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verification methods  (MathWorks Inc)


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    MathWorks Inc verification methods
    Verification Methods, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 93 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/verification+method/Simulink+Design+Verifier/ppr0576639-69-6-10
    Average 93 stars, based on 93 article reviews
    verification methods - by Bioz Stars, 2026-09
    93/100 stars

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    Article Snippet: Nowadays, FPGA devices are widely used to build control platforms in various fields of real time applications such as wireless telecommunications, image and signal processing, robotics and renewable energy systems.. In grid-connected photovoltaic (PV) systems, an effective control strategy is needed for an efficiently use of solar energy as well as for energy supplies optimization.. This paper presents an investigation, modelling and FPGA based digital controller design of a two-stage grid connected photovoltaic (GCPV) system.

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    Basics of range <t>verification,</t> exemplarily shown for the voxel coordinates (x, y) = (70,59) in patient P3 (cf table 1). Left: β+-activity profiles obtained by MC simulation and in-beam PET measurement, normalized to the maximum, as well as the corresponding dose profile are shown. The blue lines denote the location of the activity maximum, the 50 % dose fall-off (vertically, left to right) and the 20 % activity limit (horizontally). Right: The profile difference Ddiff is visualized as function of the profile shift for different analysis starting depths zmin.
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    Image Search Results


    Basics of range verification, exemplarily shown for the voxel coordinates (x, y) = (70,59) in patient P3 (cf table 1). Left: β+-activity profiles obtained by MC simulation and in-beam PET measurement, normalized to the maximum, as well as the corresponding dose profile are shown. The blue lines denote the location of the activity maximum, the 50 % dose fall-off (vertically, left to right) and the 20 % activity limit (horizontally). Right: The profile difference Ddiff is visualized as function of the profile shift for different analysis starting depths zmin.

    Journal: Physics in medicine and biology

    Article Title: Automation and uncertainty analysis of a method for in-vivo range verification in particle therapy

    doi: 10.1088/0031-9155/59/19/5903

    Figure Lengend Snippet: Basics of range verification, exemplarily shown for the voxel coordinates (x, y) = (70,59) in patient P3 (cf table 1). Left: β+-activity profiles obtained by MC simulation and in-beam PET measurement, normalized to the maximum, as well as the corresponding dose profile are shown. The blue lines denote the location of the activity maximum, the 50 % dose fall-off (vertically, left to right) and the 20 % activity limit (horizontally). Right: The profile difference Ddiff is visualized as function of the profile shift for different analysis starting depths zmin.

    Article Snippet: In the following section, the basic principles of the range verification methods by ( Knopf et al. 2008 ) and ( Min et al. 2013 ) are reviewed, which are used in the development of the MLS approach and applied to the investigated data, respectively.

    Techniques: Activity Assay

    Range verification of the proton-irradiation induced activity in P7. (a) Exemplary sagittal planes of the simulation (top) and the offline PET measurement (bottom) are displayed. (b) The corresponding normalized activity depth profiles are shown together with the dose profile. The activity fall-off thresholds of 25 % and 50 % are marked by blue lines. (c) The MLS results (top) show only small deviations in most parts of the distribution compared to the MP (bottom) calculations.

    Journal: Physics in medicine and biology

    Article Title: Automation and uncertainty analysis of a method for in-vivo range verification in particle therapy

    doi: 10.1088/0031-9155/59/19/5903

    Figure Lengend Snippet: Range verification of the proton-irradiation induced activity in P7. (a) Exemplary sagittal planes of the simulation (top) and the offline PET measurement (bottom) are displayed. (b) The corresponding normalized activity depth profiles are shown together with the dose profile. The activity fall-off thresholds of 25 % and 50 % are marked by blue lines. (c) The MLS results (top) show only small deviations in most parts of the distribution compared to the MP (bottom) calculations.

    Article Snippet: In the following section, the basic principles of the range verification methods by ( Knopf et al. 2008 ) and ( Min et al. 2013 ) are reviewed, which are used in the development of the MLS approach and applied to the investigated data, respectively.

    Techniques: Irradiation, Activity Assay

    Range verification with several offline measured activity distributions after different treatment fractions of P6. The most-likely shift for the analysis between M1 and M2 (a), M1 and M3 (b) as well as between M1 and M4 (c) is shown.

    Journal: Physics in medicine and biology

    Article Title: Automation and uncertainty analysis of a method for in-vivo range verification in particle therapy

    doi: 10.1088/0031-9155/59/19/5903

    Figure Lengend Snippet: Range verification with several offline measured activity distributions after different treatment fractions of P6. The most-likely shift for the analysis between M1 and M2 (a), M1 and M3 (b) as well as between M1 and M4 (c) is shown.

    Article Snippet: In the following section, the basic principles of the range verification methods by ( Knopf et al. 2008 ) and ( Min et al. 2013 ) are reviewed, which are used in the development of the MLS approach and applied to the investigated data, respectively.

    Techniques: Activity Assay