors for tracking,” International Conference on Information Fusion, 2019.
• J. Northrop and A. Papandreou-Suppappola, “Asymptotically efficient estimation of sea clutter intensity model parameters using log-based moments,” Asilomar Conference on Signals, Systems and Computers, November 2019.
• S. Das, B. Moraffah, S. K. Gupta and A. Papandreou-Suppappola, “Bradycardia prediction in preterm infants using nonparametric kernel density estimation,” Asilomar Conference on Signals, Systems and Computers, November 2019.
• B. Moraffah, A. Papandreou-Suppappola and M. Rangaswamy, “Nonparametric Bayesian methods and the dependent Pitman-Yor process for modeling evolution in multiple object tracking,” International Conference on Information Fusion, 2019.
• B. Moraffah and A. Papandreou-Suppappola, “Random infinite tree and dependent Poisson diffusion process for nonparametric Bayesian modeling in multiple object tracking,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5217-5221, May 2019.
• J. Ikram, A. Papandreou-Suppappola, G. Lia, A. Chattopadhyaya and P. Malatkarb, “Guided wave based inspection of integrated circuit packages using the time-frequency synchrosqueezing transform,” SPIE Smart Structures and Nondestructive Evaluation, vol. 1093, March 2019.
• S. Chakraborty, A. Banerjee, S. K. S Gupta, P. R. Christensen, A. Papandreou-Suppappola, “Multitemporal analysis of image time-series for land cover change detection and unsupervised classification of change event using spectral analysis”, American Geophysical Union (AGU) Fall Meeting, Abstract H31H-2003, December 2018.
• B. Moraffah and A. Papandreou-Supppappola, “Dependent Dirichlet process modeling and identity learning for multiple object tracking,” Asilomar Conference on Signals, Systems and Computers, pp. 1762-1766, October 2018.
• J. Northrop and A. Papandreou-Suppappola, “Estimation of compound K-distribution modeling parameters of sea clutter with unknown thermal noise power,” Asilomar Conference on Signals, Systems and Computers, pp. 2091-2095, October 2018.
• V. S. Gattani, J. S. Kota and A. Papandreou-Suppappola “Time-frequency separation of matched-waveform signatures of coexisting multimodal systems,” Asilomar Conference on Signals, Systems and Computers, pp. 2086-2090, October 2018.
• S. Chakraborty, A. Banerjee, S. Gupta, P. Christensen, A. Papandreou-Suppappola, “Automated land cover change detection and mapping from hidden parameter estimates of normalized diverse vegetation index (NDVI) time-series,” American Geophysical Union (AGU) Fall Meeting, December 2017 (abstract).
• F. Solis and A. Papandreou-Suppappola, “Multiple interface brain and head models for EEG: A surface charge approach,” Asilomar Conference on Signals, Systems and Computers, pp. 1323-1327, November 2017.
• J. S. Kota and A. Papandreou-Suppappola, “SNR threshold region prediction via singular value decomposition of the Barankin bound kernel,” Asilomar Conference on Signals, Systems and Computers, pp. 762-766, November 2017.
• S. Chakraborty, A. Banerjee, S. Gupta, A. Papandreou-Suppappola, P. Christensen, “Estimation of dynamic parameters of MODIS NDVI time series nonlinear model using particle filtering, International Geoscience and Remote Sensing Symposium, pp. 1091-1094, July 2017.
• F. Solis and A. Papandreou-Suppappola, “Surface charge method for the forward EEG problem,” Asilomar Conference on Signals, Systems and Computers, November 2016.
• J. Kota, G. M. Jacyna and A. Papandreou-Suppappola, “Nonstationary signal design for coexisting radar and communications systems,” Asilomar Conference on Signals, Systems and Computers, pp. 549-553, November 2016.
• A. Maurer, S. Hanrahan, A. O. Hebb and A. Papandreou-Suppappola, “Suppression of neurostimulation artifacts and adaptive clustering of Parkinson’s patients behavioral tasks using EEG,” Asilomar Conference on Signals, Systems and Computers, pp. 1646-1650, November 2016.
• F. Solis, A. Maurer, J. Jiang, A. Papandreou-Suppappola, “Adaptive EEG artifact suppression using Gaussian mixture modeling,” Asilomar Conference on Signals, Systems and Computers, pp. 607-611, November 2015.
• S. P. Ebenezer and A. Papandreou-Suppappola, “Multiple target track-before-detect in compound Gaussian clutter,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2539-2543, April 2015.
• S. P. Ebenezer and A. Papandreou-Suppappola, “Estimation of rapidly varying sea clutter using nearest Kronecker product approximation,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3686-3690, April 2015.
• N. Zaker, A. Dutta, A. Maurer, J. J. Zhang, S. Hanrahan, A. O. Hebb, N. Kovvali, A. Papandreou-Suppappola, “Adaptive learning of behavioral tasks for patients with Parkinson’s disease using signals from deep brain stimulation,” Asilomar Conference on Signals, Systems and Computers, pp. 208-212, November 2014.
• B. Paul, A. Papandreou-Suppappola, and D. W. Bliss, “Radar tracking waveform design in continuous space and optimization selection using differential evolution,” Asilomar Conference on Signals, Systems and Computers, pp. 2032-2036, November 2014.
• J. Kota, N. Kovvali, D. Bliss, and A. Papandreou-Suppappola, “Waveform selection for range and Doppler estimation via Barankin bound signal-to-noise ratio threshold,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4658-4662, May 2014.
• M. Zhou, J. J. Zhang, A. Papandreou-Suppappola, “Hyperbolic frequency modulation for multiple users in underwater acoustic communications,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3498-3502, May 2014.
• S. P. Ebenezer and A. Papandreou-Suppappola, “Multiple transition mode multiple target track-before-detect with partitioned sampling,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8008-8012, May 2014.
• B. O’Donnell, R. LeBaron, R. Diaz, A. Papandreou-Suppappola, “Physics-based sea clutter model for improved detection of low radar cross-section targets,” IEEE International Conference on Acoustics, Speech and Signal Processing pp. 6830-6833, May 2014.
• B. O’Donnell, A. Maurer, A. Papandreou-Suppappola, “Waveform processing for protein multi-alignment by mapping location, structure and property function attributes,” Asilomar Conference on Signals, Systems and Computers, pp. 248-252, November 2013.
• A. Malin, N. Kovvali, A. Papandreou-Suppappola, “Adaptive learning of immunosignaturing features for multi-disease pathologies,” Asilomar Conference on Signals, Systems and Computers, pp. 1301-1305, November 2013.
• M. Zhou, J. J. Zhang, A. Papandreou-Suppappola, T. M. Duman, “An underwater acoustic communication scheme with inherent scale diversity for multiple users,” MTS/IEEE OCEANS Conference, pp. 1-4, September 2013.
• A. ElMoslimany, M. Zhou, T. M. Duman, A. Papandreou-Suppappola, “A new signaling scheme for underwater acoustic communications, MTS/IEEE OCEANS Conference, pp. 1-5, September 2013.
• B. W. Larsen, H. Chung, A. Dominguez, J. Sciacca, N. Kovvali, A. Papandreou-Suppappola, D. R. Allee, “Applying matching pursuit decomposition time-frequency processing to unattended ground sensor footstep classification,” SPIE Defense, Security and Sensing Conference, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense, vol. 8711, pp. 4-13, June 2013.
• D. Huff, N. Kovvali, A. Papandreou-Suppappola, A. Chattopadhyay, “Active sensing waveform design for estimating progressive fatigue damage in structures,” SPIE Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring Conference, vol. 8694, 12 pgs, May 2013.
• B. O’Donnell, J. J. Zhang, A. Papandreou-Suppappola, M. Rangaswamy, “Waveform agile multiple target tracking using probability hypothesis density filtering,” IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 245-248, June 2012.
• S. Edla, N. Kovvali and A. Papandreou-Suppappola, “Electrocardiogram signal modeling using interacting multiple models,” Asilomar Conference on Signals, Systems and Computers, pp. 471-475, November 2011.

• S. Liu, J. J. Zhang,S. Bhat, Q. Ding, A. Papandreou-Suppappola, R. M. Narayanan, S. Kay and M. Rangaswamy, “Design and performance of an integrated waveform-agile multi-modal track-before-detect sensing system,” Asilomar Conference on Signals, Systems and Computers, pp. 1530-1534, November 2011.

• J. Northrop and A. Papandreou-Suppappola, “On the use of fractional autocorrelation to correct mismatches for chirp scale focusing for real SAR image formation,” Asilomar Conference on Signals, Systems and Computers, pp. 2084-2088, November 2011.

• A. Malin, N. Kovvali, J. J. Zhang, B. Chakraborty, A. Papandreou-Suppappola, S. Johnston
  and P. Stafford, “Adaptive learning of immunosignaturing peptide array features for biothreat detection and classification,” Asilomar Conference on Signals, Systems and Computers, pp. 1883-1887, November 2011.

• L. Miao, J. J. Zhang, C. Chakrabarti, A. Papandreou-Suppappola, and N. Kovvali, “Real-time closed-loop tracking of an unknown number of neural sources using probability hypothesis density particle filtering,” IEEE Workshop on Signal Processing Systems,
  pp. 367-372, October 2011 (nominated for Best Student Paper Award).

• B. Chakraborty, J. J. Zhang, A. Papandreou-Suppappola and D. Morrell, “Urban terrain tracking in high clutter with waveform-agility,” IEEE International Conference on
  Acoustics, Speech and Signal Processing, pp. 3640-3643, May 2011.

• D. Chakraborty, N. Kovvali, A. Papandreou-Suppappola, and A. Chattopadhyay, “Transfer learning for damage classification in structural health monitoring,” SPIE Smart Structures
  and Materials & Nondestructive Evaluation and Health Monitoring Conference, March 2011.

• B. Chakraborty, J. J. Zhang, A. Papandreou-Suppappola and D. Morrell, “Waveform-agile MIMO radar for urban terrain tracking,” IEEE Digital Signal Processing Workshop, pp. 466-471, 2011.

