Jove
Visualize
Contact Us

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

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical performance of an atrial fibrillation burden tracking algorithm: Evaluation against reference public and clinical textile electrocardiographic datasets.

Heart rhythm O2·2026
Same author

Feasibility of opportunistic peripheral bone mineral density and structural quantification using an ultra-low dose multi-detector CT.

Biomedical physics & engineering express·2026
Same author

Neonatal seizure detection from EEG using inception ResNetV2 feature extraction and XGBoost optimized with particle swarm optimization.

Scientific reports·2025
Same author

Correction: Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal.

PloS one·2024
Same author

Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.

Clinical EEG and neuroscience·2024
Same author

Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation.

Sensors (Basel, Switzerland)·2023
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Dec 6, 2025

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
11:13

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Published on: May 24, 2021

6.9K

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 summary is machine-generated.

    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.

    More Related Videos

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    18.0K
    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.0K

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
    11:13

    Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

    Published on: May 24, 2021

    6.9K
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    18.0K
    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.0K

    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.