We invite submissions of original research in topics related to the conference (see below for some examples). Please keep the deadlines in mind.
Contributions from authors attending the SampTA conference are expected to be published in IEEE Xplore.
Formatting instructions
Prepare a PDF of no more than 4 pages, including figures and tables, using the IEEE style in LaTeX:
\documentclass[conference] {IEEEtran}
cf. https://www.ieee.org/conferences/publishing/templates.html
References do not count towards the page limit; you may include as many as necessary. Please do not tweak the style-sheet to try to increase the text length. Please prepare your submission in latex and keep the sources, as they will be needed for publication.
Review and presentation
Articles will be reviewed in a single-blind fashion: only the reviewers are anonymous, while reviewers know the authors’ names.
Accepted contributions have to be presented at the conference, either as a poster presentation or a talk. No shows will not be published on IEEE Xplore. During submission authors will be asked which type of presentation they prefer. However, due to availability of time slots and contributed session planning, it is not guaranteed that the author’s preference can be accommodated.
Some examples of welcome topics
Sampling Theory: sampling of space-time deterministic or stochastic signals; sampling on manifolds and graphs; compressive sensing; sampling theory in reproducing kernel Hilbert and Banach spaces; frame theory and its applications; shift-invariant and spline-type spaces; approximation error analysis and local reconstructions; analytic number theory and lattice point methods in sampling expansions; aspects of function spaces in sampling theory.
Signal and Image Processing: audio and image processing; inverse problems on graphs; signal transforms and expansions such as wavelets and Gabor; sparse representations; information theory and communications; analog to digital conversion and quantization; phase retrieval; control theory methods in signal processing.
Data Analysis: approximation theory related to neural networks; high dimensional data analysis; manifold learning; applications of frame theory in data analysis; mathematical foundations of deep learning; probabilistic methods for data analysis; uncertainty quantification; quantum computing and quantum learning.