{"id":14,"date":"2019-08-15T16:58:16","date_gmt":"2019-08-15T23:58:16","guid":{"rendered":"https:\/\/faculty.engineering.asu.edu\/papandreou\/?page_id=14"},"modified":"2026-02-05T10:55:15","modified_gmt":"2026-02-05T17:55:15","slug":"publications","status":"publish","type":"page","link":"https:\/\/faculty.engineering.asu.edu\/papandreou\/publications\/","title":{"rendered":"Recent Publications"},"content":{"rendered":"<dl>\n<dd><a href=\"#Journal Publications\"> Journal Publications <\/a><\/dd>\n<dd><a href=\"#Conference Papers\"> Conference Papers <\/a><\/dd>\n<dd><a href=\"#Books\"> Books <\/a><\/dd>\n<dd><a href=\"#Book Chapters\"> Book Chapters <\/a><\/dd>\n<\/dl>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-medium wp-image-423\" src=\"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-content\/uploads\/sites\/39\/2020\/08\/line2-300x8.png\" alt=\"\" width=\"300\" height=\"8\" srcset=\"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-content\/uploads\/sites\/39\/2020\/08\/line2-300x8.png 300w, https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-content\/uploads\/sites\/39\/2020\/08\/line2-1024x27.png 1024w, https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-content\/uploads\/sites\/39\/2020\/08\/line2-768x21.png 768w, https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-content\/uploads\/sites\/39\/2020\/08\/line2-1536x41.png 1536w, https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-content\/uploads\/sites\/39\/2020\/08\/line2-2048x55.png 2048w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<h5> <\/h5>\n<p><a name=\"Journal Publications\"  ><\/a><\/p>\n<h5 style=\"text-align:  left\"><strong> Journal Publications  <\/strong><\/h5>\n<p><a name=\"Journal Publications\"><\/a><\/p>\n<ul>\n<li> Y. Seo, B. Moraffah, L. M. Kaplan and A. Papandreou-Suppappola, \u201cDependent Bayesian nonparametric learning for tracking in unknown time-varying environments with merged measurements,\u201d <em> IEEE Transactions on Signal Processing,<\/em> to be submitted, 2026 <\/li>\n<li>  A. Eastman and A. Papandreou-Suppappola, \u201cReal time reactivity reinforcement learning with agile sensing for tracking in unknown spectrum sharing environments,\u201d <em> IEEE Transactions on Signal Processing,<\/em> to be submitted 2026.\n<\/li>\n<li> N. Velez-Cruz and A. Papandreou-Suppappola, \u201cBayesian learning of nonlinear gene regulatory networks with<br \/>\nswitching architectures\u201d, Frontiers in Signal Processing, vol. 4, 2024 doi.org\/10.3389\/frsip.2024.1323538 <\/li>\n<li> P. Weber and A. Papandreou-Supppappola, \u201cAnalog isolated multilevel quantizer for voltage sensing while main-<br \/>\ntaining galvanic isolation,\u201d arXiv:2402.18758 [eess.SP], February 2024 <\/li>\n<li> B. Moraffah and A. Papandreou-Suppappola, \u201cMultiple object tracking with dynamic dependencies using Bayesian nonparametric modeling,&#8221; Sensors, vol. 22, January 2022 , doi: 10.3390\/s22010388<\/li>\n<li>B. Moraffah and A. Papandreou-Suppappola, \u201cBayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking,\u201d arXiv:2004.10798 [cs.LG], April 2020.<\/li>\n<li>J. Zhou, A. Papandreou-Suppappola, and C.Chakrabarti, &#8220;Parallel Gibbs sampler for wavelet-based Bayesian compressive sensing with high reconstruction accuracy,&#8221; <em>Journal of Signal Processing Systems<\/em>, to appear 2020.<\/li>\n<li>F. Solis and Antonia Papandreou-Suppappola, &#8220;Power dissipation and surface charge in EEG: application to eigenvalue structure of integral operators,&#8221; <em>IEEE Transactions on Biomedical Engineering,<\/em> vol. 67, no. 5, pp. 1232-1242, May 2020.<\/li>\n<li>J. A. Northrop and A. Papandreou-Suppappola, \u201cComputationally efficient estimation of compound K-<br \/>distributed sea clutter in thermal noise and its application to sea echo reflectivity observations,&#8221; I<em>EEE Transactions on Aerospace and Electronic Systems<\/em>, vol. 56, pp. 2340-2350, June 2020<\/li>\n<li>J. S. Kota and A. Papandreou-Suppappola, &#8221; Joint design of transmit waveforms for object tracking in coexisting multimodal sensing systems,&#8221; <em>Sensors, Special Issue Multiple Object Tracking: Making Sense of the Sensors<\/em>, Sensors vol. 19, issue 8, <a href=\"https:\/\/doi.org\/10.3390\/s19081753\">https:\/\/doi.org\/10.3390\/s19081753<\/a>, doi:10.3390\/s19081753, 2019.<\/li>\n<li>S. Chakraborty, A. Banerjee, S. K. S. Gupta, P. R. Christensen, and A. Papandreou-Suppappola, &#8220;Estimation of dynamic land cover parameters for change detection in MODIS time-series, <em>Journal of Selected Topics in Applied Earth Observations and Remote Sensing<\/em>, vol. 11, no. 6, pp. 1769-1776, June 2018.<\/li>\n<li>M. Zhou, J. J. Zhang, A. Papandreou-Suppappola, \u201cMultiple target tracking in urban environments,&#8221; <em>IEEE Transactions in Signal Processing<\/em>, vol. 64, pp. 1270-1279, March 2016.<\/li>\n<li>A. ElMoslimany, M. Zhou, T. M. Duman, A. Papandreou-Suppappola, \u201cAn underwater acoustic communications scheme exploiting biological sounds,&#8221; <em>Wireless Communications and Mobile Computing<\/em>, vol. 16, pp. 2194-2211, October 2016.<\/li>\n<li>S. P. Ebenezer and A. Papandreou-Suppappola, \u201cLow RCS target tracking in estimated rapidly-varying sea clutter using a Kronecker product approximation algorithm,&#8221; <em>IEEE Journal of Selected Topics in Signal Processing<\/em>, vol. 9, pp. 1639-1649, December 2015.<\/li>\n<li>S. P. Ebenezer and A.Papandreou-Suppappola, \u201cGeneralized recursive track-before-detect with proposal partitioning for tracking varying number of multiple targets in low SNR,&#8221; <em>IEEE Transactions on Signal Processing<\/em>, vol. 64, pp. 2819-2834, June 2016.<\/li>\n<li>B. O\u2019Donnell, A. Maurer, A. Papandreou-Suppappola, P. Stafford, \u201cTime-frequency analysis of peptide microarray data: Application to brain cancer immunosignatures,&#8221; <em>Cancer Informatics<\/em>, vol. 14, pp. 219-233, June 2015.<\/li>\n<li>M. Banavar, J. J. Zhang, B. Chakraborty, H. Kwon, Y. Li, H. Jiang, A. Spanias, C. Tepedelenlioglu, C. Chakrabarti, A. Papandreou-Suppappola, \u201cAn overview of recent advances on distributed and agile sensing algorithms and implementation,&#8221; <em>Digital Signal Processing<\/em>, pp. 1-14, vol. 39, April 2015.<\/li>\n<li>D. Chakraborty, N. Kovvali, A. Papandreou-Suppappola, and A. Chattopadhyay, \u201cAn adaptive learning damage estimation method for structural health monitoring,&#8221; <em>Journal of Intelligent Material, Systems and Structures<\/em>, (1045389X14522531, pp. 1-9, first published on April 14, 2014) pp. 125\u2013143, vol. 26, January 2015.<\/li>\n<li>S. Edla, N. Kovvali, and A. Papandreou-Suppappola, \u201cElectrocardiogram signal modeling with adaptive parameter estimation and cardiac classification using sequential Bayesian methods,&#8221; <em>IEEE Transactions on Signal Processing<\/em>, pp. 2667\u20132680, vol. 62, no. 10, May 2014.<\/li>\n<li>M. Seaver, A. Chattopadhyay, A. Papandreou-Suppappola, S. B. Kim, N. Kovvali, C. R. Farrar, M. H. Triplett and M. M. Derriso, \u201cWorkshop on transitioning structural health monitoring technology to military platforms,&#8221; <em>Journal of Intelligent Material, Systems and Structures<\/em>, vol. 24, no. 17, pp. 2063-2073, November 2013.