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cubic spline data interpolation algorithm  (MathWorks Inc)


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    MathWorks Inc cubic spline data interpolation algorithm
    Cubic Spline Data Interpolation Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/cubic+spline+data+interpolation+(spline)/pm37879326-75-6-22
    Average 90 stars, based on 1 article reviews
    cubic spline data interpolation algorithm - by Bioz Stars, 2026-09
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    Article Title: The Pharmacokinetics of Saliva and Plasma N-Oxides Following a Single Administration of a Plant-Based Bioequivalent Inorganic Nitrate Oral Supplement in an Open-Label, Phase 1, Single-Arm Study.
    Article Snippet: For pharmacokinetic modeling, the mean timepoint data were imported into Matlab (version R2023a, Mathworks, Natick, MA, USA) and interpolated at every half-hour mark using cubic Hermite spline interpolation (Matlab interp1 function with the pchip option).

    Article Title: Analysis of the fluorescent properties of vaginal fluid upon ageing.
    Article Snippet: To reconstruct this ‘real’ peak, a cubic spline data interpolation algorithm was developed which interpolated between the non-saturated sides of the curve (Matlab software, Mathworks Inc., R2021a) (figure 1).

    Article Title: Characterizing X-Ray and Solution State Conformations for a Model Qubit System: {Cr 7 Ni} Ring Rotaxanes on a Mixed Metal Triangle.
    Article Snippet: Inter-ring metalmetal distance distributions of [1(2B)3] (smoothed using cubic-spline interpolation in MATLAB) in solution (blue, sum of best-fitting 50 conformations) and in its crystal structure (magenta, normalized to the maximum of the solutionphase distribution sum); the 95% confidence range for the solution-phase distribution is highlighted in green.

    Article Title: Characterizing X-Ray and Solution State Conformations for a Model Qubit System: {Cr 7 Ni} Ring Rotaxanes on a Mixed Metal Triangle.
    Article Snippet: Inter-ring metalmetal distance distributions of [1(2E)3] (smoothed using cubic-spline interpolation in MATLAB) in solution (blue, sum of best-fitting 50 conformations) and in its crystal structure (magenta, normalized to the maximum of the solutionphase distribution sum); the 95% confidence range for the solution-phase distribution is highlighted in green.

    Article Title: Realization of Z 2 Topological Photonic Insulators Made from Multilayer Transition Metal Dichalcogenides
    Article Snippet: The raw data signals, S1 (a), is smoothed, S2 (b), using the cubic smoothing spline interpolation Matlab function with a smoothing parameter p = 10−4.

    Article Title: Application of Functional Analysis in Solving River Crossing Problems under Dynamic Water Flow Conditions
    Article Snippet: After obtaining multiple sets of data, the cubic spline interpolation method was adopted based on Matlab to carry out data fitting, and the following change curve was obtained, as shown in figure 5.

    Article Title: Characterizing X-Ray and Solution State Conformations for a Model Qubit System: {Cr 7 Ni} Ring Rotaxanes on a Mixed Metal Triangle.
    Article Snippet: Inter-ring metalmetal distance distributions of [1(2D)3] (smoothed using cubic-spline interpolation in MATLAB) in solution (blue, sum of best-fitting 50 conformations) and in its crystal structure (magenta, normalized to the maximum of the solutionphase distribution sum); the 95% confidence range for the solution-phase distribution is highlighted in green.



