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May the year 2023 bring everyone closer to the fulfilment of their dreams. We have left behind a year marked with successes on multiple fronts, including health and technology as well as a year filled with proud people risking their lives for freedom. In particular, two big movements have captured our hearts: the fierce resistance of Ukranian people against the Russian invasion of their country, and the prodemocracy uprising in Iran with women in the lead.

IEEE members are bound by the IEEE Code of Ethics [1] . By becoming IEEE members, we commit ourselves to the highest ethical and professional conduct. We agree to uphold the practices of ethical design and sustainable development; protect the privacy of others; and promptly disclose factors that might endanger the public or the environment. We strive to improve the public understanding of the implications of technology.

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.

This past May marked the beginning of our return to face-to-face events after almost three years of pandemic-forced virtual-only interactions. The first attempt was the 2022 ICASSP! Planning for large international events in the era of postpandemic uncertainty is not an easy undertaking. Of course, signal processing is all about dealing with uncertainty—and who would be better at planning than IEEE Signal Processing Society (SPS) people?

Designing a perfect filter (i.e., flat passband, sharp transition band, and highly suppressed stopband) is always the goal of digital signal processing practitioners. This goal is reachable if we make no consideration of implementation complexity. In other words, the challenge of designing a high-performance filter is to leverage the distortion tradeoff in the passband, transition band, and stopband. 

Fourier theory is the backbone of signal processing (SP) and communication engineering. It has been widely used in almost all branches of science and engineering in numerous applications since its inception. However, Fourier representations such as Fourier series (FS) and Fourier transform (FT) may not exist for some signals that fail to fulfill a predefined set of Dirichlet conditions (DCs). 

In separate projects, research teams based in Spain and Germany are using signal processing to help develop new ways of creating distortion-free brain imaging and detecting deceptively fake photographic images.

This article discusses the contradiction between the exploding energy demand of artificial intelligence (AI) and the information and communication (ICT) industry as a whole and the parallel strong request for energy sobriety imposed by the need to mitigate the impact of climate change and the anticipated collapse of civilization as we know it.

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.

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.

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