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matlab parallel computing toolbox  (MathWorks Inc)


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    Structured Review

    MathWorks Inc matlab parallel computing toolbox
    Matlab Parallel Computing Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 385 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+-r2020b/Parallel+Computing+Toolbox/pmc13045672-9-0-0
    Average 95 stars, based on 385 article reviews
    matlab parallel computing toolbox - by Bioz Stars, 2026-09
    95/100 stars

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    Related Articles

    other:

    Article Title: Protocol for identifying and comparing neuronal ensembles using different algorithms within a graphical user interface
    Article Snippet: MATLAB Parallel Computing Toolbox , MathWorks , https://www.mathworks.com/products/parallel-computing.html.

    Article Title: Protocol for identifying and comparing neuronal ensembles using different algorithms within a graphical user interface
    Article Snippet: MATLAB Statistics and Machine Learning Toolbox , MathWorks , https://www.mathworks.com/products/statistics.html.

    Article Title: From statistics to deep learning in single-molecule fluorescence resonance energy transfer analysis.
    Article Snippet: MASH-FRET [9,26], a MATLAB-based toolbox, uses the BOBA-FRET’s analysis and simulation framework, while adding workflows for molecule localisation and dynamic characterisation. iSMS [27] and SMART [28] offer graphical interfaces for data analysis and visualisation, and enhance standard HMM by incorporating VB and the Bayesian Information Criterion (BIC) while estimating the number of states.

    Article Title: A quantitative framework for multiscale analysis of Candida albicans biofilm development
    Article Snippet: Image processing in MATLAB We use MATLAB’s image processing toolbox to analyse experimental images for the hyphal tracking, area coverage, and biofilm front light-sheet analyses presented in this work.

    Software:

    Article Title: Contextual camera controls during a collaboration session in a heterogenous computing platform
    Article Snippet: .. Non-limiting examples of available AI algorithms, software, and libraries that may be utilized within embodiments of systems and methods described herein include, but are not limited to: PYTHON, OPENCV, INCEPTION, THEANO, TORCH, PYTORCH, PYLEARN2, NUMPY, BLOCKS, TENSORFLOW, MXNET, CAFFE, LASAGNE, KERAS, CHAINER, MATLAB Deep Learning, CNTK, MatConvNet (a MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for Deep Learning from Rasmus Berg Palm), BigDL, Cuda-Convnet (a fast C++/CUDA implementation of convolutional or feed-forward neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.Ish, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPoT, ConvNet, ELEKTRONN, OpenNN, NEURALDESIGNER, Theano Generalized Hebbian Learning, Apache SINGA, Lightnet, and SimpleDNN. ..

    Article Title: Systems and methods for connecting a conference room to an ongoing meeting session
    Article Snippet: Such AI/ML model(s) may implement: a neural network (e.g., artificial neural network, deep neural network, convolutional neural network, recurrent neural network, autoencoders, reinforcement learning, etc.), fuzzy logic, deep learning, deep structured learning hierarchical learning, Support Vector Machine (SVM) (e.g., linear SVM, nonlinear SVM, SVM regression, etc.), decision tree learning (e.g., classification and regression tree or “CART”), Very Fast Decision Tree (VFDT), ensemble methods (e.g., ensemble learning, Random Forests, Bagging and Pasting, Patches and Subspaces, Boosting, Stacking, etc.), dimensionality reduction (e.g., Projection, Manifold Learning, Principal Components Analysis, etc.), or the like. .. Non-limiting examples of available AI/ML algorithms, models, software, and libraries that may be utilized within embodiments of systems and methods described herein include, but are not limited to: PYTHON, OPENCV, INCEPTION, THEANO, TORCH, PYTORCH, PYLEARN2, NUMPY, BLOCKS, TENSORFLOW, MXNET, CAFFE, LASAGNE, KERAS, CHAINER, MATLAB Deep Learning, CNTK, MatConvNet (a MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for Deep Learning from Rasmus Berg Palm), BigDL, Cuda-Convnet (a fast C++/CUDA implementation of convolutional or feed-forward neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPOT, ConvNet, ELEKTRONN, OpenNN, NEURALDESIGNER, Theano Generalized Hebbian Learning, Apache SINGA, Lightnet, and SimpleDNN. ..

    Article Title: Systems and methods for remotely provisioning facial recognition data to heterogeneous computing platforms
    Article Snippet: .. Non-limiting examples of software and libraries which may be utilized within embodiments of systems and methods described herein to perform AI modeling operations include, but are not limited to: PYTHON, OPENCV, scikit-learn, INCEPTION, THEANO, TORCH, PYTORCH, PYLEARN2, NUMPY, BLOCKS, TENSORFLOW, MXNET, CAFFE, LASAGNE, KERAS, CHAINER, MATLAB Deep Learning, CNTK, MatConvNet (a MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for Deep Learning from Rasmus Berg Palm), BigDL, Cuda-Convnet (a fast C++/CUDA implementation of convolutional or feed-forward neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPoT, ConvNet, ELEKTRONN, OpenNN, NEURALDESIGNER, Theano Generalized Hebbian Learning, Apache SINGA, Lightnet, and SimpleDNN. ..

    Control:

    Article Title: “A novel adaptive gesture recognition framework for bionic hands using Stacked Autoencoder (SAE), Adaptive Bayesian Feature Selection (ABFS), MODWT, and Hybrid sEMG–MMG Sensor Modality”
    Article Snippet: Integration Architecture: Sensor signals are preprocessed in MATLAB using MODWT and fed through ABFS and SAE to classify gestures. .. Classified gestures are converted into control commands and transmitted to ADAMS using a real-time data exchange interface (MATLAB Simulink + ADAMS/Controls). ..



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