Books

• I. Kyriakides, D. Morrell, and A. Papandreou-Suppappola, Adaptive High-Resolution Sensor Waveform Design for Tracking, Synthesis Lectures on Algorithms and Software in Engineering, Morgan & Claypool Publishers, 109 pages, 2010.
• S. P. Sira, A. Papandreou-Suppappola and D. Morrell, Advances in Waveform-Agile Sensing for Tracking, Synthesis Lectures on Algorithms and Software in Engineering, Series on Algorithms and Software, Morgan & Claypool Publishers, 83 pages, 2008.
• A. Papandreou-Suppappola, ed., Applications in Time-Frequency Signal Processing. Boca Raton, Florida: CRC Press, October 2002.

Book Chapters

• A. Papandreou-Suppappola and S. Suppappola, “Time-frequency signal analysis and classification using matching pursuits,” in Time-Frequency Signal Analysis and Processing (B. Boashash, ed.), 2nd edition, Academic Press, pp. 701–743, 2016 (ISBN: 9780123984999).
• A. Papandreou-Suppappola, B. Iem, and G. F. Boudreaux-Bartels, “Time-frequency symbols for statistical signal processing,” in Time-Frequency Signal Analysis and Processing, (B. Boashash, ed.), 2nd edition, Academic Press, pp. 871–913, 2016 (ISBN: 9780123984999).
• A. Papandreou-Suppappola, F. Hlawatsch, and G. F. Boudreaux-Bartels, “Power time-frequency representations and their applications,” in Time-Frequency Signal Analysis and Processing, (B. Boashash, ed.), 2nd edition, Academic Press, pp. 531–573, 2016 (ISBN: 9780123984999).
• B. O’Donnell, A. Maurer, and A. Papandreou-Suppappola, “Biosequence time-frequency processing: Pathogen detection and identification,” in Excursions in Harmonic Analysis, Volume 3, (R. Balan, M. Begue, J. J. Benedetto, W. Czaja, and K. A. Okoudjou, Eds.), Springer Verlag Heidelberg, 2015.
• A. Papandreou-Suppappola, J. Zhang, B. Chakraborty, Y. Li, D. Morrell, S. P. Sira, “Adaptive waveform design for tracking,” Chapter 16 in Waveform Design and Diversity for Advanced Radar Systems, (F. Gini, A. De Maio, and L. Patton, Eds.), IET Peter Peregrinus, pp. 407-444, 2012.
• A. Papandreou-Suppappola, C. Ioana and J. Zhang, “Time-Varying Wideband Channel Modeling and Applications,” Chapter 9 in Wireless Communications over Rapidly Time-Varying Channels, (Franz Hlawatsch and Gerald Matz, ed.), Academic Press, pp. 375-411, 2011.
• J. Zhang and A. Papandreou-Suppappola, “Waveform Design and Diversity for Shallow Water Environments,” Chapter C-65, in Principles of Waveform Diversity and Design, (M. Wicks, E. Mokole, S. Blunt, R. Schneible, V. Amuso, Eds), Raleigh, NC: SciTech Publishing, Inc., 2010.
• Y. Li, S. P. Sira, A. Papandreou-Suppappola and D. Morrell, “Waveform time-frequency characterization for dynamically configured sensor systems,” Chapter B-21 in Principles of Waveform Diversity and Design, (M. Wicks, E. Mokole, S. Blunt, R. Schneible, V. Amuso, Eds), Raleigh, NC: SciTech Publishing, Inc., 2010.
• A. Papandreou-Suppappola, “Time-Frequency Processing of Time-Varying Signals with Nonlinear Group Delay,” in Wavelets and Signal Processing, (L. Debnath, ed.), Birkhaüser-Verlag, New York, pp. 311–359, 2003.
• A. Papandreou-Suppappola, “Time-Varying Processing: Tutorial on Principles and Practice,” in Applications in Time-Frequency Signal Processing, (A. Papandreou-Suppappola, ed.), Florida: CRC Press, pp. 1–84, 2002.
• A. Papandreou-Suppappola, “Time-frequency representations covariant to group delay shifts,” in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, (B. Boashash, ed.), ch. 5.6, pp. 203–212, Elsevier, Oxford, UK, 2003.
• A. Papandreou-Suppappola and S. Suppappola, “Time-frequency signal analysis and classification using matching pursuits,” in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, (B. Boashash, ed.), ch. 12.2, pp. 510–518, Elsevier, 2003.
• A. Papandreou-Suppappola, B. Iem, and G. F. Boudreaux-Bartels, “Time-frequency symbols for statistical signal processing,” in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, (B. Boashash, ed.), ch. 9.2, pp. 382–391, Elsevier, 2003.
• A. Papandreou-Suppappola, F. Hlawatsch, and G. F. Boudreaux-Bartels, “Power time-frequency representations and their applications,” in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, (B. Boashash, ed.), ch. 15.3, pp. 643–650, Elsevier, 2003.