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iteratively reweighted least squares algorithm with the bisquare weighting function matlab robustfit  (MathWorks Inc)


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    MathWorks Inc iteratively reweighted least squares algorithm with the bisquare weighting function matlab robustfit
    Iteratively Reweighted Least Squares Algorithm With The Bisquare Weighting Function Matlab Robustfit, 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/least-square+algorithm+matlab/pm39656649-95-10-17
    Average 90 stars, based on 1 article reviews
    iteratively reweighted least squares algorithm with the bisquare weighting function matlab robustfit - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Cortical tracking of speakers' spectral changes predicts selective listening.
    Article Snippet: A social scene is particularly informative when people are distinguishable.. To understand somebody amid a “cocktail party” chatter, we automatically index their voice.. This ability is underpinned by parallel processing of vocal spectral contours from speech sounds, but it has not yet been established how this occurs in the brain’s cortex.

    Article Title: Glomerular Filtration Rate Measurement Utilizing Transdermal Detection Methodology
    Article Snippet: Abstract: Background: The well-known accuracy limitations of estimated GFR (eGFR) currently employed in clinical practice present barriers to optimal care for patients with, or at-risk for, decreased kidney function.. A point-of-care glomerular filtration rate (GFR) measurement methodology has the potential to address these limitations.. We prospectively assessed transdermal detection of the novel fluorescent GFR tracer agent relmapirazin in participants having normal or impaired kidney function across all human skin colors on the Fitzpatrick Skin Scale (FSS).

    Article Title: Improving Stroke Treatment Using Magnetic Nanoparticle Sensors to Monitor Brain Thrombus Extraction.
    Article Snippet: The inverse cubic function was fitted using the default least-squares algorithm in MATLAB.

    Article Title: Improving Stroke Treatment Using Magnetic Nanoparticle Sensors to Monitor Brain Thrombus Extraction.
    Article Snippet: The inverse cubic function was fitted using the default least-squares algorithm in MATLAB. (a) (b) Sensors 2025, 25, x FOR PEER REVIEW 14 of 17 (c) (d) Figure 13.

    Article Title: Human cardiac tissues produce lower contractile stress and exhibit slower cross-bridge cycling in type 2 diabetes.
    Article Snippet: muscle under all metabolite conditions in MATLAB, using a nonlinear least-squares algorithm.

    Article Title: Improving Stroke Treatment Using Magnetic Nanoparticle Sensors to Monitor Brain Thrombus Extraction.
    Article Snippet: The inverse cubic function was fitted using the default least-squares algorithm in MATLAB. (a) (b) Figure 12.

    Article Title: Development and Application of a Quantitative Model for Proximate and Ultimate Analysis of Flue-Cured Tobacco Based on Near-Infrared Spectroscopy
    Article Snippet: Partial least-squares algorithm written by MATLAB software was used to correlate the contents of VM, FC, and A in 149 calibration samples with the corresponding NIR spectra.

    Article Title: Molecular basis for the increased fusion activity of the Ebola virus glycoprotein epidemic variant A82V: Insights from simulations and experiments.
    Article Snippet: Curve fitting of FCS autocorrelation data and smFRET dwell time histograms were performed in Matlab with a nonlinear least-squares algorithm.



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    MathWorks Inc nonnegative linear least-squares spectral unmixing algorithm matlab isqnonneg
    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral <t>unmixing</t> algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.
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    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral <t>unmixing</t> algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.
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    Image Search Results


    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

    Journal: Journal of Biomedical Optics

    Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

    doi: 10.1117/1.JBO.29.1.016008

    Figure Lengend Snippet: SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

    Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

    Techniques: Imaging

    Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

    Journal: Journal of Biomedical Optics

    Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

    doi: 10.1117/1.JBO.29.1.016008

    Figure Lengend Snippet: Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

    Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

    Techniques: