Last updated: August 2026

Journal papers

[Information on Impact factors quartiles and rankings are related to the year of publication. Acronyms used: SCI: Scimago quartile, JCR: Journal Citation Report quartile]. Journal rankings are taken from the JCR website.]

  1. C. Giladi, Y. Sepulcre and T. Trigano, ‘‘Intensity and Dictionary Estimation for Nuclear Spectroscopy Using a Sparse Double Optimization’’, in IEEE Transactions on Instrumentation and Measurement, 2026. 13 pages. DOI: https://doi.org/10.1109/TIM.2026.3709451 . (SCI: Q1, JCR: Q1, 5-year IF 6.0, ranking: 54/368, GR h-5 124)
    View copyright and download preprint © [2026] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  2. C. Lin, Y. Huang, Z. Chen, S. Gu, D. Bykhovsky, T. Trigano, X. Zheng, Y. Zhu, ‘‘Semi-Supervised Energy Spectrum Estimation in High Count-Rate Nuclear Spectrometry’’, in IEEE Transactions on Instrumentation and Measurement, Vol. 75, 2512816, 2026. DOI: https://doi.org/10.1109/TIM.2026.3706156 (SCI: Q1, JCR: Q1, 5-year IF 6.0, ranking: 54/368, GR h-5 124)
    View copyright and download preprint © [2026] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  3. Y. Sepulcre and T. Trigano, “Sparser LASSO Induced by Convolutional Expansion (SLICE) for Robust Peak Localization in Gamma Spectroscopy”, in Signal Processing, Vol. 249, Dec. 2026. DOI: https://10.1016/j.sigpro.2026.110740 (SCI: Q1, JCR: Q2, 5 year IF 3.9, ranking 130/368, GR h-5 66)
    View copyright and download preprint © [2026] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ . Click here to download the preprint


  4. C. Lin, X. Zheng, T. Trigano, D. Bykhovsky, Y. Zhu and L. Tian, “Spectroscopic Pulse Embeddings by Contrastive Learning from Unlabeled Data for Pile-Up Analysis”, in Sensors, Vol. 26(7), 2138, Mar. 2026. DOI: https://doi.org/10.3390/s26072138 (SCI: Q1, JCR: Q2, 5 year IF 3.7, ranking 134/368, GR h-5 210)
    View copyright and download preprint © [2026] Open access publication, available freely by clicking on the DOI.


  5. A. Mishra, A. Krief, M.M. Sahoo, A. Schachter, I. Gondac, N. Dudai, T. Trigano and I. Herrmann, Spectroscopic analysis reveals an opposite pattern between carnosic and rosmarinic acids concentration in rosemary (Salvia rosmarinus), in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol. 350, 127392, Apr. 2026. DOI: https://10.1016/j.saa.2025.127392 (SCI: Q1, JCR: Q1, 5-year IF 3.4, ranking 5/44, GR h-5 79)

  6. A. Mishra, A. Krief, M.M. Sahoo, A. Schachter, I. Gondac, N. Dudaic, T. Trigano, I. Herrmann, “Pre- and post-harvest spectral estimation of selected phytochemicals in rosemary”, in Computers and Electronics in Agriculture, Vol. 244, 111501, Mar. 2026. DOI: https://doi.org/10.1016/j.compag.2026.111501 SCI: Q1, JCR: Q1, 5-year IF 9.3, 2/94, GR h-5 138)

  7. Z. Chen, X. Zheng, Y. Zhu, T. Trigano, Y. Huang and Y. Zhang, “Parallel implementation of spectral pileup correction and Gaussian noise suppression using CUDA heterogeneous architecture”, in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 1082, Part 1, 170929, Feb. 2026. DOI: https://doi.org/10.1016/j.nima.2025.170929. (SCI: Q2, JCR: Q3, 5-year IF: 1.4, ranking 57/79, GR h-5 48)
    View copyright and download preprint © [2026] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ . Click here to download the preprint


  8. T. Trigano, Y. Sepulcre, M. Masika, A. Perez and D. Luengo, “FDP-TF: a Fast Two-Pass Trend Filtering for ECG Delination”, in Computers in Biology and Medicine, Vol. 197, Part A, 110927, Oct. 2025. DOI: https://doi.org/10.1016/j.compbiomed.2025.110927. (SCI: Q1, JCR: Q1, 5-year IF 6.5, ranking 26/177, GR h-5 140)
    View copyright and download preprint © [2025] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ . Click here to download the preprint


