The design and artificial realization of a controller of pulse coupling feedback

In this paper a controller of pulse coupling feedback （PCF） is designed to control chaotic systems. Control principles and the technique to select the feedback coefficients are introduced. This controller is theoretically studied with a three dimensional （3D） chaotic system. The artificial simulation results show that the chaotic system can be stabilized to different periodic orbits by using the PCF method, and the number of the periodic orbits are 2^n×3^m p （n and m are integers）. Therefore, this control method is effective and practical.

Study on the Methane Coupling under Pulse Corona Plasma by Using CO_2 as Oxidant

In this paper, the conversion of CO2/CH4 by using pulse corona plasma was studied at atmospheric pressure and ambient temperature. The effects of ratio of CO2/CH4, pulse voltage and repeated frequency of plasma discharge were first studied in the system.

Core-Excited Molecules by Resonant Intense X-Ray Pulses Involving Electron-Rotation Coupling

It has been reported that electron-rotation coupling plays a significant role in diatomic nuclear dynamics induced by intense VUV pulses [Phys. Rev. A 102(2020) 033114;Phys. Rev. Res. 2(2020) 043348]. As a further step, we present here investigations of the electron-rotation coupling effect in the presence of Auger decay channel for core-excited molecules, based on theoretical modeling of the total electron yield(TEY), resonant Auger scattering(RAS) and x-ray absorption spectra(XAS) for two showcases of CO and CH^(+) molecules excited by resonant intense x-ray pulses. The Wigner D-functions and the universal transition dipole operators are introduced to include the electron-rotation coupling for the core-excitation process. It is shown that with the pulse intensity up to 10^(16) W/cm^(2), no sufficient influence of the electron-rotation coupling on the TEY and RAS spectra can be observed. This can be explained by a suppression of the induced electron-rotation dynamics due to the fast Auger decay channel, which does not allow for effective Rabi cycling even at extreme field intensities,contrary to transitions in optical or VUV range. For the case of XAS, however, relative errors of about 10% and 30% are observed for the case of CO and CH^(+), respectively, when the electron-rotation coupling is neglected.It is concluded that conventional treatment of the photoexcitation, neglecting the electron-rotation coupling,can be safely and efficiently employed to study dynamics at the x-ray transitions by means of electron emission spectroscopy, yet the approximation breaks down for nonlinear processes as stimulated emission, especially for systems with light atoms.

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.

Spatio-temporal measurements of overshoot phenomenon in pulsed inductively coupled discharge

Pulse inductively coupled plasma has been widely used in the microelectronics industry,but the existence of overshoot phenomenon may affect the uniformity of plasma and generate high-energy ions,which could damage the chip.The overshoot phenomenon at various spatial locations in pulsed inductively coupled Ar and Ar/CF_(4) discharges is studied in this work.The electron density,effective electron temperature,relative light intensity,and electron energy probability function(EEPF) are measured by using a time-resolved Langmuir probe and an optical probe,as a function of axial and radial locations.At the initial stage of pulse,both electron density and relative light intensity exhibit overshoot phenomenon,i.e.,they first increase to a peak value and then decrease to a convergent value.The overshoot phenomenon gradually decays,when the probe moves away from the coils.Meanwhile,a delay appears in the variation of the electron densities,and the effective electron temperature decreases,which may be related to the reduced strength of electric field at a distance,and the consequent fewer high-energy electrons,inducing limited ionization and excitation rate.The overshoot phenomenon gradually disappears and the electron density decreases,when the probe moves away from reactor centre.In Ar/CF_(4) discharge,the overshoot phenomenon of electron density is weaker than that in the Ar discharge,and the plasma reaches a steady density within a much shorter time,which is probably due to the more ionization channels and lower ionization thresholds in the Ar/CF_(4) plasma.

Comparative study of travelling-wave and numerical solutions for the coupled short pulse (CSP) equation

The Lie symmetry analysis is performed for the coupled short plus （CSP） equation. We derive the infinitesimals that admit the classical symmetry group. Five types arise depending on the nature of the Lie symmetry generator. In all types, we find reductions in terms of system of ordinary differential equations, and exact solutions of the CSP equation are derived, which are compared with numerical solutions using the classical fourth-order Runge-Kutta scheme.

Modeling of the nanoparticle coagulation in pulsed radio-frequency capacitively coupled C_2H_2 discharges

The role of pulse parameters on nanoparticle property is investigated self-consistently based on a couple of fluid model and aerosol dynamics model in a capacitively coupled parallel-plate acetylene（C2H2） discharge. In this model, the mass continuity equation, momentum balance equation, and energy balance equation for neutral gas are taken into account.Thus, the thermophoretic force arises when a gas temperature gradient exists. The typical results of this model are positive and negative ion densities, electron impact collisions rates, nanoparticle density, and charge distributions. The simulation is performed for duty ratio 0.4/0.7/1.0, as well as pulse modulation frequency from 40 kHz to 2.7 MHz for pure C2H2 discharges at a pressure of 500 mTorr. We find that the pulse parameters, especially the duty ratio, have a great affect on the dissociative attachment coefficient and the negative density. More importantly, by decreasing the duty ratio, nanoparticles start to diffuse to the wall. Under the action of gas flow, nanoparticle density peak is created in front of the pulse electrode,where the gas temperature is smaller.

