https://github.com/gagniuc/mix-two-signals-in-ruby

This is an implementation designed in Ruby. This implementation is able to mix two signals/vectors (A and B) in arbitrary proportions. This source code uses a novel mathematical model published in the journal Chaos. The model is called Spectral Forecast.
https://github.com/gagniuc/mix-two-signals-in-ruby

algorithm algorithms code mix numerical-analysis numerical-methods ruby signal signal-processing source waveform

Last synced: 7 months ago
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This is an implementation designed in Ruby. This implementation is able to mix two signals/vectors (A and B) in arbitrary proportions. This source code uses a novel mathematical model published in the journal Chaos. The model is called Spectral Forecast.

• Host: GitHub
• URL: https://github.com/gagniuc/mix-two-signals-in-ruby
• Owner: Gagniuc
• License: mit
• Created: 2022-03-04T22:18:50.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-16T20:45:22.000Z (over 3 years ago)
• Last Synced: 2025-01-15T07:31:52.906Z (9 months ago)
• Topics: algorithm, algorithms, code, mix, numerical-analysis, numerical-methods, ruby, signal, signal-processing, source, waveform
• Language: Ruby
• Homepage:
• Size: 9.77 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE.md

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README

          
# Mix two signals in Ruby

This is an implementation designed in Python that is able to blend two signals in arbitrary proportions. This source code uses a novel mathematical model published in the journal [Chaos](https://aip.scitation.org/doi/10.1063/1.5120818). The model is called Spectral Forecast. The Mix-two-signals implementation is a demo that is able to mix two signals (A and B) in arbitrary proportions. Different cases can be seen, with two different waveform signals that are combined depending on a value d, called a distance. The value of d can be arbitrary chosen between zero and a value Max(d), which is defined as the maximum value found above the two vectors that represent these signals. In this specific case d = 33. The output is the M signal calculated from the two signals A and B, such as:

M = 15.37,35.12,51.12,57.17,47.89,43.08,60.35,67.91,63.72,48.03,33.99

The Spectral Forecast equation adapted to signals can be observed below:

![screenshot](https://github.com/Gagniuc/Waveform-mixing-with-Spectral-Forecast-in-JS/blob/main/img/spectral%20forecast%20signals.png?raw=true)

What can you expect from the code above? The effect of the above source code in the case of longer signals can be seen in a [graphical form](https://gagniuc.github.io/Waveform-mixing-with-Spectral-Forecast-in-JS/) below:

![screenshot](https://github.com/Gagniuc/Waveform-mixing-with-Spectral-Forecast-in-JS/blob/main/img/sf(0).gif?raw=true)

![screenshot](https://github.com/Gagniuc/Waveform-mixing-with-Spectral-Forecast-in-JS/blob/main/img/sf(2).gif?raw=true)

![screenshot](https://github.com/Gagniuc/Waveform-mixing-with-Spectral-Forecast-in-JS/blob/main/img/sf(3).gif?raw=true)

# References

- Paul A. Gagniuc et al. Spectral forecast: A general purpose prediction model as an alternative to classical neural networks. Chaos 30, 033119 (2020).

- Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, Hoboken, NJ, USA, 2021, ISBN: 9781119697961.