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。

新医科视域下“计算机网络技术”课程中的医学仿真应用

“计算机网络技术”是医科高校信息管理专业学生的一门重要课程,实验教学则是提高学生应用能力的基础保障。本文针对传统课程理论与实验分离、实验内容与医学环境脱节的问题,采用仿真软件Packet Tracer的项目实验设计,培养学生解决医院网络工程问题的能力。通过实验项目的训练,学生能够实际操作网络设备,了解网络的配置和管理,同时也能够通过仿真软件模拟医院网络环境,提升解决实际问题的能力。这种综合实验教学方法取得了良好的教学效果,有较高的推广价值。

Network traffic classification:Techniques,datasets,and challenges

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.

Dynamic Deep Learning for Enhanced Reliability in Wireless Sensor Networks: The DTLR-Net Approach

In the world of wireless sensor networks(WSNs),optimizing performance and extending network lifetime are critical goals.In this paper,we propose a new model called DTLR-Net(Deep Temporal LSTM Regression Network)that employs long-short-term memory and is effective for long-term dependencies.Mobile sinks can move in arbitrary patterns,so the model employs long short-term memory(LSTM)networks to handle such movements.The parameters were initialized iteratively,and each node updated its position,mobility level,and other important metrics at each turn,with key measurements including active or inactive node ratio,energy consumption per cycle,received packets for each node,contact time,and interconnect time between nodes,among others.These metrics aid in determining whether the model can remain stable under a variety of conditions.Furthermore,in addition to focusing on stability and security,these measurements assist us in predicting future node behaviors as well as how the network operates.The results show that the proposed model outperformed all other models by achieving a lifetime of 493.5 s for a 400-node WSN that persisted through 750 rounds,whereas other models could not reach this value and were significantly lower.This research has many implications,and one way to improve network performance dependability and sustainability is to incorporate deep learning approaches into WSN dynamics.

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.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

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.

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.

Research on Sleeve Grouting Density Detection Based on the Impact Echo Method

Grouting defects are an inherent challenge in construction practices,exerting a considerable impact on the operational structural integrity of connections.This investigation employed the impact-echo technique for the detection of grouting anomalies within connections,enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes.A series of grouting completeness assessments were meticulously conducted,taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement.The findings revealed that:(i)the energy distribution for the highstrength concrete cohort predominantly occupied the frequency bands 42,44,45,and 47,whereas for other groups,it was concentrated within the 37 to 40 frequency band;(ii)the delineation of empty sleeves was effectively discernible by examining the wavelet packet energy ratios across the spectrum of frequencies,albeit distinguishing between sleeves with 50%and full grouting density proved challenging;and(iii)the wavelet packet energy analysis yielded variable detection outcomes contingent on the material attributes of the sleeves,demonstrating heightened sensitivity when applied to ultrahigh-performance concrete matrices and GFRP-reinforced steel bars.

Adaptive Hybrid Forward Error Correction Coding Scheme for Video Transmission

This paper proposes an adaptive hybrid forward error correction(AH-FEC)coding scheme for coping with dynamic packet loss events in video and audio transmission.Specifically,the proposed scheme consists of a hybrid Reed-Solomon and low-density parity-check(RS-LDPC)coding system,combined with a Kalman filter-based adaptive algorithm.The hybrid RS-LDPC coding accommodates a wide range of code length requirements,employing RS coding for short codes and LDPC coding for medium-long codes.We delimit the short and medium-length codes by coding performance so that both codes remain in the optimal region.Additionally,a Kalman filter-based adaptive algorithm has been developed to handle dynamic alterations in a packet loss rate.The Kalman filter estimates packet loss rate utilizing observation data and system models,and then we establish the redundancy decision module through receiver feedback.As a result,the lost packets can be perfectly recovered by the receiver based on the redundant packets.Experimental results show that the proposed method enhances the decoding performance significantly under the same redundancy and channel packet loss.

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。

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.

