Given the success of the previous edition named “Nonstationary Signal Analysis in Geophysics 2020” (NoSAG20), we plan to organize for Summer 2023 a second edition, which will start with a three-day Summer School (May 15 - 17, ref. Program). The goal of the Summer School is to bring out the knowledge about non-linear data analysis tools for signal processing to the next generation of researchers. The School will consist in three courses of 8 hours which will be given by international experts in the Mathematics for Signal Processing and Geophysical applications. Please find the Lecturer names in the Invited Lecturers & Speakers tab.
The topics of the courses will include reviews of modern signal processing tools, such as Synchrosqueezing Wavelet, Empirical Mode Decomposition, Fast Iterative Filtering, et cetera. Each course will include tutorials focused on possible applications to geophysical systems and other applications. Hence the attendees will have the opportunity to learn about the most modern algorithms for non-linear data analysis directly from world experts and they will have a chance to practice their applications to real life problems.
The Summer School will be followed by a 1-day Capacity Building workshop (May 18, ref. Program) about Radio Sciences techniques for Space Weather, in which the students will focus on how techniques for Time-Frequency analysis are applied in the Space Weather domain, with a particular regard to Radio Sciences.
The two-day Conference that will close the event (May 19 - 20, ref. Program) will give to the young researchers the opportunity to meet and listen to talks given by top researchers working both in the development of new tools for signal processing, their mathematical analysis, their applications to modern geophysical and other applied fields problems, as well as to discover new open problems in Geophysics and other fields of research. Talks will span from inverse problems, like the determination of the unknown number of active sub-signals of a blind-source composite signal, to separation methods for multicomponent nonstationary signals with crossing instantaneous frequencies and chirps, passing by the analysis of big data by means of machine learning and deep learning approaches, the development and analysis of multivariate and multidimensional data analysis techniques, the time-frequency representation filtering and enhancing, and the development of comprehensive adaptive harmonic models to represent composite signals. We plan to have poster sessions throughout the conference, to provide young researchers the opportunity to showcase their work and research activities.