Specific Emitter Identification Based on Visibility Graph Entropy

The specific emitter identification （SEI） technique some external feature measurements of the signal. determines the unique emitter of a given signal by using It has recently attracted a great deal of attention because many applications can benefit from it. This work addresses the SEI problem using two methods, namely, the normalized visibility graph entropy （NVGE） and the normalized horizontal visibility graph entropy （NHVGE） based on treating emitters as nonlinear dynamical systems. Firstly, the visibility graph （VG） and the horizontal visibility graph （HVG） are used to convert the instantaneous amplitude, phase and frequency of received signals into graphs. Then, based on the information captured by the VG and the HVG, the normalized Shannon entropy （NSE） calculated from the corresponding degree distributions are utilized as the rf fingerprint. Finally, four emitters from the same manufacturer are utilized to evaluate the performance of the two methods. Experimental results demonstrate that both the NHVGE-based method and NVGE-based method are quite effective and they perform much better than the method based on the normalized permutation entropy （NPE） in the case of a small amount of data. The NVGE-based method performs better than the NHVGE-based method since the VG can extract more information than the HVG does. Moreover, our methods do not distinguish between the transient signal and the steady-state signal, making it practical.

Application of Graph Entropy in CRISPR and Repeats Detection in DNA Sequences

We analyzed DNA sequences using a new measure of entropy. The general aim was to analyze DNA sequences and find interesting sections of a genome using a new formulation of Shannon like entropy. We developed this new measure of entropy for any non-trivial graph or, more broadly, for any square matrix whose non-zero elements represent probabilistic weights assigned to connections or transitions between pairs of vertices. The new measure is called the graph entropy and it quantifies the aggregate indeterminacy effected by the variety of unique walks that exist between each pair of vertices. The new tool is shown to be uniquely capable of revealing CRISPR regions in bacterial genomes and to identify Tandem repeats and Direct repeats of genome. We have done experiment on 26 species and found many tandem repeats and direct repeats (CRISPR for bacteria or archaea). There are several existing separate CRISPR or Tandem finder tools but our entropy can find both of these features if present in genome.

scGET:Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development,cell fate commitment represents a critical transition or“tipping point”of embryonic differentiation,at which there is a drastic and qualitative shift of the cell populations.In this study,we presented a computational approach,scGET,to explore the gene–gene associations based on single-cell RNA sequencing(scRNAseq)data for critical transition prediction.Specifically,by transforming the gene expression data to the local network entropy,the single-cell graph entropy(SGE)value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level.Being applied to five scRNA-seq datasets of embryonic differentiation,scGET accurately predicts all the impending cell fate transitions.After identifying the“dark genes”that are non-differentially expressed genes but sensitive to the SGE value,the underlying signaling mechanisms were revealed,suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation.The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.