RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

A Personalized Digital Code from Unique Genome Fingerprinting Pattern for Use in Identification and Application on Blockchain

With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each individual and does not change over time. Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research. In this study, each individual’s genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself. The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome. The final output is in the form of a 24 numerical code with each number having three possible combinations. The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.

Radio Frequency Fingerprint-Based Satellite TT&C Ground Station Identification Method

This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.

A Molecular Approach to Identification of the Chinese Drug“pu Gong Ying”(Herba Taraxaci)and Six Adulterants byDNA Fingerprinting Using Random Primed PolymeraseChain Resaction(PCR)

DNA fingerprinting among members of the Chinese drug Pu Gong Ying(Taraxacum mongolicum Hand,-Mazz.)and six adulterants of Tu Gong Ying were demonstrated with random-primed polymerase chain reaction(PCR)including arbitrarily primed polymerase chain reaction(AP-PCR)and random amplified polymorphic DNA(RAPD).Distinctive,reproducible genomic fingerprints from DNA from 7 species belonged to Compositae were generated with two long(20 and 24 mer)and one short(10 mer)randomly chosen primers.The Pu Gong Ying can be differentiated from six species of Tu Gong Ying according to the banding pattems of their amplified DNA on agarose gels.The results showed that AP-PCR and RAPD methods can be used for identifying Chinese drugs.Moreover,the Similarity Indexes of the genomic DNA fingerprints showed that Pu Gong Ying and its adulterants are unrelated.Therefore,AP-PCR and RAPD methods can be used for identifying Chinese drugs.

Identification of Porphyra lines using computerized DNA fingerprinting

RAPD (Randomly Amplified Polymorphic DNA) analysis was performed with filaments of 15 Porphyra lines representing four important groups (P. yezoensis, P. haitanensis, P. katadai var. Hemiphylla and P. digospermatangia). Eight stable and repeatable RAPD bands amplified with two primers, OPN-02 and OPJ-18, were selected for the construction of DNA fingerprinting. The RAPD results were scored based on the presence or absence of each of the 8 bands and then converted to computer language expressed with two digitals, 1 and 0, which represented the presence (numbered as 1) or absence (numbered as 0) of each band, respectively. Based on these results, a model DNA fingerprint and a computerized DNA fingerprint were constructed. In the constructed DNA fingerprint, each Porphyra line has its unique fingerprinting pattern and can be easily distinguished from each other. Later, a software, named as PhGI, was designed based on this DNA fingerprinting. It can be used in practical Porphyra line identification.

Bohai crude oil identification by gas chromatogram fingerprinting quantitative analysis coupled with cluster analysis

By gas chromatogram, six crude oils fingerprinting distributed in four oilfields and four oil platforms were analyzed and the corre- sponding normal paraffin hydrocarbon （ including pristane and phytane） concentration was obtained by the internal standard methed. The normal paraffin hydrocarbon distribution patterns of six crude oils were built and compared. The cluster analysis on the normal paraffin hydrocarbon concentration was conducted for classification and some ratios of oils were used for oils comparison. The results indicated： there was a clear difference within different crude oils in different oil fields and a small difference between the crude oils in the same oil platform. The normal paraffin hydrocarbon distribution pattern and ratios, as well as the cluster analysis on the nomad paraffin hydrocarbon concentration can have a better differentiation result for the crude oils with small difference than the original gas chromatogram.

Comparison of DUS testing and SNP fingerprinting for variety identification in cucumber

