Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

FEATURE EXTRACTION OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

A method is proposed for the analysis of vibration signals from components ofrotating machines, based on the wavelet packet transformation (WPT) and the underlying physicalconcepts of modulation mechanism. The method provides a finer analysis and better time-frequencylocalization capabilities than any other analysis methods. Both details and approximations are splitinto finer components and result in better-localized frequency ranges corresponding to each node ofa wavelet packet tree. For the punpose of feature extraction, a hard threshold is given and theenergy of the coefficients above the threshold is used, as a criterion for the selection of the bestvector. The feature extraction of a vibration signal is accomplished by computing thereconstruction signal and its spectrum. When applied to a rolling bear vibration signal featureextraction, the proposed method can lead to be very effective.

Investigation on the automatic parameters extraction of pulse signals based on wavelet transform

This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prerequisite is to detect the corners of pulse signals correctly. Up to now,the pulse parameters are mostly acquired by marking the pulse corners manually,which is an obstacle to modernize pulse diagnosis. Therefore,a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately,which can be expected to facilitate the modernization of pulse diagnosis.

Extraction of periodic signals in chaotic secure communication using Duffing oscillators

This paper presents a novel approach to extract the periodic signals masked by a chaotic carrier. It verifies that the driven Duffing oscillator is immune to the chaotic carrier and sensitive to certain periodic signals. A preliminary detection scenario illustrates that the frequency and amplitude of the hidden sine wave signal can be extracted from the chaotic carrier by numerical simulation. The obtained results indicate that the hidden messages in chaotic secure communication can be eavesdropped utilizing Duffing oscillators.

Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.

PARTIALLY BLIND EXTRACTION OF CONTINUOUS CHAOTIC SIGNALS FROM A LINEAR MIXTURE

In this paper, we address the problem of blind extraction and separation of a continuous chaotic signal from a linear mixture consisting of some chaotic signal and/or random signals. The problem of blind extraction is firstly formulated as a problem of the synchronization-based parameter estimation. Then an efficient least square based parameter estimation method is introduced to determine the desired extracting vector. The proposed blind signal extraction scheme is applicable to blind separation of chaotic signals by formulating the separation problem as the extraction of each chaotic source. Numerical simulation shows that the proposed approach can blindly extract and separate the desired chaotic signals and it is also robust to measurement noise.

ESPRIT-Based Feature Extraction of Helicopter Acoustic Signal

Aim To extract harmonic frequencies of helicopter acoustic signal as features for hel icopter identification. Methods Estimation of signal parameters via rotational invariance techniques(ESPRIT) was selected to extract harmonic frequencies from really measured helicopter acoustic signal and an algorithm based on the SVD TLS was used. Results ESPRIT correctly extracted harmonic frequencies of helicopter using the data of limited length under the variousflight conditions. Conclusion ESPRIT is an effective method of extracting harmonic frequencies and using harmonic frequencies of helicopter acoustic signal to recognize helicopter is feasible.

Pulse-to-pulse periodic signal sorting features and feature extraction in radar emitter pulse sequences

A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.

Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal

The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise.The relationship between windowing transform andfiltering is analyzed first in the paper.The advantage of adjustable time-frequency window of wavelet transform is introduced.Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed.The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed,and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded.Thirdly,by extending the harmonic wavelet transform,the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper.Then comparing with the Hanning window,the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown,and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented.Finally,the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise,and the satisfactory results are achieved.The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.

A novel dense spectrum correction algorithm for extracting vibration signals in internal combustion engine and its application

The algorithm of dense spectrum correction has been raised and proved based on the correction of discrete spectrum by fast Fourier transform.The result of simulation shows that such algorithm has advantages of high accuracy and small amount of calculation.The algorithm has been successfully applied to the analysis of vibration signals from internal combustion engine.To calculate discrete spectrum,fast Fourier transform has been used to calculate the discrete spectrum by the signals acquired by the sensors on the oil pan,and the signal has been extracted from the mixed signals.

