The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform

Moreno Aróstegui, Juan Manuel - One of the best experts on this subject based on the ideXlab platform.

  • Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding
    'Elsevier BV', 2013
    Co-Authors: Ballesteros Larrotta, Dora Maria, Moreno Aróstegui, Juan Manuel
    Abstract:

    This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the Biorthogonal Base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is Based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.Peer ReviewedPostprint (published version

  • Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding
    1
    Co-Authors: Ballesteros Larrotta, Dora Maria, Moreno Aróstegui, Juan Manuel
    Abstract:

    This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the Biorthogonal Base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is Based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.Peer Reviewe

Ballesteros Larrotta, Dora Maria - One of the best experts on this subject based on the ideXlab platform.

  • Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding
    'Elsevier BV', 2013
    Co-Authors: Ballesteros Larrotta, Dora Maria, Moreno Aróstegui, Juan Manuel
    Abstract:

    This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the Biorthogonal Base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is Based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.Peer ReviewedPostprint (published version

  • Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding
    1
    Co-Authors: Ballesteros Larrotta, Dora Maria, Moreno Aróstegui, Juan Manuel
    Abstract:

    This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the Biorthogonal Base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is Based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.Peer Reviewe