Sampling Theory, Signal Processing, and Data Analysis (SaSiDa) is a journal focusing on the mathematical aspects of sampling theory, signal processing, and data analysis. Welcomes papers on the mathematics of data science and machine learning. Encourages cross-disciplinary advances and interactions. Publishes high-quality research papers, survey articles, and seminal theoretical papers. Covers a wide range of topics from traditional Fourier analytic methods to cutting-edge techniques like Compressive Sensing, Atomic Decomposition, and Deep Learning.
⚡ Speed vs Prestige
How does this journal balance review speed with impact level?
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2007
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Algebra and Number TheoryQ2
AnalysisQ2
Computational MathematicsQ2
Radiology, Nuclear Medicine and ImagingQ2
Signal ProcessingQ2
Subject Classification
Web of Science Categories
MathematicsMathematics, Applied
Scopus Categories
Radiology, Nuclear Medicine and ImagingComputational MathematicsAnalysisSignal ProcessingAlgebra and Number Theory
Research Topics (OpenAlex)
Mathematical Analysis and Transform MethodsSparse and Compressive Sensing TechniquesImage and Signal Denoising MethodsMathematical functions and polynomialsDigital Filter Design and ImplementationNumerical methods in inverse problemsMathematical Approximation and IntegrationMathematical Dynamics and FractalsAdvanced Harmonic Analysis ResearchTopological and Geometric Data Analysis