<\/li>\n<li>L. Miao, J. J. Zhang, C. Chakrabarti, and A. Papandreou-Suppappola, \u201cEfficient Bayesian tracking of multiple sources of neural activity: Algorithms and real-time FPGA implementation,&#8221; <em>IEEE Transactions on Signal Processing<\/em>, vol. 61, pp. 633-647, February 2013.<\/li>\n<li>I. Kyriakides, D. Morrell and A. Papandreou-Suppappola, \u201cAdaptive highly localized waveform design for multiple target tracking,&#8221; <em>EURASIP Journal on Advances in Signal Processing<\/em>, vol. 180, pp. 1-17, August 2012.<\/li>\n<li>L. Miao, S. Michael, N. Kovvali, C. Chakrabarti, and A. Papandreou-Suppappola, \u201cMulti-source neural activity estimation and sensor scheduling: Algorithms and hardware implementation,&#8221; <em>Journal of Signal Processing Systems<\/em>, vol. 70, pp. 145-162, February 2013.<\/li>\n<li>L. Miao, J. J. Zhang, C. Chakrabarti, A. Papandreou-Suppappola, \u201cAlgorithm and parallel implementation of particle filtering and its use in waveform-agile sensing,&#8221; <em>Journal of Signal Processing Systems<\/em>, vol. 65, pp. 211-227, November 2011. (<em>invited<\/em>).<\/li>\n<li>L. Ravichandran, A. Papandreou-Suppappola, Z. Lacroix, A. Spanias, and C. Legendre, \u201cWaveform Mapping and Time-Frequency Processing of DNA and Protein Sequences,&#8221; <em>IEEE Transactions on Signal Processing<\/em>, vol. 59, no. 9, pp. 4210-4224, September 2011.<\/li>\n<li>N. F. Josso, J. J. Zhang, A. Papandreou-Suppappola, C. Ioana, T. M. Duman, \u201cNonstationary system analysis methods for underwater communications,&#8221; <em>EURASIP Journal on Advances in Signal Processing, Special Issue on: Recent Advances in Theory and Methods for Nonstationary Signal Analysis<\/em>, vol. 11, article ID 807472, 14 pages, 2011.<\/li>\n<\/ul>\n<hr \/>\n<p><a name=\"Conference Papers\"><\/a><\/p>\n<h5 style=\"text-align: left\"><strong> Conference Papers <\/strong><a name=\"Conference Papers\"><\/a><\/h5>\n<p>&nbsp;<\/p>\n<ul>\n<li> A. Eastman and A. Papandreou-Suppappola, \u201cAction selection methods to explore spectrum sharing environments using double Q-learning-while-tracking,\u201d <em> Asilomar Conference on Signals, Systems and Computers,<\/em> 2025\n<\/li>\n<li>A. Eastman, Y. Seo and A. Papandreou-Suppappola, \u201cDynamic adaptive inference with reinforcement learning in time-varying future generation sensing environments,\u201d <em> Asilomar Conference on Signals, Systems and Computers,<\/em> 2025\n<\/li>\n<li>Y. Seo, L.  M. Kaplan and A. Papandreou-Suppappola, \u201cTracking multiple objects with dynamic dependencies and merged measurements,\u201d <em> Asilomar Conference on Signals, Systems and Computers,<\/em>  2025\n<\/li>\n<li>P. Zhang, C. Chakrabarti and A. Papandreou-Suppappola, \u201cFeature-based learning for computationally efficient estimation in high-frequency time-varying spectrum-sharing environments,\u201d <em> Asilomar Conference on Signals, Systems and Computers, <\/em> 2025\n<\/li>\n<li>Y. Zhang, B. Moraffah and A. Papandreou-Suppappola, \u201cInterference mitigation in spectrum sharing environments using time-frequency processing and feature clustering,\u201d <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 514-518, 2022.\n<\/li>\n<li>N. Velez-Cruz, B. Moraffah and A. Papandreou-Suppappola, \u201cSwitching Langevin dynamics for gene regulatory networks,\u201d <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 1316-1320, 2022.\n<\/li>\n<li>N. Velez-Cruz, B. Moraffah and A. Papandreou-Suppappola, \u201cSequential Bayesian inference using stochastic models of gene regulatory networks,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 568-572, 2021.\n<\/li>\n<li>J. Ikram, A. Chattopadhyay and A. Papandreou-Suppappola, \u201cSynchrosqueezing transform matched to nonlinear group delay for mode estimation of ultrasonic guided waves,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 558-562, 2021.\n<\/li>\n<li>J. Ikram, A. Chattopadhyay and A. Papandreou-Suppappola, \u201cUnsupervised mode extraction and group velocity estimation for ultrasonic guided waves propagating in dispersive material,\u201d Asilomar Conference on Signals, Systems and Computers, pp. 343-347,  2020.\n<\/li>\n<li>B. Moraffah, C. Brito, B. Venkatesh and A. Papandreou-Suppappola \u201cUse of hierarchical Dirichlet processes to integrate dependent observations from multiple disparate sensors for tracking,&#8221; <em>International Conference on Information Fusion<\/em>, 2019.\n<\/li>\n<li>J. Northrop and A. Papandreou-Suppappola, \u201cAsymptotically efficient estimation of sea clutter intensity model parameters using log-based moments,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, November 2019.\n<\/li>\n<li>S. Das, B. Moraffah, S. K. Gupta and A. Papandreou-Suppappola, \u201cBradycardia prediction in preterm infants using nonparametric kernel density estimation,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, November 2019.\n<\/li>\n<li>B. Moraffah, A. Papandreou-Suppappola and M. Rangaswamy, \u201cNonparametric Bayesian methods and the dependent Pitman-Yor process for modeling evolution in multiple object tracking,&#8221; <em>International Conference on Information Fusion<\/em>, 2019.\n<\/li>\n<li>B. Moraffah and A. Papandreou-Suppappola, \u201cRandom infinite tree and dependent Poisson diffusion process for nonparametric Bayesian modeling in multiple object tracking,\u201d <em>IEEE International Conference on Acoustics, Speech and Signal Processing<\/em>, pp. 5217-5221, May 2019.\n<\/li>\n<li>J. Ikram, A. Papandreou-Suppappola, G. Lia, A. Chattopadhyaya and P. Malatkarb, &#8220;Guided wave based inspection of integrated circuit packages using the time-frequency synchrosqueezing transform,&#8221; <em>SPIE Smart Structures and Nondestructive Evaluation<\/em>, vol. 1093, March 2019.\n<\/li>\n<li>S. Chakraborty, A. Banerjee, S. K. S Gupta, P. R. Christensen, A. Papandreou-Suppappola, &#8220;Multitemporal analysis of image time-series for land cover change detection and unsupervised classification of change event using spectral analysis&#8221;, <em>American Geophysical Union (AGU) Fall Meeting<\/em>, Abstract H31H-2003, December 2018.\n<\/li>\n<li>B. Moraffah and A. Papandreou-Supppappola, \u201cDependent Dirichlet process modeling and identity learning for multiple object tracking,\u201d <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 1762-1766, October 2018.\n<\/li>\n<li>J. Northrop and A. Papandreou-Suppappola, \u201cEstimation of compound K-distribution modeling parameters of sea clutter with unknown thermal noise power,\u201d <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 2091-2095, October 2018.\n<\/li>\n<li>V. S. Gattani, J. S. Kota and A. Papandreou-Suppappola \u201cTime-frequency separation of matched-waveform signatures of coexisting multimodal systems,\u201d <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 2086-2090, October 2018.\n<\/li>\n<li>S. Chakraborty, A. Banerjee, S. Gupta, P. Christensen, A. Papandreou-Suppappola, &#8220;Automated land cover change detection and mapping from hidden parameter estimates of normalized diverse vegetation index (NDVI) time-series,&#8221; <em>American Geophysical Union (AGU) Fall Meeting<\/em>, December 2017 (abstract).