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GWRA: grey wolf based reconstruction algorithm for compressive sensing signals.

Ahmed Aziz1, Karan Singh2, Ahmed Elsawy1

  • 1Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary
This summary is machine-generated.

A new Grey Wolf Reconstruction Algorithm (GWRA) improves data reconstruction in compressive sensing (CS). This technique offers superior accuracy and efficiency compared to existing greedy and swarm-based methods.

Keywords:
Average normalized mean squared errorCompressive sensingGreedy-based reconstruction algorithmGrey wolf optimizerMean absolute percentage errorReconstruction algorithms

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Area of Science:

  • Signal Processing
  • Data Compression
  • Optimization Algorithms

Background:

  • Compressive Sensing (CS) enables efficient signal acquisition and data compression.
  • Reconstructing original data from compressed signals is a key challenge in CS.
  • Greedy-based algorithms offer fast and computationally efficient solutions for CS reconstruction.

Purpose of the Study:

  • To introduce a novel optimization algorithm, the Grey Wolf Reconstruction Algorithm (GWRA).
  • To enhance the accuracy and efficiency of data reconstruction in Compressive Sensing.

Main Methods:

  • Developed GWRA by integrating a reversible greedy algorithm with the Grey Wolf Optimizer.
  • Validated the GWRA technique through extensive simulations.

Main Results:

  • GWRA demonstrated significant reductions in reconstruction error.
  • Achieved lower mean absolute percentage error and average normalized mean squared error compared to existing methods.
  • Outperformed traditional greedy algorithms (Sum Product, OMP, CoSaMP, FBP) and swarm-based techniques (BA, PSO).

Conclusions:

  • The proposed GWRA offers superior performance for data reconstruction in Compressive Sensing.
  • GWRA presents a promising advancement over existing greedy and swarm-based reconstruction techniques.
  • This algorithm effectively addresses the challenge of accurate and efficient data recovery in CS applications.