ARGA-Unet:Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

A Novel Telecommunication System:HAPS

This paper has identified the main research and development programs in HAPS and their applications across the world.The spectrum and licensing issues are presented according to the latest ITU recommendations.To make HAPS play a vital role in the integrated telecommunication and broadcast network in the future,some proposals are given.

KubeTelecom:一种面向5G网络切片的多云多容器集群管理与运维引擎

面向运营商多种网络云化应用的多云多容器集群管理与运维引擎,提出并详细介绍了KubeTelecom的设计架构以及实现方案。从5G切片场景的承载需求入手,分析了KubeTelecom为上层应用平台提供多容器集群管理与编排能力以及数据驱动的综合运维能力。展望了未来电信云平台通过集成KubeTelecom模块,可以为多种云原生应用提供统一的资源管理和运维能力,为运营商的多种DICT业务生态发展提供坚实的技术支持。

Deep Reinforcement Learning-Based Collaborative Routing Algorithm for Clustered MANETs

Flexible adaptation to differentiated quality of service(QoS)is quite important for future 6G network with a variety of services.Mobile ad hoc networks(MANETs)are able to provide flexible communication services to users through self-configuration and rapid deployment.However,the dynamic wireless environment,the limited resources,and complex QoS requirements have presented great challenges for network routing problems.Motivated by the development of artificial intelligence,a deep reinforcement learning-based collaborative routing(DRLCR)algorithm is proposed.Both routing policy and subchannel allocation are considered jointly,aiming at minimizing the end-to-end(E2E)delay and improving the network capacity.After sufficient training by the cluster head node,the Q-network can be synchronized to each member node to select the next hop based on local observation.Moreover,we improve the performance of training by considering historical observations,which can improve the adaptability of routing policies to dynamic environments.Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion.In addition,the effectiveness of the routing policy in a dynamic environment is verified.

Integrated Sensing and Communication Enabled Multiple Beamwidth and Power Allocation for Connected Automated Vehicles

Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.

Modified Computational Ranking Model for Cloud Trust Factor Using Fuzzy Logic

Through the use of the internet and cloud computing,users may access their data as well as the programmes they have installed.It is now more challenging than ever before to choose which cloud service providers to take advantage of.When it comes to the dependability of the cloud infrastructure service,those who supply cloud services,as well as those who seek cloud services,have an equal responsibility to exercise utmost care.Because of this,further caution is required to ensure that the appropriate values are reached in light of the ever-increasing need for correct decision-making.The purpose of this study is to provide an updated computational ranking approach for decision-making in an environment with many criteria by using fuzzy logic in the context of a public cloud scenario.This improved computational ranking system is also sometimes referred to as the improvised VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)method.It gives users access to a trustworthy assortment of cloud services that fit their needs.The activity that is part of the suggested technique has been broken down into nine discrete parts for your convenience.To verify these stages,a numerical example has been evaluated for each of the six different scenarios,and the outcomes have been simulated.

Visible Light Communication for Vehicular Applications: A Novel Architecture with Proof-of-Concept Prototype

When there is an increasing interest in visible light communication(VLC), outdoor vehicle VLC has emerged as a promising candidate technology for future intelligent transportation systems. However, in VLC based vehicular applications, several challenges impede successful commercial application of VLC based products. This article first provides a thorough overview of the existing challenges. To overcome these challenges, we propose a novel architecture with tracking and environment sensing ability for practical vehicular applications. Moreover, a proof-ofconcept prototype is implemented to validate the feasibility of the proposed system. Experimental and simulation results show that the proposed VLC system can provide reliable communications with a bit-error rate less than 10-4for vehicles under strong interference lights. Finally, based on the evaluations, we propose some key design issues for future studies in this research area.

Architecture for Cellular Enabled Integrated Communication and Sensing Services

There is growing interest in the integrated sensing and communication(ISAC)to extend the 5G+/6G network capabilities by introducing sensing capability.While the solutions for mono-static or bi-static ISAC have shown feasibility and benefits based on existing 5G physical layer design,whether and how to coordinate multiple ISAC devices to better exert networking performance are rarely discussed.3 rd Partnership Project(3GPP)has initiated the ISAC use cases study,and the follow-up studies for network architecture could be anticipated.In this article,we focus on gNB-based sensing mode and propose ISAC functional framework with given of highlevel service procedures to enable cellular based ISAC services.In the proposed ISAC framework,three types of network functions for sensing service as Sensing Function(SF),lightweight-Edge Sensing Function(ESF)and full-version-ESF are designed with interaction with network nodes to fulfill the latency requirements of ISAC use cases.Finally,with simulation evaluations and hardware testbed results,we further verify the performance benefit and feasibility to enable ISAC in 5G for the gNB-based sensing mode with new design on SF and related signaling protocols.

