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matlab matlab  (MathWorks Inc)


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    MathWorks Inc matlab matlab
    Matlab Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 324 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/geometric+coil+compression+matlab+code/MATLAB+Compiler/ppr0620945-295-16-17
    Average 96 stars, based on 324 article reviews
    matlab matlab - by Bioz Stars, 2026-10
    96/100 stars

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    other:

    Article Title: An Integrated Framework for Automated Image Segmentation and Personalized Wall Stress Estimation of Abdominal Aortic Aneurysms.
    Article Snippet: The script can be compiled into a standalone executable (e.g., via MATLAB Compiler) that runs with the free MATLAB Runtime, so end users do not require a MATLAB license [45].

    Article Title: RUBAT Studio: A Unified Workbench for Multichannel Bioacoustic Data Acquisition
    Article Snippet: RUBAT Studio v4.0 was packaged using the MATLAB Application Compiler to produce standalone executables requiring only the freely distributable MATLAB Runtime.

    Article Title: Autophagy dysfunction in iPSCs-derived neurons and midbrain organoids carrying a SNCA triplication.
    Article Snippet: Neural stat compiler files were used for data analysis in MATLAB.

    Article Title: Predicting Cell Adhesion States on Nanopillar Arrays with a Nano‐Bio Interface Model: From Modeling to Functional Device Design
    Article Snippet: Nanopillar arrays (NAs) are widely employed as versatile nanostructures for biological applications.. Cells on NAs adopt three adhesion states—“top”, “middle”, and “bottom”—each corresponding to distinct biological functions.. Although these adhesion states are influenced by NAs geometry, existing models overlook the role of the membrane reservoir—an intrinsic cellular property—in regulating membrane tension during cell settling, resulting in an incomplete mechanistic understanding and limited predictive capability.

    Article Title: An Integrated Framework for Automated Image Segmentation and Personalized Wall Stress Estimation of Abdominal Aortic Aneurysms
    Article Snippet: The script can be compiled into a standalone executable (e.g., via MATLAB Compiler) that runs with the free MATLAB Runtime, so end users do not require a MATLAB license [ ].

    Generated:

    Article Title: WAH- i : Optimising Microphone Array Geometry for Customised Localisation Accuracy
    Article Snippet: .. To support deployment beyond a MATLAB development environment, standalone executables were generated using the MATLAB Compiler ( ). ..



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    geometric coil compression matlab code  (MathWorks Inc)
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    MathWorks Inc geometric coil compression matlab code
    Computational scaling with respect to image size for CG and HSS based reconstruction methods, see Figure 1 for algorithm flow-diagrams. R = 3 acceleration is applied to the T2 weighted images. A 10−6 tolerance is assumed for all algorithms to ensure consistent final image error. All methods include 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. The Jacobi pre-conditioner is used for all CG methods. The use of Cartesian optimized coil <t>compression</t> from 32 to 8-channels is explored for the Matrix Free method. The smallest and largest reconstruction times for HSS-Inverse are identified with arrows.
    Geometric Coil Compression Matlab Code, 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/geometric+coil+compression+matlab+code/pmc04167172-179-12-15
    Average 90 stars, based on 1 article reviews
    geometric coil compression matlab code - by Bioz Stars, 2026-10
    90/100 stars
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    Computational scaling with respect to image size for CG and HSS based reconstruction methods, see Figure 1 for algorithm flow-diagrams. R = 3 acceleration is applied to the T2 weighted images. A 10−6 tolerance is assumed for all algorithms to ensure consistent final image error. All methods include 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. The Jacobi pre-conditioner is used for all CG methods. The use of Cartesian optimized coil compression from 32 to 8-channels is explored for the Matrix Free method. The smallest and largest reconstruction times for HSS-Inverse are identified with arrows.

    Journal: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine

    Article Title: Fast Reconstruction for Multi-channel Compressed Sensing Using a Hierarchically Semiseparable Solver

    doi: 10.1002/mrm.25222

    Figure Lengend Snippet: Computational scaling with respect to image size for CG and HSS based reconstruction methods, see Figure 1 for algorithm flow-diagrams. R = 3 acceleration is applied to the T2 weighted images. A 10−6 tolerance is assumed for all algorithms to ensure consistent final image error. All methods include 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. The Jacobi pre-conditioner is used for all CG methods. The use of Cartesian optimized coil compression from 32 to 8-channels is explored for the Matrix Free method. The smallest and largest reconstruction times for HSS-Inverse are identified with arrows.

    Article Snippet: When investigating the impact of coil compression for CG based approaches, the Geometric Coil Compression MATLAB code associated with [ 11 ] was used.

    Techniques:

    Computational scaling of the HSS-Inverse method with respect to the number of parallel imaging channels and acceleration factor. A 10−6 tolerance is assumed for 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. Cartesian optimized coil compression is used to reduce from 32 to 8-channels. R = 2, 3, and 4 under-sampling is examined.

    Journal: Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine

    Article Title: Fast Reconstruction for Multi-channel Compressed Sensing Using a Hierarchically Semiseparable Solver

    doi: 10.1002/mrm.25222

    Figure Lengend Snippet: Computational scaling of the HSS-Inverse method with respect to the number of parallel imaging channels and acceleration factor. A 10−6 tolerance is assumed for 5 iterations of Split Bregman with a TV weighting β = 3 · 10−3 and soft-thresholding ε = 2 · 10−1. Cartesian optimized coil compression is used to reduce from 32 to 8-channels. R = 2, 3, and 4 under-sampling is examined.

    Article Snippet: When investigating the impact of coil compression for CG based approaches, the Geometric Coil Compression MATLAB code associated with [ 11 ] was used.

    Techniques: Imaging, Sampling