基于Packet Tracer的物联网实训仿真平台开发

构建以思科(Cisco)公司的Packet Tracer软件为依托的物联网实训教学仿真平台,旨在将计算机网络专业的知识与技能串联起来,完成一个典型的、较复杂的、接近企业级物联网项目的设计与研发,根据岗位能力目标要求,采用基于实际应用的典型物联网工程项目,根据学生的认知能力和技能水平,按照教学需要进行适当重构后在仿真平台予以部署和实现,采用“项目引领,任务驱动”的教学方法,将仿真项目分解为若干个工作任务,每个任务围绕教学目标各有侧重,涵盖相应知识要点,按照由浅入深、循序渐进的教学规律,融“教、学、做”于一体,以达到强化学生动手操作能力、培养学生工程项目意识,具备物联网项目实施和运维等工程应用能力的目的。

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。

Pre-filtering based automatic composition of multi-QoS semantic web services

To support the rapid automatic services composition and fulfill multi-quality of service （multi- QoS） demand, we propose a novel approach to realize services composition automatically by a prefihering process. Aimed at a set of web services with similar functionality and different quality of service （QoS） , a semantic services chain is given and a corresponding constructing algorithm is proposed to construct the data structure. A pre-filtering process is put forward to find whether a composition service before planning exists. It can avoid aborted planning. An optimal planning algorithm is proposed which can choose the most suitable service from a lot of similar candidate services based on semantic service chains and multi-QoS values. The algorithms can improve the correctness and automation performances of automated semantic web services composition. As an example, a concrete composite process is analyzed. Experimental results show the validity of the composite process.

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.

Suppression of seismic random noise by deep learning combined with stationary wavelet packet transform

Many traditional denoising methods,such as Gaussian filtering,tend to blur and lose details or edge information while reducing noise.The stationary wavelet packet transform is a multi-scale and multi-band analysis tool.Compared with the stationary wavelet transform,it can suppress high-frequency noise while preserving more edge details.Deep learning has significantly progressed in denoising applications.DnCNN,a residual network;FFDNet,an efficient,fl exible network;U-NET,a codec network;and GAN,a generative adversative network,have better denoising effects than BM3D,the most popular conventional denoising method.Therefore,SWP_hFFDNet,a random noise attenuation network based on the stationary wavelet packet transform(SWPT)and modified FFDNet,is proposed.This network combines the advantages of SWPT,Huber norm,and FFDNet.In addition,it has three characteristics:First,SWPT is an eff ective featureextraction tool that can obtain low-and high-frequency features of different scales and frequency bands.Second,because the noise level map is the input of the network,the noise removal performance of diff erent noise levels can be improved.Third,the Huber norm can reduce the sensitivity of the network to abnormal data and enhance its robustness.The network is trained using the Adam algorithm and the BSD500 dataset,which is augmented,noised,and decomposed by SWPT.Experimental and actual data processing results show that the denoising eff ect of the proposed method is almost the same as those of BM3D,DnCNN,and FFDNet networks for low noise.However,for high noise,the proposed method is superior to the aforementioned networks.

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。

仿真软件Packet Tracer在计算机网络技术课程教学中的应用

本文探讨了仿真软件Packet Tracer在计算机网络技术课程教学中的应用。文章首先介绍了Packet Tracer软件的优势及在计算机网络课程中的重要性。接着,从理论教学出发,阐述了该软件如何直观展示网络原理、模拟OSI与TCP/IP模型以及将抽象概念具体化。在实验教学中,Packet Tracer助力学生构建网络拓扑、配置设备、跟踪数据包、分析协议,并进行故障排除与性能优化。文章还提出了线上线下混合教学、分组实验与协作学习等教学方法,并通过具体教学案例展示了Packet Tracer的应用效果。最后,总结了教学效果,并对未来计算机网络技术教学进行了展望。

