Support spinor machine

Kanjamapornkul, Kabin; Pinčák, Richard; Chunithipaisan, Sanphet; Bartoš, Erik

doi:10.1016/j.dsp.2017.07.023

Cited by 17 publications

(9 citation statements)

References 37 publications

(47 reference statements)

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“…The gene state of central dogma can be induced without transitive layer of protein as principle fiber bundle P [Aµ] . The underlying connection (a gauge potential of genotype) appears as a Yang-Mills field F µν [39]. We have cycle and co-cyle β of superspace of living organism X t defining a Jacobian over the supermanifold fiber g ij = ∂si ∂sj .…”

Section: Discussionmentioning

confidence: 99%

The Chern–Simons current in time series of knots and links in proteins

Capozziello

Pinčák

2018

Annals of Physics

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A superspace model of knots and links for DNA time series data is proposed to take into account the feedback loop from docking to undocking state of protein-protein interactions. In particular, the direction of interactions between the 8 hidden states of DNA is considered. It is a E8×E8 unified spin model where the genotype, from active and inactive side of DNA time data series, can be considered for any living organism. The mathematical model is borrowed from loop-quantum gravity and adapted to biology. It is used to derive equations for gene expression describing transitions from ground to excited states, and for the 8 coupling states between geneon and anti-geneon transposon and retrotransposon in trash DNA. Specifically, we adopt a modified Grothendieck cohomology and a modified Khovanov cohomology for biology. The result is a Chern-Simons current in (8 + 3) extradimensions of a given unoriented supermanifold with ghost fields of protein structures. The 8 dimensions come from the 8 hidden states of spinor field of genetic code. The extradimensions come from the 3 types of principle fiber bundle in the secondary protein.

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Section: Discussionmentioning

confidence: 99%

The Chern–Simons current in time series of knots and links in proteins

Capozziello

Pinčák

2018

Annals of Physics

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“…It is a transition function between state and hidden state as explained in the diagram of active and passive state of gene expression moduli state space equation. A detailed discussion of this point can be found in [39]. For the trash area of DNA, we have a new definition of the system of moduli state space.…”

Section: Algebraic Construction Of Trash Dnamentioning

confidence: 99%

The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

et al. 2018

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A Chern-Simons current, coming from ghost and anti-ghost fields of supersymmetry theory, can be used to define a spectrum of gene expression in new time series data where a spinor field, as alternative representation of a gene, is adopted instead of using the standard alphabet sequence of bases A, T, C, G, U . After a general discussion on the use of supersymmetry in biological systems, we give examples of the use of supersymmetry for living organism, discuss the codon and anti-codon ghost fields and develop an algebraic construction for the trash DNA, the DNA area which does not seem active in biological systems. As a general result, all hidden states of codon can be computed by Chern-Simons 3 forms. Finally, we plot a time series of genetic variations of viral glycoprotein gene and host T-cell receptor gene by using a gene tensor correlation network related to the Chern-Simons current. An empirical analysis of genetic shift, in host cell receptor genes with separated cluster of gene and genetic drift in viral gene, is obtained by using a tensor correlation plot over time series data derived as the empirical mode decomposition of Chern-Simons current.

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“…And recently, other researchers introduce gray-scale algorithm into the training process of SVM and combines the feature parameters extracted by K-means clustering algorithm to complete the modulation pattern classification of MPSK and MQAM signals [11]. Moreover, the author of [12] uses the mathematical structure of the wedge product on the SVM to extend the support vector machine to the support spinor machine, and extends the original field from the vector field to the spinor field, so as to establish a new classifier model on the mathematical principle of the SVM.…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Classification Based on Novel Feature Extraction Algorithms

Zhang

Sun

2020

IEEE Access

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In order to solve the problems that the accuracy of modulation recognition algorithms of MSK and MQAM signals is not ideal under the condition of low signal-to-noise ratio (SNR) in Additional White Gaussian Noise (AWGN) environment, two novel features, the differential nonlinear phase Peak Factor (PF) and the reciprocal of amplitude envelope variance of cyclic spectrum at zero frequency cross section after difference and forth power processing, are constructed, which can complete the recognition of MSK signal and the classification of MPSK and MQAM signals respectively. This paper proposes a mixed recognition algorithm based on the two new features and other classical features, and design a tree shaped multi-layer smooth support vector machine classifier based on feature selection algorithm (FS_DT-SSVM) to recognize eleven kinds of digital modulation signals. The simulation results illustrate that the algorithm can achieve the classification of the modulation signals {2FSK, MSK, 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM} with small SNR. When the SNR is not less than −1dB, the recognition rate of the classifier for all signals exceed 97%, which validates the effectiveness of the proposed modulation recognition algorithm.

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Support spinor machine

Cited by 17 publications

References 37 publications

The Chern–Simons current in time series of knots and links in proteins

The Chern–Simons current in time series of knots and links in proteins

The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

Automatic Modulation Classification Based on Novel Feature Extraction Algorithms

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