  9. Y. Huang, C. Lin, D. Bykhovsky, T. Trigano, Z. Chen, X. Zheng, and Y. Zhu, “Deep Learning Based Energy Spectrum Estimation for High Counting Rate Nuclear Spectrometry”, in IEEE Transactions on Instrumentation and Measurement, Vol. 74, 2533014, May. 2025. DOI: https://doi.org/10.1109/TIM.2025.3573370 (SCI: Q1, JCR: Q1, 5-year IF 6.0, ranking: 54/368, GR h-5 124)
    View copyright and download preprint © [2025] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  10. A. Squicciarini, T. Trigano and D. Luengo, “Jensen-Tsallis Divergence for Supervised Classification under Data Imbalance”, in Machine Learning, Vol 114(7), pp. 1-16, Springer, 2025. https://doi.org/10.1007/s10994-025-06791-4 (SCI: Q1, JCR: Q2, 5-year IF 6.6, ranking 101/204, GR h-5 63)

  11. D. Bykhovsky, Z. Chen, Y. Huang, X. Zheng and T. Trigano, Advanced Spectroscopy Time-Domain Signal Simulator for the Development of Machine and Deep Learning Algorithms, in IEEE Sensors Letters, Vol 9(4), pp. 1-4, Apr. 2025. DOI: https://doi.org/10.1109/LSENS.2025.3544656 (Q2, IF 2.2, GR h-5 34)
    View copyright and download preprint © [2025] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  12. Y. Huang, X. Zheng, Y. Zhu, T. Trigano, Z. Chen and D. Bykhovsky, Deep learning based pile-up correction algorithm for spectrometric data under high count rate measurements, in Sensors, Special Issue on Spectral Detection Technology, Sensors and Instruments, Vol 25(5), 1464, Feb. 2025. DOI: https://doi.org/10.3390/s25051464 (Q2, IF 3.4, GR h-5 191)
    View copyright and download preprint © [2025] Open access publication, available freely by clicking on the DOI.


  13. Z. Chen, D. Bykhovsky, X. Zheng, T. Trigano, Y. Zhu, GaSim: A Python Class to Generate Simulated Time Signals for Gamma Spectroscopy, in SoftwareX, Vol 29(2), 102037, https://doi.org/10.1016/j.softx.2025.102037 (Q2, IF 2.4, GR h-5 45)
    View copyright and download preprint © [2025] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ . Click here to download the preprint


  14. Y. Tzror, M. Bezner, S. Deri, T. Trigano, K. Ben-Harush, Nanofilament organization in highly tough fibers based on lamin proteins, Journal of the Mechanical Behavior of Biomedical Materials, 160, 106748, 2024. http://dx.doi.org/10.2139/ssrn.4868528 (Q1, IF 3.3, GR h-5 83)

  15. T. Trigano and D. Bykhovsky, Deep Learning Based Method for Activity Estimation from Short-Duration Gamma Spectroscopy Recordings, in IEEE Transactions in Instrumentation and Measurement, Aug. 2024, https://doi.org/10.1109/TIM.2024.3449943 (Q1, IF 5.6, GR h-5 139)
    View copyright and download preprint © [2024] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  16. T. Trigano, S. Talala and D. Luengo, Adaptive Trend Filtering for ECG Denoising and Delineation, in IEEE Journal of Biomedical and Health Informatics, Vol 27(12), Dec. 2023, pp. 5755-5766.,https://doi.org/10.1109/jbhi.2023.3314983 (Q1, GR 91, IF 7.7)
    View copyright and download preprint © [2023] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  17. T. Trigano and Z. Fradkin, Fast Algorithm for Time Decay Estimation with Applications to Electrostatic Ion Beam Traps, in Measurement Science and Technology, Vol 34(2), pp. 025701, 2023, https://doi.org/10.1088/1361-6501/ac9c23. (Q2, GR 42, IF 2.398).

  18. T. Trigano and D. Luengo, Intracardiac ECG Pulse Localization using Overlapping Block Sparse Reconstruction, in Biomedical Signal Processing and Control, Vol. 73(1), pp. 103921, Jan. 2023,https://doi.org/10.1016/j.bspc.2022.103921 (Q1, GR 69, IF 5.076)
    View copyright and download preprint © [2023] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ . Click here to download the preprint


  19. M. Khayat, S. Deri, D. Wolf, T. Trigano, O. Medalia, K. Ben-Harush, Biomimetic nuclear lamin fibers with remarkable toughness and stiffness, in International Journal of Biological Macromolecules, Vol 163, pp 2060-2067, Nov. 2020, https://doi.org/10.1016/j.ijbiomac.2020.09.113 (Q2, GR 123, IF 8.025).

  20. T. Trigano, S. Vaknin and D. Luengo, Fast Proximal Optimization for Sparse Reconstruction with Dictionaries Based on Translated Waveforms, in Signal Processing, Vol 169(4), paper 107379, April 2020, https://doi.org/10.1016/j.sigpro.2019.107379 (Q1, GR 71, IF 4.729).
    View copyright and download preprint © [2020] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ . Click here to download the preprint


  21. T. Trigano and Y. Bechor, Fast Background Removal of JPEG Images Based on HSV Polygonal Cuts for a Foot Scanner Device, Journal of Real-Time Image Processing, 17(4), pp. 981-992, 2019, https://doi.org/10.1007/s11554-019-00850-5 (Q2, GR 32, IF 2.293).

  22. D. Luengo, D. Meltzer and T. Trigano, An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals, Applied Science, 8(12), 2018, https://doi.org/10.3390/app8122569 (Q2, GR 105, IF 2.638).
    View copyright and download preprint © [2018] Open access publication, available freely by clicking on the DOI.


  23. T. Trigano and Y. Sepulcre, Data-driven Parameter Selection in Activity Estimation for Nuclear Spectroscopy , Signal Processing, 151, 99-106, 2018, https://doi.org/10.1016/j.sigpro.2018.05.006 (Q1, GR 71, IF 4.729).
    View copyright and download preprint © [2018] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ . Click here to download the preprint


  24. D. Bykhovsky and T. Trigano, Numerical Generation of Compound Random Processes with Arbitrary Autocorrelation Functions, Fluctuations and Noise Letters, Nov. 2017, https://doi.org/10.1142/S0219477518500013 (Q3, GR 17).

  25. T. Trigano, I. Shevtsov and D. Luengo, CoSA : an Accelerated ISTA Algorithm for Dictionaries Based on Translated Waveforms, Signal Processing, 139, 131-135, Oct. 2017, https://doi.org/10.1016/j.sigpro.2017.04.004 (Q1, GR 71, IF 4.729).
    View copyright and download preprint © [2017] Publised by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license <https://creativecommons.org/licenses/by-nc-nd/4.0/> . Click here to download the preprint


  26. T. Trigano and Y. Cohen, Intensity Estimation of Spectroscopic Signals with an Improved Sparse Reconstruction Algorithm, IEEE Signal Processing Letters, Vol. 24(5), pp. 530-534, 2017, 10.1109/LSP.2017.2680839 (Q1, GR 62, IF 3.201).
    View copyright and download preprint © [2017] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  27. T. Trigano, Y. Sepulcre and Y. Ritov, Sparse Reconstruction Algorithm for Nonhomogeneous Counting Rate Estimation, IEEE Transactions in Signal Processing, Vol. 65(2), pp 372-385, 2017, 10.1109/TSP.2016.2620104 (Q1, GR 98, IF 4.875).
    View copyright and download preprint © [2016] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  28. T. Trigano, I. Gildin and Y. Sepulcre, Pileup Correction Algorithm Using an Iterated Sparse Reconstruction Method, IEEE Signal Processing Letters, Vol. 22(9), pp. 1392-1396, 2015, 10.1109/LSP.2015.2406911 (Q1, GR 62, IF 3.201).
    View copyright and download preprint © [2015] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  29. T. Trigano, E. Barat, T. Dautremer and T. Montagu, Fast Digital Filtering of Spectrometric Data for Pile-Up Correction, IEEE Signal Processing Letters, Vol. 22(7), pp. 973-977, July. 2015, 10.1109/LSP.2014.2377352 (Q1, GR 62, IF 3.201).
    View copyright and download preprint © [2015] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  30. D. Luengo, S. Monzon, T. Trigano, J. Via and A. Artes-Rodrigues, Blind Analysis of Atrial Fibrillation Electrograms: Sparsity-Aware Formulation, Integrated Computer Aided Engineering, Vol. 22-1, pp.71-85, 2015, https://doi.org/10.3233/ICA-140471 (Q1, GR 24, IF 6.137).

  31. Y. Sepulcre, T. Trigano and Y. Ritov, Sparse Regression Algorithm for Counting Rate Estimation in Nuclear Spectrometry, IEEE Transactions on Signal Processing, Vol 61(17), 4347-4359, September, 2013, 10.1109/TSP.2013.2264811 (Q1, GR 98, IF 4.875).
    View copyright and download preprint © [2013] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  32. R. Goot, T. Trigano, S. Tapuchi and J. Gavan, Adaptative Allocation of Power Transmission for HAPs, in Annals of Telecommunications, Vol 67, 1-7, June 2012, https://doi.org/10.1007/s12243-012-0303-1 (Q2, GR 24, IF 1.901).

  33. J. Gavan, T. Trigano, S. Tapuchi and A. Kuperman, Mitigation of Mobile Radio Parasitic Radiations Effect: a Review, in Journal of Communication Engineering Systems, Vol 2(1), 1-11 2012, Q4.

  34. T. Trigano, U. Isserles and Y. Ritov, Semiparametric curve alignment and shift density estimation for biological data, IEEE Transactions in Signal Processing, Vol 59(5), 1970-1984, May, 2011, 10.1109/TSP.2011.2113179 (Q1, GR 98, IF 4.875).
    View copyright and download preprint © [2011] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  35. T. Trigano, T. Montagu, E. Moulines, F. Roueff and A. Souloumiac, Statistical Pile-Up Correction Method for HPGe Detectors, IEEE Transactions on Signal Processing, Vol 55(10), 4871-4881, 2008, 10.1109/TSP.2007.896300 (Q1, GR 98, IF 4.875).
    View copyright and download preprint © [2007] IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Click here to download the preprint


  36. E. Moulines, F. Roueff, A. Souloumiac and T. Trigano, 2007, Nonparametric Inference about Photon Energy from Indirect Measurements, Bernoulli 13(2), 365-388, 2007, 10.3150/07-BEJ5184 (Q1, GR 41, IF 1.68)
    View copyright and download preprint © [2007] Open access publication, available freely by clicking on the DOI.


Book Chapters

  1. Z. Chen, X. Kong, X. Zheng, Y. Zhu and T. Trigano, Parallel pileup correction for nuclear spectrometric data on many-core accelerators in Smart Computing and Communications, M. Liu, Z. Liu and C. Zhang (eds), Lecture Notes in Computer Science, vol. 13828, pp 258-267, Springer, 2022, (https://doi.org/10.1007/978-3-031-28124-2_24)

  2. D. Meltzer, D. Luengo and T. Trigano, ECG-based Biometric Recognition, in Teaching and Subjects on Biomedical Engineering, pp 157-171, Acco CV, Leuven, Belgium, 2021

  3. D. Luengo, D. Oses and T. Trigano, Digital Signal Processing of ECG and PCG signals, in Teaching and Subjects on Biomedical Engineering, pp 132-156, Acco CV, Leuven, Belgium, 2021

  4. D. Luengo, D. Meltzer and T. Trigano, Overcomplete Multiscale Dictionaries for Efficient Representations of ECG Signals, in Computer Aided Systems Theory, Moreno-Diaz, R., Pichler, F., Quesada-Arencibia, A. (eds), Lecture Notes in Computer Science, vol. 12014, pp 331-338, 2020, (https://doi.org/10.1007/978-3-030-45096-0_41)

  5. T. Trigano and Y. Sepulcre, Regularized Sparse Representation for Spectrometric Pulse Separation and Counting Rate Estimation, in Latent Variable Analysis and Source Separation, Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds), Lecture Notes in Computer Science, Vol 7191, 188-195, 2012, (https://doi.org/10.1007/978-3-642-28551-6_24)

  6. T. Trigano, U. Isserles, T. Montagu and Y. Ritov, Semiparametric curve alignment and shift density estimation : ECG data processing revisited, in Signal Processing, S. Miron (Ed), InTechOpen, pp. 217-240, 2010, (https://doi.org/10.5772/8526).