基于强化双树复小波包变换的风电机组偏航轴承损伤识别

针对风电机组偏航轴承损伤识别问题,提出了基于强化双树复小波包变换的损伤识别方法。首先,通过双树复小波包变换与线性峭度结合对不同分解层数下的分量计算平均线性峭度值,确定最优分解层数;其次,对最优分解所得小波系数及尺度系数进行幅值调制,进而增强不同信号成分的能量;然后,采用散布熵指标确定各分量最佳调制系数并通过双树复小波包逆变换得到修正信号;最后,对修正信号作归一化平方包络谱分析提取故障特征频率。结果表明:所提方法能够实现复杂工况下偏航轴承损伤类型的准确识别,具有一定工程参考价值。

按病种分值付费(DIP)政策实施现状及比较研究

以广州、东营、厦门、宜昌、泸州5个DIP试点城市为例,对各统筹区政策实施现状进行梳理,比较各统筹区的实施政策,分析政策设计成因、实施成效及存在问题,发现各统筹区在总额预算管理、病组管理、结算清算等技术规范设置上仍存在一定差异。从政策、医保、医疗多个维度提出完善统筹区的后续配套政策、优化并逐步统一统筹区技术规范、完善并发挥各级医保部门的管理职能、强化医疗机构内部精细化管理的建议。

不同速率下松散颗粒直剪试验声发射特征

松散颗粒堆积体在自然界和工业生产活动中广泛存在.为研究其力学性质和失稳过程,基于声发射(acoustic emission,AE)技术探究松散体剪切过程的声学特征演化规律.首先,分析松散颗粒在不同剪切速率下的AE特征参数;其次,结合加载过程的力学特征对AE演化阶段进行划分;最后,利用频谱变化和小波包能量占比进一步验证松散颗粒剪切破坏的AE演化规律.结果表明:能量和振铃计数随剪切过程而逐步增大,且剪切速率越快,能量和振铃计数增幅越大;小事件数与大事件数的比值(b值)在剪切过程中逐渐降低,剪切速率越大,b值越小;不同速率下的颗粒抗剪强度约为140kPa,剪切力峰值集中在400N左右,振铃计数、AE能量与b值在剪切运动过程中的变化与剪切破坏阶段密切相关;频谱重心会随剪切过程逐步降低,从大约350 kHz降低至250kHz,同时,较低频带能量占比增加、较高频带能量占比减少,导致频谱重心不断下移.

基于Packet Tracer的BGP路由协议实践教学仿真

在西部地区有些高校缺乏网络实验设备,很难满足网络工程专业的高级路由技术课程中实践教学的需求,提出了一种在Packet Tracer模拟环境下实现BGP路由协议的配置,并对实践结果进行了验证。通过仿真实验,可以降低实验投资成本,是一种可行的、廉价的高效解决方案。

基于Packet Tracer软件的小型局域网络设计与仿真

小型局域网络作为一种常见的网络,其设计思路与实现过程成为网络工程课程教学中重要的实验内容。为了让学生更好地掌握网络工程的设计与实现过程,采用案例驱动方式设计了一个小型局域网络实践题目,详细阐述了如何运用Cisco Packet Tracer对网络进行设计与仿真实现。应用结果表明,此项目实践在网络工程课程教学中取得了良好效果。

基于Packet Tracer的路由综合实验设计与实现

阐述了网络层路由的基本概念、工作原理及特点,通过Packet Tracer仿真设计了静态路由、动态路由综合实验,实现了不同网络结点之间的通信,让学生能够从理论上和实践上更好地掌握网络层路由技术。

Packet Tracer软件在无线网络技术实验教学中的应用

介绍了Packet Tracer软件包含的无线网络仿真设备及其加密认证技术。借助Packet Tracer软件完成了无线网络技术课程的实验教学,由无线网卡、无线接入点和无线路由器构成无线网络,通过对无线网络的配置介绍了仿真实验过程。学生通过完成仿真实验,培养了组建、使用和管理无线网络的技能,加深了对无线网络技术理论及概念的掌握和理解。