Design of an Interconnection Architecture and Sizing of Two (2) EPC Core Networks: The Case of Orange-Guinea

With the arrival of the 4G and 5G,the telecommunications networks have experienced a large expansion of these networks.That enabled the integration of many services and adequate flow,thus enabling the operators to respond to the growing demand of users.This rapid evolution has given the operators to adapt,their methods to the new technologies that increase.This complexity becomes more important,when these networks include several technologies to access different from the heterogeneous network like in the 4G network.The dimensional new challenges tell the application and the considerable increase in demand for services and the compatibility with existing networks,the management of mobility intercellular of users and it offers a better quality of services.Thus,the proposed solution to meet these new requirements is the sizing of the EPC(Evolved Packet Core)core network to support the 5G access network.For the case of Orange Guinea,this involves setting up an architecture for interconnecting the core networks of Sonfonia and Camayenne.The objectives of our work are of two orders:(1)to propose these solutions and recommendations for the heart network EPC sizing and the deployment to be adopted;(2)supply and architectural interconnection in the heart network EPC and an existing heart network.In our work,the model of traffic in communication that we use to calculate the traffic generated with each technology has link in the network of the heart.

VLAN网络中Trunk与VTP技术的应用与仿真研究

文章介绍VLAN的应用,以及Trunk和VTP技术的原理。针对不同VLAN拓扑及应用场景讨论Trunk和VTP技术在VLAN网络通信中的应用与实现;基于Cisco Packet Tracer仿真软件,利用VLAN相关协议以及Trunk和VTP技术创建用户逻辑分组、进行拓扑设计、搭建跨交换机的VLAN网络仿真平台,对实际应用场景进行模拟和仿真。通过仿真验证Trunk和VTP技术在VLAN网络通信中的高效性和可靠性,为组建真实的企业网络环境提供可借鉴的参考。

Where Is Phase Velocity in Minkowski Space?

In the special theory of relativity, massive particles can travel at neither the speed of light c nor faster. Meanwhile, since the photon was quantized, many have thought of it as a point particle. How pointed? The idea could be a mathematical device or physical simplification. By contrast, the preceding notion of wave-group duality has two velocities: a group velocity vg and a phase velocity vp. In light vp = vg = c;but it follows from special relativity that, in massive particles, vp > c. The phase velocity is the product of the two best measured variables, and so their product constitutes internal motion that travels, verifiably, faster than light. How does vp then appear in Minkowski space? For light, the spatio-temporal Lorentz invariant metric is s2=c2t2−x2−y2−z2, the same in whatever frame it is viewed. The space is divided into 3 parts: firstly a cone, symmetric about the vertical axis ct > 0 that represents the world line of a stationary particle while the conical surface at s = 0 represents the locus for light rays that travel at the speed of light c. Since no real thing travels faster than the speed of light c, the surface is also a horizon for what can be seen by an observer starting from the origin at time t = 0. Secondly, an inverted cone represents, equivalently, time past. Thirdly, outside the cones, inaccessible space. The phase velocity vp, group velocity vg and speed of light are all equal in free space, vp = vg = c, constant. By contrast, for particles, where causality is due to particle interactions having rest mass mo > 0, we have to employ the Klein-Gordon equation with s2=c2t2−x2−y2−z2+mo2c2. Now special relativity requires a complication: vp.vg = c2 where vg c and therefore vp > c. In the volume outside the cones, causality due to light interactions cannot extend beyond the cones. However, since vp > c and even vp >> c when wavelength λ is long, extreme phase velocities are then limited in their causal effects by the particle uncertainty σ, i.e. to vgt ± σ/ω, where ω is the particle angular frequency. This is the first time the phase range has been described for a massive particle.

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。

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。

面向智能家居应用的物联网“场景分解式”实训设计

通过PacketTracer模拟物联网智能家居场景,并将所涉及的理论知识进行分解后纳入实训课程,使学生能够循序渐进地掌握智能设备、网络、物联网等方面知识和技术,同时理解雾计算的概念以及在智能家居场景中选择雾计算的必要性,培养解决物联网工程领域复杂工程问题的能力。

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。