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executable mex files  (MathWorks Inc)


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    MathWorks Inc executable mex files
    Executable Mex Files, 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/matlab+mex/MATLAB+Coder/pmc05688953-402-10-9
    Average 95 stars, based on 169 article reviews
    executable mex files - 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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    Image Search Results


    a – b , run times of Tiff readers and writers for libtiff (MATLAB), tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .

    Journal: Nature Methods

    Article Title: Image processing tools for petabyte-scale light sheet microscopy data

    doi: 10.1038/s41592-024-02475-4

    Figure Lengend Snippet: a – b , run times of Tiff readers and writers for libtiff (MATLAB), tifffile (Python), and Cpp-Tiff versus the number of frames. c – d , run times of Zarr readers and writers, comparing the MATLAB interface of Zarr, native Zarr (Python), and Cpp-Zarr across different numbers of frames. In panels a-d , all images are in unit16 format with a frame size of 512 × 1,800 (xy), and the benchmark results are the absolute run times for Fig. . e – f , run times of Tiff readers and writers for libtiff (MATLAB) and Cpp-Tiff versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. g – h , run times of Zarr readers and writers for the MATLAB interface of Zarr, and Cpp-Zarr versus the number of CPU cores for a unit16 image stack of size 512 × 1,800 × 20,000. i – k , data size and read/write times versus compression level for lz4 and zstd compressors for a uint16 image stack of size 512 × 1,800 × 30,000. The benchmarks were run ten times independently on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). All 24 cores were allocated for panels a – d and i – k , and varying numbers of CPU cores were allocated for panels e – h . Data are shown as mean ± s.d. in panels a – h and j – k .

    Article Snippet: Our Tiff reader/writer leverages the capabilities of the libtiff library in C++ with the MATLAB MEX interface.

    Techniques:

    a , Performance gains of our Cpp-Tiff reader versus the conventional Tiff reader in MATLAB and tifffile reader in Python versus the number of frames in 3D stacks. b , Performance gains of Cpp-Tiff writer versus the conventional Tiff writer in MATLAB and the tifffile writer in Python versus the number of frames in 3D stacks. c , Performance gains of our Cpp-Zarr reader versus the conventional Zarr reader (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. d , Performance gains of Cpp-Zarr writers versus the conventional Zarr writer (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. The images have a uint16 frame size of 512 × 1,800 ( xy ) in all cases. The benchmarks were run independently ten times on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). Data are shown as the mean ± s.d.

    Journal: Nature Methods

    Article Title: Image processing tools for petabyte-scale light sheet microscopy data

    doi: 10.1038/s41592-024-02475-4

    Figure Lengend Snippet: a , Performance gains of our Cpp-Tiff reader versus the conventional Tiff reader in MATLAB and tifffile reader in Python versus the number of frames in 3D stacks. b , Performance gains of Cpp-Tiff writer versus the conventional Tiff writer in MATLAB and the tifffile writer in Python versus the number of frames in 3D stacks. c , Performance gains of our Cpp-Zarr reader versus the conventional Zarr reader (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. d , Performance gains of Cpp-Zarr writers versus the conventional Zarr writer (MATLAB interface of Zarr) and native Zarr in Python versus the number of frames in 3D stacks. The images have a uint16 frame size of 512 × 1,800 ( xy ) in all cases. The benchmarks were run independently ten times on a 24-core CPU computing node (dual Intel Xeon Gold 6146 CPUs). Data are shown as the mean ± s.d.

    Article Snippet: Our Tiff reader/writer leverages the capabilities of the libtiff library in C++ with the MATLAB MEX interface.

    Techniques: