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hardware description language hdl coder  (MathWorks Inc)


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    MathWorks Inc hardware description language hdl coder
    Hardware Description Language Hdl Coder, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 231 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/hardware description language hdl coder/product/MathWorks Inc
    Average 96 stars, based on 231 article reviews
    hardware description language hdl coder - by Bioz Stars, 2026-04
    96/100 stars

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    FIGURE 8 (A) MATLAB Model for standard voltage; (B) Waveform for standard voltage.

    Journal: Frontiers in Energy Research

    Article Title: Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach

    doi: 10.3389/fenrg.2024.1435704

    Figure Lengend Snippet: FIGURE 8 (A) MATLAB Model for standard voltage; (B) Waveform for standard voltage.

    Article Snippet: For the simulation environment, the FPGA model was validated using the following environment: • Software: MATLAB R2021b • MATLAB HDL Coder • Xilinx Vivado 2020.2 • Hardware: • Xilinx Zynq-7000 SoC FPGA • Clock frequency: 100 MHz • FPGA resources: 53,200 LUTs, 106,400 FlipFlops, 140 BRAMs Concerning the dataset used, the dataset for training and testing the Extreme Learning Machine (ELM) classifier was generated using simulated power quality disturbances.

    Techniques:

    FIGURE 10 (A) MATLAB Model for voltage sag and voltage surge; (B) Waveform for voltage sag and voltage surge.

    Journal: Frontiers in Energy Research

    Article Title: Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach

    doi: 10.3389/fenrg.2024.1435704

    Figure Lengend Snippet: FIGURE 10 (A) MATLAB Model for voltage sag and voltage surge; (B) Waveform for voltage sag and voltage surge.

    Article Snippet: For the simulation environment, the FPGA model was validated using the following environment: • Software: MATLAB R2021b • MATLAB HDL Coder • Xilinx Vivado 2020.2 • Hardware: • Xilinx Zynq-7000 SoC FPGA • Clock frequency: 100 MHz • FPGA resources: 53,200 LUTs, 106,400 FlipFlops, 140 BRAMs Concerning the dataset used, the dataset for training and testing the Extreme Learning Machine (ELM) classifier was generated using simulated power quality disturbances.

    Techniques:

    FIGURE 11 (A) MATLAB Model for transitional oscillations; (B) Waveform for transitional oscillations.

    Journal: Frontiers in Energy Research

    Article Title: Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach

    doi: 10.3389/fenrg.2024.1435704

    Figure Lengend Snippet: FIGURE 11 (A) MATLAB Model for transitional oscillations; (B) Waveform for transitional oscillations.

    Article Snippet: For the simulation environment, the FPGA model was validated using the following environment: • Software: MATLAB R2021b • MATLAB HDL Coder • Xilinx Vivado 2020.2 • Hardware: • Xilinx Zynq-7000 SoC FPGA • Clock frequency: 100 MHz • FPGA resources: 53,200 LUTs, 106,400 FlipFlops, 140 BRAMs Concerning the dataset used, the dataset for training and testing the Extreme Learning Machine (ELM) classifier was generated using simulated power quality disturbances.

    Techniques: