Tom Trigano

@sce.ac.il

Department of Electrical Engineering



                    

https://researchid.co/tomtrigano

From 2006 to 2008 I worked as a postdoctoral fellow at the Hebrew University of Jersualem in the department of statistics. Since 2008 I am a senior lecturer in the Shamoon College of Engineering, Israel. My main research interests include applied statistics, statistical signal processing, pattern recognition and machine learning with applications to spectroscopy and biomedical applications.

EDUCATION

Tom (Thomas) Trigano was born in Paris, France in 1978, and received an M.Sc. in engineering from the Telecom Paris Tech (France) and an M.Sc in Applied Probability from Paris VI University (France) in 2001. He recieved the Ph.D. degree in signal processing from the Telecom Paris Tech in 2005

RESEARCH INTERESTS

Signal Processing, Machine Learning, Compressed Sensing, Applied Statistics, Biomedical applications, Nuclear Spectroscopy

43

Scopus Publications

398

Scholar Citations

13

Scholar h-index

17

Scholar i10-index

Scopus Publications

  • Deep Learning Based Pile-Up Correction Algorithm for Spectrometric Data Under High-Count-Rate Measurements
    Yiwei Huang, Xiaoying Zheng, Yongxin Zhu, Tom Trigano, Dima Bykhovsky, and Zikang Chen
    MDPI AG
    Gamma-ray spectroscopy is essential in nuclear science, enabling the identification of radioactive materials through energy spectrum analysis. However, high count rates lead to pile-up effects, resulting in spectral distortions that hinder accurate isotope identification and activity estimation. This phenomenon highlights the need for automated and precise approaches to pile-up correction. We propose a novel deep learning (DL) framework plugging count rate information of pile-up signals with a 2D attention U-Net for energy spectrum recovery. The input to the model is an Energy–Duration matrix constructed from preprocessed pulse signals. Temporal and spatial features are jointly extracted, with count rate information embedded to enhance robustness under high count rate conditions. Training data were generated using an open-source simulator based on a public gamma spectrum database. The model’s performance was evaluated using Kullback–Leibler (KL) divergence, Mean Squared Error (MSE) Energy Resolution (ER), and Full Width at Half Maximum (FWHM). Results indicate that the proposed framework effectively predicts accurate spectra, minimizing errors even under severe pile-up effects. This work provides a robust framework for addressing pile-up effects in gamma-ray spectroscopy, presenting a practical solution for automated, high-accuracy spectrum estimation. The integration of temporal and spatial learning techniques offers promising prospects for advancing high-activity nuclear analysis applications.

  • GaSim: A python class to generate simulated time signals for gamma spectroscopy
    Zikang Chen, Dima Bykhovsky, Xiaoying Zheng, Tom Trigano, and Yongxin Zhu
    Elsevier BV

  • Advanced Spectroscopy Time-Domain Signal Simulator for the Development of Machine and Deep Learning Algorithms
    Dima Bykhovsky, Zikang Chen, Yiwei Huang, Xiaoying Zheng, and Tom Trigano
    Institute of Electrical and Electronics Engineers (IEEE)

  • Nanofilament organization in highly tough fibers based on lamin proteins
    Yael Tzror, Mark Bezner, Shani Deri, Tom Trigano, and Kfir Ben-Harush
    Elsevier BV

  • Deep Learning Based Method for Activity Estimation from Short-Duration Gamma Spectroscopy Recordings
    Tom Trigano and Dima Bykhovsky
    Institute of Electrical and Electronics Engineers (IEEE)

  • Adaptive Trend Filtering for ECG Denoising and Delineation
    Tom Trigano, Shlomi Talala, and David Luengo
    Institute of Electrical and Electronics Engineers (IEEE)
    Standard recordings of electrocardiograhic signals are contaminated by a large variety of noises and interferences, which impair their analysis and the further related diagnosis. In this paper, we propose a method, based on compressive sensing techniques, to remove the main noise artifacts and to locate the main features of the pulses in the electrocardiogram (ECG). The motivation is to use Trend Filtering with a varying proximal parameter, in order to sequentially capture the peaks of the ECG, which have different functional regularities. The practical implementation is based on an adaptive version of the ADMM (alternating direction method of multiplier) algorithm. We present results obtained on simulated signals and on real data illustrating the validity of this approach, showing that results in peak localization are very good in both cases and comparable to state of the art approaches.