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    (Top) A single exponential decay function vs. time, representing the relaxation modulus of a single mode Maxwell fluid. (Bottom) A generic function resembling the normalised mean square displacement vs. time of an optically trapped particle suspended into a non-Newtonian fluid. Equations and are represented by a finite number of ‘sampled’ points and a continuous (pink) line. The points are also interpolated by means of three MATLAB built-in <t>interpolation</t> functions: Spline, PCHIP and Makima. The insets show the relative absolute error of each interpolation function with respect of either of Eqs. and , as calculated using Eq. . The time window of the inset encompasses the final three points of the main graph, where the relative error is at its highest.
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    (Top) A single exponential decay function vs. time, representing the relaxation modulus of a single mode Maxwell fluid. (Bottom) A generic function resembling the normalised mean square displacement vs. time of an optically trapped particle suspended into a non-Newtonian fluid. Equations and are represented by a finite number of ‘sampled’ points and a continuous (pink) line. The points are also interpolated by means of three MATLAB built-in <t>interpolation</t> functions: Spline, PCHIP and Makima. The insets show the relative absolute error of each interpolation function with respect of either of Eqs. and , as calculated using Eq. . The time window of the inset encompasses the final three points of the main graph, where the relative error is at its highest.
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    (Top) A single exponential decay function vs. time, representing the relaxation modulus of a single mode Maxwell fluid. (Bottom) A generic function resembling the normalised mean square displacement vs. time of an optically trapped particle suspended into a non-Newtonian fluid. Equations and are represented by a finite number of ‘sampled’ points and a continuous (pink) line. The points are also interpolated by means of three MATLAB built-in <t>interpolation</t> functions: Spline, PCHIP and Makima. The insets show the relative absolute error of each interpolation function with respect of either of Eqs. and , as calculated using Eq. . The time window of the inset encompasses the final three points of the main graph, where the relative error is at its highest.
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    (Top) A single exponential decay function vs. time, representing the relaxation modulus of a single mode Maxwell fluid. (Bottom) A generic function resembling the normalised mean square displacement vs. time of an optically trapped particle suspended into a non-Newtonian fluid. Equations and are represented by a finite number of ‘sampled’ points and a continuous (pink) line. The points are also interpolated by means of three MATLAB built-in <t>interpolation</t> functions: Spline, PCHIP and Makima. The insets show the relative absolute error of each interpolation function with respect of either of Eqs. and , as calculated using Eq. . The time window of the inset encompasses the final three points of the main graph, where the relative error is at its highest.
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    cubic spline data interpolation (spline) function  (MathWorks Inc)
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    (Top) A single exponential decay function vs. time, representing the relaxation modulus of a single mode Maxwell fluid. (Bottom) A generic function resembling the normalised mean square displacement vs. time of an optically trapped particle suspended into a non-Newtonian fluid. Equations and are represented by a finite number of ‘sampled’ points and a continuous (pink) line. The points are also interpolated by means of three MATLAB built-in <t>interpolation</t> functions: Spline, PCHIP and Makima. The insets show the relative absolute error of each interpolation function with respect of either of Eqs. and , as calculated using Eq. . The time window of the inset encompasses the final three points of the main graph, where the relative error is at its highest.
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    (Top) A single exponential decay function vs. time, representing the relaxation modulus of a single mode Maxwell fluid. (Bottom) A generic function resembling the normalised mean square displacement vs. time of an optically trapped particle suspended into a non-Newtonian fluid. Equations and are represented by a finite number of ‘sampled’ points and a continuous (pink) line. The points are also interpolated by means of three MATLAB built-in interpolation functions: Spline, PCHIP and Makima. The insets show the relative absolute error of each interpolation function with respect of either of Eqs. and , as calculated using Eq. . The time window of the inset encompasses the final three points of the main graph, where the relative error is at its highest.

    Journal: Scientific Reports

    Article Title: i-RheoFT: Fourier transforming sampled functions without artefacts

    doi: 10.1038/s41598-021-02922-8

    Figure Lengend Snippet: (Top) A single exponential decay function vs. time, representing the relaxation modulus of a single mode Maxwell fluid. (Bottom) A generic function resembling the normalised mean square displacement vs. time of an optically trapped particle suspended into a non-Newtonian fluid. Equations and are represented by a finite number of ‘sampled’ points and a continuous (pink) line. The points are also interpolated by means of three MATLAB built-in interpolation functions: Spline, PCHIP and Makima. The insets show the relative absolute error of each interpolation function with respect of either of Eqs. and , as calculated using Eq. . The time window of the inset encompasses the final three points of the main graph, where the relative error is at its highest.

    Article Snippet: Therefore, here we have compared the effectiveness of the following three interpolation functions already built-in MATLAB: a cubic spline data interpolation (Spline) (as the one used in ), a modified Akima piecewise cubic Hermite interpolation (Makima) and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) .

    Techniques:

    Mean relative absolute error (MRAE) vs. the density of initial experimental points (DIP) of the three MATLAB built-in interpolation functions: Spline, PCHIP and Makima. (Top) The MRAE is evaluated with respect to Eq. . (Bottom) The MRAE is evaluated with respect to Eq. .

