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Related Concept Videos

Transformers01:26

Transformers

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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The Ideal Transformer01:26

The Ideal Transformer

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Related Experiment Video

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Deep Image Steganography Using Transformer and Recursive Permutation.

Zhiyi Wang1, Mingcheng Zhou1, Boji Liu1

  • 1School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel image steganography scheme using Transformer models for superior feature extraction and recursive permutation for enhanced secret image encryption, improving overall security performance.

Keywords:
data hidingdeep learningimage encryptionimage steganographytransformer

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Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Image steganography is crucial for secure data hiding within digital images.
  • Deep learning advancements have significantly improved steganography techniques.
  • Existing methods face challenges in robust feature extraction and encryption.

Purpose of the Study:

  • To propose a novel image steganography scheme leveraging Transformer models.
  • To enhance secret image security through a recursive permutation encryption algorithm.
  • To demonstrate the superiority of the proposed Transformer-based approach over existing deep learning models.

Main Methods:

  • Utilized Transformer architecture for advanced feature extraction in steganography.
  • Developed a recursive permutation algorithm for robust image encryption.
  • Conducted extensive experiments to validate the scheme's effectiveness and performance.

Main Results:

  • The Transformer model outperformed state-of-the-art deep learning models in feature extraction for steganography.
  • The recursive permutation encryption algorithm demonstrated strong security attributes.
  • The integrated scheme significantly improved steganography performance and security.

Conclusions:

  • The proposed Transformer-based image steganography scheme offers enhanced security and performance.
  • Recursive permutation effectively secures secret images, complementing the steganography process.
  • This research advances the field of secure image steganography using deep learning.