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hdl generation tools  (MathWorks Inc)


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    MathWorks Inc hdl generation tools
    Hdl Generation Tools, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 241 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlabs+regionprops+algorithm/HDL+Coder/pmc09538980-258-24-5
    Average 96 stars, based on 241 article reviews
    hdl generation tools - by Bioz Stars, 2026-10
    96/100 stars

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    other:

    Article Title: Sparse path codes and methods
    Article Snippet: However, instead of compiling to the native instruction set of a multipurpose microprocessor, the source code is compiled to a “hardware description language,” such as VHDL (an IEEE standard), using a compiler such as HDLCoder (available from MathWorks).

    Article Title: Towards the transformation of MATLAB models into FPGA-Based hardware accelerators
    Article Snippet: , MATLAB, HDL Coder, Quartus , Cyclone V (DE1-SoC) , No , No , Accuracy, Area, Power.

    Article Title: FPGA technology in healthcare: A comprehensive review of hardware and software solutions for diagnostics, imaging, and patient care
    Article Snippet: The processing of provided signals was coded using the HDL Coder tool of MATLAB (for Spartan-6) and Vivado HLS platform (for Kintex-7).

    Article Title: Systems and methods for automatically generating computer programming code and schedules and comparing their performance
    Article Snippet: Exemplary code generators include the HDL Coder, the Simulink Coder, the Embedded Coder, and the Simulink PLC Coder products from The Math Works, Inc., and the TargetLink coded generation system from dSpace GmbH.

    Article Title: Methods and arrangements to aid recycling
    Article Snippet: However, instead of compiling to the native instruction set of a multipurpose microprocessor, the source code is compiled to a “hardware description language,” such as VHDL (an IEEE standard), using a compiler such as HDLCoder (available from MathWorks).

    Article Title: System for mitigating the problem of deepfake media content using watermarking
    Article Snippet: To realize such an implementation, the relevant module(s) (e.g., embedding and reading digital watermarks, hashing, fingerprinting, feature extraction, per above, with respect to image, audio, video or text content) are first implemented using a general purpose computer, using software such as MATLAB (from Mathworks, Inc.).

    Software:

    Article Title: Towards the transformation of MATLAB models into FPGA-Based hardware accelerators.
    Article Snippet: AMD’s Vitis Model Composer, integrated as a Simulink add-on, supports rapid prototyping of FPGA-oriented systems [12]. .. Similarly, MATLAB Coder and Hardware Description Language (HDL) Coder facilitate the conversion of algorithms developed in MATLAB into C/C++ or HDL representations, thereby bridging software-oriented models with hardware-oriented implementations [13]. ..

    Article Title: Towards the transformation of MATLAB models into FPGA-Based hardware accelerators
    Article Snippet: AMD’s Vitis Model Composer, integrated as a Simulink add-on, supports rapid prototyping of FPGA-oriented systems . .. Similarly, MATLAB Coder and Hardware Description Language (HDL) Coder facilitate the conversion of algorithms developed in MATLAB into C/C + + or HDL representations, thereby bridging software-oriented models with hardware-oriented implementations . ..



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    matlab regionprops algorithm  (MathWorks Inc)
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    MathWorks Inc matlab regionprops algorithm
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    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm <t>regionprops</t> in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)
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    matlab algorithm 'regionprops  (MathWorks Inc)
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    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm <t>regionprops</t> in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)
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    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm regionprops in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)

    Journal: Bulletin of Mathematical Biology

    Article Title: Waves in a Stochastic Cell Motility Model

    doi: 10.1007/s11538-023-01164-1

    Figure Lengend Snippet: In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm regionprops in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)

    Article Snippet: In ( a ), we only show the simulation of wave integrated up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T=20$$\end{document} T = 20 because the solution remains in the background state; afterwards, the other three figures are shown up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T=100$$\end{document} T = 100 (Color figure online) In order to quantify this notion of optimality in the noise intensity, we must first quantify the size and shape of the patterns in Fig. b and c. Using MATLAB’s regionprops algorithm, we can automatically detect the patches with a high value for the activator u (see “Appendix A” for details), giving us the possibility to compute the number of activation events and determine the width and duration of each event, see Fig. a.

    Techniques: Activation Assay, Standard Deviation