Patents

  1. L. Schwartz, T. Trigano, Y. Bechor. Process to isolate object of interest in image, US Patent US10417772B2, https://patents.google.com/patent/US10417772B2/en, 2019

  2. Y. Mashiach, M. Tal, T. Trigano and Y. Sepulcre, A method of improving the precision of counting rate estimation of radioactive sources, Israeli patent IL209613A0, 30 pages, 2011.

  3. E. Barat, T. Dautremer, T. Trigano and T. Brisset, Measurement and treatment of a signal comprising stacks of elementary pulses, US patent 11579757, 40 pages, 2008.

  4. E. Barat, T. Dautremer, T. Trigano and T. Brisset, System and apparatus of processing of a signal consisting in pileups of individual pulses, European patent 2005773039, 49 pages, 2007.

Conference proceedings

  1. C. Lin, Z. Chen, C. Feng, S. Gu, X. Zheng, Y. Zhu, T. Trigano , and D. Bykhovsky, An open X-ray spectrometric dataset for deep learning-based pile-up correction Proceedings of WASA 2025.

  2. D. Luengo, A. Treytl, P. Arras, K. Kornijenko, G. Tabunshchyk and T. Trigano, Improving Quality of Life through the Engineering Education. Case Study, Proceedings of E-TEMS, 9-11 March 2022, Bilbao, Spain, pp 190-195, 2022. 10.1109/E-TEMS53558.2022.9944453

  3. D. Luengo, J. Via and T. Trigano, Efficient Iteratively Rewighted LASSO Algorithm for Cross-Products Penalized Sparse Solutions, Proceedings of EURASIP Conference in Signal Processing (EUSIPCO, Qualis rank: B1), 18-21 January 2021, Amsterdam, Netherlands, 5 pages, 2020, 10.23919/Eusipco47968.2020.9287804

  4. D. Luengo, D. Meltzer and T. Trigano, Sparse ECG Representation with a Multi-Scale Dictionary Derived from Real-World Signals, 41th International Conference on Telecommunications and Signal Processing, 04-06 July 2018, Athens, Greece, 4 pages, 2018, 0.1109/TSP.2018.8441329

  5. T. Trigano, V. Kolesnikov, D. Luengo Garcia and A. Artes_Rodrigues, Grouped Sparsity Algorithm for Multichannel Intracardiac ECG Synchronization, Proceedings of EURASIP Conference in Signal Processing (EUSIPCO, Qualis rank: B1), 01-05 September 2014, Lisbon, Portugal, 2014, https://ieeexplore.ieee.org/document/6952547

  6. D. Luengo Garcia, J. Via, S. Monzon, T. Trigano and A. Artes-Rodrigues, Cross-Products LASSO, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 26-31 May 2013, Vancouver, Canada, 5 pages, 2013, 10.1109/ICASSP.2013.6638840

  7. M. Lopatin, N. Moskovitch, T. Trigano and Y. Sepulcre, Pileup Attenuation for Spectroscopic Signals Using A Sparse Reconstruction, in Proceedings of 27-th IEEE Convention of Electrical and Electronics Engineers in Israel, 14-17 November 2012, Eilat, Israel, 5 pages, 2012, 10.1109/EEEI.2012.6377045

  8. S. Monzon, D. Luengo Garcia, T. Trigano and A. Artes-Rodrigues, Sparse Spectral Analysis Of Atrial Fibrillation Electrograms, in 2012 IEEE International Workshop on Machine Learning for Signal Processing, 23-26 September 2012, Santander, Spain, 5 pages, 2012, 10.1109/MLSP.2012.6349721

  9. Y. Sepulcre and T. Trigano, Iterated Sparse Reconstruction for Activity Estimation in Nuclear Spectroscopy, Proceedings of EURASIP Conference in Signal Processing (EUSIPCO, Qualis rank: B1), 27-31 August 2012, Bucharest, Romania, 5 pages, 2012, https://ieeexplore.ieee.org/document/6334066

  10. T. Trigano, Y. Sepulcre, M. Roitman and U. Aferiat, On Nonhomogeneous Activity Estimation in Gamma Spectrometry using Sparse Signal Representation, in 2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, Nice, France, 649-652, 2011. 10.1109/SSP.2011.5967784