Variety identification plays an important role in protecting the intellectual property of varieties,ensuring seed quality,and encouraging breeding innovation.Currently,morphological evaluation in the field,such as distinctness,uniformity,and stability(DUS)testing,and DNA fingerprinting in the laboratory using molecular markers are two dominant methods used for variety identification.Few studies have compared the results of these approaches,and the relationship between the two methods is obscure.In this study,134 dominant cucumber varieties were evaluated using 50 DUS testing traits and genotyped by 40 single nucleotide polymorphisms(SNPs).The 40 SNPs were developed in our previous study and arewell suited for variety identification.In the DUS testing,significant positive or negative correlations among 50 DUS traits were observed,and 20 core traits,including 15 fruit traits,were further selected to increase field inspection efficiency.This suggested that fruit shape plays an important role in variety identification.The ratio of fruit length/diameter was themost important trait,explaining 9.2%of the phenotypic variation.In the DNA fingerprinting test,the 40 SNPs were highly polymorphic and could distinguish all of the 134 cucumber varieties,and 14 core SNPs were selected to improve the identification rate.Interestingly,the population structure analysis of 134 cucumber varieties by phenotypic data in the DUS test was in accordance with the genotypic data from the DNA fingerprinting,indicating that all varieties could be divided into the same four subgroups:European type,North China type,South China type,and hybrids of the North China and South China types.Moreover,linear correlativity of distinguishment for each pair of varieties was observed between the DUS test and the DNA fingerprinting.These results indicated that these two methods have good application in future research,especially for the scaled-up analysis of hundreds of varieties.

Application of a Step-by-Step Fingerprinting Identification Method on a Spilled Oil Accident in the Bohai Sea Area

In recent years,oil spill accidents occur frequently in the marine area of China.Finding out the spilled oil source is a key step in the relevant investigation.In this paper,a step-by-step fingerprinting identification method was used in a spilled oil accident in the Bohai Sea in 2002.Advanced chemical fingerprinting and data interpretation techniques were used to characterize the chemical composition and determine the possible sources of two spilled oil samples.The original gas chromatography -flame ionization detec-tion (GC-FID) chromatogram of saturated hydrocarbons was compared.The gas chromatography-mass spectrometry (GC/MS) chromatograms of aromatic hydrocarbons terpane and sterane,n-alkane and poly-aromatic hydrocarbons (PAHs) were analyzed.The correlation analysis on diagnostic ratios was performed with Student’s t-test.It is found that the oil fingerprinting of the spilled oil (designated as sz1) from the polluted sand beach was identical with the suspected oil (designated as ky1) from a nearby crude oil refinery factory.They both showed the fingerprinting character of mixed oil.The oil fingerprinting of the spilled oil (designated as ms1) collected from the port was significantly different from oil ky1 and oil sz1 and was with a lubricating oil fingerprint character.The identification result not only gave support for the spilled oil investigation,but also served as an example for studying spilled oil accidents.

A new reaction system to determine nonlinear chemical fingerprint and its use in Panax ginseng identification method based on double reaction system

A new reaction system to determine nonlinear chemical fingerprint(NCF)and its use in identification method based on double reaction system was researched.Panax ginsengs,such as ginseng,American ginseng and notoginseng were identified by the method.The NCFs of the three samples of Panax ginsengs were determined through two nonlinear chemical systems,namely system 1 consisting of sample components,H2SO4,MnSO4,NaBrO3,acetone and the new system,system 2 consisting of sample components,H2SO4,(NH4)4Ce(SO4)2,NaBrO3 and citric acid.The comparison between the results determined through systems 1 and 2 shows that the speed to determine NCF through system 2 is much faster than that through system 1;for systems 1 and 2,the system similarities of the same kind of samples are≥98.09%and 99.78%,respectively,while those of different kinds of samples are≤63.04%and 86.34%,respectively.The results to identify the kinds of some samples by system similarity pattern show that both the accuracies of identification methods based on single system 1 and 2 are≥95.6%,and the average values are 97.1%and 96.3%,respectively;the accuracy of the method based on double system is≥97.8%,and the average accuracy is 99.3%.The accuracy of the method based on double system is higher than that based on any single system.

