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Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
425
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

302
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....
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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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,...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

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The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
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Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
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Related Experiment Video

Updated: Dec 21, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K

Interval type-2 fuzzy logic system based similarity evaluation for image steganography.

Zubair Ashraf1, Mukul Lata Roy1, Pranab K Muhuri1

  • 1Department of Computer Science, South Asian University, Akbar Bhavan, Chanakyapuri, New Delhi 110021, India.

Heliyon
|May 19, 2020
PubMed
Summary

This study introduces an interval type-2 fuzzy logic system (IT2 FLS) for image steganography, improving data hiding in non-edge regions. The IT2 FLS-LSB method enhances similarity detection for more secure and efficient least significant bit (LSB) steganography.

Keywords:
Computer scienceData hidingImage steganographyInterval type-2 fuzzy logic systemSimilarity measure

Related Experiment Videos

Last Updated: Dec 21, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K

Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Image steganography conceals data within images, often targeting non-edge regions due to pixel similarity.
  • Traditional similarity measures for steganography lack perceptual accuracy, relying on mathematical rather than human-like perception.

Purpose of the Study:

  • To propose a novel image steganography method using an interval type-2 fuzzy logic system (IT2 FLS) for enhanced neighboring pixel similarity detection.
  • To develop and evaluate the IT2 FLS-LSB method against Type-1 Fuzzy Logic System (T1FLS-LSB) and Euclidean distance-based (SM-LSB) methods.

Main Methods:

  • Developed an interval type-2 fuzzy logic system (IT2 FLS) to calculate perceptual similarity between neighboring image pixels.
  • Implemented the IT2 FLS-LSB steganographic method, embedding data in pixels with high similarity scores using the least significant bit (LSB) technique.
  • Created comparative methods: T1FLS-LSB and SM-LSB for evaluation.

Main Results:

  • The IT2 FLS-LSB method demonstrated superior performance in embedding secret data within image non-edge regions.
  • Evaluated steganographic methods using Peak Signal-to-Noise Ratio (PSNR), Universal Quality Index (UQI), and Structural Similarity Measure (SSIM).
  • The proposed IT2 FLS-LSB method achieved high payload capacity and effectiveness compared to existing steganographic techniques.

Conclusions:

  • The interval type-2 fuzzy logic system effectively captures perceptual similarity for improved image steganography.
  • The IT2 FLS-LSB method offers a robust and efficient approach to secure data embedding in digital images.
  • The study validates the efficacy of fuzzy logic systems in advancing steganographic techniques.