  • Fast algorithm for time decay estimation with applications to electrostatic ion beam traps
    T Trigano and Z Fradkin
    IOP Publishing
    Abstract The ability of peptide trapping in an electrostatic ion beam trap (EIBT) is used for the measurement of renin substrate lifetime dependence from the pressure. The time decay estimation is traditionally obtained by optimization of nonlinear curve-fitting in the least-squares sense. This paper presents a novel algorithm to address this problem, using a numerical differentiation method as the basis for lifetime estimation. Simulations results show that the proposed method provides results similar to those obtained with the classical approach, but is faster by about two orders of magnitude. An experimental result is detailed, which shows the adequacy of this algorithm for the real-life monitoring of decay measurements, not only for EIBT, but also for other processes such as luminescence where exponential decay is involved.

  • Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators
    Zikang Chen, Xiangcong Kong, Xiaoying Zheng, Yongxin Zhu, and Tom Trigano
    Springer Nature Switzerland


  • Improving Quality of Life Through Engineering Education. A Case Study
    David Luengo, Albert Treytl, Stephanie Nestawal, Peter Arras, Kinga Korniejenko, Galyna Tabunshchyk, and Tom Trigano
    IEEE
    By definition, a smart, sustainable city is technologically enabled, connected and agile to address economic, environmental and social challenges. The success of a smart city, however, also relies on the competencies of its citizens, suggesting a re-modelling of engineering education towards a learner-centred, competency-based education. This paper gives an overview of the challenges encountered throughout implementing the BIOART multinational curriculum development project. A project that tackled the challenges for an inclusive, resilient smart city concept by developing novel teaching and learning content and adopting innovative educational methodologies in biomedical engineering, a.o. in the context of an internationally organised summer school and online hackathon.

  • Efficient iteratively reweighted lasso algorithm for cross-products penalized sparse solutions
    David Luengo, Javier Via, and Tom Trigano
    IEEE
    In this paper, we describe an efficient iterative algorithm for finding sparse solutions to a linear system. Apart from the well-known L1 norm regularization, we introduce an additional cost term promoting solutions without too-close activations. This additional term, which is expressed as a sum of cross-products of absolute values, makes the problem non-convex and difficult to solve. However, the application of the successive convex approximations approach allows us to obtain an efficient algorithm consisting in the solution of a sequence of iteratively reweighted LASSO problems. Numerical simulations on randomly generated waveforms and ECG signals show the good performance of the proposed method.

  • Biomimetic nuclear lamin fibers with remarkable toughness and stiffness
    Maayan Khayat, Shani Deri, David Wolf, Tom Trigano, Ohad Medalia, and Kfir Ben-Harush
    Elsevier BV

  • Fast background removal of JPEG images based on HSV polygonal cuts for a foot scanner device
    T. Trigano and Y. Bechor
    Springer Science and Business Media LLC


  • Overcomplete Multi-scale Dictionaries for Efficient Representation of ECG Signals
    David Meltzer, David Luengo, and Tom Trigano
    Springer International Publishing

  • An efficient method to learn overcomplete multi-scale dictionaries of ECG signals
    David Luengo, David Meltzer, and Tom Trigano
    MDPI AG
    The electrocardiogram (ECG) was the first biomedical signal for which digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (QRS complexes and other waveforms, such as the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, such as the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies a fast sparse inference algorithm to model the signal using the constructed dictionary. As a result, our method is much more efficient from a computational point of view than other existing algorithms, thus becoming amenable to dealing with long recordings from multiple patients. Regarding the dictionary construction, we located first all the QRS complexes in the training database, then we computed a single average waveform per patient, and finally we selected the most representative waveforms (using a correlation-based approach) as the basic atoms that were resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet’s PTB database show the good performance of the proposed approach.


  • Sparse ECG Representation with a Multi-Scale Dictionary Derived from Real-World Signals
    David Luengo, David Meltzer, and Tom Trigano
    IEEE
    The electrocardiogram (ECG) was the first biomedical signal where digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (the QRS complexes and other waveforms like the P and T waves) and periods of inactivity (corresponding to isoelectric intervals like the PQ or ST segments), plus noise and interferences. In this work, we show how to construct a realistic multi-scale dictionary using waveforms recorded from realworld patients and how to apply this dictionary to obtain a sparse representation of ECG signals. Simulations on realworld records from Physionet show the good performance of the proposed approach.

