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.

Specific Emitter Identification for IoT Devices Based on Deep Residual Shrinkage Networks

Specific emitter identification can distin-guish individual transmitters by analyzing received signals and extracting inherent features of hard-ware circuits.Feature extraction is a key part of traditional machine learning-based methods,but manual extrac-tion is generally limited by prior professional knowl-edge.At the same time,it has been noted that the per-formance of most specific emitter identification meth-ods degrades in the low signal-to-noise ratio(SNR)environments.The deep residual shrinkage network(DRSN)is proposed for specific emitter identification,particularly in the low SNRs.The soft threshold can preserve more key features for the improvement of performance,and an identity shortcut can speed up the training process.We collect signals via the receiver to create a dataset in the actual environments.The DRSN is trained to automatically extract features and imple-ment the classification of transmitters.Experimental results show that DRSN obtains the best accuracy un-der different SNRs and has less running time,which demonstrates the effectiveness of DRSN in identify-ing specific emitters.

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.

Isolation and Identification of a Specific cDNA Mapping to the Bam HI-I2 and -LFragments within the Inverted Repeats ofUnique Long Re-gion (IRL) in the Genom e ofMarek′s Disease Herpesvirus (MDV) Oncogenic Strain Beijing-1

Objective\ To understand the transcription of BamHI L DNA fragment from genome of strong virulent GA strain of Marek′s disease herpesvirus (MDV) in lymphoblastoid tumor tissue induced by oncogenic strain Beijing 1 (a specific local strain in China) of MDV. Methods\ Two oligonucleotide primers were synthesized according to the reported sequence of \%meq\% gene an ideal oncogenic candidate and our previously determined sequence of BamHI L fragment of Marek′s disease herpesvirus (MDV), respectively. Reverse transcriptase PCR(RT PCR) assay was performed by using these primers and the mRNA as a template which was isolated from visceral lymphoblastoid tumors obtained from chickens artificially infected with strain Beijing 1 of oncogenic MDV. Southern blot molecular hybridization was further carried out to detect the product of RT PCR with digoxigenin labeled nucleotide probe from BamHI I2 and L fragment in the gene library of MDV strain GA, respectively. Results\ Two probes could simultaneously hybridize this cDNA amplified by RT PCR with a length of about 730 bp. Conclusion\ It is suggested that \%meq\% transcription could extend from the right hand end of BamHI I2 to the adjacent BamHI L, and the BamHI L region was likely to be transcribed in MDV induced lymphoblastoid tumors.

Single sample electromagnetic spectrum recognition utilizing fractional Fourier transform

Electromagnetic Spectrum(EMS)recognition is vital in spectrum control,interference location,electronic countermeasures,etc.However,samples of high-value targets are incredibly scarce,even single,and are easily overwhelmed by noise and numerous low-value targets,resulting in poor recognition accuracy using traditional methods.Furthermore,the great similarity between samples from the same manufacturer,model,and batch,makes Specific Emitter Identification(SEI)with the EMS especially challenging.Based on the powerful extension and extraction ability of the Fractional Fourier Transform(FrFT)for detailed features,this paper proposes a novel algorithm for the EMS recognition under a single-sample condition.The proposed method constructs a feature matrix FrFT-M from the results of the FrFT under specific orders for each sample.Then,the most relevant item,obtained by analyzing the correlations among FrFT-Ms between the unidentified sample and known samples,determines the optimal recognition.Three simple tests are conducted,including two simulations considering fifteen basic waveforms and six typical radar signals,and one experiment using STM32 microcontroller boards.The detection results of simulated and experimental data show that the accuracies of all three cases are higher than 86%,even for samples of the same model.Our method is promising and may have significant value in other fields.

