Decoding Algorithm by Cooperation Between Hartmann Rudolph Algorithm and a Decoder Based on Syndrome and Hash

Alaoui, My Seddiq El Kasmi; Nouh, Saïd

doi:10.4018/ijncr.2021010102

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…There are other methods that use non-algebraic techniques, such as genetic algorithms that belong to evolutionary techniques [18]- [19]. We have also found a work that uses local search to find errors [20]- [21].…”

Section: Related Workmentioning

confidence: 91%

“…Figure 4 presents the main classes of decoding techniques. Some decoders are based on algebraic theory such as the algorithms developed through solving nonlinear multivariate equations obtained from the identities of Newton [13]- [14], the Berlekamp-Massey algorithm [15] which is based on the calculation of syndromes and the definition of an error locator polynomial, the algorithm of Chase [16] and the algorithm of Hartmann Rudolf [17]- [18]. However, some algebraic techniques, aforementioned, require a large wide of computational operations, in terms of sum and product, in the used finite field.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for decoding linear block codes: case of multi-class logistic regression model

Imrane

Nouh

Mehdi

et al. 2021

IJEECS

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<p>Facing the challenge of enormous data sets variety, several machine learning-based algorithms for prediction (e.g, Support vector machine, multi layer perceptron and logistic regression) have been highly proposed and used over the last years in many fields. Error correcting codes (ECCs) are extensively used in practice to protect data against damaged data storage systems and against random errors due to noise effects. In this paper, we will use machine learning methods, especially multi-class logistic regression combined with the famous syndrome decoding algorithm. The main idea behind our decoding method which we call logistic regression decoder (LRDec) is to use the efficient multi-class logistic regression models to find errors from syndromes in linear codes such as bose, ray-chaudhuri and hocquenghem (BCH), and the quadratic residue (QR). Obtained results of the proposed decoder have a significant benefit in terms of bit error rate (BER) for random binary codes. The comparison of our decoder with many competitors proves its power. The proposed decoder has reached a success percentage of 100% for correctable errors in the studied codes.</p>

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Section: Related Workmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Machine learning for decoding linear block codes: case of multi-class logistic regression model

Imrane

Nouh

Mehdi

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

FocMech-Flow: Automatic Determination of P-Wave First-Motion Polarity and Focal Mechanism Inversion and Application to the 2021 Yangbi Earthquake Sequence

Fang

Xiao

et al. 2023

Applied Sciences

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P-wave first-motion polarity is important for the inversion of earthquake focal mechanism solutions. The focal mechanism solution can further contribute to our understanding of the source rupture process, the fault structure, and the regional stress field characteristics. By using the abundant focal mechanism solutions of small and moderate earthquakes, we can deepen our understanding of fault geometry and the seismogenic environment. In this paper, we propose an automatic workflow, FocMech-Flow (Focal Mechanism-Flow), for identifying P-wave first-motion polarity and focal mechanism inversion with deep learning and applied it to the 2021 Yangbi earthquake sequence. We use a deep learning model named DiTingMotion to detect the P-wave first-motion polarity of 2389 waveforms, resulting in 98.49% accuracy of polarity discrimination compared with human experts. The focal mechanisms of 112 earthquakes are obtained by using the CHNYTX program, which is 3.7 times more than that of the waveform inversion method, and the results are highly consistent. The analysis shows that the focal mechanisms of the foreshock sequence of the Yangbi earthquake are highly consistent and are all of the strike-slip type; the focal mechanisms of the aftershock sequence are complex, mainly the strike-slip type, but there are also reverse and normal fault types. This study shows that the deep learning method has high reliability in determining the P-wave first-motion polarity, and FocMech-Flow can obtain a large number of focal mechanism solutions from small and moderate earthquakes, having promising application in fine-scale stress inversion.

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Decoding Algorithm by Cooperation Between Hartmann Rudolph Algorithm and a Decoder Based on Syndrome and Hash

Cited by 2 publications

References 9 publications

Machine learning for decoding linear block codes: case of multi-class logistic regression model

Machine learning for decoding linear block codes: case of multi-class logistic regression model

FocMech-Flow: Automatic Determination of P-Wave First-Motion Polarity and Focal Mechanism Inversion and Application to the 2021 Yangbi Earthquake Sequence

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