  • Numerical Generation of Compound Random Processes with an Arbitrary Autocorrelation Function
    Dima Bykhovsky and Tom Trigano
    World Scientific Pub Co Pte Lt
    The generation of non-Gaussian random processes with a given autocorrelation function (ACF) is addressed. The generation is based on a compound process with two components. Both components are solutions of appropriate stochastic differential equations (SDEs). One of the components is a Gaussian process and the other one is non-Gaussian with an exponential ACF. The analytical study shows that a compound combination of these processes may be used for the generation of a non-Gaussian random process with a required ACF. The results are verified by two numerical examples.

  • Cosa: An accelerated ISTA algorithm for dictionaries based on translated waveforms
    Tom Trigano, Igor Shevtsov, and David Luengo
    Elsevier BV

  • Intensity Estimation of Spectroscopic Signals with an Improved Sparse Reconstruction Algorithm
    Tom Trigano and Jonas Cohen
    Institute of Electrical and Electronics Engineers (IEEE)
    One of the main measurements performed in a nuclear spectroscopy experiment is the activity of an unknown radioactive source. The use of digital apparatus and physical perturbations, known as pile-up effect, make this measurement difficult when the activity of the source is high. In recent contributions, the use of compressive sensing methods yielded good estimates of this activity. This letter presents an improvement of a previously described method. It takes into account the fact that the signal used for the activity estimation is sampled, and introduces another plug-in estimation to counterbalance the bias introduced by the sampling. Results on simulations and real data validate the proposed approach, but illustrate that a good fit between the dictionary used and the signal at hand is required.

  • Sparse reconstruction algorithm for nonhomogeneous counting rate estimation
    Thomas Trigano, Yann Sepulcre, and Yaracov Ritov
    Institute of Electrical and Electronics Engineers (IEEE)
    One of the main objectives of nuclear spectroscopy is the estimation of the counting rate of unknown radioactive sources. Recently, we proposed an algorithm based on a sparse reconstruction of the time signal in order to estimate precisely this counting rate, under the assumption that it remained constant over time. Computable bounds were obtained to quantify the performances. This approach, based on a postprocessed approach of a non-negative sparse regression of the time signal, performed well even when the activity of the source was high. The purpose of this paper is to present an extension of the previous method for an activity varying over time. It relies on the same preliminary sparse reconstruction. However, the postprocessed and plug-in steps are made differently to fit the nonhomogeneous framework. The adapted bounds are presented, and results on simulations illustrate the advantages and limitations of this method.

  • Pileup correction algorithm using an iterated sparse reconstruction method
    Tom Trigano, Ilia Gildin, and Yann Sepulcre
    Institute of Electrical and Electronics Engineers (IEEE)
    Every radioactive source can be characterized by an histogram obtained after collecting the energies of photons emitted from the source, also called energy spectrum. However, when the activity of this source is high, a physical phenomenon known as the pile-up effect distorts direct measurements, resulting in a significant distortion of the energy spectrum. We suggest in this letter an iterative algorithm to attenuate the pile-up effect and enhance the resulting energy spectra. It is based on iterations of a post-processed, non-negative, version of the Least Absolute Shrinkage and Selection Operator (LASSO). Results on simulations and real data illustrate the improvement obtained by the proposed method.

  • Fast digital filtering of spectrometric data for pile-up correction
    T. Trigano, E. Barat, T. Dautremer, and T. Montagu
    Institute of Electrical and Electronics Engineers (IEEE)
    This letter considers a problem stemming from the analysis of spectrometric data. When performing experiments on highly radioactive matter, electrical pulses recorded by the spectrometer tend to overlap, thus yielding severe distortions when computing the histogram of the pulses' energies. In this letter, we propose a fast recursive algorithm which estimates efficiently this histogram from measurements of the duration and energies of overlapping pulses. Its good performances are shown both on simulations and real data. Furthermore, its lower algorithmic complexity makes it more fitting for real-time implementation.

  • Blind analysis of atrial fibrillation electrograms: A sparsity-aware formulation
    David Luengo, Sandra Monzón, Tom Trigano, Javier Vía, and Antonio Artés-Rodríguez
    IOS Press
    The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.

RECENT SCHOLAR PUBLICATIONS

  • Deep Learning Based Pile-Up Correction Algorithm for Spectrometric Data Under High-Count-Rate Measurements
    Y Huang, X Zheng, Y Zhu, T Trigano, D Bykhovsky, Z Chen
    Sensors (Basel, Switzerland) 25 (5), 1464 2025

  • Advanced Spectroscopy Time-Domain Signal Simulator for the Development of Machine and Deep Learning Algorithms
    D Bykhovsky, Z Chen, Y Huang, X Zheng, T Trigano
    IEEE Sensors Letters 2025

  • GaSim: A python class to generate simulated time signals for gamma spectroscopy
    Z Chen, D Bykhovsky, X Zheng, T Trigano, Y Zhu
    SoftwareX 29, 102037 2025

  • Nanofilament organization in highly tough fibers based on lamin proteins
    Y Tzror, M Bezner, S Deri, T Trigano, K Ben-Harush
    Journal of the Mechanical Behavior of Biomedical Materials 160, 106748 2024

  • Deep Learning Based Method for Activity Estimation from Short-Duration Gamma Spectroscopy Recordings
    T Trigano, D Bykhovsky
    IEEE Transactions on Instrumentation and Measurement 2024

  • Adaptive trend filtering for ECG denoising and delineation
    T Trigano, S Talala, D Luengo
    IEEE Journal of Biomedical and Health Informatics 27 (12), 5755-5766 2023

  • Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators
    T Trigano
    Smart Computing and Communication: 7th International Conference, SmartCom 2023

  • Intracardiac ECG pulse localization using overlapping block sparse reconstruction
    T Trigano, D Luengo
    Biomedical Signal Processing and Control 79, 103921 2023

  • Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators
    Z Chen, X Kong, X Zheng, Y Zhu, T Trigano
    International Conference on Smart Computing and Communication, 258-267 2022

  • Fast algorithm for time decay estimation with applications to electrostatic ion beam traps
    T Trigano, Z Fradkin
    Measurement Science and Technology 34 (2), 025701 2022

  • Improving Quality of Life Through Engineering Education. A Case Study
    D Luengo, A Treytl, S Nestawal, P Arras, K Korniejenko, G Tabunshchyk, ...
    2022 IEEE European Technology and Engineering Management Summit (E-TEMS 2022

  • Efficient iteratively reweighted lasso algorithm for cross-products penalized sparse solutions
    D Luengo, J Va, T Trigano
    2020 28th European Signal Processing Conference (EUSIPCO), 2045-2049 2021

  • Digital signal processing of ECG and PCG signals
    D Luengo, D Oss, T Trigano
    Teaching and subjects on bio-medical engineering, 177-211 2021

  • ECG-Based Biometric Recognition
    D Meltzer, D Luengo, T Trigano
    Teaching and subjects on bio-medical engineering, 155 2021

  • Biomimetic nuclear lamin fibers with remarkable toughness and stiffness
    M Khayat, S Deri, D Wolf, T Trigano, O Medalia, K Ben-Harush
    International journal of biological macromolecules 163, 2060-2067 2020

  • Fast background removal of JPEG images based on HSV polygonal cuts for a foot scanner device
    T Trigano, Y Bechor
    Journal of Real-Time Image Processing 17 (4), 981-992 2020

  • Fast proximal optimization for sparse reconstruction with dictionaries based on translated waveforms
    T Trigano, S Vaknin, D Luengo
    Signal Processing 169, 107379 2020

  • Efficient Iteratively reweighted LASSO algorithm for cross-products penalized sparse solutions
    D Luengo Garca, J Va Rodrguez, T Trigano
    Institute of Electrical and Electronics Engineers, Inc. 2020

  • Process to isolate object of interest in image
    L Schwartz, T Trigano, Y Bechor
    US Patent 10,417,772 2019

  • FAR applied
    D Luengo, D Meltzer, T Trigano
    Selected Papers from the 2018 41st International Conference on 2019

