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

Parallel Resonance01:23

Parallel Resonance

284
The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
284
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

144
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....
144
Discrete Fourier Transform01:15

Discrete Fourier Transform

441
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
441
Weighted Mean00:57

Weighted Mean

5.5K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging.

Sensors (Basel, Switzerland)·2022
Same author

Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery.

Sensors (Basel, Switzerland)·2021
Same author

Robust Signaling for Bursty Interference.

Entropy (Basel, Switzerland)·2020
Same author

Mask Responses for Single-Pixel Terahertz Imaging.

Scientific reports·2018

Related Experiment Video

Updated: Sep 26, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.9K

Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing.

Udaya S K P Miriya Thanthrige1, Peter Jung2,3, Aydin Sezgin1

  • 1Institute of Digital Communication Systems, Ruhr University Bochum, 44801 Bochum, Germany.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary

This study introduces a novel method for detecting internal material defects using MIMO radar. The advanced technique enhances accuracy and speed by combining joint rank and sparsity minimization with deep learning.

Keywords:
algorithm unfoldingclutter suppressioncompressive sensingdefects detectionreweighted norm

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K
Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible
14:44

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Published on: May 13, 2025

1.2K

Related Experiment Videos

Last Updated: Sep 26, 2025

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.9K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K
Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible
14:44

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Published on: May 13, 2025

1.2K

Area of Science:

  • Non-destructive testing
  • Electromagnetic sensing
  • Signal processing

Background:

  • Detecting internal material defects in layered structures is challenging due to surface clutter.
  • Sophisticated signal separation is crucial for accurate defect identification.
  • Layered material responses often exhibit low-rank properties, suitable for advanced modeling.

Purpose of the Study:

  • To develop an improved method for detecting internal material defects in layered structures.
  • To address the limitations of conventional signal separation techniques in cluttered environments.
  • To enhance the accuracy and convergence speed of defect detection algorithms.

Main Methods:

  • Utilizing compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar.
  • Proposing a joint rank and sparsity minimization approach.
  • Implementing a non-convex, double-reweighted iterative algorithm for low-rank and sparse component estimation.
  • Applying deep learning for parameter tuning (algorithm unfolding).

Main Results:

  • The proposed double-reweighted approach achieves higher accuracy than conventional nuclear norm and ℓ1-norm minimization.
  • The iterative algorithm effectively estimates low-rank and sparse contributions.
  • Deep learning-based parameter tuning significantly improves accuracy and convergence speed.
  • Numerical results demonstrate superior performance compared to existing methods.

Conclusions:

  • The joint rank and sparsity minimization, enhanced by deep learning, offers a powerful solution for material defect detection.
  • This method effectively overcomes clutter interference in MIMO radar systems.
  • The approach shows significant improvements in accuracy and efficiency for non-destructive testing applications.