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matlab code  (MathWorks Inc)


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    MathWorks Inc matlab code
    Matlab Code, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 169 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/principal+component+analysis+matlabs+function+pca/MATLAB+Coder/pmc10831957__mmc2-827-0-0
    Average 95 stars, based on 169 article reviews
    matlab code - by Bioz Stars, 2026-09
    95/100 stars

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    Related Articles

    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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    The top-3 independent <t>components</t> of the spiking response of each trial type. (A) shows each stimulation type and the corresponding independent components over the trial time. Positive coefficients are correlated with spiking activity while negative coefficients are anti-correlated with spiking activity. In the scatter plots below, each component is shown as an axis and each trial is plotted as a point within the three dimensions. Exemplar trials are highlighted and shown in insets with spike rate over time. (B) shows how the component weights (boxes) scale the component shapes to describe the features of the mean firing rate of an example channel. The corresponding blue and green arrows point to the deviations in mean firing rate while the purple arrow and line generally indicate the background firing rate that are captured by the respective component and its weight. (C) shows the reconstruction (shaded yellow) of the mean spike rate of an example channel (black line) using the descriptive weightings of the independent components.
    Principal Components Analysis (Pca; Matlab R2017a + ‘Pca’ Function With ‘Algorithm’ Parameter Set To ‘Svd), 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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    The top-3 independent components of the spiking response of each trial type. (A) shows each stimulation type and the corresponding independent components over the trial time. Positive coefficients are correlated with spiking activity while negative coefficients are anti-correlated with spiking activity. In the scatter plots below, each component is shown as an axis and each trial is plotted as a point within the three dimensions. Exemplar trials are highlighted and shown in insets with spike rate over time. (B) shows how the component weights (boxes) scale the component shapes to describe the features of the mean firing rate of an example channel. The corresponding blue and green arrows point to the deviations in mean firing rate while the purple arrow and line generally indicate the background firing rate that are captured by the respective component and its weight. (C) shows the reconstruction (shaded yellow) of the mean spike rate of an example channel (black line) using the descriptive weightings of the independent components.

    Journal: Frontiers in Neuroscience

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex

    doi: 10.3389/fnins.2023.1151309

    Figure Lengend Snippet: The top-3 independent components of the spiking response of each trial type. (A) shows each stimulation type and the corresponding independent components over the trial time. Positive coefficients are correlated with spiking activity while negative coefficients are anti-correlated with spiking activity. In the scatter plots below, each component is shown as an axis and each trial is plotted as a point within the three dimensions. Exemplar trials are highlighted and shown in insets with spike rate over time. (B) shows how the component weights (boxes) scale the component shapes to describe the features of the mean firing rate of an example channel. The corresponding blue and green arrows point to the deviations in mean firing rate while the purple arrow and line generally indicate the background firing rate that are captured by the respective component and its weight. (C) shows the reconstruction (shaded yellow) of the mean spike rate of an example channel (black line) using the descriptive weightings of the independent components.

    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Techniques: Activity Assay

    Combined independent component analysis of the sensory response and its modulation. (A) shows the mean weights of the components sorted by stimulation type and area which are displayed in (B) . Positive values point to the presence of that component in the response while negative values indicate an inverse relationship; the error bars show the standard error of the mean. (C) displays the prediction of area and lesion volume for component 2 and 3 scores by the GLME model as compared to a linear fit. (D) highlights the changes in the component scores between Solenoid (yellow) and ICMS + Solenoid trials (purple) for each channel in an experimental block of an exemplar animal. (E) shows the reconstructed rates for each stimulation type by area. The mean component scores were used to weight each component and reconstruct the average response in spiking to stimulation.

    Journal: Frontiers in Neuroscience

    Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex

    doi: 10.3389/fnins.2023.1151309

    Figure Lengend Snippet: Combined independent component analysis of the sensory response and its modulation. (A) shows the mean weights of the components sorted by stimulation type and area which are displayed in (B) . Positive values point to the presence of that component in the response while negative values indicate an inverse relationship; the error bars show the standard error of the mean. (C) displays the prediction of area and lesion volume for component 2 and 3 scores by the GLME model as compared to a linear fit. (D) highlights the changes in the component scores between Solenoid (yellow) and ICMS + Solenoid trials (purple) for each channel in an experimental block of an exemplar animal. (E) shows the reconstructed rates for each stimulation type by area. The mean component scores were used to weight each component and reconstruct the average response in spiking to stimulation.

    Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

    Techniques: Blocking Assay