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

Controller Configurations01:22

Controller Configurations

380
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
380
Electron Configurations02:46

Electron Configurations

26.3K
Electron configurations and orbital diagrams can be determined by applying the Aufbau principle (each added electron occupies the subshell of lowest energy available), Pauli exclusion principle (no two electrons can have the same set of four quantum numbers), and Hund’s rule of maximum multiplicity (whenever possible, electrons retain unpaired spins in degenerate orbitals).
The relative energies of the subshells determine the order in which atomic orbitals are filled (1s, 2s, 2p, 3s, 3p,...
26.3K
Electron Configuration of Multielectron Atoms03:26

Electron Configuration of Multielectron Atoms

65.0K
The alkali metal sodium (atomic number 11) has one more electron than the neon atom. This electron must go into the lowest-energy subshell available, the 3s orbital, giving a 1s22s22p63s1 configuration. The electrons occupying the outermost shell orbital(s) (highest value of n) are called valence electrons, and those occupying the inner shell orbitals are called core electrons. Since the core electron shells correspond to noble gas electron configurations, we can abbreviate electron...
65.0K
State Space Representation01:27

State Space Representation

573
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
573
Control Volume and System Representations01:16

Control Volume and System Representations

1.6K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.6K
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

208
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
208

You might also read

Related Articles

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

Sort by
Same author

Quality Improvement Synthetic Aperture Radar (SAR) Images Using Compressive Sensing (CS) With Moore-Penrose Inverse (MPI) and Prior From Spatial Variant Apodization (SVA).

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images.

Sensors (Basel, Switzerland)·2021
Same author

Ultra-High Resolution Imaging Method for Distributed Small Satellite Spotlight MIMO-SAR Based on Sub-Aperture Image Fusion.

Sensors (Basel, Switzerland)·2021
Same author

Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion.

Sensors (Basel, Switzerland)·2021
Same author

InSAR Signal and Data Processing.

Sensors (Basel, Switzerland)·2020
Same author

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial-Temporal Correlation.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: Feb 3, 2026

Production of a SARS-CoV-2 Virus-Like-Particle System to Investigate Viral Life Cycles In Vitro
09:26

Production of a SARS-CoV-2 Virus-Like-Particle System to Investigate Viral Life Cycles In Vitro

Published on: June 6, 2025

1.1K

SAR Target Configuration Recognition via Product Sparse Representation.

Ming Liu1,2, Shichao Chen3, Fugang Lu4

  • 1Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an 710062, China. mliu@snnu.edu.cn.

Sensors (Basel, Switzerland)
|October 24, 2018
PubMed
Summary
This summary is machine-generated.

A new product sparse representation (PSR) algorithm enhances synthetic aperture radar (SAR) target recognition by combining sparse representation with a product model. This method effectively reduces speckle noise for improved accuracy and robustness.

Keywords:
product modelsparse representation (SR)synthetic aperture radar (SAR)target configuration recognition

More Related Videos

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.6K
Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.5K

Related Experiment Videos

Last Updated: Feb 3, 2026

Production of a SARS-CoV-2 Virus-Like-Particle System to Investigate Viral Life Cycles In Vitro
09:26

Production of a SARS-CoV-2 Virus-Like-Particle System to Investigate Viral Life Cycles In Vitro

Published on: June 6, 2025

1.1K
Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.6K
Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.5K

Area of Science:

  • Computer Vision and Image Processing
  • Pattern Recognition
  • Remote Sensing

Background:

  • Sparse Representation (SR) is a powerful technique for pattern recognition tasks.
  • Synthetic Aperture Radar (SAR) images are susceptible to multiplicative speckle noise, which complicates target recognition.
  • Existing methods may struggle with the unique noise characteristics of SAR imagery.

Purpose of the Study:

  • To develop an advanced algorithm for SAR target configuration recognition that addresses speckle noise.
  • To improve the accuracy and robustness of SAR image analysis.
  • To leverage the strengths of both sparse representation and product modeling for SAR data.

Main Methods:

  • Proposed a novel Product Sparse Representation (PSR) algorithm tailored for SAR images.
  • Utilized a product model to capture essential SAR image characteristics.
  • Modeled SAR speckle noise using Gamma distribution and employed a q-statistical approach to obtain sparse vectors.

Main Results:

  • The PSR algorithm demonstrated superior performance in SAR target recognition compared to existing state-of-the-art methods.
  • Experimental validation on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database confirmed the algorithm's effectiveness.
  • The proposed method showed robustness under various operating conditions and noise levels.

Conclusions:

  • The Product Sparse Representation (PSR) algorithm offers a significant advancement for SAR target recognition.
  • The integration of product modeling and sparse representation effectively mitigates speckle noise.
  • The algorithm provides higher recognition rates and improved robustness for SAR image analysis.