Christian Jutten

Univ. Grenoble-Alpes

My three years of service as the editor-in-chief (EIC) of Signal Processing Magazine ( SPM ) are now coming to a close. During the past three years, many of us were deeply affected by serious political, social, and environmental events such as the war in Ukraine; protests for freedom in Iran; coups d’état in Africa; the COVID-19 pandemic; seisms...

The objectives of IEEE Signal Processing Magazine ( SPM ) are to propose, for any IEEE Signal Processing Society (SPS) member and beyond, a wide range of tutorial articles on both methods and applications in signal and image processing. The articles are divided into different categories: feature articles, column and forum articles, and articles...

The ICASSP 2023 conference in Rhodes, Greece, was remarkable from multiple perspectives. Notably, this was the first fully in-person ICASSP after three consecutive virtual conferences, which were necessitated by the COVID-19 pandemic. Attendees fully embraced the opportunity to engage in live interactions and reestablish their networks.

It is our great pleasure to introduce the second part of this special issue to you! The IEEE Signal Processing Society (SPS) has completed 75 years of remarkable service to the signal processing community. The eight selected articles included in this second part are clear portraits of that. As the review process for these articles took longer, howe...

The 75th anniversary of the IEEE Signal Processing Society (SPS) is an ideal time to look at the rapid advances in our field and the many ways that these increasingly powerful technologies have transformed our professions and the world. This is not just a time to celebrate past achievements and pat ourselves on the back, but also to educate young s...

Je crois invinciblement que la science et la paix triompheront de l’ignorance et de la guerre ( I believe invincibly that science and peace will triumph over ignorance and war )

In previous editorials, SPS President Athina Petropulu and I had the opportunity to say a few words about ethics, especially taking into account the usefulness of our research projects, for humanity and Earth, in a wide sense. In the current energy crisis and the explosion of costs, this issue becomes still more important, and I believe that it mus...

Blind source separation (BSS) techniques have revealed to be promising approaches for, among other, biomedical signal processing applications. Specifically, for the noninvasive extraction of fetal cardiac signals from maternal abdominal recordings, where conventional filtering schemes have failed to extract the complete fetal ECG components. From p...

First, I would like to wish you and your loved ones a nice new year filled with health and happiness. The last few years have been challenging for various reasons: the COVID-19 pandemic, climatic events, and the war in Ukraine, to name a few. It seems impossible to be able to stop the megalomania and madness of some human beings. It also seems diff...

Among the methods for training Multilayer Perceptron networks, backpropagation is one of the most used ones on problems of supervised learning. However, it presents some limitations, such as local convergence and the a priori choice of the network topology. Another possible approach for training is to use Genetic Algorithms to optimize the weights...

Ethics and scientific integrity are key concepts in scientific research.

Ethics in science is essential for various reasons and is a duty for scientists. The full sense of the word ethics may differ according to languages and countries. For instance, in France, we typically make a distinction between ethics and scientific integrity, while scientific integrity is a part of ethics in the United States. For instance, the...

As humans, we cannot be indifferent to the increasing number of dramatic events taking place in the world: fires, tornadoes, floods, and—recently—the collapse of a huge block of the Marmolada glacier in the Italian Alps. All are clear evidence to the global warming of the Earth. As scientists in signal and image processing and in the data sciences,...

The July issue of IEEE Signal Processing Magazine ( SPM ) is a special issue focused on “Explainability in Data Science: Interpretability, Reproducibility, and Replicability.” With increased enthusiasm for machine learning, it is a very timely topic, and I invite every IEEE Signal Processing Society (SPS) member to read these very instructive pa...

“Science without conscience is only ruin of the soul” said François Rabelais. This centuries-old quote still resonates, today maybe louder than ever. I began to write this editorial at the end of February when Russian tanks and soldiers invaded Ukraine and waves of bombers began dropping their bombs on Ukrainian cities, targeting civilian buildings...

For me and, probably, many readers, each issue of IEEE Signal Processing Magazine ( SPM ) is the opportunity and pleasure to learn something new in the area of signal and image processing. In addition to lecture notes, tips-and-tricks articles, special reports, and so on, which propose interesting and clever solutions to typical signal or image...

