Application Progress of Teaching in the Form of Traffic Short Videos in Nursing Education

With the rapid development of the information technology era,the teaching quality requirements continue to surge,and the mode of education in colleges and universities has also carried out certain innovations.The integration of modern information technology into the teaching process and the combination of medical content has become a new hotspot for reform and innovation of medical education at home and abroad.In this paper,we will describe the application of traffic short videos as the main teaching form in nursing education in domestic and foreign studies,and the role of the application of this teaching form in the improvement of theoretical knowledge and clinical skills of nursing students,as well as the impact on the cultivation of nursing students’professional cognition,communication skills,critical thinking,etc.,with the aim of providing new perspectives for the subsequent nursing education.

Variable bit rate video traffic modeling by multiplicative multifractal model

Multiplicative multifractal process could well modal video traffic. The multiplier distributions in the multiplicatire multifractal model for video traffic are investigated and it is found that Gaussian is not suitable for describing the multipliers on the small time scales. A new statistical distribution-symmetric Pareto distribution is introduced. It is applied instead of Gaussian for the multipliers on those scales. Based on that, the algorithm is updated so that symmetric pareto distribution and Gaussian distribution are used to model video traffic but on different time scales. The simulation results demonstrate that the algorithm could model video traffic more accurately.

On use of the alpha stable self-similar stochastic process to model aggregated VBR video traffic

The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.

Nonlinear Dynamic Analysis of MPEG-4 Video Traffic

The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The priraeipal compohems analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts.So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear be havior and can be characterized by its power spectral density,principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.

Statistical Multiplexing of Video Traffic and Data Traffic

This paper studies statistical multiplexing performance by input of video traffic and data traffic. The inputs have different Qos requirements such as loss and delay jitter. By applying a modified FBM model, we present methods to estimate effective bandwidth of the aggregated traffic. Simulations were performed to evaluate effective bandwidth. The comparison between the estimation and the simulation shows that the estimations can give correct data for the effective bandwidths in terms of our interests. The analysis of gain by using priority multiplexing also addresses proper Qos configuration for the inputs in order to achieve positive gains.

Software Defined Traffic Engineering for Improving Quality of Service

The Software Defined Networking(SDN) paradigm separates the control plane from the packet forwarding plane, and provides applications with a centralized view of the distributed network state. Thanks to the flexibility and efficiency of the traffic flow management, SDN based traffic engineering increases network utilization and improves Quality of Service(QoS). In this paper, an SDN based traffic scheduling algorithm called CATS is proposed to detect and control congestions in real time. In particular, a new concept of aggregated elephant flow is presented. And then a traffic scheduling optimization model is formulated with the goal of minimizing the variance of link utilization and improving QoS. We develop a chaos genetic algorithm to solve this NP-hard problem. At the end of this paper, we use Mininet, Floodlight and video traces to simulate the SDN enabled video networking. We simulate both the case of live video streaming in the wide area backbone network and the case of video file transferring among data centers. Simulation results show that the proposed algorithm CATS effectively eliminates network congestions in subsecond. In consequence, CATS improves the QoS with lower packet loss rate and balanced link utilization.

Modeling the MPEG-1 Video Traffic

Now, the problem of modeling MPEG 1 video traffic still needs studying further. Based on the analysis of statistical characteristics of this kind of traffic, this paper presents a new traffic model. Simulation results show that the proposed model can reflect the statistical characteristics of the real MPEG 1 video traffic well.

Framework for Freeway Auto-surveillance from Traffic Video

Video based surveillance systems have been widely used on freeway for traffic monitoring, as the cameras can provide the most intuitionistic information. In order to manage all the traffic videos automatically, in this paper, a distributed real-time auto- surveillance system is presented. The freeway traffic videos are taken as input video from Pan Tilt Zoom （PTZ） camera, and then produces an analysis of the states and activity of the vehicles in the region of interested （ROI）, if there is any abnormal instance, an alarm and corresponding traffic video are sent to awake surveillants by Ethernet. To achieve this functionality, our system relies on three main procedures. The first one initializes the system. It detects the ROI of the scene, and performs the camera calibration to remove the perspective effect of the incoming image. The second one segments moving vehicles from the images, eliminate shadow and tracks them real-time. It uses a set of methods to obtain the background of the image, extracts the moving regions and tracks these moving regions by matching them between frames of the video sequence to obtain high-level information such as color, size, velocity, and trajectories of moving vehicles. In the third procedure, activities of vehicles are analyzed based on a series of preset situations which would happen on freeway. The detail information of each vehicle and the global statistical information are checked to find out any abnormal instance, and then triggered an alarm. We present details of the system, together with experiment results which demonstrate the accuracy and time responses.