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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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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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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Aliasing01:18

Aliasing

284
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Sample Handling01:02

Sample Handling

170
Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Related Experiment Video

Updated: Oct 11, 2025

A Method to Study Adaptation to Left-Right Reversed Audition
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Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding.

Yoga Samudra1, Tohari Ahmad1

  • 1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

Heliyon
|November 29, 2021
PubMed
Summary

This study enhances data security by embedding sensitive information into audio files. The improved method offers higher quality steganography and ensures data reversibility for secure cloud computing applications.

Keywords:
Audio processingData hidingData protectionInformation securityInfrastructure

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Area of Science:

  • Computer Science
  • Information Security
  • Digital Forensics

Background:

  • Cloud computing and network applications process sensitive data, posing security and privacy risks due to inadequate public system mechanisms.
  • Data compromise leads to severe security and privacy issues for users, necessitating robust protection methods.
  • Existing data embedding techniques, like audio steganography, face limitations in payload capacity and cover data quality.

Purpose of the Study:

  • To address the limitations of existing steganography methods regarding payload capacity and stego-data quality.
  • To improve prediction error expansion-based algorithms and introduce a novel mirroring embedded sample scheme for audio steganography.
  • To achieve high-quality, reversible data embedding in audio covers for enhanced data security.

Main Methods:

  • Improved prediction error expansion algorithm.
  • Designed a mirroring embedded sample scheme to minimize distortion in the cover audio.
  • Focused on forcing processed audio samples to closely resemble original ones.

Main Results:

  • The proposed method significantly improves stego-data quality for larger payloads compared to existing algorithms.
  • Achieved a signal-to-noise ratio (SNR) exceeding 100 dB, surpassing that of compared methods.
  • Demonstrated the reversibility of the method, allowing full reconstruction of both payload and cover audio.

Conclusions:

  • The enhanced audio steganography method effectively balances payload capacity and cover data integrity.
  • The technique offers superior quality and reversibility, making it suitable for securing sensitive data in networked applications.
  • This research contributes a more secure and efficient approach to data hiding in audio signals.