MOST CITED SCHOLAR PUBLICATIONS

  • Sparse Regression Algorithm for Activity Estimation in Spectrometry
    Y Sepulcre, T Trigano, Y Ritov
    IEEE Transactions on Signal Processing 61 (17), 4347-4359 2013
    Citations: 33

  • Statistical pileup correction method for HPGe detectors
    T Trigano, A Souloumiac, T Montagu, F Roueff, E Moulines
    IEEE Transactions on Signal Processing 55 (10), 4871-4881 2007
    Citations: 26

  • Semiparametric curve alignment and shift density estimation for biological data
    T Trigano, U Isserles, Y Ritov
    IEEE Transactions on Signal Processing 59 (5), 1970-1984 2011
    Citations: 25

  • Sparse spectral analysis of atrial fibrillation electrograms
    S Monzn, T Trigano, D Luengo, A Artes-Rodriguez
    2012 IEEE International Workshop on Machine Learning for Signal Processing, 1-6 2012
    Citations: 23

  • Cross-products LASSO
    D Luengo, J Va, S Monzn, T Trigano, A Arts-Rodrguez
    2013 IEEE International Conference on Acoustics, Speech and Signal 2013
    Citations: 19

  • Pile-up correction algorithms for nuclear spectrometry
    T Trigano, T Dautremer, E Barat, A Souloumiac
    Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech 2005
    Citations: 18

  • Pileup correction algorithm using an iterated sparse reconstruction method
    T Trigano, I Gildin, Y Sepulcre
    IEEE Signal Processing Letters 22 (9), 1392-1395 2015
    Citations: 17

  • Blind analysis of atrial fibrillation electrograms: a sparsity-aware formulation
    D Luengo, S Monzn, T Trigano, J Va, A Arts-Rodrguez
    Integrated Computer-Aided Engineering 22 (1), 71-85 2015
    Citations: 17

  • Fast digital filtering of spectrometric data for pile-up correction
    T Trigano, E Barat, T Dautremer, T Montagu
    IEEE Signal Processing Letters 22 (7), 973-977 2014
    Citations: 16

  • CoSA: An accelerated ISTA algorithm for dictionaries based on translated waveforms
    T Trigano, I Shevtsov, D Luengo
    Signal Processing 139, 131-135 2017
    Citations: 15

  • Nonparametric inference of photon energy distribution from indirect measurement
     Moulines, F Roueff, A Souloumiac, T Trigano
     2007
    Citations: 14

  • Traitement statistique du signal spectromtrique: tude du dsempilement de spectre en nergie pour la spectromtrie Gamma
    T Trigano
    Tlcom ParisTech 2005
    Citations: 14

  • Sparse reconstruction algorithm for nonhomogeneous counting rate estimation
    T Trigano, Y Sepulcre, Y Ritov
    IEEE Transactions on Signal Processing 65 (2), 372-385 2016
    Citations: 13

  • Grouped sparsity algorithm for multichannel intracardiac ECG synchronization
    T Trigano, V Kolesnikov, D Luengo, A Arts-Rodrguez
    2014 22nd European Signal Processing Conference (EUSIPCO), 1537-1541 2014
    Citations: 13

  • On nonhomogeneous activity estimation in gamma spectrometry using sparse signal representation
    T Trigano, Y Sepulcre, M Roitman, U Aferiat
    2011 IEEE Statistical Signal Processing Workshop (SSP), 649-652 2011
    Citations: 13

  • An efficient method to learn overcomplete multi-scale dictionaries of ECG signals
    D Luengo, D Meltzer, T Trigano
    Applied Sciences 8 (12), 2569 2018
    Citations: 12

  • Intensity estimation of spectroscopic signals with an improved sparse reconstruction algorithm
    T Trigano, J Cohen
    IEEE Signal Processing Letters 24 (5), 530-534 2017
    Citations: 10

  • Sparse ECG representation with a multi-scale dictionary derived from real-world signals
    D Luengo, D Meltzer, T Trigano
    2018 41st International Conference on Telecommunications and Signal 2018
    Citations: 9

  • Process to isolate object of interest in image
    L Schwartz, T Trigano, Y Bechor
    US Patent 10,417,772 2019
    Citations: 8

  • Pileup attenuation for spectroscopic signals using a sparse reconstruction
    M Lopatin, N Moskovitch, T Trigano, Y Sepulcre
    2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, 1-5 2012
    Citations: 7