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Related Concept Videos

Downsampling01:20

Downsampling

493
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
493
Upsampling01:22

Upsampling

499
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
499

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Related Experiment Video

Updated: Dec 6, 2025

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
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MNDL Sparsity Order Selection for Compressed Sensing with Application in ECG Compression.

Mahdi Shamsi, Tohid Yousefi Rezaii, Soosan Beheshti

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study introduces an automated method for estimating noise variance in compressed sensing, improving sparse signal recovery and signal-to-noise ratio (SNR) for applications like ECG compression.

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    Area of Science:

    • Signal Processing
    • Compressed Sensing
    • Statistical Inference

    Background:

    • Determining sparsity order is crucial for compressed sensing but often overlooks noise variance.
    • Existing model order selection methods may not fully account for observation and measurement noise.
    • Minimum Noiseless Description Length (MNDL) offers robust order selection but requires noise variance estimation.

    Purpose of the Study:

    • To develop a novel, automated method for estimating observation noise variance within the MNDL framework.
    • To enable simultaneous estimation of signal-to-noise ratio (SNR) and sparsity order without prior assumptions.
    • To enhance sparse signal recovery in compressed sensing applications.

    Main Methods:

    • A new automated approach is integrated into the MNDL order selection method.
    • The method estimates observation noise variance and SNR concurrently with sparsity order.
    • No prior knowledge or assumptions about noise variance are required.

    Main Results:

    • The proposed automated MNDL method successfully estimates SNR and sparsity order.
    • Simulations for ECG compression demonstrate improved parameter estimation error.
    • The method leads to significant SNR improvement compared to existing approaches.

    Conclusions:

    • The developed automated MNDL method provides a robust solution for sparse signal recovery.
    • It eliminates the need for manual SNR estimation, simplifying the process.
    • The approach shows practical advantages in real-world applications like ECG compression.