    Journal: Scientific Reports

    Article Title: i-RheoFT: Fourier transforming sampled functions without artefacts

    doi: 10.1038/s41598-021-02922-8

    Figure Lengend Snippet: Mean relative absolute error (MRAE) vs. the density of initial experimental points (DIP) of the three MATLAB built-in interpolation functions: Spline, PCHIP and Makima. (Top) The MRAE is evaluated with respect to Eq. . (Bottom) The MRAE is evaluated with respect to Eq. .

    Article Snippet: Therefore, here we have compared the effectiveness of the following three interpolation functions already built-in MATLAB: a cubic spline data interpolation (Spline) (as the one used in ), a modified Akima piecewise cubic Hermite interpolation (Makima) and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) .

    Techniques:

    Mean relative absolute error (MRAE) of the frequency-dependent complex moduli determined by Fourier transforming (via Eq. ) the interpolation functions shown in Fig. (top & bottom) for DIP values ranging from 1/4 to 1.

    Journal: Scientific Reports

    Article Title: i-RheoFT: Fourier transforming sampled functions without artefacts

    doi: 10.1038/s41598-021-02922-8

    Figure Lengend Snippet: Mean relative absolute error (MRAE) of the frequency-dependent complex moduli determined by Fourier transforming (via Eq. ) the interpolation functions shown in Fig. (top & bottom) for DIP values ranging from 1/4 to 1.

    Article Snippet: Therefore, here we have compared the effectiveness of the following three interpolation functions already built-in MATLAB: a cubic spline data interpolation (Spline) (as the one used in ), a modified Akima piecewise cubic Hermite interpolation (Makima) and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) .

    Techniques:

    (Top) Eq. and (bottom) Eq. drawn as continuous (pink) lines by using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^4$$\end{document} 10 4 experimental points linearly spaced in time. A white noise having a SNR \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=50$$\end{document} = 50 is added to the experimental data, which are then interpolated by means of three MATLAB built-in interpolation functions: Spline, PCHIP and Makima. The insets highlight the detrimental effects on the interpolation process due to the presence of noise, both at short and long time scales.

    Journal: Scientific Reports

    Article Title: i-RheoFT: Fourier transforming sampled functions without artefacts

    doi: 10.1038/s41598-021-02922-8

    Figure Lengend Snippet: (Top) Eq. and (bottom) Eq. drawn as continuous (pink) lines by using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^4$$\end{document} 10 4 experimental points linearly spaced in time. A white noise having a SNR \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$=50$$\end{document} = 50 is added to the experimental data, which are then interpolated by means of three MATLAB built-in interpolation functions: Spline, PCHIP and Makima. The insets highlight the detrimental effects on the interpolation process due to the presence of noise, both at short and long time scales.

    Article Snippet: Therefore, here we have compared the effectiveness of the following three interpolation functions already built-in MATLAB: a cubic spline data interpolation (Spline) (as the one used in ), a modified Akima piecewise cubic Hermite interpolation (Makima) and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) .

    Techniques:

    Mean relative absolute error (MRAE) of the frequency-dependent complex moduli determined by Fourier transforming (via Eq. ) the interpolation functions shown in Fig. (top & bottom respectively) for SNR values ranging from 1 to 350 dB. The error bars represent one standard deviation of uncertainty calculated over ten repeats.

    Journal: Scientific Reports

    Article Title: i-RheoFT: Fourier transforming sampled functions without artefacts

    doi: 10.1038/s41598-021-02922-8

    Figure Lengend Snippet: Mean relative absolute error (MRAE) of the frequency-dependent complex moduli determined by Fourier transforming (via Eq. ) the interpolation functions shown in Fig. (top & bottom respectively) for SNR values ranging from 1 to 350 dB. The error bars represent one standard deviation of uncertainty calculated over ten repeats.

    Article Snippet: Therefore, here we have compared the effectiveness of the following three interpolation functions already built-in MATLAB: a cubic spline data interpolation (Spline) (as the one used in ), a modified Akima piecewise cubic Hermite interpolation (Makima) and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) .

    Techniques: Standard Deviation