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

Small-signal modeling and parameter extraction method for a multigate GaAs pHEMT switch

This paper presents an accurate small-signal model for multi-gate GaAs pHEMTs in switching-mode.The extraction method for the proposed model is developed.A 2-gate switch structure is fabricated on a commercial 0.5μm AlGaAs/GaAs pHEMT technology to verify the proposed model.Excellent agreement has been obtained between the measured and simulated results over a wide frequency range.

Reliability Evaluation on Weak Signal Extraction for Airgun Source Surveys

Based on the data synthesis simulation and the actual processing of the airgun seismic source signal,three quantitative indicators of signal-to-noise ratio,waveform correlation coefficient and phase offset,are superimposed. We systematically evaluate the functions of the following three stack methods including linearity,phase weighting and S-transform in the extraction of weak signals under strong background noise and quantitatively estimate the reliability of the stack results. Through the comprehensive discussion of the above three methods of stack results,the preliminary comparative analysis believes that the linear stack signal-to-noise ratio is low,but the waveform distortion is minimal; the phase-weighted superimposed signal-to-noise ratio is high and the phase offset is small,but the results of the waveform quality and linear stack are larger than the deviation; the S-transform stack has a relatively higher signal-to-noise ratio and a small loss of waveform amplitude,but there is a certain phase shift phenomenon. It is therefore suggested that linear stack technology should be used when the requirements of both waveform quality and time precision are high. However,the selection of the stack method when the airgun source excitation is limited should be emphasized. If high fidelity is required, the S-transform stack method should be selected; if the required time is high,accuracy can be selected by phase-weighted stack method to achieve reasonable extraction of weak signals.

A HOS-based Blind Signal Extraction Method for Chaotic MIMO Systems

A novel method to extract multiple input and multiple output (MIMO) chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic (HOS) technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.

Extraction of Partial Discharge Acoustic Signal by Wavelet Transform with Teager′s Energy Operator

To develop a measurement system for monitoring partial discharge （PD） without the effect of external interferences,an algorithm of PD signal extraction based on wavelet transform with Teager＇s energy operators was presented. Acoustic signal generated by PD was selected to remove excessive interfering signals and electromagnetic interferences. Acoustic signals were collected and decomposed into I0 levels by wavelet transform into approximation and detail components. “Daubechies 25” was proved to be the most suitable mother wavelet for the extraction of PD acoustic signals. Compared with conventional wavelet denoising method, Teager＇s energy operators were adopted to the PD signal reconstruction and the signal to noise ratio was in creased by 20%-25% inthe experiment,without lost in energy and pulse amplitude.

A Direct Parameter-Extraction Method for GaInP/GaAs Heterojunction Bipolar Transistors Small-Signal Model

An accurate and broad-band method for hetero-junction bipolar transistors(HBT) small-signal model parameters-extraction is presented in this paper. An equivalent circuit forthe HBT under a forward-bias condition is proposed for extraction of accessresistance and parasiticinductance. This method differs from previous ones by extracting the c-quivalent circuit parameterswithout using special test structure or global numerical optimization techniques. The mainadvantage of this method is that a unique and physically meaningful set of intrinsic parameters isextracted from impedance and admittance representation of the measured S-pa-rameters in thefrequency range of 1-12 GHz under different bias conditions. The method yields a deviation of lessthan 5% between measured and modeled S-parameters.

Effect of spinal cord extracts after spinal cord injury on proliferation of rat embryonic neural stem cells and Notch signal pathway in vitro

