Showing 4 open source projects for "tracking tools"

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    GNSS-SDR

    GNSS-SDR

    An open source software-defined GNSS receiver

    An open source software-defined Global Navigation Satellite Systems (GNSS) receiver written in C++ and based on the GNU Radio framework.
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    Downloads: 1,300 This Week
    Last Update:
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  • 2
    Qi - Quantitative Imaging (PIV and more)

    Qi - Quantitative Imaging (PIV and more)

    A collection of MATLAB tools for PIV, PTV, particle sizing, and more.

    Qi is a collection of MATLAB tools for the quantitative analysis of flow field images. Our Particle Image Velocimetry (PIV) tool, prana, implements a Robust Phase Correlation kernel for PIV analysis, and now includes Particle Tracking Velocimetry and sizing tools. Tools for pressure calculation, proper orthogonal decomposition (POD), and 3d imaging are under development.
    Downloads: 5 This Week
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  • 3
    Vibrato Toolbox

    Vibrato Toolbox

    Vibrato Toolbox: Parametric Analysis of Musical Vibrato

    Vibrato Toolbox: Parametric Analysis of Musical Vibrato from High-Precision Magnitude/Frequency Tracking Mingfeng Zhang ECE, Univ. of Rochester Part I Analysis of individual music tones This MATLAB toolbox provides signal analysis tools for time-frequency analysis and for parametric analysis of musical vibrato Part II Analysis of multiple music tones or multiple harmonic partials This toolbox allows you to explore a group of audio files, and obtains its related parameters of musical vibrato. ...
    Downloads: 0 This Week
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  • 4
    Kernel Adaptive Filtering Toolbox

    Kernel Adaptive Filtering Toolbox

    a Matlab benchmarking toolbox for kernel adaptive filtering

    ...A Matlab benchmarking toolbox for kernel adaptive filtering. Kernel adaptive filtering algorithms are online and adaptive regression algorithms based on kernels. They are suitable for nonlinear filtering, prediction, tracking and nonlinear regression in general. This toolbox includes algorithms, demos, and tools to compare their performance. See the included README file for a list of included algorithms and more details. If you use this toolbox in your research please cite: @inproceedings{vanvaerenbergh2013comparative, author = {Van Vaerenbergh, Steven and Santamar{\'i}a, Ignacio}, booktitle = {2013 IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE)}, title = {A Comparative Study of Kernel Adaptive Filtering Algorithms}, year = {2013}, note = {Software available at \url{https://github.com/steven2358/kafbox/}} }
    Downloads: 0 This Week
    Last Update:
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