  11. T. Trigano, Y. Sepulcre, M. Tal and Y. Mashiach, Sparse Regression Algorithm for Activity Estimation in Gamma Spectrometry, in Proceedings of 26th IEEE Convention of Electrical and Electronics Engineers in Israel, 17-20 November 2010, Eilat, Israel, 268-272, 2010, 10.1109/EEEI.2010.5661973

  12. T. Trigano, U. Isserles and Y. Ritov, Semiparametric Density Estimation of Shifts for alignment of ECG data, in EURASIP Signal Processing Conference (EUSIPCO, Qualis rank: B1), August 25-29 2008, Lausanne, Switzerland, 1-5, 2008 https://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569101580.pdf

  13. T. Dautremer, E. Barat and T. Trigano, Nonparametric Bayesian Estimation of Censored counters Intensity from the indicator data, AIP Conference Proceedings, Vol 872(485), International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Paris, France, 8 pages, 2006, https://doi.org/10.1063/1.2423310

  14. T. Trigano, F. Roueff, E. Moulines, A. Souloumiac and T. Montagu, Energy Spectrum Reconstruction for HPGe Detectors Using Analytical Pile-Up Correction, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 14-19 May 2006, Toulouse, France, Volume 3, III-VII, 2006, 10.1109/ICASSP.2006.1660723

  15. T. Trigano, F. Roueff, A. Souloumiac and E. Moulines, Nonparametric Inference for Pile-up Correction in Nuclear Spectrometry, IEEE Statistical Signal Processing Workshop (SSP), 17-20 July 2005, Bordeaux, France, 754-750, 2005, 10.1109/SSP.2005.1628693.

  16. T. Trigano, E. Barat, T. Dautremer and A. Souloumiac, Pile-up Correction Algorithms for Nuclear Spectrometry, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP, Qualis rank: A1), 23-23 March 2005, Philadelphia, USA, iv/441 - iv/444, 2005, 10.1109/ICASSP.2005.1416040.

  17. T. Trigano, D. Mostefa and G. Chollet, Implementation of a classification tree in a ASV system, Proceedings of the COST-275 Workshop, Paris, 6 pages, 2002.

  18. T. Trigano, D. Mostefa, G. Chollet and D. Petrovska, Description of the ENST Speaker Verification System, NIST Speaker Recognition Workshop, Washington, 5 pages, 2002.

Other publications

Technical reports

  1. T. Trigano, Deconvolution methods for discrete histograms: multiplicative gradient algorithm for denoising energy spectra, CEA Saclay, Technical Report 05-031, 29 pages, 2005.

  2. T. Trigano, Numerical inversion of the Laplace transform: application for pileup correction of continuous-time signals for spectrometry, CEA Saclay, Technical Report 04-015, 44 pages, 2004.

  3. E. Barat, T. Dautremer and T. Trigano, Stochastic modeling for the photonic signal: pileup correction algorithm for discrete-time signals from spectrometry, CEA Saclay, Technical Report 03-018, 44 pages, 2003.

Theses

  1. T. Trigano, Statistical spectromatric signal processing: pileup correction algorithms for gamma spectrometry, PhD thesis (French), 159 pages, 2005.

  2. T. Trigano, On the asymptotic properties of Hill’s estimator, M.S. thesis, Paris 6 University, 47 pages, 2001.

Courses booklets

  1. T. Trigano and Dima Bykhovsky, Digital Signal Processing, course booklet for Digital Signal Processing taught at SCE, 120 pages, 2023 (Hebrew).

  2. T. Trigano, Digital Image Processing, course booklet for Medical Image Processing 1 taught at Tel-Aviv University, 132 pages, 2022 (English).

  3. T. Trigano, Mathematical Methods Using Python, course booklet for Numerical Methods with Python taught at SCE, 84 pages, 2019 (English).

  4. T. Trigano, Introduction to random processes, course booklet for Random Signals taught at Afeka, 81 pages, 2018 (Hebrew).

  5. T. Trigano, Information Theory, course booklet for Information Theory taught at SCE, 98 pages, 2017 (Hebrew).

  6. T. Trigano, Signals and Systems, course booklet for Signals and Systems taught at SCE, 98 pages, 2016 (Hebrew).

  7. T. Trigano, O. Ben Harush and M. Nanikashvili Introduction to Probability and Statistics, course booklet for Probability and Statistics taught at SCE, 134 pages, 2011 (Hebrew).