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 students and innovators about the history of our profession, the challenges we have overcome, and the breakthroughs that have led to the incredible growth of Signal Processing (SP). More importantly, this reflection will help shape our path forward, by inspiring new innovations, and also bringing awareness of the ethical issues associated with evolving and emerging technologies. This awareness will help us to develop meaningful safeguards and ensure responsible use of these technologies.

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 difficult to reverse climate changes brought on by habits that we would have to radically change and industrial lobbying focused solely on profits at whatever cost. I fear that based on all of the disasters we have been experiencing, no one sound person can challenge the climate changes taking place around the world.

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 must be considered carefully in all our projects. Scientific integrity is another topic that I often discuss as it is actually a duty for all researchers for many of the reasons I developed in my November 2022 editorial [1] .

Solving a Source separation problem using a maximum likelihood approach offers the possibility to encode, in addition to the mutual statistical independence of the sources, additional prior information on these signals by specifying their probability distribution functions. Such setting also corresponds to some specific choice of the non-linear functions in independent component analysis (ICA) algorithms based on non-linear decorrelation (See chapter \ref{chap:1} of this book). The Bayesian inference strategy offers an additional flexibility by allowing to take into account the noise statistics and to account for prior information on the mixing coefficients. The purpose of this chapter is to present the Bayesian approach for source separation. The general framework of Bayesian estimation will be presented in the first section of this chapter. It includes the specification of the likelihood resulting from the statistical description of the noise and the formulation of statistical models encoding the available information on the sought source signals and mixing coefficients. Resulting algorithms in the case of linear or nonlinear mixing models in the context of physical-chemical sensing will be presented. The Bayesian approach will be illustrated through some examples of case studies based on spectral data resulting from spectrometry measurements.

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 in special issues, the specificities of which are detailed on the SPM webpage “Information for Authors - SPM”: https://signalprocessingsociety.org/publications-resources/ieee-signal-processing-magazine/information-authors-spm .

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)

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, however, they could not be included in the first part of the special issue, and we are glad to bring them to you now.

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 in Turkey, Syria, and Morocco; huge floods in Libya and India; gigantic fires in North America and Southern Europe; and an avalanche of stones in the Alps, to name a few. In such a context, I believe that the IEEE slogan, “Advancing Technology for Humanity,” is incredibly relevant and timely. It also must be viewed in a wider sense, including the preservation of Earth and sustainable development. In point of fact, what would become of humanity without Earth? I believe that we must always have this in mind when contemplating our future projects, asking for funding, and while teaching.

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.

This chapter gives a general overview of the source separation problem and its underlying hypotheses, and of methods and algorithms for its solving, with an emphasis on the context of data processing in physical/chemical sensing applications. After explaining the motivations of source separation from the signal processing viewpoint, we present the mathematical formulation of the source separation problem, which can then be viewed as a matrix (or tensor) factorization model, for which there are essential indeterminacies. For achieving suitable solutions, it is necessary to assume priors on the source signals and on the mixing process. We especially focus on independent component analysis (ICA), a classical approach based on mutual statistical independence of the sources. We then give some examples of chemical and physical applications that can be formulated as source separation problems, and we provide a discussion on the main properties of the measurements and of the related mixing models. Then, we briefly present some separation approaches that can exploit these properties using various tools and methods which will be developed in the book. Finally, we describe the organization of the book and the notations used along all the chapters.

It is our great pleasure to introduce the first 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. When the Society was founded in 1948, we couldn’t imagine, for instance, how wireless networks of smartphones would be able to connect us easily at all times, or that an image processing algorithm would be able to detect cancer in a few seconds. Those are just simple examples of the immense technological progress over the past 75 years, which became possible thanks in great part to the dedicated work of professional members of the SPS.

“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, hospitals, and schools. This dramatic event was ...

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 segmentation. In this work, we proposed a framework to segment MS lesions in FluidAttenuated Inversion Recovery (FLAIR) and T2 MRI images by modified U-Net and modified Attention U-Net. For this purpose, we developed some extra preprocessing on MRI scans, made modifications in the loss function of U-Net and Attention U-Net, and proposed using the union of FLAIR and T2 predictions to reach a better performance. Results show that the union of FLAIR and T2 predicted masks by the modified Attention U-Net reaches the performance of 82.30% in terms of Dice Similarity Coefficient (DSC) in the test dataset, which is a considerable improvement compared to the previous works.

INTRODUCTION ...............................................................................................................1 Context.....................................................................................................................1 Living Learning Communities...........................................................................1 The Mount Leadership Society Scholars Program ............................................2 Purpose of the Study ................................................................................................3 Study Methodology..................................................................................................3 Limitations ...............................................................................................................4 Definition of Terms..................................................................................................5

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 papers.

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 this purpose are either greedy and unreliable or computationally costly. In this paper, we propose a new performance index, called CorrIndex, whose reliability can be proved theoretically. Moreover, compared to previous performance indices, it has a low computational cost. Theoretical results and computer experiments demonstrate these advantages of CorrIndex compared to other indices.(c) 2022 Elsevier B.V. All rights reserved.