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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.
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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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The important convolution properties include width, area, differentiation, and integration properties.
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Downsampling01:20

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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.
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Convolution computations can be simplified by utilizing their inherent properties.
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The Residual ISI for Which the Convolutional Noise Probability Density Function Associated with the Blind Adaptive Deconvolution Problem Turns Approximately Gaussian.

Entropy (Basel, Switzerland)·2022
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A Novel Technique for Achieving the Approximated ISI at the Receiver for a 16QAM Signal Sent via a FIR Channel Based Only on the Received Information and Statistical Techniques.

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Updated: Nov 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Improved Approach for the Maximum Entropy Deconvolution Problem.

Shay Shlisel1, Monika Pinchas1

  • 1Department of Electrical and Electronic Engineering, Ariel University, Ariel 40700, Israel.

Entropy (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a new method for blind adaptive deconvolution using the Generalized Gaussian density function to model convolutional noise. The proposed technique improves equalization performance, reducing convergence time for 16 Quadrature Amplitude Modulation (QAM) systems.

Keywords:
Generalized Gaussian Distribution (GGD)Laplace integralblind equalizationdeconvolutionedgeworth expansionmaximum entropy

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Area of Science:

  • Digital Communications
  • Signal Processing
  • Information Theory

Background:

  • The Gaussian probability density function (pdf) is commonly used for convolutional noise in blind adaptive deconvolution but is only accurate in later stages.
  • Previous attempts using Edgeworth Expansion and Maximum Entropy for 16 Quadrature Amplitude Modulation (QAM) showed limited or parameter-dependent improvements.

Purpose of the Study:

  • To develop a novel equalization method for blind adaptive deconvolution.
  • To accurately model convolutional noise pdf for 16 QAM signals without requiring additional parameters.

Main Methods:

  • Applied the Generalized Gaussian density (GGD) function and Edgeworth Expansion to approximate the convolutional noise pdf.
  • Developed a new equalization algorithm based on this improved noise model.
  • Evaluated performance using simulations for the hard channel case.

Main Results:

  • The proposed method achieved improved equalization performance, particularly in terms of convergence time.
  • Convergence was observed approximately 15,000 symbols faster compared to the original Maximum Entropy algorithm.
  • The new approach eliminated the need for additional predefined parameters required by previous methods.

Conclusions:

  • The Generalized Gaussian density function provides an effective model for convolutional noise in 16 QAM blind adaptive deconvolution.
  • The novel equalization method based on GGD significantly reduces the number of symbols needed for reliable decision-making.
  • This advancement offers a more efficient and practical solution for equalization in digital communication systems.