Preparation and Identification of Monoclonal Antibodies against Chicken Immunoglobulin G

[ Objective] To prepare monoclonal antibodies against chicken immunoglobulin G （IgG） and improve the diagnostic level of specific antibodies in chickens. [ Method] Chicken IgG was isolated by saturated ammonium sulfate and purified by Sephadex G-200 column chromatography. Then the BALB/c mice were immunized by the chicken IgG, and the spleen cells were fused with mouse myeloma cells SP2/0. Finally, the positive hybridoma cells were screened and detected by indirect enzyme-linked immunosorbent assay （ELISA）. [ Result] Four hybridoma cell strains secre- ting monoclonal antibodies against chicken IgG were obtained and named as C44, C45, C67 and C68, and their ascites titers in indirect ELISA were 1 ： 640 000, 1 ： 320 000, 1 ： 640 000 and 1 ： 80 000, respectively. The monoclonal antibodies secreted by C44 and C45 could recognize light chains of chicken IgG and those secreted by C,67 and C68 could recognize heavy chains of chicken IgG. They all could not recognize IgG from duck, rabbit and swine. Additionally, the Ig type identification results showed that they all belonged to IgGl. [ Conclusion] Four cell strains of obtained hybridoma can stably produce the monoclonal antibodies against chicken IgG.

Fast infectious diseases diagnostics based on microfuidic biochip system

Molecular diagnostics is one of the most important tools currently in use for clinical pathogen detection due to its high sensitivity,specificity,and low consume of sample and reagent is keyword to low cost molecular diagnostics.In this paper,a sensitive DNA isothermal amplifi-cation method for fast clinical infectious diseases diagnostics at aM concentrations of DNA was developed using a polycarbonate(PC)microfuidic chip.A portable confocal optical fuo-rescence detector was specifically developed for the microfuidic chip that was capable of highly sensitive real-time detection of amplified products for sequence-specific molecular identification near the optical diffraction limit with low background.The molecular diagnostics of Listeria monocytogenes with nucleic acid extracted from stool samples was performed at a minimum DNA template concentration of 3.65 aM,and a detection limit of less than five copies of genomic DNA.Contrast to the general polymerase chain reaction(PCR)at eppendorf(EP)tube,the detection time in our developed method was reduced from 1.5h to 45 min for multi-target parallel detection,the consume of sample and reagent was dropped from 25μL to 1.45μL.This novel microfuidic chip system and method can be used to develop a micro total analysis system as a clinically relevant pathogen molecular diagnostics method via the amplification of targets,with potential applications in biotechnology,medicine,and clinical molecular diagnostics.

Near-infrared carbon nanodots for effective identification and inactivation of Gram-positive bacteria

An unacceptable increase in antibacterial resistance has arisen due to the abuse of multiple classes of broad-spectrum antibiotics.Therefore,it is significant to develop new antibacterial agents,especially those that can accurately identify and kill specific bacteria.Herein,we demonstrate a kind of perilla-derived carbon nanodots(CNDs),integrating intrinsic advantages of luminescence and photodynamic,providing the opportunity to accurately identify and kill specific bacteria.The CNDs have an exotic-doped andπ-conjugated core,vitalizing them near-infrared(NIR)absorption and emission properties with photoluminescence quantum yield of 21.1%;hydrophobic chains onto the surface of the CNDs make them to selectively stain Gram-positive bacteria by insertion into their membranes.Due to the strong absorption in NIR region,reactive oxygen species are in situ generated by the CNDs onto bacterial membranes under 660 nm irradiation,and 99.99%inactivation efficiency against Gram-positive bacteria within 5 min can be achieved.In vivo results demonstrate that the CNDs with photodynamic antibacterial property can eliminate the inflammation of the area affected by methicillin-resistant Staphylococcus aureus(MRSA),and enabling the wound to be cured quickly.

Preparation of Surface-enhanced Raman Scattering（SERS）-active Optical Fiber Sensor by Laser-induced Ag Deposition and Its Application in Bioidentification of Biotin/Avidin

A surface-enhanced Raman scattering（SERS） optical fiber sensor was prepared by the laser-induced deposition ofAg nanoparticle membrane on a silica optical fiber tip, which was applied to the real time SERS spectral monitoring on the biorecognition of biotin/avidin. The bioidentification of biotin/avidin was carried out through a indirect method, in which the bioidentification is based on the SERS response signal of a labeled dye（Atto610） after its fluorescence has been quenched totally by the deposited Ag nanoparticle membrane. By SERS monitoring the bioidentification process of biotin/avidin, it has been found that this recognition process is finished in 40 min. The lowest detection concentration of biotin is 1.0 × 10^-7 mg/mL. This research is promising in the application of immunoassays on line and in vivo.