Applied and Computational Harmonic Analysis

Applied and Computational Harmonic Analysis - ISSN 1063-5203
Source Normalized Impact per Paper (SNIP): 1.776 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 1.589 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 2.094 (2015) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© Thomson Reuters Journal Citation Reports 2015
5 Year Impact Factor: 2.376 (2015) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2014 to the previous five years and divided by the source items published in the previous five years.
© Journal Citation Reports 2015, Published by Thomson Reuters
Volumes: Volumes 42-43
Issues: 6 issues
ISSN: 10635203

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Description

Applied and Computational Harmonic Analysis (ACHA) is an interdisciplinary journal that publishes high-quality papers in all areas of mathematical sciences related to the applied and computational aspects of harmonic analysis, with special emphasis on innovative theoretical development, methods, and algorithms, for information processing, manipulation, understanding, and so forth. The objectives of the journal are to chronicle the important publications in the rapidly growing field of data representation and analysis, to stimulate research in relevant interdisciplinary areas, and to provide a common link among mathematical, physical, and life scientists, as well as engineers. Applied and computational harmonic analysis covers, in the broadest sense, topics that include but not limited to:

I Signal and Function Representations

• continuous and discrete wavelet transform

• wavelet frames

• wavelet algorithms

•local time-frequency and time-scale basis functions

• multi-scale and multi-level methods

• refinable functions

II Representation of Abstract and High-dimensional Objects

• diffusion wavelets and geometry

• harmonic analysis on graphs and trees

• sparse data representation

• compressive sampling

• compressed sensing

• matrix completion

• random matrices and projections

• data dimensionality reduction

• high-dimensional integration

III Application Areas

• data compression

• signal and image processing

• learning theory and algorithms

• computer-aided geometric design

• extra large data analysis and understanding

• data recovery and image inpainting

• data mining

• hyperspectral imaging

• novel sensors and systems