Intelligent reflecting surface-assisted federated learning in multi-platoon collaborative networks

Inspired by mobile edge computing(MEC),edge learning has gained a momentum by directly performing model training at network edge without sending massive data to a centralized data center.However,the quality of model training will be affected by the limited communication and computing resources of network edge.In this paper,how to improve the training performance of a federated learning system aided by intelligent reflecting surface(IRS)over vehicle platooning networks is studied,where multiple platoons train a shared federated learning model.Multi-platoon cooperation can alleviate the pressure of data processing caused by the limited computing resources of single platoon.Meanwhile,IRS can enhance the inter-platoon communication in a cost-effective and energy-efficient manner.Firstly,the federated learning optimization problem of maximizing the learning accuracy is formulated by jointing platoon scheduling,bandwidth allocation and phase shifts at the IRS to maximize the number of scheduled platoon.Specif-ically,in the proposed learning architecture each platoon updates the learning model with its own data and uploads it to the global model through IRS-based wireless networks.Then,a method based on sequential optimization algorithm(SOA)and a group-based optimization method are analyzed for single IRS aided and large-scale IRS aided commu-nication,respectively.Finally,a platoon scheduling scheme is designed based on the communication reliability and computing reliability of platoons.Simulation results demonstrate that large-scale IRS assisted communication can effectively improve the reliability of multi-user communication networks.The scheduling scheme based on learning reliability balances the communication performance and computing performance of platoons.

400 Gbit/s彩光系统在IP网络中的应用探讨

随着互联网流量的持续增长,IP网络400 Gbit/s链路的规模部署迫在眉睫。彩光技术的进展为400 Gbit/s系统的部署带来了更多的选择。分析了400 Gbit/s主流接口标准化情况,对400 Gbit/s彩光接口、400 Gbit/s白光接口、40 Gbit/s彩光接口进行了比较,并分析了路由器设备支出情况,提出了400 Gbit/s彩光在IP网络不同场景中的应用策略,为400 Gbit/s链路规模部署提供了参考。

基于RFID的医疗设备远程管理系统设计

为提高医院内设备定位精度,设计基于RFID的医疗设备远程管理系统。通过阅读器、电子标签和处理器组成RFID节点,引入AS3992芯片作为核心芯片,实现数据处理和编码,执行远程指令。采用自由空间传播模型计算路径损耗产生值,通过三角定位数学方法计算待定标签位置,采用RFID定位方法确定标签的最近邻居,实现医疗设备的远程定位。实验结果表明,在没有障碍物的情况下,所提系统设备定位误差少于1 m,在有障碍物的情况下,定位误差在2 m左右,证明所提出系统定位准确性较高,满足系统的设计需求。

知识图谱在光传送网络智能运维中的应用研究

为了解决光传送网络传统运维模式成本高、效率低、智能程度弱等问题,分析了知识图谱技术在电信行业的研究现状以及面临的挑战。首先,提出了基于知识图谱的光传送网络智能运维系统架构,重点研究了网络状态感知、知识图谱构建、搜索能力开放等关键技术以及典型应用场景;然后,以智能割接在中国电信某市分公司的应用为案例,对其应用成效进行了分析,测试表明智能割接上线后,整体割接效率提升了约3倍;最后,对基于知识图谱的光传送网络智能运维发展趋势进行了展望。

50 Gbit/s前传光模块关键技术研究与应用展望

随着全球5G网络的规模化商用及移动互联网流量的迅猛增长,更为丰富的无线频谱资源得以释放,高速率光模块在无线前传网络中的需求日益凸显。聚焦于50 Gbit/s前传光模块的关键技术,深入剖析了前传网络的两种主要组网方式,分析了前传链路的典型传输距离及链路损耗情况,探讨了10 km场景下粗波分复用(coarse wavelength division multiplex,CWDM)中各波长的最大色散值和最小色散值,并研究了相应的色散补偿策略,通过实验验证了不同多径干扰(multi-path interference,MPI)强度下系统光功率代价的变化,详细分析了多径干扰噪声对系统误码性能的潜在影响,为前传光模块参数的优化提供了重要的理论依据。此外,深入研究了50 Gbit/s前传光模块的光电芯片技术方案,对多种不同类型光模块的参数性能进行了严格的测试,提出了针对50 Gbit/s前传光模块光电接口参数的标准化建议,详细介绍了当前50 Gbit/s前传光模块在国内外的标准现状,并展望了未来应用的发展趋势。

