基于Packet Tracer的虚拟医疗网络设计与性能分析

在Packet Tracer模拟环境中针对虚拟医疗网络进行设计与性能分析。系统需求与设计部分详细考虑了医疗网络的功能需求、拓扑设计和安全性措施。通过Packet Tracer模拟环境的建立,成功模拟了医院内部网络、医疗设备网络和远程连接,符合实际医疗场景。在性能分析中,使用Packet Tracer工具对带宽、延迟、安全性和可靠性进行了全面评估。通过优化网络配置,确保实时医疗应用的高效运行,同时强调了网络的安全性和可靠性。这一综合性的设计和性能分析为虚拟医疗网络的实际部署提供了有力的支持。

Quantum Mechanics: Harmonic Wave-Packets, Localized by Resonant Response in Dispersion Dynamics

From a combination of Maxwell’s electromagnetism with Planck’s law and the de Broglie hypothesis, we arrive at quantized photonic wave groups whose constant phase velocity is equal to the speed of light c = ω/k and to their group velocity dω/dk. When we include special relativity expressed in simplest units, we find that, for particulate matter, the square of rest mass , i.e., angular frequency squared minus wave vector squared. This equation separates into a conservative part and a uniform responsive part. A wave function is derived in manifold rank 4, and from it are derived uncertainties and internal motion. The function solves four anomalies in quantum physics: the point particle with prescribed uncertainties;spooky action at a distance;time dependence that is consistent with the uncertainties;and resonant reduction of the wave packet by localization during measurement. A comparison between contradictory mathematical and physical theories leads to similar empirical conclusions because probability amplitudes express hidden variables. The comparison supplies orthodox postulates that are compared to physical principles that formalize the difference. The method is verified by dual harmonics found in quantized quasi-Bloch waves, where the quantum is physical;not axiomatic.

Position-aware packet loss optimization on service function chain placement

The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method

Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration.

基于PacketTracer和Onenet的物联网家居系统

针对实物物联网家居成本过高、搭建过程复杂的问题,文中使用PacketTracer和OneNet云平台设计了一种物联网智能家居系统。该系统使用PacketTracer方法进行了设计,并通过MCU将智能家居设备远程接入OneNet云平台。测试结果表明,与传统的实物物联网家居相比,该系统不仅缩短了设计周期,还能实现对家居设备的远程控制。

Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

基于Packet Tracer的智慧校园网络设计及仿真

为了适应现代教育的发展,对传统校园网络进行智能化改造。针对智慧校园网络的需求,着重介绍了设计思路,并使用Packet Tracer网络仿真软件构建了智慧校园网络模型,利用冗余设计、端口聚合、负载均衡等技术使校园网络更加可靠、稳定。该模型主要利用多种传感器、物联网服务器、SBC/MCU微处理器以及智能网关实现不同的物联网功能,用户可以利用手机或PC终端远程登录物联网服务器,监控校内电子设备、网络设备。该设计能够为智慧校园网络的升级改造提供参考,有力推进校园教育信息化建设。

基于Packet Tracer的VLAN间路由仿真实验设计

每个虚拟局域网(VLAN)在逻辑上都相当于一个独立的网络,拥有自己的广播域,VLAN之间不能直接通信,但是有时候又需要在不同网段间跨VLAN进行通信,因此研究使用Packet Tracer模拟软件实现了不同VLAN间路由的配置。

Cisco packet tracer在计算机网络实训中的应用

随着信息技术的飞速发展,计算机网络实训已成为信息技术教育中不可或缺的一环。在这一过程中,实训工具的选择至关重要,Cisco Packet Tracer提供了一个虚拟的实验环境,用户可以在其中模拟各种网络设备,如交换机、路由器和服务器等,并通过拖拽的方式构建网络拓扑结构。该软件作为一款功能强大的网络模拟软件,凭借其直观的操作界面、丰富的设备库和灵活的组网功能,在计算机网络实训中得到广泛应用。本文就Cisco packet tracer在计算机网络实训中的应用进行探讨。

Radar Target Discrimination based on waveletPackets for Reduced data Storage

In order to storage resource of a radar recognition system, schemes for reducing data storage and for correlation discrimination of radar based on wavelet packets were proposed Experiment results at various signal-to-noise ratios were given The given.ability of the reduced data method's validity are supported by experimental results. Using optimal basis can get higher successful recognition rate using rigid wavelet basis.

