Aims and scope

The journal "Sampling Theory, Signal Processing, and Data Analysis” is a continuation of the journal "Sampling Theory in Signal and Image Processing” and focuses on the mathematics relating to sampling theory, signal processing, data analysis, and associated recovery problems from partial or indirect information. It aims at inducing interactions leading to cross-disciplinary advances.

The focus of the journal is on high quality research papers.  Well-written survey or expository articles about cutting-edge research topics, summarizing the state of the art, will also be considered. Seminal theoretical papers or papers that significantly advance understanding of applications or resolve important standing issues are particularly welcome.

The scope includes theoretical, computational and significant applied aspects of the following topics:
Sampling Theory, Signal and Image Processing, Data Analysis, reaching from traditional Fourier analytic to cutting edge methods such as Compressive Sensing, Atomic Decomposition and  Deep Learning.

Topics covered by the journal:

Sampling Theory 
· sampling of space-time deterministic or stochastic signals 
· sampling on the sphere and on more general manifolds
· sampling on graphs
· compressive sensing
· sampling theory in reproducing kernel Hilbert and Banach spaces
· frame theory and its applications in sampling theory
· shift-invariant and spline-type spaces 
· approximation error analysis and local reconstructions 
· analytic number theory and lattice point methods in sampling expansions
· sampling in coorbit theory and group representations
· aspects of function spaces in sampling theory (Sobolev spaces, Besov spaces, Wiener amalgam spaces and others) 
· operator and functional analytic methods related to the above topics

Signal and Image Processing
· audio and image processing
· signal processing and inverse problems on graphs
· signal transforms such as the scattering transform
· wavelets, shearlets, Gabor expansions
· atomic decompositions and related transforms
· information theory and communications
· analog to digital conversion and quantization
· phase retrieval problems
· control theory methods in signal processing
· interaction between inverse problems, signal analysis, and image processing
· operator and functional analytic methods related to the above topics

Data Analysis
· machine learning and neural networks
· high dimensional data analysis and manifold learning
· application of frame theory in data analysis
· mathematical foundations of deep learning
· probabilistic methods for data analysis
· reproducing kernel methods in machine learning and data analysis
· inverse problems, data assimilation and uncertainty quantification
· sparsity in data analysis
· quantum computing and quantum learning
· operator and functional analytic methods related to the above topics