version 0.7.2

Bug-fix release

In Brief:

  • Generates coding and decoding matrices.

  • Probabilistic decoding: Belief Propagation algorithm.

  • Images transmission simulation (channel model: AGWN).

  • Sound transmission simulation (channel model :AGWN).

Image coding-decoding example:

https://media.giphy.com/media/l4KicsAauqIWjeFR6/giphy.gif https://media.giphy.com/media/l0COHC49bK6g7yIPm/giphy.gif

Sound coding-decoding example:

Sound Transmission

Installation

From pip:

$ pip install --upgrade pyldpc

Tutorials:

Jupyter notebooks:

Many changes in tutorials in v.0.7.0

  • Users’ Guide:

1- LDPC Coding-Decoding Simulation

2- Images Coding-DecodingTutorial

3- Sound Coding-DecodingTutorial

4- LDPC Matrices Construction Tutorial

  • For LDPC construction details:

1- pyLDPC Construction(French)

2- LDPC Images Functions Construction

3- LDPC Sound Functions Construction

version 0.7.1

Contains:

  1. Coding and decoding matrices Generators:

    • Regular parity-check matrix using Callager’s method.

    • Coding Matrix G both non-systematic and systematic.

  2. Coding function adding Additive White Gaussian Noise.

  3. Decoding functions using Probabilistic Decoding (Belief propagation algorithm):

    • Default BP algorithm.

    • Full-log BP algorithm.

  4. Images transmission sub-module:

    • Coding and Decoding Grayscale and RGB Images.

    • Pixel by pixel coding & decoding (small matrices)

    • Row by row coding & decoding (large sparse matrices)

    • BER: Bit Error Rate function.

  5. Sound transmission sub-module:

    • Coding and Decoding audio files.

    • BER_audio: Bit Error Rate function.

What’s new:

  • Bug in using full rank parity check matrices fixed.

  • 5 to 10 times faster decoding.

  • Compatibility of scipy.sparse.csr objects (CSR format) and numpy arrays.

  • Row by row image decoding (More efficient than pixel coding) using large matrices.

  • 4 times faster coding.

In the upcoming versions:

  • Use of large matrices (csr) in sound transmission sub-module.

  • Library of ready-to-use large matrices (csr).

  • Text Transmission functions.

Contact:

Please contact hicham.janati@ensae.fr for any bug encountered / any further information.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.7.9)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for hjanati from gravatar.com hjanati

Credits

Author: Hicham Janati

License

MIT

Tags

codes ldpc error detection decoding coding pyldpc

Classifiers

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