基于Smart Packets的主动网络管理模型研究

主动网络是一种新型的网络体系,它不仅可以传送数据包,而且还可以执行数据包中特定的运算任务。主动网络为用户提供了可编程的接口,用户可通过网络中的节点动态地注入所需的服务。Smart Packets采用紧缩格式和安全代码编写其中的程序,它能够支持主动网络管理。本文讨论了传统网络管理中存在的问题,分析了主动网络管理体系结构与管理机制。重点研究了Smart Packets的结构、管理机制、包的格式、编程语言和设计目标,以及安全性。

基于Packet Tracer的MQTT协议仿真实现

MQTT协议是物联网设备云边传输最常用的通信协议之一。对于物联网相关专业、学科的学生来说,理解MQTT协议的工作原理、数据包结构、消息服务质量等非常重要。通过在Packet Tracer仿真软件上进行MQTT协议网络仿真结构设计、网络连通性配置、程序设计以及相应数据包抓取,仿真MQTT协议的工作过程,分析协议数据包的结构。经过仿真分析与理论分析的对比,进一步加深对MQTT协议的理解。

基于Cisco Packet Tracer的虚拟仿真软件在“计算机网络技术”实验教学中的应用研究

作者介绍了Cisco Packet Tracer虚拟仿真软件的功能和优点,并根据当前“计算机网络技术”课程的实验教学现状,以“交换机虚拟局域网”实验为例说明Cisco Packet Tracer如何在“计算机网络技术”课程中实现虚拟仿真实验教学。通过问卷调查可知,虚拟仿真实验能够改善实验教学效果,降低实验成本,培养学生的实践操作能力和分析解决问题能力。

OPTIMAL DISTRIBUTED FUSION ALGORITHM WITH ONE-STEP OUT-OF-SEQUENCE ESTIMATES

The transmission modes of multi-hop and broadcasting for Wireless Sensor Networks(WSN)often make random and unknown transmission delays appear,so multisensor data fusion based ondelayed systems attracts intense attention from lots of researchers.The existing achievements for thedelayed fusion all focus on Out-Of-Sequence Measurements(OOSM)problem which has many dis-advantages such as high communication cost,low computational efficiency,huge computational com-plexity and storage requirement,bad real-time performance and so on.In order to overcome theseproblems occurred in the OOSM fusion,the Out-Of-Sequence Estimates(OOSE)are considered tosolve the delayed fusion for the first time.Different from OOSM which belongs to the centralized fusion,the OOSE scheme transmits local estimates from local sensors to the central processor and is thus thedistributed fusion;thereby,the OOSE fusion can not only avoid the problems suffered in the OOSMfusion but also make the design of fusion algorithm highly simple and easy.Accordingly,a novel optimallinear recursive prediction weighted fusion method is proposed for one-step OOSE problem in this letter.As a tradeoff,its fusion accuracy is slightly lower than that of the OOSM method because the currentOOSM fusion is a smooth estimate and OOSE gets a prediction estimate.But,the smooth result of theOOSE problem also has good fusion accuracy.Performance analysis and computer simulation show thatthe total performance of the proposed one-step OOSE fusion algorithm is better than the current one-step OOSM fusion in the practical tracking systems.