基于5G网络的RedCap部署方案研究

RedCap作为轻量化的5G技术,是5G Rel-17版本定义的新终端类型,可有效兼顾行业对于技术性能和部署成本的并存需求。一般认为,这种轻量级5G技术的核心价值在于与5G NR网络优异特性的融合,即如何基于运营商已部署的5G无线网引入RedCap是其中的关键问题。结合运营商无线网现状,通过分析RedCap与无线网络侧相关的功能特性,提出引入RedCap后的无线网组网部署方案,包括无线网升级方案、参数配置方案以及信令开销策略等,可为国内各运营商在方案制定及工程实施上提供参考。

基于联合神经网络的投诉预测模型研究

对影响电信运营商重复投诉的关键因素进行深入探讨,旨在提高服务质量并构建风险预测模型。基于运营商客服数据,研究采用了Logistic回归、BP神经网络以及二者联合建模的方法。Logistic回归模型确定了5个主要影响因素,预测重复投诉发生的概率,精度达到80.0%。BP神经网络则选取了81个影响因素,预测精度为90.6%。在此基础上,构建了联合模型,其精度高达92.8%。实际应用于某省会电信运营商后,重复投诉率下降了3.2%,成效显著,为提高电信运营商服务质量、降低重复投诉率提供了有力支持,对我国电信行业发展具有重要意义。

基于马尔可夫决策的钢铁产成品订单分配模型研究

物流订单分配是钢铁产成品销售过程中的重要环节,对销售过程的整体体验和全流程的良性循环起着至关重要的作用。在实际生产过程中,传统的人工分单模式已难以适应长时间维度的发展要求。为了充分考虑一个时间周期内销售的物流成本最低以及承运司机的收益较高,同时确保企业的长期收益,本文用多目标约束,建立马尔可夫决策模型,引入KM算法执行二分图匹配,基于司机收益最大化和企业成本最小的多目标优化,最大化钢铁商品总交易量为长期的最终目标,结合价值函数和多属性的多目标优化函数,形成车辆和货物的完整匹配决策。以钢铁企业的真实业务数据为例,对数据进行预处理后筛选合适特征进行模型训练并验证算法的正确性和可用性。结果表明,该模型相较于传统的订单分配方法,可以更好地解决钢铁企业在订单分配场景下的需求。

Distributed Application Addressing in 6G Network

To ensure the extreme performances of the new 6G services,applications will be deployed at deep edge,resulting in a serious challenge of distributed application addressing.This paper traces back the latest development of mobile network application addressing,analyzes two novel addressing methods in carrier network,and puts forward a 6G endogenous application addressing scheme by integrating some of their essence into the 6G network architecture,combining the new 6G capabilities of computing&network convergence,endogenous intelligence,and communication-sensing integration.This paper further illustrates how that the proposed method works in 6G networks and gives preliminary experimental verification.

基于融合小波函数与暗通道的图像重构算法

围绕图像识别中的图像去雾模块,在暗通道条件下,对含雾图像的重构算法展开研究。针对去雾图像的颜色失真严重、图像清晰度较低等问题,利用改进的小波函数到暗通道算法多次融合对源图像进行去雾处理,不断调整图像的像素色阶使其达到一个最佳范围,并且使图像的色彩失真在一定程度上获得补偿,以此实现图像去雾的目的。仿真实验表明,利用多次融合的改进小波函数在暗通道的去雾图像重构效果最佳,重构后有更好的清晰度,在多方面的表现均优于其它算法。

基于机器学习技术的返乡发展人群预测模型研究与应用

随着经济的发展和一线城市生活压力的增大,越来越多的人迁移城市以及返回家乡发展,为了高效服务用户和提升用户产品使用体验,提出基于LightGBM、CatBoost等算法来预测返乡发展人群,并进行了异构模型融合。通过模型对比,所提融合模型有更好的效果,可以为服务和产品提供依据,减少流失优化感知,提高市场保有率。

5G共建共享网络可视化安全态势感知研究

在5G共建共享网络中,海量安全日志和告警审计使得安全数据缺乏分析、安全事件处置效率低下.面对威胁感知能力缺失、安全管理困难等诸多痛点,提出了5G共建共享网络可视化安全态势感知模型和可视化解决方案.对空口、运维、系统、传输、配置核查5大领域面临的攻击检测进行全面安全态势感知,进行了测试验证.对安全事件进行关联分析,识别跨域攻击链,发现攻击者核心意图,帮助客户实现主动、动态、可度量、可视化的5G共建共享网络安全、稳定运营及安全产业的快速发展.