A comparative study on the spectral characteristics of nanosecond pulsed discharges in atmospheric He and a He+2.3%H_(2)O mixture

Nanosecond pulsed discharges at atmospheric pressure in a pin-to-pin electrode configuration are well reproducible in time and space, which is beneficial to the fundamentals and applications of low-temperature plasmas. In this experiment, the discharges in helium(He) and He with 2.3%water vapor(H_(2)O) are driven by a series of 10 ns overvoltage pulses(~13 k V). Special attention is paid to the spectral characteristics obtained in the center of discharges by time-resolved optical emission spectroscopy. It is found that in helium, the emission of atomic and molecular helium during the afterglow is more intense than that in the active discharge, while in the He+2.3%H_(2)O mixture, helium emission is only observed during the discharge pulse and the molecular helium emission disappears. In addition, the emissions of OH(A-X) and Hα present similar behavior that increases sharply during the falling edge of the voltage pulse as the electrons cool down rapidly. The gas temperature is set to remain low at 540 K by fitting the OH(A-X) band. A comparative study on the emission of radiative species(He, He_(2), OH and H)is performed between these two discharge cases to derive their main production mechanisms. In both cases, the dominant primary ion is He^(+) at the onset of discharges, but their He^(+) charge transfer processes are quite different. Based on these experimental data and a qualitative discussion on the discharge kinetics, with regard to the present discharge conditions, it is shown that the electron-assisted three-body recombination processes appear to be the significant sources of radiative OH and H species in high-density plasmas.

Global dynamics of a novel multi-group model for computer worms

In this paper, we study worm dynamics in computer networks composed of many autonomous systems. A novel multigroup SIQR （susceptible-infected-quarantined-removed） model is proposed for computer worms by explicitly considering anti-virus measures and the network infrastructure. Then, the basic reproduction number of worm R0 is derived and the global dynamics of the model are established. It is shown that if R0 is less than or equal to 1, the disease-free equilibrium is globally asymptotically stable and the worm dies out eventually, whereas, if R0 is greater than 1, one unique endemic equilibrium exists and it is globally asymptotically stable, thus the worm persists in the network. Finally, numerical simulations are given to illustrate the theoretical results.

Epidemic spreading in scale-free networks including the effect of individual vigilance

In this paper, we study the epidemic spreading in scale-flee networks and propose a new susceptible-infected- recovered （SIR） model that includes the effect of individual vigilance. In our model, the effective spreading rate is dynamically adjusted with the time evolution at the vigilance period. Using the mean-field theory, an analytical result is derived. It shows that individual vigilance has no effect on the epidemic threshold. The numerical simulations agree well with the analytical result. Purthermore, we investigate the effect of individual vigilance on the epidemic spreading speed. It is shown that individual vigilance can slow the epidemic spreading speed effectively and delay the arrival of peak epidemic infection.

Spatial characteristics of nanosecond pulsed micro-discharges in atmospheric pressure He/H2O mixture by optical emission spectroscopy

Atmospheric pressure micro-discharges in helium gas with a mixture of 0.5%water vapor between two pin electrodes are generated with nanosecond overvoltage pulses.The temporal and spatial characteristics of the discharges are investigated by means of time-resolved imaging and optical emission spectroscopy with respect to the discharge morphology,gas temperature,electron density,and excited species.The evolution of micro-discharges is captured by intensified CCD camera and electrical properties.The gas temperature is diagnosed by a two-temperature fit to the ro-vibrational OH(A^(2)Σ^(+)–X^П(2),0–0)emission band and is found to remain low at 425 K during the discharge pulses.The profile of electron density performed by the Stark broadening of Ha 656.1-nm and He I 667.8-nm lines is uniform across the discharge gap at the initial of discharge and reaches as high as 10^(23)m^(-3).The excited species of He,OH,and H show different spatio-temporal behaviors from each other by the measurement of their emission intensities,which are discussed qualitatively in regard of their plasma kinetics.

Least squares weighted twin support vector machines with local information

A least squares version of the recently proposed weighted twin support vector machine with local information(WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algorithm, called least squares weighted twin support vector machine with local information(LSWLTSVM), for generating binary classifiers based on two non-parallel hyperplanes. Two modified primal problems of WLTSVM are attempted to solve, instead of two dual problems usually solved. The solution of the two modified problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in WLTSVM. Moreover, two extra modifications were proposed in LSWLTSVM to improve the generalization capability. One is that a hot kernel function, not the simple-minded definition in WLTSVM, is used to define the weight matrix of adjacency graph, which ensures that the underlying similarity information between any pair of data points in the same class can be fully reflected. The other is that the weight for each point in the contrary class is considered in constructing equality constraints, which makes LSWLTSVM less sensitive to noise points than WLTSVM. Experimental results indicate that LSWLTSVM has comparable classification accuracy to that of WLTSVM but with remarkably less computational time.

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

A Clustering Analysis Method for Massive Music Data

Clustering analysis plays a very important role in the field of data mining,image segmentation and pattern recognition.The method of cluster analysis is introduced to analyze NetEYun music data.In addition,different types of music data are clustered to find the commonness among the same kind of music.A music data-oriented clustering analysis method is proposed:Firstly,the audio beat period is calculated by reading the audio file data,and the emotional features of the audio are extracted;Secondly,the audio beat period is calculated by Fourier transform.Finally,a clustering algorithm is designed to obtain the clustering results of music data.

Estimating Brain Functional Networks Based on Spatiotemporal Higher-Order Correlations for Autism Identification

Brain functional network (BFN) has become an important tool for the analysis and diagnosis of brain diseases, and how to build a high-quality BFN based on resting-state functional magnetic resonance imaging (rs-fMRI) has become a growing concern in the neuroscience community. Although some methods have been proposed to construct a high-quality BFN, they only encode the spatial characteristics of the ROIs, ignoring the temporal characteristics. As a result, it becomes challenging to accurately capture the true state of the brain. To address this problem, we propose a novel method to construct a higher-order BFN, considering both temporal and spatial domain characteristics. In particular, we get the characteristics of the temporal domain by differentiating the rs-fMRI signal itself, and then we integrate the information of the spatial domain and temporal domain to build a high-order BFN. To evaluate the proposed method, we conduct our experiments on ABIDE database to identify subjects with Autism Spectrum Disorder (ASD) from normal controls. Experimental results show that our method can achieve higher performance than baseline methods.