The Propagation of Wave Packets and Its Relationship with the Subtropical Jet over Southern China in January 2008

The propagation of wave packets and its relationship with the subtropical jet was investigated for the period 26 29 January 2008 over southern China using ECMWF Interim re-analysis data. Wave packets propagated from the north to the south side of an upper front with eastward development along the upper front during this period. Due to the eastward development of propagation, the acceleration of geostrophic westerly winds shifted eastward along the front. There were two primary sources of the propagation of wave packets at around 30°N. The first was the temperature inversion layer below 500 hPa, and the second was baroclinic zones located along the polarward flank of the subtropical jet in the middle and upper troposphere. Most wave packets propagated horizontally from the baroclinic zones and then converged on the zero meridional gradients of zonal winds.

A CLASS OF BIDIMENSIONAL NONSEPARABLEWAVELET PACKETS

2-band wavelet packets in L-2 (R-s) were constructed in [3]. In this note, a way to construct bidimensional orthonormal wavelet packets related to the dilation matrix M = ((1)(1) (1)(-1)) is obtained. M-wavelets are used ill quincunx subsampling in two dimensions for image processing. What is more., the approach of this paper can be generalized to construct wavelet packets in L-2 (R-s) related to a general diltion matrix.

Characterizing Internet Backbone Traffic Based on Deep Packets Inpection and Deep Flows Inspection

Based on the massive data collected with a passive network monitoring equipment placed in China＇s backbone, we present a deep insight into the network backbone traffic and evaluate various ways for inproving traffic classifying efficiency in this pa- per. In particular, the study has scrutinized the net- work traffic in terms of protocol types and signatures, flow length, and port distffoution, from which mean- ingful and interesting insights on the current Intemet of China from the perspective of both the packet and flow levels are derived. We show that the classifica- tion efficiency can be greatly irrproved by using the information of preferred ports of the network applica- tions. Quantitatively, we find two traffic duration thresholds, with which 40% of TCP flows and 70% of UDP flows can be excluded from classification pro- cessing while the in^act on classification accuracy is trivial, i.e., the classification accuracy can still reach a high level by saving 85% of the resources.

Interference of dissociating wave packets in the I_2 molecule driven by femtosecond laser pulses

The interference between two dissociating wave packets of the I2 molecule driven by femtosecond laser pulses is theoreticaly studied by using the time-dependent quantum wave packet method. Both the internuclear distance-and velocity-dependent density functions are calculated and discussed. It is demonstrated that the interference pattern is determined by the phase difference and the delay time between two pump pulses. With two identical pulses with a delay time of 305 fs and a FWHM of 20 fs, more interference fringes can be observed, while with two pump pulses with a delay time of 80 fs and a FWHM of 20 fs, only a few interference fringes can be observed.

Configuration for Predicting Travel-Time Using Wavelet Packets and Support Vector Regression

Travel-time prediction has gained significance over the years especially in urban areas due to increasing traffic congestion. In this paper, the basic building blocks of the travel-time prediction models are discussed, with a small review of the previous work. A model for the travel-time prediction on freeways based on wavelet packet decomposition and support vector regression (WDSVR) is proposed, which used the multi-resolution and equivalent frequency distribution ability of the wavelet transform to train the support vector machines. The results are compared against the classical support vector regression (SVR) method. Our results indicated that the wavelet reconstructed coefficient when used as an input to the support vector machine for regression performed better (with selected wavelets only), when compared with the support vector regression model (without wavelet decomposition) with a prediction horizon of 45 minutes and more. The data used in this paper was taken from the California Department of Transportation (Caltrans) of District 12 with a detector density of 2.73, experiencing daily peak hours except most weekends. The data was stored for a period of 214 days accumulated over 5-minute intervals over a distance of 9.13 miles. The results indicated MAPE ranging from 12.35% to 14.75% against the classical SVR method with MAPE ranging from 12.57% to 15.84% with a prediction horizon of 45 minutes to 1 hour. The basic criteria for selection of wavelet basis for preprocessing the inputs of support vector machines are also explored to filter the set of wavelet families for the WDSVR model. Finally, a configuration of travel-time prediction on freeways is presented with interchangeable prediction methods.