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least-squares algorithms built into matlab r2022b  (MathWorks Inc)


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    MathWorks Inc least-squares algorithms built into matlab r2022b
    Least Squares Algorithms Built Into Matlab R2022b, 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/pm38197980-228-13-12
    Average 90 stars, based on 1 article reviews
    least-squares algorithms built into matlab r2022b - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: 3D finite element models reveal regional fatty infiltration modulates tibialis anterior force generating capacity in FSHD
    Article Snippet: The segmentations were used to define contour points of the TA muscle volume, central aponeurosis to tendon, and tibia (MATLAB R2022b, Natick, MA, USA).

    Article Title: Urethral and bladder dosimetry and urinary toxicity in prostate cancer patients undergoing SBRT with and without intra-prostatic boost
    Article Snippet: Using MATLAB R2022b, Wilcoxon rank-sum test was used to test the hypothesis of an increase in median value from cohort B to cohort A.

    Article Title: Biological profits of irrational computations in the orbitofrontal cortex
    Article Snippet: Models were implemented and trained using MATLAB R2022b with the VBA toolbox [ ].

    Article Title: Refining orthognathic outcomes: 3D accuracy with patient-specific implants for facial asymmetry correction
    Article Snippet: Data were analyzed using Matlab (R2022b, The MathWorks, USA) and R statistical software (version 4.3.3; R Development Core Team, Vienna, Austria, 2024).

    Article Title: MRI contrast accumulation in features of cerebral small vessel disease: blood-brain barrier dysfunction or elevated vascular density?
    Article Snippet: The rest of the DCE and BBB leakage analyses were performed in MATLAB R2022b (Natick, Massachusetts: The MathWorks Inc) using in-house tools.

    Article Title: Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
    Article Snippet: The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods.. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models.. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques.

    Software:

    Article Title: Experimental study on DEM parameters calibration for organic fertilizer by the particle swarm optimization - backpropagation neural networks.
    Article Snippet: .. This study used the Matlab R2022b software to set up machine learning methods. ..

    Article Title: Temporal variations of δ 13 C-CH 4 in rice paddies dominated by the plant-mediated pathway
    Article Snippet: Valve sequencer control software (sampling control) , Picarro Inc. , Included with instrument. .. MATLAB R2022b (data processing) , Commercially Available Software (Mathworks) , https://www.mathworks.com/. .. Python 3.9 (statistical analysis and scripting) , Python Software Foundation , https://www.python.org.



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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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    MathWorks Inc least-squares algorithms built into matlab r2022b
    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: