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Showing 1 to 6 of 6 for “"Signal Denoising"”.

  1. Graph Learning and Optimization for Irregular-Structured Signal Processing

    Graph Signal Processing (GSP) extends harmonic analysis tools, such as Fourier transforms and wavelets, to discrete signals defined on finite graphs, enabling tasks like signal denoising, prediction, and interpolation on irregular domains. A critical first step in GSP is to learn an appropriate …

    york Repository record for Graph Learning and Optimization for Irregular-Structured Signal Processing (opens in a new tab)

  2. Time-Frequency Analysis and Filtering based on the Short-Time Fourier Transform

    … (TF) domain provides a convenient platform for signal analysis by involving the dimension of time in the frequency representation of a signal. A straightforward way to acquire localized knowledge about the frequency content of the signal at different times is to perform the Fourier transform …

    kings Repository record for Time-Frequency Analysis and Filtering based on the Short-Time Fourier Transform (opens in a new tab)

  3. Partial discharge denoising for power cables

    … based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in …

    strathclyde Repository record for Partial discharge denoising for power cables (opens in a new tab)

  4. Enhancing rainfall measurement using microwave links at three scales

    The application of microwave signals in telecommunication has advanced in recent decades. Hence the CMLs (Commercial Microwave Links) have been spread all over the lands. Meanwhile, the decrease in power of the signal due to rainfall has raised the idea of measuring rainfall with microwave …

    waikato-masters Repository record for Enhancing rainfall measurement using microwave links at three scales (opens in a new tab)

  5. Adaptive sparse representations and their applications

    "The sparsity of signals and images in a certain transform domain or dictionary has been exploited in many applications in signal processing, image processing, and medical imaging. Analytical sparsifying transforms such as Wavelets and DCT have been widely used in compression standards. Recently, …

    uiuc Repository record for Adaptive sparse representations and their applications (opens in a new tab)

  6. Characterization of Structure-Borne Tire Noise Using Virtual Sensing

    Various improvements which have been made to the vehicle (reduced engine noise, reducedaerodynamic related NVH), have resulted in tire road noise as the dominant source of thevehicle interior noise. Generally, vehicle interior noise has two main sources, 1) travellinglow frequency excitation below …

    vt Repository record for Characterization of Structure-Borne Tire Noise Using Virtual Sensing (opens in a new tab)