Multiple Sclerosis (MS) is a Central Nervous System (CNS) disease that Magnetic Resonance Imaging (MRI) system can detect and segment its lesions. Artificial Neural Networks (ANNs) recently reached a noticeable performance in finding MS lesions from MRI. U-Net and Attention U-Net are two of the most successful ANNs in the field of MS lesion segment...

Permutation and scaling ambiguities are relevant issues in tensor decomposition and source separation algorithms. Although these ambiguities are inevitable when working on real data sets, it is preferred to eliminate these uncertainties for evaluating algorithms on synthetic data sets. As shown in the paper, the existing performance indices for thi...

First of all, I wish you and your relatives a very happy new year. I hope that 2022 will differ from the two previous years, in which the COVID-19 pandemic disrupted many of our lives, both personal and professional. Even if virtual events can have some advantages, I hope that the main conferences and workshops in 2022 will be held face to face or...

Representing data by means of graph structures identifies one of the most valid approach to extract information in several data analysis applications. This is especially true when multimodal datasets are investigated, as records collected by means of diverse sensing strategies are taken into account and explored. Nevertheless, classic graph signal...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Signal Processing Magazine ( SPM ) is a fully edited magazine. This means that, after the review process, the final files provided by the authors are processed by a team of editors under the supervision and the responsibility of the magazine’s ma...

Presents the introductory editorial for this issue of the publication.

Objective: Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel...

This paper proposes an efficient algorithm for robust sensor placement with the purpose of recovering a source signal from noisy measurements. To model uncertainty on the spatially-variant sensors gain and on the spatially correlated noise, we assume that both are realizations of Gaussian processes. Since the signal to noise ratio (SNR) is also unc...

Presents the introductory editorial for this issue of the publication.

The spectral signatures of the materials contained in hyperspectral images, also called endmembers ( EMs ), can be significantly affected by variations in atmospheric, illumination, and environmental conditions that typically occur within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the EMs, which p...

The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem. Numerous algorithms have been developed for this problem, which are based on the exact or approximate joint diagonalization of second or higher order cumulant matrices/tensors of multichannel data. While a great body of research has b...

Modern data analytics take advantage of ensemble learning and transfer learning approaches to tackle some of the most relevant issues in data analysis, such as lack of labeled data to use to train the analysis models, sparsity of the information, and unbalanced distributions of the records. Nonetheless, when applied to multimodal datasets (i.e., da...

Presents the introductory editorial for this issue of the publication.

Presents information on new editors for this issue of the publication.

Smoothness priors and quadratic variation (QV) regularization are widely used techniques in many applications ranging from signal and image processing, computer vision, pattern recognition, and many other fields of engineering and science. In this contribution, an extension of such algorithms to band-stop smoothing filters (BSSFs) is investigated....

Multimodal learning, also known as multi-view learning, data integration, or data fusion, is an emerging field in signal processing, machine learning, and pattern recognition domains. It aims at building models, learned from several related and complementary modalities, in order to increase the generalization performances of a predictive learning m...

Presents the introductory editorial for this issue of the publication.

A brain–computer interface (BCI) is a common device for communication between the human brain and a computer. This article investigates the efficiency of using a 3-D interface for BCI machines. For this purpose, the P300 speller, which is a BCI device that enables the user to spell characters on a screen using brain waves, is modified. The classica...

The paper proposes a framework for unification of the penalized least-squares optimization (PLSO) and forward-backward filtering scheme. It provides a mathematical proof that forward-backward filtering (zero-phase IIR filters) can be presented as instances of PLSO. On the basis of this result, the paper then represents a unifying approach to the de...

Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth's surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this article, we aim at predicting the maximum information extraction that ca...

Objective: We present a transfer learning method for datasets with different dimensionalities, coming from different experimental setups but representing the same physical phenomena. We focus on the case where the data points are symmetric positive definite (SPD) matrices describing the statistical behavior of EEG-based brain computer interfaces (...

Hyperspectral image unmixing has proven to be a useful technique to interpret hyperspectral data, and is a prolific research topic in the community. Most of the approaches used to perform linear unmixing are based on convex geometry concepts, because of the strong geometrical structure of the linear mixing model. However, many algorithms based on c...