Objective:To investigate the effect of the spinal cord extracts(SCE)after spinal cord injuries(SCIs)on the proliferation of rat embryonic neural stem cells(NSCs)and the expressions of mRNA of Notch1 as well as of Hes1 in this process in vitro.Methods:The experiment was conducted in 4 different mediums:NSCs+PBS(Group A-blank control group),NSCs+SCE with healthy SD rats(Croup B-normal control group),NSCs+SCE with SD rats receiving sham-operation treatment(Croup C-sham-operation group)and NSCs+SCE with SCIs rats(Group D-paraplegic group).Proliferative abilities of 4 different groups were analyzed by MTT chromatometry after co-culture for 1,2,3,4 and 5 d,respectively.The expressions of Notch 1 and Hes1 mRNA were also detected with RT-PCR after co-culture for 24 and 48 h,respectively.Results:After co-culture for 1,2,3,4 and 5 d respectively,the MTT values of group D were significantly higher than those of group A,group B and group C(P<0.05).However,there were no significantly differences regarding MTT values between group A,group B and group C after co-culture for 1,2,3,4 and 5 d,respectively(P>0.05).Both the expressions of Notch1 and Hes1 mRNA of group D were significantly higher than those of other 3 groups after co-culture for 24 h and 48 h as well(P<0.05).But there was no difference oin expressions of Notch1 and Hes1 mRNA among group A,group B and group C after co-culture for 24 h and 48 h(P>0.05).There was no difference in expressions of Notch1and Hes1 mRNA between 24 h and 48 h treatment in group D.Conclusions:SCE could promote the proliferation of NSCs.It is demonstrated that the microenvironment of SCI may promote the proliferation of NSCs.Besides,SCE could increase the expression of Notch1 and Hes1 mRNA of NSC.It can be concluded that the Notch signaling pathway activation is one of the mechanisms that locally injured microenvironment contributes to the proliferation of ENSC after SCIs.This process may be performed by up-regulating the expressions of Notch1 and Hes1 gene.

Weak signal extraction of micro-motor rotor unbalance based on all-phase fast Fourier transform

To improve the dynamic balancing accuracy of the micro-motor rotor,an unbalanced vibration feature extraction based on an all-phase fast Fourier transform(APFFT)method is proposed.The amplitude and phase of the signal are extracted by spectrum analysis after windowing the unbalanced signal.The mathematical model is derived to simulate the weak signal of rotor unbalance.The simulation results show that this method is accurate in extracting the weak signal of the rotor under different noise levels.The micro-motor rotor unbalanced test system is developed for experimental validations.The accuracy and stability of the vibration amplitude and phase extracted by the APFFT are better than the accuracy and stability from the hardware filtering method.The rotor unbalance is reduced by more than 80%.Furthermore,secondary balance of the rotor after the first balance is carried out.The proposed method can still extract the residual unbalance of the rotor.The results demonstrated that the proposed method can achieve a reduction rate of 90%and the accuracy is within 5mg,verifying the effectiveness of the proposed method for high-precision rotor dynamic balance.

Diarylheptanoid-myricanone isolated from ethanolic extract of Myrica cerifera shows anticancer effects on HeLa and PC3 cell lines:signalling pathway and drug-DNA interaction

OBJECTIVE： To test if myricanone （02H2405）, a cyclic diarylheptanoid, has anticancer effects on two different cancer cell lines HeLa and PC3. The present study was conducted with a note on the drug-DNA interaction and apoptotic signalling pathway. METHODS： Several studies like cytotoxicity, nuclear damage, annexin-V-fluorescein isothiocyanate （FITC）/propidium iodide （PI）-Iabelled apoptotic assay and cell cycle arrest, immunoblot and reverse transcriptase-polymerase chain reaction （RT-PCR） were used following standard protocols. Circular dichroism （CD） spectroscopy was also done to evaluate whether myricanone effectively interacted with DNA to bring about conformational changes that could strongly inhibit the cancer cell proliferation. RESULTS： Myricanone showed a greater cytotoxic effect on PC3 cells than on HeLa cells. Myricanone promoted G0/G arrest in HeLa cells and S phase arrest in PC3 cells. Nuclear condensation and annexin V-FITC/PI studies revealed that myricanone promoted apoptotic cell death. CD spectroscopic data indicated that myricanone had an interaction with calf thymus DNA that changed DNA structural conformation. RT-PCR and immunoblot studies revealed that myricanone activated the apoptotic signalling cascades through down-regulation of transcription factors like nuclear factor-KB （NF-KB） （p65）, and signal transducers and activators of transcription 3 （STAT3）; cell cycle regulators like cyclin D1, and survivin and other signal proteins like Bcl-2 and up-regulation of Bax, caspase-9 and caspase-3. CONCLUSION： Myricanone induced apoptosis in both types of cancer cells by triggering caspase activation, and suppression of cell proliferation by down-regulation of NF-KB and STAT3 signalling cascades, which makes it a suitable candidate for possible use in the formulation of therapeutic alent for combatin cancer.