matlab-based framework hawkspex® flow Search Results


matlab-based framework hawkspex® flow  (MathWorks Inc)
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matlab-based implementation  (MathWorks Inc)
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nirs analyzir toolbox v.615  (MathWorks Inc)
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matlab-based gui  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
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video s6 matlab based encoder  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
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matlab-based toolkit  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
Matlab Based Toolkit, 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
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matlab-based functions  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
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matlab-based work  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
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matlab-based script  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
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matlab-based toolbox  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
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simulink/matlab-based design  (MathWorks Inc)
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Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.
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anisotropic diffusion  (MathWorks Inc)
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MathWorks Inc anisotropic diffusion
Absolute computational performance (in milliseconds) of ( a ) bilateral filtering , ( b ) <t>anisotropic</t> diffusion , ( c ) BLS-GSM , and ( d ) nonlocal means denoising for different input sizes. A comparison is made between our proposed GPU-based Quasar framework and alternative implementations that are available to the scientific community: bilateral filter (ImageJ and MATLAB based), anisotropic diffusion (ImageJ and MATLAB based), BLS-GSM (MATLAB ) and nonlocal means denoising (ImageJ and MATLAB based). For each algorithm, we consider inputs of 256 2 , 512 2 , 1024 2 , 2048 2 , and 4096 2 pixels. In general, our Quasar implementation performs 10 to 100 times faster compared with the existing software packages. Notice that the obtained speedup increases as the input image size increases due to the fact that GPUs are able to process more pixels in parallel.
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Image Search Results


Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.

Journal: Frontiers in Physiology

Article Title: CardioMEA: comprehensive data analysis platform for studying cardiac diseases and drug responses

doi: 10.3389/fphys.2024.1472126

Figure Lengend Snippet: Comparison of open-source data analysis platforms for cardiac data obtained through MEAs.

Article Snippet: Graphical user interface (GUI) , MATLAB-based GUI , MATLAB-based GUI , Python-based GUI , Web-based GUI.

Techniques: Comparison

Absolute computational performance (in milliseconds) of ( a ) bilateral filtering , ( b ) anisotropic diffusion , ( c ) BLS-GSM , and ( d ) nonlocal means denoising for different input sizes. A comparison is made between our proposed GPU-based Quasar framework and alternative implementations that are available to the scientific community: bilateral filter (ImageJ and MATLAB based), anisotropic diffusion (ImageJ and MATLAB based), BLS-GSM (MATLAB ) and nonlocal means denoising (ImageJ and MATLAB based). For each algorithm, we consider inputs of 256 2 , 512 2 , 1024 2 , 2048 2 , and 4096 2 pixels. In general, our Quasar implementation performs 10 to 100 times faster compared with the existing software packages. Notice that the obtained speedup increases as the input image size increases due to the fact that GPUs are able to process more pixels in parallel.

Journal: Nature Communications

Article Title: An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

doi: 10.1038/s41467-020-14529-0

Figure Lengend Snippet: Absolute computational performance (in milliseconds) of ( a ) bilateral filtering , ( b ) anisotropic diffusion , ( c ) BLS-GSM , and ( d ) nonlocal means denoising for different input sizes. A comparison is made between our proposed GPU-based Quasar framework and alternative implementations that are available to the scientific community: bilateral filter (ImageJ and MATLAB based), anisotropic diffusion (ImageJ and MATLAB based), BLS-GSM (MATLAB ) and nonlocal means denoising (ImageJ and MATLAB based). For each algorithm, we consider inputs of 256 2 , 512 2 , 1024 2 , 2048 2 , and 4096 2 pixels. In general, our Quasar implementation performs 10 to 100 times faster compared with the existing software packages. Notice that the obtained speedup increases as the input image size increases due to the fact that GPUs are able to process more pixels in parallel.

Article Snippet: A comparison is made between our proposed GPU-based Quasar framework and alternative implementations that are available to the scientific community: bilateral filter (ImageJ and MATLAB based), anisotropic diffusion (ImageJ and MATLAB based), BLS-GSM (MATLAB ) and nonlocal means denoising (ImageJ and MATLAB based).

Techniques: Diffusion-based Assay, Comparison, Software