\n<\/li>\n<li>F. Solis and A. Papandreou-Suppappola, &#8220;Multiple interface brain and head models for EEG: A surface charge approach,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 1323-1327, November 2017.\n<\/li>\n<li>J. S. Kota and A. Papandreou-Suppappola, \u201cSNR threshold region prediction via singular value decomposition of the Barankin bound kernel,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>,  pp. 762-766, November 2017.\n<\/li>\n<li>S. Chakraborty, A. Banerjee, S. Gupta, A. Papandreou-Suppappola, P. Christensen, \u201cEstimation of dynamic parameters of MODIS NDVI time series nonlinear model using particle filtering, <em>International Geoscience and Remote Sensing Symposium<\/em>, pp. 1091-1094, July 2017.\n<\/li>\n<li>F. Solis and A. Papandreou-Suppappola, &#8220;Surface charge method for the forward EEG problem,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, November 2016.\n<\/li>\n<li>J. Kota, G. M. Jacyna and A. Papandreou-Suppappola, &#8220;Nonstationary signal design for coexisting radar and communications systems,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 549-553, November 2016.\n<\/li>\n<li>A. Maurer, S. Hanrahan, A. O. Hebb and A. Papandreou-Suppappola, &#8220;Suppression of neurostimulation artifacts and adaptive clustering of Parkinson\u2019s patients behavioral tasks using EEG,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 1646-1650, November 2016.\n<\/li>\n<li>F. Solis, A. Maurer, J. Jiang, A. Papandreou-Suppappola, \u201cAdaptive EEG artifact suppression using Gaussian mixture modeling,\u201d <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 607-611, November 2015.\n<\/li>\n<li>S. P. Ebenezer and A. Papandreou-Suppappola, \u201cMultiple target track-before-detect in compound Gaussian clutter,\u201d <em>IEEE International Conference on Acoustics, Speech and Signal Processing<\/em>, pp. 2539-2543, April 2015.\n<\/li>\n<li>S. P. Ebenezer and A. Papandreou-Suppappola, \u201cEstimation of rapidly varying sea clutter using nearest Kronecker product approximation,\u201d <em>IEEE International Conference on Acoustics, Speech and Signal Processing<\/em>, pp. 3686-3690, April 2015.\n<\/li>\n<li>N. Zaker, A. Dutta, A. Maurer, J. J. Zhang, S. Hanrahan, A. O. Hebb, N. Kovvali, A. Papandreou-Suppappola, \u201cAdaptive learning of behavioral tasks for patients with Parkinson\u2019s disease using signals from deep brain stimulation,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 208-212, November 2014.\n<\/li>\n<li>B. Paul, A. Papandreou-Suppappola, and D. W. Bliss, \u201cRadar tracking waveform design in continuous space and optimization selection using differential evolution,\u201d <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 2032-2036, November 2014.\n<\/li>\n<li>J. Kota, N. Kovvali, D. Bliss, and A. Papandreou-Suppappola, &#8220;Waveform selection for range and Doppler estimation via Barankin bound signal-to-noise ratio threshold,&#8221; <em>IEEE International Conference on Acoustics, Speech and Signal Processing<\/em>, pp. 4658-4662, May 2014.\n<\/li>\n<li>M. Zhou, J. J. Zhang, A. Papandreou-Suppappola, &#8220;Hyperbolic frequency modulation for multiple users in underwater acoustic communications,&#8221; <em>IEEE International Conference on Acoustics, Speech and Signal Processing<\/em>, pp. 3498-3502, May 2014.\n<\/li>\n<li>S. P. Ebenezer and A. Papandreou-Suppappola, &#8220;Multiple transition mode multiple target track-before-detect with partitioned sampling,&#8221; <em>IEEE International Conference on Acoustics, Speech and Signal Processing<\/em>, pp. 8008-8012, May 2014.\n<\/li>\n<li>B. O\u2019Donnell, R. LeBaron, R. Diaz, A. Papandreou-Suppappola, &#8220;Physics-based sea clutter model for improved detection of low radar cross-section targets,&#8221; <em>IEEE International Conference on Acoustics, Speech and Signal Processing<\/em> pp. 6830-6833, May 2014.\n<\/li>\n<li>B. O\u2019Donnell, A. Maurer, A. Papandreou-Suppappola, &#8220;Waveform processing for protein multi-alignment by mapping location, structure and property function attributes,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 248-252, November 2013.\n<\/li>\n<li>A. Malin, N. Kovvali, A. Papandreou-Suppappola, &#8220;Adaptive learning of immunosignaturing features for multi-disease pathologies,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 1301-1305, November 2013.\n<\/li>\n<li>M. Zhou, J. J. Zhang, A. Papandreou-Suppappola, T. M. Duman, &#8220;An underwater acoustic communication scheme with inherent scale diversity for multiple users,&#8221; <em>MTS\/IEEE OCEANS Conference<\/em>, pp. 1-4, September 2013.\n<\/li>\n<li>A. ElMoslimany, M. Zhou, T. M. Duman, A. Papandreou-Suppappola, &#8220;A new signaling scheme for underwater acoustic communications, <em>MTS\/IEEE OCEANS Conference<\/em>, pp. 1-5, September 2013.\n<\/li>\n<li>B. W. Larsen, H. Chung, A. Dominguez, J. Sciacca, N. Kovvali, A. Papandreou-Suppappola, D. R. Allee, &#8220;Applying matching pursuit decomposition time-frequency processing to unattended ground sensor footstep classification,&#8221; <em>SPIE Defense, Security and Sensing Conference, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense<\/em>, vol. 8711, pp. 4-13, June 2013.\n<\/li>\n<li>D. Huff, N. Kovvali, A. Papandreou-Suppappola, A. Chattopadhyay, &#8220;Active sensing waveform design for estimating progressive fatigue damage in structures,&#8221; <em>SPIE Smart Structures and Materials &amp; Nondestructive Evaluation and Health Monitoring Conference<\/em>, vol. 8694, 12 pgs, May 2013.\n<\/li>\n<li>B. O\u2019Donnell, J. J. Zhang, A. Papandreou-Suppappola, M. Rangaswamy, &#8220;Waveform agile multiple target tracking using probability hypothesis density filtering,&#8221; <em>IEEE Sensor Array and Multichannel Signal Processing Workshop<\/em>, pp. 245-248, June 2012.\n<\/li>\n<li>S. Edla, N. Kovvali and A. Papandreou-Suppappola, &#8220;Electrocardiogram signal modeling using interacting multiple models,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 471-475, November 2011.<\/li>\n<li>\n<p>S. Liu, J. J. Zhang,S. Bhat, Q. Ding, A. Papandreou-Suppappola, R. M. Narayanan, S. Kay and M. Rangaswamy, \u201cDesign and performance of an integrated waveform-agile multi-modal track-before-detect sensing system,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 1530-1534, November 2011.<\/p>\n<\/li>\n<li>\n<p>J. Northrop and A. Papandreou-Suppappola, &#8220;On the use of fractional autocorrelation to correct mismatches for chirp scale focusing for real SAR image formation,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 2084-2088, November 2011.<\/p>\n<\/li>\n<li>\n<p>A. Malin, N. Kovvali, J. J. Zhang, B. Chakraborty, A. Papandreou-Suppappola, S. Johnston <br \/>and P. Stafford, \u201cAdaptive learning of immunosignaturing peptide array features for biothreat detection and classification,&#8221; <em>Asilomar Conference on Signals, Systems and Computers<\/em>, pp. 1883-1887, November 2011.<\/p>\n<\/li>\n<li>\n<p>L. Miao, J. J. Zhang, C. Chakrabarti, A. Papandreou-Suppappola, and N. Kovvali, \u201cReal-time closed-loop tracking of an unknown number of neural sources using probability hypothesis density particle filtering,&#8221; <em>IEEE Workshop on Signal Processing Systems<\/em>, <br \/>pp. 367-372, October 2011 (nominated for Best Student Paper Award).<\/p>\n<\/li>\n<li>\n<p>B. Chakraborty, J. J. Zhang, A. Papandreou-Suppappola and D. Morrell, \u201cUrban terrain tracking in high clutter with waveform-agility,&#8221;  <em>IEEE International Conference on <\/em><br \/><em>Acoustics, Speech and Signal Processing<\/em>, pp. 3640-3643, May 2011.<\/p>\n<\/li>\n<li>\n<p>D. Chakraborty, N. Kovvali, A. Papandreou-Suppappola, and A. Chattopadhyay, &#8220;Transfer learning for damage classification in structural health monitoring,&#8221; <em>SPIE Smart Structures<\/em><br \/><em>and Materials &amp; Nondestructive Evaluation and Health Monitoring Conference<\/em>, March 2011.<\/p>\n<\/li>\n<li>\n<p> B. Chakraborty, J. J. Zhang, A. Papandreou-Suppappola and D. Morrell, &#8220;Waveform-agile MIMO radar for urban terrain tracking,&#8221;  <em>IEEE Digital Signal Processing Workshop<\/em>, pp. 466-471, 2011.<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<p><a name=\"Books\"><\/a><\/p>\n<h5 style=\"text-align: left\"><strong> Books <\/strong><\/h5>\n<p><a name=\"Books\"><\/a><\/p>\n<ul>\n<li>I. Kyriakides, D. Morrell, and A. Papandreou-Suppappola, <em>Adaptive High-Resolution Sensor Waveform Design for Tracking<\/em>, Synthesis Lectures on Algorithms and Software in Engineering, Morgan &amp; Claypool Publishers, 109 pages, 2010.<\/li>\n<li>S. P. Sira, A. Papandreou-Suppappola and D. Morrell, <em>Advances in Waveform-Agile Sensing for Tracking<\/em>, Synthesis Lectures on Algorithms and Software in Engineering, Series on Algorithms and Software, Morgan &amp; Claypool Publishers, 83 pages, 2008.<\/li>\n<li>A. Papandreou-Suppappola, ed., <em>Applications in Time-Frequency Signal Processing<\/em>. Boca Raton, Florida: CRC Press, October 2002.\n<\/li>\n<\/ul>\n<hr \/>\n<p><a name=\"Book Chapters\"><\/a><\/p>\n<h5 style=\"text-align: left\"><strong> Book Chapters<\/strong><\/h5>\n<p><a name=\"Book Chapters\"><\/a><\/p>\n<ul>\n<li>A. Papandreou-Suppappola and S. Suppappola, \u201cTime-frequency signal analysis and classification using matching pursuits,\u201d in <em>Time-Frequency Signal Analysis and Processing<\/em> (B. Boashash, ed.), 2nd edition, Academic Press, pp. 701\u2013743, 2016 (ISBN: 9780123984999).<\/li>\n<li>A. Papandreou-Suppappola, B. Iem, and G. F. Boudreaux-Bartels, \u201cTime-frequency symbols for statistical signal processing,\u201d in <em>Time-Frequency Signal Analysis and Processing<\/em>, (B. Boashash, ed.), 2nd edition, Academic Press, pp. 871\u2013913, 2016 (ISBN: 9780123984999).<\/li>\n<li>A. Papandreou-Suppappola, F. Hlawatsch, and G. F. Boudreaux-Bartels, \u201cPower time-frequency representations and their applications,&#8221; in <em>Time-Frequency Signal Analysis and Processing<\/em>, (B. Boashash, ed.), 2nd edition, Academic Press, pp. 531\u2013573, 2016 (ISBN: 9780123984999).<\/li>\n<li>B. O\u2019Donnell, A. Maurer, and A. Papandreou-Suppappola, \u201cBiosequence time-frequency processing: Pathogen detection and identification,\u201d in <em>Excursions in Harmonic Analysis, Volume 3<\/em>, (R. Balan, M. Begue, J. J. Benedetto, W. Czaja, and K. A. Okoudjou, Eds.), Springer Verlag Heidelberg, 2015.<\/li>\n<li>A. Papandreou-Suppappola, J. Zhang, B. Chakraborty, Y. Li, D. Morrell, S. P. Sira, \u201cAdaptive waveform design for tracking,\u201d Chapter 16 in <em>Waveform Design and Diversity for Advanced Radar Systems<\/em>, (F. Gini, A. De Maio, and L. Patton, Eds.), IET Peter Peregrinus, pp. 407-444, 2012.