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ai for signals and images -凯发k8网页登录

wavelet-based techniques for machine learning and deep learning, gpu acceleration, hardware deployment, signal labeling

wavelet techniques are effective for obtaining sparse, compressive data representations or features, which you can use in machine learning and deep learning workflows. wavelet toolbox™ supports deployment of multiscale feature extraction algorithms through matlab® coder™ and gpu coder™ for a number of targets. to take advantage of the performance benefits offered by a modern graphics processing unit (gpu), certain wavelet toolbox functions can perform operations on a gpu. these functions provide gpu acceleration for your workflows. wavelet toolbox also provides functionality to perform signal labeling.

categories


  • multiresolution analysis, wavelet time scattering, continuous wavelet transform, nondecimated discrete wavelet transform, wigner-ville distribution, mel spectrogram

  • wavelet image scattering, 2-d continuous wavelet transform, shearlets, stationary wavelet transform

  • feature extraction on gpus for machine learning and deep learning workflows

  • c/c code generation, gpu code generation, raspberry pi®, nvidia® jetson®

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