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coder matlab coder  (MathWorks Inc)


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    MathWorks Inc coder matlab coder
    Coder Matlab Coder, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 171 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/real-time+implementations+matlab/MATLAB+Coder/10__1002_slash_oca__2117-176-2-2
    Average 95 stars, based on 171 article reviews
    coder matlab coder - by Bioz Stars, 2026-09
    95/100 stars

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    other:

    Article Title: Towards the transformation of MATLAB models into FPGA-Based hardware accelerators
    Article Snippet: The forward pass of the reference model is independently reimplemented in MATLAB scripts and in manually optimized HLS-based C + + code, while the HDL Coder workflow is used as a comparative baseline.

    Article Title: Method and system for measuring, predicting and optimizing human alertness
    Article Snippet: The algorithms for group-average and individualized predictions were written in MATLAB, translated to C with MATLAB Coder, and then compiled into a native library.

    Article Title: Predictive temperature control of electric two wheeler hub motor using gradient aware neural regulation with degradation tracking and fault tolerant multi condition torque adaptation.
    Article Snippet: All artificial neural network (ANN) models were converted into fixed-point C code using MATLAB Coder and Embedded Coder toolchains.

    Article Title: Real-time Covid-19 diagnosis on embedded IoT platforms
    Article Snippet: The trained model was converted into an embedded-compatible format using MATLAB Coder and GPU Coder, generating CUDA-accelerated code optimized for the GPU resources available on the NVIDIA Jetson platform.

    Article Title: Towards the transformation of MATLAB models into FPGA-Based hardware accelerators.
    Article Snippet: The forward pass of the reference model is independently reimplemented in MATLAB scripts and in manually optimized HLS-based C++ code, while the HDL Coder workflow is used as a comparative baseline.

    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 . ..

    Blocking Assay:

    Article Title: Predictive temperature control of electric two wheeler hub motor using gradient aware neural regulation with degradation tracking and fault tolerant multi condition torque adaptation.
    Article Snippet: .. Once trained, the ANN model is exported as a lightweight C-code block using MATLAB CoderTM for embedded deployment. ..



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    ( A ) Example 2 s of simulated observed data (thick curves) and the true phase (thin blue curves) for each scenario. ( B ) For each scenario, example spectra of (B.i) the signal, and (B.ii) the observation (i.e., signal plus noise). Spectra were estimated for 10 s segments using the function ‘pmtm’ in MATLAB, to compute a multitaper estimate with frequency resolution 1 Hz and nine tapers. ( C ) The phase error for each estimation method (see legend) and simulation scenario. In each box plot, the central mark indicates the median; the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively; the whiskers indicate the most extreme data points not considered outliers. Figure 2—source data 1. Circular standard deviation for all methods.

    Journal: eLife

    Article Title: A state space modeling approach to real-time phase estimation

    doi: 10.7554/eLife.68803

    Figure Lengend Snippet: ( A ) Example 2 s of simulated observed data (thick curves) and the true phase (thin blue curves) for each scenario. ( B ) For each scenario, example spectra of (B.i) the signal, and (B.ii) the observation (i.e., signal plus noise). Spectra were estimated for 10 s segments using the function ‘pmtm’ in MATLAB, to compute a multitaper estimate with frequency resolution 1 Hz and nine tapers. ( C ) The phase error for each estimation method (see legend) and simulation scenario. In each box plot, the central mark indicates the median; the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively; the whiskers indicate the most extreme data points not considered outliers. Figure 2—source data 1. Circular standard deviation for all methods.

    Article Snippet: The computed parameters were nearly identical between the two implementations and the time to estimate the parameters (which, as above, would need to be done only once per experiment) ranged from 2 to 100 s. The phase estimates of the two implementations have a mean circular difference of 2.06 degrees ( ) with a circular standard deviation of 0.65 degree; this variation in phase estimates likely arises from differences in filtering in the TORTE real-time (causal filtering on individual buffers) implementation and the offline MATLAB real-time (acausal filtering across all data) implementation.

    Techniques: Standard Deviation

    ( A ) Open Ephys GUI for using SSPE. The user specifies the number of frequencies to track, the center frequencies to track, the frequencies of interest for phase calculation and output (FOI), variance for the FOI, and the observation error. Observation error determines the effective bandwidth for each frequency. ( B ) Histogram of the circular standard deviation between MATLAB (offline) and TORTE (real-time) implementations of the SSPE. Small variation results from causal low pass filtering in TORTE and acausal filtering in the offline phase estimates. ( C ) Time to evaluate phase versus buffer size. Longer buffer sizes from TORTE require longer calculation time for application of SSPE. However, the calculation time is approximately two orders of magnitude smaller than the buffer size.

    Journal: eLife

    Article Title: A state space modeling approach to real-time phase estimation

    doi: 10.7554/eLife.68803

    Figure Lengend Snippet: ( A ) Open Ephys GUI for using SSPE. The user specifies the number of frequencies to track, the center frequencies to track, the frequencies of interest for phase calculation and output (FOI), variance for the FOI, and the observation error. Observation error determines the effective bandwidth for each frequency. ( B ) Histogram of the circular standard deviation between MATLAB (offline) and TORTE (real-time) implementations of the SSPE. Small variation results from causal low pass filtering in TORTE and acausal filtering in the offline phase estimates. ( C ) Time to evaluate phase versus buffer size. Longer buffer sizes from TORTE require longer calculation time for application of SSPE. However, the calculation time is approximately two orders of magnitude smaller than the buffer size.

    Article Snippet: The computed parameters were nearly identical between the two implementations and the time to estimate the parameters (which, as above, would need to be done only once per experiment) ranged from 2 to 100 s. The phase estimates of the two implementations have a mean circular difference of 2.06 degrees ( ) with a circular standard deviation of 0.65 degree; this variation in phase estimates likely arises from differences in filtering in the TORTE real-time (causal filtering on individual buffers) implementation and the offline MATLAB real-time (acausal filtering across all data) implementation.

    Techniques: Standard Deviation

    Fig. 4. Structure of ANFIS MPPT controller.

    Journal: IEEE Access

    Article Title: A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization

    doi: 10.1109/access.2019.2931547

    Figure Lengend Snippet: Fig. 4. Structure of ANFIS MPPT controller.

    Article Snippet: MATLAB/Simulink implemented ANFIS controller interfaced with a real-time dSPACE control board that generates optimal duty ratio for SEPIC converter.

    Techniques:

    Fig. 6. Complete structure of Hybrid ANFIS-ABC control. Fig. 7. Inverter control strategy.

    Journal: IEEE Access

    Article Title: A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization

    doi: 10.1109/access.2019.2931547

    Figure Lengend Snippet: Fig. 6. Complete structure of Hybrid ANFIS-ABC control. Fig. 7. Inverter control strategy.

    Article Snippet: MATLAB/Simulink implemented ANFIS controller interfaced with a real-time dSPACE control board that generates optimal duty ratio for SEPIC converter.

    Techniques: Control

    Fig. 26. Comparison of RMSE of ANFIS-ABC, ANFIS-PSO and

    Journal: IEEE Access

    Article Title: A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization

    doi: 10.1109/access.2019.2931547

    Figure Lengend Snippet: Fig. 26. Comparison of RMSE of ANFIS-ABC, ANFIS-PSO and

    Article Snippet: MATLAB/Simulink implemented ANFIS controller interfaced with a real-time dSPACE control board that generates optimal duty ratio for SEPIC converter.

    Techniques: Comparison