Jove
Visualize
Contact Us
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 Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

816
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
816
Upsampling01:22

Upsampling

682
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...
682
Aliasing01:18

Aliasing

744
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
744
Downsampling01:20

Downsampling

745
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...
745
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

388
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
388
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

414
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
414

You might also read

Related Articles

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

Sort by
Same author

Development and External Validation of a Machine Learning Model for 10-Year Ischemic Stroke Risk Prediction in Diverse Populations.

medRxiv : the preprint server for health sciences·2026
Same author

Where risk becomes visible: a layered fixed-policy framework for diabetic kidney disease screening in type 2 diabetes.

medRxiv : the preprint server for health sciences·2026
Same author

Four Consecutive False Negative Newborn Screens in a Patient with Classical Congenital Adrenal Hyperplasia: A Case Report

Journal of clinical research in pediatric endocrinology·2026
Same author

Development and validation of a two-stage machine learning model for personalised type 2 diabetes screening in the All of Us Research Program and UK Biobank.

BMJ open·2026
Same author

A 7-year experience in adenoidectomy with endoscopic radiofrequency volume reduction.

Scientific reports·2025
Same author

Optimizing glucocorticoid therapy in congenital adrenal hyperplasia and analog conditions: the intersection of dose reduction, patient care, and coverage in the US.

Frontiers in endocrinology·2025

Related Experiment Video

Updated: Mar 14, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

Michael M Abdel-Sayed1, Ahmed Khattab1, Mohamed F Abu-Elyazeed1

  • 1Electronics and Communications Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt.

Journal of Advanced Research
|September 28, 2016
PubMed
Summary

Compressed sensing reconstructs signals efficiently using the Reduced-set Matching Pursuit (RMP) algorithm. RMP offers improved accuracy and speed over existing greedy methods and L1 minimization for sparse signal acquisition.

Keywords:
Compressed sensingMatching pursuitRestricted isometry propertySparse signal reconstruction

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.9K
Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

574

Related Experiment Videos

Last Updated: Mar 14, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.3K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.9K
Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

574

Area of Science:

  • Signal Processing
  • Information Theory
  • Applied Mathematics

Background:

  • Compressed sensing (CS) allows sparse signal acquisition below the Nyquist rate.
  • Traditional L1 minimization for CS reconstruction is computationally intensive.
  • Greedy algorithms offer lower complexity but varying accuracy.

Purpose of the Study:

  • Introduce the Reduced-set Matching Pursuit (RMP) algorithm for compressed sensing.
  • Improve reconstruction time and accuracy in sparse signal recovery.
  • Address limitations of existing greedy algorithms in value selection.

Main Methods:

  • Developed the Reduced-set Matching Pursuit (RMP) greedy algorithm.
  • Implemented signal pruning to exclude incorrectly selected values.
  • Introduced the normalized time-error product metric for evaluating performance.

Main Results:

  • RMP achieves higher reconstruction accuracy than existing greedy algorithms.
  • RMP demonstrates significantly lower computational complexity.
  • RMP outperforms L1 minimization on the normalized time-error product metric.
  • Performance validated in both noiseless and noisy conditions.

Conclusions:

  • RMP is an efficient and accurate greedy algorithm for compressed sensing.
  • The algorithm offers a superior trade-off between reconstruction time and error.
  • RMP presents a viable alternative to computationally expensive reconstruction methods.