In this paper, we are interested in optimal sensor placement for signal extraction. Recently, a new criterion based on output signal to noise ratio has been proposed for sensor placement. However, to solve the optimization problem, a greedy approach is used over a grid, which is not optimal. To improve this method, we present an optimization approa...

The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what pr...

In hyperspectral imaging, spectral unmixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene and their relative proportions in every pixel. While a linear mixing model was used for a long time, the complex nature of the physicochemical phenomena that affect the sp...

Smoothness priors is a well-known and most commonly used method in the analysis of stochastic processes making it very useful in the field of stochastic signal processing. It is particularly suited for smoothing the noisy data and detrending the time-series signals. The method is based on an optimization problem where the n-th order derivative of t...

Objective: This research explores absence seizures using data recorded from different layers of somatosensory cortex of four genetic absence epilepsy rats from Strasbourg (GAERS). Localizing the active layers of somatosensory cortex (spatial analysis) and investigating the dynamics of recorded seizures (temporal analysis) are the main goals of thi...

Text mining, as a special case of data mining, refers to the estimation of knowledge or parameters necessary for certain purposes, such as unsupervised clustering by observing various documents. In this context, the topic of a document can be seen as a hidden variable, and words are multi-view variables related to each other by a topic. The main go...

We propose a new algorithm for finding a sparse solution of a linear system of equations using l0 minimization. The proposed algorithm relies on approximating the non-smooth l0 (pseudo) norm with a differentiable function. Unlike other approaches, we utilize a particular definition of l0 norm which states that the l0 norm of a vector can be compute...

Objective: Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel...

In this study, we analyze the absence epileptic seizures using the data recorded from different layers of somatosensory cortex of absence epileptic rats. We aim to (1) extract the epileptic activities or sources generating the seizures, and (2) investigate the temporal changes of seizures. To achieve our goals, we describe the recorded seizures by...

Abstract: This paper focuses on the optimal sensor placement problem with the purpose of signal extraction in an underdetermined noisy setting. Assuming prior information on the spatial gain of the measured signal and on the spatial noise correlation, we propose a sensor placement criterion based on the maximization of the average signal to noise r...

Hyperspectral image unmixing has proven to be a useful technique to interpret hyperspectral data, and is a prolific research topic in the community. Most of the approaches used to perform linear unmixing are based on convex geometry concepts, because of the strong geometrical structure of the linear mixing model. However, two main phenomena lead to...

Hyperspectral image unmixing has proven to be a useful technique to interpret hyperspectral data, and is a prolific research topic in the community. Most of the approaches used to perform linear unmixing are based on convex geometry concepts, because of the strong geometrical structure of the linear mixing model. However, two main phenomena lead to...

In hyperspectral imaging, spectral unmixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene and their relative proportions in every pixel. While a linear mixing model was used for a long time, the complex nature of the physical mixing processes, led to shift the...

In hyperspectral imaging, spectral unmixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene and their relative proportions in every pixel. While a linear mixing model was used for a long time, the complex nature of the physical mixing processes, led to shift the...

The measure timetable plays a critical role for the accuracy of the estimator. This article deals with the optimization of the schedule of measures for observing a random process in time using a Kalman filter, when the length of the process is finite and fixed, and a fixed number of measures are available. The measuring devices are allowed to diffe...

Hyperspectral images for remote sensing provide much more information than conventional imaging techniques, allowing a precise identification of the materials in the observed scene, but their limited spatial resolution makes that observations are usually mixtures of the contributions of several materials. The spectral unmixing problem aims at recov...

Objective: This paper presents a Transfer Learning approach for dealing with the statistical variability of electroencephalographic (EEG) signals recorded on different sessions and/or from different subjects. This is a common problem faced by brain-computer interfaces (BCI) and poses a challenge for systems that try to reuse data from previous rec...

Let $ A = \{A_{ij} \}_{i, j \in I}$, where I is an index set, be a doubly indexed family of matrices, where $A_{ij}$ is $n_i \times n_j$. For each $i \in I$, let $ V_i$ be an $n_i$-dimensional vector space. We say A is reducible in the coupled sense if there exist subspaces, $ U_i \subseteq V_i$, with $ U_i \neq \{0\}$ for at least one $i \in I$, a...

