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Springer Nature
Sampling Theory Signal Processing and Data Analysis Template
Write in a clean editor, then format for Sampling Theory Signal Processing and Data Analysis in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Sampling Theory Signal Processing and Data Analysis format
Sampling Theory Signal Processing and Data Analysis is a peer-reviewed journal published by Springer Nature, covering Mathematical Analysis and Transform Methods, Sparse and Compressive Sensing Techniques, Image and Signal Denoising Methods.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Sampling Theory Signal Processing and Data Analysis 12, 45–58 (2023).
Formats any DOI in Sampling Theory Signal Processing and Data Analysis style. No sign-up. |
| Publishes research in | Mathematical Analysis and Transform Methods Sparse and Compressive Sensing Techniques Image and Signal Denoising Methods Mathematical functions and polynomials Digital Filter Design and Implementation |
| ISSN | 2730-5716 |
| Citation impact (2-yr) | 1.06 |
| h-index | 10 |
| i10-index | 10 |
| Total citations | 363 |
| Article processing charge | $2,750 |
| Top institutions publishing here | Chemnitz University of Technology |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Sampling Theory Signal Processing and Data Analysis per year
Citation impact of Sampling Theory Signal Processing and Data Analysis by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Sampling Theory Signal Processing and Data Analysis
Abstract In this paper, we establish a quantitative estimate for Durrmeyer-sampling type operators in the general framework of Orlicz spaces, using a suitable modulus of smoothness defined by the involved modular functional. As a consequence of the above result, we can deduce quantitative estimates in several instances of Orlicz spaces, such as $$L^p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">…
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Abstract G -equivariant convolutional neural networks (GCNNs) is a geometric deep learning model for data defined on a homogeneous G -space $$\mathcal {M}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>M</mml:mi> </mml:math> . GCNNs are designed to respect the global symmetry in $$\mathcal {M}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>M</mml:mi> </mml:math> , thereby facilitating learning. In this paper, we analyze GCNNs on homogeneous…
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Abstract In this paper, we derive a new reconstruction method for real non-harmonic Fourier sums, i.e., real signals which can be represented as sparse exponential sums of the form $$f(t) = \sum _{j=1}^{K} \gamma _{j} \, \cos (2\pi a_{j} t + b_{j})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>f</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:msubsup><mml:msub><mml:mi>γ</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mspace/><mml:mo>cos</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mi>π</mml:mi><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>b</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> , where the frequency parameters $$a_{j} \in {\mathbb {R}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>∈</mml:mo><mml:mi>R</mml:mi></mml:mrow></mml:math>…
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Sampling Theory Signal Processing and Data Analysis template — frequently asked questions
How do I write a paper in the Sampling Theory Signal Processing and Data Analysis format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the Sampling Theory Signal Processing and Data Analysis template. When you export, DocuGuru compiles the paper into the official Springer Nature format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does Sampling Theory Signal Processing and Data Analysis use?
Sampling Theory Signal Processing and Data Analysis uses Superscript numbered (Nature) references, shown as superscript numerals in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: 1. Smith, A., Jones, B. & Lee, C. A representative article title. Sampling Theory Signal Processing and Data Analysis 12, 45–58 (2023).
Do I need to know LaTeX to submit to Sampling Theory Signal Processing and Data Analysis?
No. DocuGuru generates the sn-jnl LaTeX class and compiles the PDF for you in the background, so you get a Springer Nature-ready Sampling Theory Signal Processing and Data Analysis document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
Can I import an existing draft into the Sampling Theory Signal Processing and Data Analysis template?
Yes. Paste or upload your current manuscript — Word, LaTeX, Markdown, or plain text — and DocuGuru reflows it into the Sampling Theory Signal Processing and Data Analysis format with correct headings, figures, tables, and superscript citations.
Who publishes Sampling Theory Signal Processing and Data Analysis?
Sampling Theory Signal Processing and Data Analysis is a multidisciplinary journal published by Springer Nature. DocuGuru's Sampling Theory Signal Processing and Data Analysis template matches Springer Nature's official submission format.
Can I export a submission-ready Sampling Theory Signal Processing and Data Analysis PDF?
Yes — DocuGuru produces a PDF built with the official Sampling Theory Signal Processing and Data Analysis template (the sn-jnl class) that is ready to submit to Springer Nature, together with the matching LaTeX source files.
How much does the Sampling Theory Signal Processing and Data Analysis template cost?
You can start writing in the Sampling Theory Signal Processing and Data Analysis template for free. Exporting the final submission-ready Sampling Theory Signal Processing and Data Analysis PDF and LaTeX source is part of DocuGuru's paid plans — see the app for current pricing.