PCNN based image processing and feature extraction of dual-bypass gas metal arc weld pool

In manual welding process, skilled welders can adjust the welding parameters to ensure the weld quality through their observation of the weld pool surface. In order to acquire useful information of the weld pool for control of the welding process and realizing the automatic welding, the measurement system of DB-GMA W process was established and the weld pool image was obtained by passive vision. Then, three image processing algorithms, Sobel, Canny, and pulse coupled neural network （PCNN） were detailed and applied to extracting the edge of the DB-GMA weld pool. In addition, a scheme was proposed for calculating the length, maximum width and superficial area of the weld pool under different welding conditions. The compared results show that the PCNN algorithm can be used for extracting the edge of the weld pool and the obtained information is more useful and accurate. The calculated results coincide with the actual measurement well, which demonstrates that the proposed algorithm is effective, its imaging processing time is required only 20 ms, which can completely meet the requirement of real-time control.

Effect of ballistic electrons on ultrafast thermomechanical responses of a thin metal film

The ultrafast thermomechanical coupling problem in a thin gold film irradiated by ultrashort laser pulses with different electron ballistic depths is investigated via the ultrafast thermoelasticity model. The solution of the problem is obtained by solving finite element governing equations. The comparison between the results of ultrafast thermomechanical coupling responses with different electron ballistic depths is made to show the ballistic electron effect. It is found that the ballistic electrons have a significant influence on the ultrafast thermomechanical coupling behaviors of the gold thin film and the best laser micromachining results can be achieved by choosing the specific laser technology（large or small ballistic range）.In addition, the influence of simplification of the ultrashort laser pulse source on the results is studied, and it is found that the simplification has a great influence on the thermomechanical responses, which implies that care should be taken when the simplified form of the laser source term is applied as the Gaussian heat source.

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the lleld of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform （NSCT）. The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima （MM） algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network （PCNN）. The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.

Pulse Coupled Neural Network Edge-Based Algorithm for Image Text Locating

This paper presents a method for locating text based on a simplified pulse coupled neural network （PCNN）. The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing. The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector. The different edges are integrated using an automatically adjusted weighting coefficient. Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system. This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem. The algorithm is a simplified PCNN edge-based （PCNNE） algorithm. Three comparison tests are used to evaluate the algorithm. Tests on large data sets show PCNNE efficiently detects texts with various colors, font sizes, positions, and uneven illumination. This method outperforms several traditional methods both in text detection rate and text detection accuracy.

Conservation laws of the complex short pulse equation and coupled complex short pulse equations

In this paper, the complex short pulse equation and the coupled complex short pulse equations that can describe the ultra-short pulse propagation in optical fibers are investigated. The two complex nonlinear models are turned into multi-component real models by proper transformations. Lie symmetries are obtained via the classical Lie group method, and the results for the coupled complex short pulse equations contain the existing results as particular cases.Based on the linearizing operator and adjoint linearizing operator for the two real systems,adjoint symmetries can be obtained. Explicit conservation laws are constructed using the symmetry/adjoint symmetry pair(SA) method. Relationships between the nonlinear selfadjointness method and the SA method are investigated.

Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

Improved image filter based on SPCNN

By extraction of the thoughts of non-linear model and adaptive model match, an improved Nagao filter is brought. Meanwhile a technique based on simplified pulse coupled neural network and used for noise positioning, is put forward. Combining the two methods above, we acquire a new method that can restore images corrupted by salt and pepper noise. Experiments show that this method is more preferable than other popular ones, and still works well while noise density fluctuates severely.

基于PCNN图像分割的医学图像融合算法

为充分提取源图像间的互补信息,改进传统的图像融合算法在亮度维持、能量保留、边缘信息保持等方面的不足,本文提出了基于脉冲耦合神经网络(pulse coupled neural network, PCNN)图像分割的医学图像融合算法。该算法综合了非下采样剪切波变换(non-subsampled shearlet transform, NSST)与PCNN。首先,选取标准差较大的源图像作为被分割图像,标准差较小的源图像作为参照图像,将源图像进行NSST分解,获取源图像低频子带系数和高频子带系数;在低频融合中,利用参数自适应的PCNN对被分割图像的低频子带进行分割,根据分割结果获取融合低频子带系数;在高频融合中,采用以区域能量和与拉普拉斯能量和两者的乘积作为判断函数,获取融合高频子带系数;利用NSST逆变换获取融合图像。最后,应用本文提出的算法,对脑萎缩、急性中风和高血压性脑病等3组电脑断层扫描/磁共振成像(computerized tomography/magnetic resonance imaging, CT/MRI)图像进行了融合仿真,并将仿真结果与2018年后国际刊上提出的5种算法的融合图像进行比较。结果表明,应用本文提出的融合算法得到的图像,有效地增强了不同模态间的信息互补,保持了融合图像与源图像具有相同明亮程度,又保留了源图像低亮度部分的边缘信息,更加符合人眼视觉特性,具有更高的客观评价指标。

Image copy-move forgery passive detection based on improved PCNN and self-selected sub-images

Image forgery detection remains a challenging problem.For the most common copy-move forgery detection,the robustness and accuracy of existing methods can still be further improved.To the best of our knowledge,we are the first to propose an image copy-move forgery passive detection method by combining the improved pulse coupled neural network(PCNN)and the self-selected sub-images.Our method has the following steps:First,contour detection is performed on the input color image,and bounding boxes are drawn to frame the contours to form suspected forgery sub-images.Second,by improving PCNN to perform feature extraction of sub-images,the feature invariance of rotation,scaling,noise adding,and so on can be achieved.Finally,the dual feature matching is used to match the features and locate the forgery regions.What’s more,the self-selected sub-images can quickly obtain suspected forgery sub-images and lessen the workload of feature extraction,and the improved PCNN can extract image features with high robustness.Through experiments on the standard image forgery datasets CoMoFoD and CASIA,it is effectively verified that the robustness score and accuracy of proposed method are much higher than the current best method,which is a more efficient image copy-move forgery passive detection method.