Rapid Identification and Subtyping of Enterobacter cloacae Clinical Isolates Using Peptide Mass Fingerprinting

Objective To establish a domestic database of Enterobacteria cloacae （E. cloacae）, and improve the identification efficiency using peptide mass fingerprinting. Methods Peptide mass fingerprinting was used for the identification and subtyping of E. cloacae. Eighty-seven strains, identified based on hsp60 genotyping, were used to construct and evaluate a new reference database. Results Compared with the original reference database, the identification efficiency and accuracy of the new reference database was greatly improved at the species level. The first super reference database for E. cloacae identification was also constructed and evaluated. Based on the super reference database and the main spectra projection dendrogram, E. cloacae strains were divided into two clades. Conclusion Peptide mass fingerprinting is a powerful method to identify and subtype E. cloacae, and the use of this method will allow us to obtain more information to understand the heterogeneous organism E. cloacae.

Investigation and Application of Automatic Fingerprint Identification Based on Fuzzy Pattern Recognition

Fingerprint image is a typical non-restraint image that has some uncertainty, which makes it difficult to perform identification using classical approach. Therefore, fuzzy pattern recognition is applied to match individual query by searching the entire template database. The fuzzy maximum subordinate principle is used to solve shift matching. Through experimenting and analyzing, the approximate principle fuzzy method is employed by selecting fuzzy characteristics and determining the similarity function to achieve the further accuracy. Theoretical and experimental results show this approach is effective and reasonable.

Rice seed identification by computerized AFLP-DNA fingerprinting

We developed a computerized seed identification system. Fifteen rice varietiesthat were widely used in China were analyzed by AFLP fingerprinting. 12 primerpairs were screened, In order to simplify the procedure and cut down the cost inseed identification. the least number of primer pairs for practical seed identifi-cation should be seleeled. In this study. 3 primer pairs were selected. They

Human Face Identification

Automatic face identification poses an exacting problem in feature extraction, pattern analysis and recognition. Owing to its wide potential for applications efforts have been made and some systems have been established catering for some aspects of the problem. The-state-of -the-art is summarized. The author's work on face feature analysis and extraction is also briefly introduced. These features, which are extracted from one monochrome frontal view image of the face, include the profile feature, the shape of the face contour, the eye region statistics and the generic measures across the face. Then the problems to be solved and the prospects of futher work and applications of automatic face identification are concluded.

Study on HPLC-DAD Fingerprint of Xueshuan Xinmaining Capsules

[Objectives]To establish the HPLC-DAD fingerprint of Xueshuan Xinmaining Capsule(XXC).[Methods]The chromatographic conditions for the analysis of XXC solution were as follows:XSelect HSS T3 column;acetonitrile-0.1%phosphoric acid water was used as mo-bile phase,gradient elution;flow rate:1.0 mL/min;column temperature 30℃;The injection volume is 10μL.The quality of XXC samples produced by different manufacturers was evaluated by similarity evaluation and cluster analysis.[Results]In theHPLC-dad fingerprints of 15 batches of XXC,23 common peaks were identified and 9 peaks were identified,and the similarity was greater than 0.95.According to the re-sults of cluster analysis,15 batches of XXC samples could be divided into two categories,S2,S5,S6,S7 and S8 batches belonged to category Ⅰ,and the rest batches belonged to category Ⅱ.[Conclusions]In this study,a representative and universal identification method of Xxc HPLC-DAD fingerprint was established.The method has high precision,stability and repeatability,is simple and reliable,and provides a pow-erful reference for further improving the quality evaluation system of XXC.

Electrochemical Identification of Yulania spp. by Fingerprinting of Leaves Using Glassy Carbon Electrode

In this communication,we used electrochemical sensor for recording the electrochemical profiles of eleven species of Yulania spp.from leaf extract.Two solvents and two buffer conditions were used for electrochemical fingerprints collection.Their electrochemical fingerprints can be converted to different patterns and consequently for species recognition.The results indicate the pattern recognition is much convenient than that of the recognition of species directly using voltammetric signal.The current information in electrochemical fingerprinting represents the type and amount of electrochemically active molecules,which linked to the genetic differences among the plants.Therefore,the electrochemical fingerprints were applied for further phylogenetic study.The phylogenetic tree deduced from voltametric curves is divided into three main groups.The first clade contains Y.denudate,Liriodendron chinense,Y.cylindrica,Y.biondii,Y.sprengeri.The second clade contains Y.zenii,Y.liliiflora,Y.kobus,and Y.amoena.The third clade contains Y.×soulangeana,Manglietia fordiana and Y.sinostellata.In addition,Y.salicifolia is not in these main clades.The results demonstrate that electrochemical fingerprinting can be used as a com-plementary tool in the study of phylogenetics.