This paper deals with the identifiability of joint independent subspace analysis (JISA). JISA is a recently-proposed framework that subsumes independent vector analysis (IVA) and independent subspace analysis (ISA). Each underlying mixture can be regarded as a dataset; therefore, JISA can be used for data fusion. In this paper, we assume that each...

In the context of nonlinear Blind Source Separation (BSS), the Post-Nonlinear (PNL) model is of great importance due to its suitability for practical nonlinear problems. Under certain mild constraints on the model, Independent Component Analysis (ICA) methods are valid for performing source separation, but requires use of Higher-Order Statistics (H...

In medical applications, quantitative analysis of breath may open new prospects for diagnosis or for patient monitoring. To detect acetone, a breath biomarker for diabetes, we use a single metal-oxide (MOX) gas sensor working in a dual temperature mode. We propose a linear-quadratic model to describe the mixing model mapping gas concentrations to M...

This paper presents a data-driven approach for analyzing multivariate time series. It relies on the hypothesis that high-dimensional data often lie on a low-dimensional manifold whose geometry may be revealed using manifold learning techniques. We define a notion of distance between multi-variate time series and use it to determine a low-dimensiona...

This research temporally explores absence epileptic seizures using depth cortical data recorded from different layers of the somatosensory cortex of Genetic Absence Epilepsy Rats from Strasbourg (GAERS). We characterize the recorded absence seizures by a linear combination of a few static and dynamic sources. Retrieving these sources from the recor...

In the context of Post-Nonlinear (PNL) mixtures, source separation based on Second-Order Statistics (SOS) is a challenging topic due to the inherent difficulties when dealing with nonlinear transformations. Under the assumption that sources are temporally colored, the existing SOS-inspired methods require the use of Higher-Order Statistics (HOS) as...

In this paper, we discuss the joint blind source separation (JBSS) of real-valued Gaussian stationary sources with uncorrelated samples from a new perspective. We show that the second-order statistics of the observations can be reformulated as a coupled decomposition of several tensors. The canonical polyadic decomposition (CPD) of each such tensor...

The aim of our work is to quantify two gases (acetone and ethanol) diluted in an air buffer using only a single metal oxide (MOX) sensor. We took advantage of the low selectivity of the MOX sensor, exploiting a dual-temperature mode. Working at two temperatures of the MOX sensitive layer allowed us to obtain diversity in the measures. Two virtual s...

Hyperspectral images provide much more information than conventional imaging techniques, allowing a precise identification of the materials in the observed scene, but because of the limited spatial resolution, the observations are usually mixtures of the contributions of several materials. The spectral unmixing problem aims at recovering the spectr...

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regre...

This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a non-smooth and non-convex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up...

This paper introduces a novel algorithm for the online estimate of the Riemannian mixture model parameters. This new approach counts on Riemannian geometry concepts to extend the well-known Titterington approach for the online estimate of mixture model parameters in the Euclidean case to the Riemannian manifolds. Here, Riemannian mixtures in the Ri...

Data representation plays an important role in performance of machine learning algorithms. Since data usually lacks the desired quality, many efforts have been made to provide a more desirable representation of data. Among many different approaches, sparse data representation has gained popularity in recent years. In this paper, we propose a new sp...

Hyperspectral image unmixing is a source separation problem whose goal is to identify the signatures of the materials present in the imaged scene (called endmembers), and to estimate their proportions (called abundances) in each pixel. Usually, the contributions of each material are assumed to be perfectly represented by a single spectral signature...

Objective: This paper tackles the problem of transfer learning in the context of EEG-based Brain Computer Interface (BCI) classification. In particular the problems of cross-session and cross-subject classification are considered. These problems concern the ability to use data from previous sessions or from a database of past users to calibrate an...

Riemannian geometry has been found accurate and robust for classifying multidimensional data, for instance, in brain-computer interfaces based on electroencephalography. Given a number of data points on the manifold of symmetric positive-definite matrices, it is often of interest to embed these points in a manifold of smaller dimension. This is nec...

In this paper, a novel approach for performing Blind Source Separation (BSS) in nonlinear mixtures is proposed, and their separability is studied. It is shown that this problem can be solved under a few assumptions, which are satisfied in most practical applications. The main idea can be considered as transforming a time-invariant nonlinear BSS pro...