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Related Concept Videos

State Space Representation01:27

State Space Representation

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...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
Stereoisomerism02:52

Stereoisomerism

Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula.
Transition metal complexes often exist as geometric isomers, in which the same atoms are connected through the same types of bonds but with differences in their orientation in space. Coordination complexes with two different ligands in the cis and trans positions from a ligand of interest form isomers. For example, the octahedral [Co(NH3)4Cl2]+ ion has two isomers (Figure 1) In the cis...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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 sampling...
Stereoisomers02:32

Stereoisomers

On the basis of mirror symmetry, stereoisomers of an organic molecule can be further classified into diastereomers and enantiomers. Diastereomers are stereoisomers that are not mirror images of each other. Substituted alkenes, such as the cis and trans isomers of 2-butene, are diastereomers, as these molecules exhibit different spatial orientations of their constituent atoms, are not mirror images of each other, and do not interconvert. Here, the interconversion is suppressed due to restricted...

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Related Experiment Video

Updated: May 28, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

URSMamba: Universal remote sensing image steganography using state space model.

Chao Yang1, Shiyuan Wang2, Ying Huang3

  • 1School of Computer Science, China University of Geosciences, Wuhan, 430074, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

URSMamba introduces a novel approach to remote sensing image steganography using a State Space Model. This method enhances both concealing and revealing abilities for confidential data transmission in high-dimensional imagery.

Keywords:
Deep learningImage steganographyRemote sensing imageState space model

Related Experiment Videos

Last Updated: May 28, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

  • Computer Science
  • Information Security
  • Remote Sensing

Background:

  • Image steganography is crucial for secure data transmission.
  • Remote sensing images present unique challenges due to complex distributions and multiple spectral bands.
  • Existing methods often overlook the specific requirements of remote sensing image steganography.

Purpose of the Study:

  • To develop a universal steganography method for remote sensing images.
  • To address the challenges of concealing and revealing abilities and hiding capacity in complex remote sensing data.
  • To improve the performance of image steganography on multi-spectral remote sensing images.

Main Methods:

  • Proposed URSMamba, a State Space Model-based universal remote sensing image steganography.
  • Introduced Low-High Frequency Mamba Block (LHfreMB) for global and local feature extraction.
  • Utilized Spectral Mamba Block (SpectralMB) for rich spectral information extraction.
  • Developed Spatial-Spectral Dynamic Fusion (SSDF) block for adaptive feature integration.

Main Results:

  • URSMamba demonstrates superior performance on multi-spectral remote sensing images.
  • Achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) for cover/stego and secret/recovery image pairs.
  • Outperformed state-of-the-art methods, showing 0.54 dB and 1.65 dB gains for 8-band images.
  • Also delivered high-quality results on natural images with 3.25 dB and 2.96 dB improvements.

Conclusions:

  • URSMamba effectively models complex ground distributions and spectral features in remote sensing images.
  • The proposed method offers enhanced concealing and revealing capabilities for secure remote sensing data.
  • URSMamba provides a robust and high-performance solution for remote sensing image steganography.