Isolation, Phenotypic Identification and Fingerprint by RAPD-PCR of Oxalotrophoic Burkholderia cepacia from Egyptian Fertile Soil

Soil oxalate degrading bacteria is an important source for removing of toxic oxalate compounds. Isolation, screening, characterization, identification and fingerprint of the soil oxalate degrading bacteria from Egyptian fertile soil was aimed in this work. Isolated bacteria were first characterized using API 20 NE system. Nine RAPD primers were used in genetic fingerprinting of the characterized bacteria using RAPD-PCR. The preliminary characterization and polyphasic taxonomy for mesophilic, aerobic oxalate-degrading bacteria isolated from Egyptian fertile soil including auxanography using API galleries confirmed that the isolate belonged to the genus Burkholderia cepacia, the percentage of identity was 99.8% for 5167777 profiles. Fingerprinting by RAPD-PCR showed a DNA fingerprint ranging from 0 to 9 bands, over a size range of 344 bp to 3,280 bp.

Fingerprint Identification by Artificial Neural Network

Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by their minutiae using Artificial Neural Networks （ANN）, which reveals to be highly effective. The ANN method implemented is a based on Multi-Layer Perceptron （MLP） model, which utilizes the algorithm of retro-propagation of gradient during the learning process. In such a process, the mean square error generated represents the specific parameter for the identification phase by comparing a fingerprint taken from a crime scene with those of a reference database.

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.

Face Identification Using Multiwavelet Transform and Multiwavelet Network

Interest in face identification systems has increased significantly due to the emergence of significant commercial opportunities in surveillance and security applications. In this paper, an approach is developed for combining the MWT （multiwavelet transform） with a MWN （multiwavelet network） as face identification algorithm. Only quarter of the approximation of the multiwavelet of the face image will be used as input to the MWN where the approximation quarter of the resultant multiwavelet of the face image will be segmented into four parts. These parts will be treated as 3D representation of the face image and will be given to the MWN. This makes multiwavelets a well designed tool for face identification. The multiwavelet shows promise in combining the desirable feature of the face image. A fast procedure for computing the MWT is introduced. The algorithm developed in this paper are tested on a data base consisting of 480 face images. The proposed algorithm outperform the other methods where a 100% identification was achieved using the mentioned data base.

Identification of commercial cultivars of Agaricus bisporus in China using genome-wide microsatellite markers

Agaricus bisporus is one of the most widely cultivated mushrooms in the world. Commercial cultivars are usually phenotypically alike and easy to be copied by isolating tissue cultures. This brings great challenges to distinguish different cultivars and to protect new varieties. Thus, techniques for the accurate identification of cultivars are essentially required. In this study, we accurately identified 11 commercial cultivars of A. bisporus released in China by using microsatellite(SSR, simple sequence repeat) markers. SSR markers were developed by mining the genome sequence. A total of 3 134 SSRs were identified, of which 1 490 SSRs were distributed in gene models, and 1 644 in the intergenic regions. A total of 17 polymorphic primer pairs were developed and SSR fingerprints were constructed for all the commercial cultivars. These SSR markers generated a total of 73 alleles, with an average of 4.29 per locus. Specifically, the primer combination of AB_SSR_2341 and AB_SSR_2590 could distinguish all the 11 commercial cultivars. The similarity coefficients of the 11 commercial cultivars were between 0.56 and 0.95 indicating that some of them were close related. Our results provide an efficient technique for the identification of A. bisporus cultivars in China, which can also facilitate the marker-assisted breeding in the future.