<\/li>\n<li>A. Papandreou-Suppappola, C. Ioana and J. Zhang, \u201cTime-Varying Wideband Channel Modeling and Applications,\u201d Chapter 9 in <em>Wireless Communications over Rapidly Time-Varying Channels<\/em>, (Franz Hlawatsch and Gerald Matz, ed.), Academic Press, pp. 375-411, 2011.<\/li>\n<li>J. Zhang and A. Papandreou-Suppappola, \u201cWaveform Design and Diversity for Shallow Water Environments,\u201d Chapter C-65, in <em>Principles of Waveform Diversity and Design<\/em>, (M. Wicks, E. Mokole, S. Blunt, R. Schneible, V. Amuso, Eds), Raleigh, NC: SciTech Publishing, Inc., 2010.<\/li>\n<li>Y. Li, S. P. Sira, A. Papandreou-Suppappola and D. Morrell, \u201cWaveform time-frequency characterization for dynamically configured sensor systems,\u201d Chapter B-21 in <em>Principles of Waveform Diversity and Design<\/em>, (M. Wicks, E. Mokole, S. Blunt, R. Schneible, V. Amuso, Eds), Raleigh, NC: SciTech Publishing, Inc., 2010.<\/li>\n<li>A. Papandreou-Suppappola, \u201cTime-Frequency Processing of Time-Varying Signals with Nonlinear Group Delay,\u201d in <em>Wavelets and Signal Processing<\/em>, (L. Debnath, ed.), Birkha\u00fcser-Verlag, New York, pp. 311\u2013359, 2003.<\/li>\n<li>A. Papandreou-Suppappola, \u201cTime-Varying Processing: Tutorial on Principles and Practice,\u201d in <em>Applications in Time-Frequency Signal Processing<\/em>, (A. Papandreou-Suppappola, ed.), Florida: CRC Press, pp. 1\u201384, 2002.<\/li>\n<li>A. Papandreou-Suppappola, \u201cTime-frequency representations covariant to group delay shifts,\u201d in <em>Time-Frequency Signal Analysis and Processing: A Comprehensive Reference<\/em>, (B. Boashash, ed.), ch. 5.6, pp. 203\u2013212, Elsevier, Oxford, UK, 2003.<\/li>\n<li>A. Papandreou-Suppappola and S. Suppappola, \u201cTime-frequency signal analysis and classification using matching pursuits,\u201d in <em>Time-Frequency Signal Analysis and Processing: A Comprehensive Reference<\/em>, (B. Boashash, ed.), ch. 12.2, pp. 510\u2013518, Elsevier, 2003.<\/li>\n<li>A. Papandreou-Suppappola, B. Iem, and G. F. Boudreaux-Bartels, \u201cTime-frequency symbols for statistical signal processing,\u201d in <em>Time-Frequency Signal Analysis and Processing: A Comprehensive Reference<\/em>, (B. Boashash, ed.), ch. 9.2, pp. 382\u2013391, Elsevier, 2003.<\/li>\n<li>A. Papandreou-Suppappola, F. Hlawatsch, and G. F. Boudreaux-Bartels, \u201cPower time-frequency representations and their applications,&#8221; in <em>Time-Frequency Signal Analysis and Processing: A Comprehensive Reference<\/em>, (B. Boashash, ed.), ch. 15.3, pp. 643\u2013650, Elsevier, 2003.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Journal Publications Conference Papers Books Book Chapters Journal Publications Y. Seo, B. Moraffah, L. M. Kaplan and A. Papandreou-Suppappola, \u201cDependent Bayesian nonparametric learning for tracking in unknown time-varying environments with merged measurements,\u201d IEEE Transactions on [&hellip;]<\/p>\n","protected":false},"author":81,"featured_media":456,"parent":0,"menu_order":11,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-14","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/pages\/14","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/users\/81"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/comments?post=14"}],"version-history":[{"count":0,"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/pages\/14\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/media\/456"}],"wp:attachment":[{"href":"https:\/\/faculty.engineering.asu.edu\/papandreou\/wp-json\/wp\/v2\/media?parent=14"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}