Neural network techniques for adaptive multiuser demodulation

Adaptive methods for performing multiuser demodulation in a direct-sequence spread-spectrum multiple-access (DS/SSMA) communication environment are investigated. In this scenario, the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. This prohibitive complexity has spawned the area of research on suboptimal receivers with moderate complexity. Adaptive algorithms for detection allow for reception when the communication environment is either unknown or changing. Motivated by previous work with radial basis functions (RBF's) for performing equalization, RBF networks that operate with knowledge of only a subset of the system parameters are studied. Although this form of detection has been previously studied (group detection) when the system parameters are known, in this work, neural network techniques are employed to adaptively determine unknown system parameters. This approach is further bolstered by the fact that the optimal detector in the synchronous case can be implemented by a RBF network when all of the system parameters are known. The RBF network's performance (with estimated parameters) is compared with the optimal synchronous detector, the decorrelating detector and the single layer perceptron detector. Clustering techniques and adaptive least mean squares methods are investigated to determine the unknown system parameters. This work shows that the adaptive radial basis function network attains near optimal performance and is robust in realistic communication environments     

SDH传输网集中监控与网管的优化

结合原哈尔滨网通sDH传输网监控情况,针对sDH城域网在多点网管同时监控时,不同网管系统之间出现的数据同步、告警同步,以及考虑节约二次投资费用等,提出了我们的解决方案.还介绍网管分布情况,针对华为SDH网管的功能提出网管系统优化方案.部分用户接入层SDH网络尚未接入网管监控系统,也是需要处理的问题.通过这些措施,提高了原哈尔滨网通传输网的维护管理效率.     

Training techniques for neural network applications in ATM

The main problems of adaptive ATM quality of service (QoS) control methods using neural networks were the exponentially wide range of the output target and the real-time training data sampling. But new practical techniques to overcome these problems may open new neural network applications. In this article, the framework of connection admission control (CAC) is described as a typical example of neural-network-based QoS estimation and two practical techniques, called relative target method and virtual output buffer method, are presented to enhance the neural network performance in CAC     

Packet filter optimization techniques for high-speed network monitoring

Effective network monitoring is vital for a growing number of control and management applications typically found in present-day networks. The ever-increasing link speeds and the complexity of monitoring applications’ needs have exposed severe limitations of existing monitoring techniques. A majority of the current monitoring tasks require only a small subset of all observed packets, which share some common properties such as identical header fields or similar patterns in their data. In order to capture only these useful packets, a large set of expressions needs to be evaluated. This evaluation should be done as efficiently as possible when monitoring multi-gigabit networks. To speed up this packet classification process, this article presents different packet filter optimization techniques. Complementary to existing approaches, we propose an adaptive optimization algorithm which dynamically reconfigures the filter expressions based on the currently observed traffic pattern. The performance of the algorithms is validated both analytically and by means of the implementation in a network monitoring framework. The various characteristics of the algorithms are investigated, including their performance in an operational network intrusion detection system.     

Efficient network folding techniques for routing permutations inVLSI

Network folding is a technique for realizing permutations on N elements using interconnection networks with M input (and output) terminals, where M相似文献   

T-MPLS网络的OAM监测处理

T-MPLS是面向连接的技术,是MPLS在传送网中的应用,在MPLS的基础上增加了具有传送风格的面向连接的端到端的OAM功能.主要介绍了T-MPLS层网络中OAM的定义,OAM包的数据格式及OAM故障管理功能的监测处理方式.     

光传送网的光监控信道技术

介绍了光传送网的功能分层结构、网络的生存性及安全性能要求,确定了光传送网的管理功能,着重分析了ＯＡＤＭ／ＯＸＣ系统光监控信道技术。     

Alarm driven supervision for telecommunication network: I - Off-line scenarios generation

The use of model-based reasoning techniques allows the needed supervision knowledge to be acquired in a more systematic, robust and evolutive way than expertise. Nevertheless, one of the main problems is that the combinatory complexity associated with model-based techniques rarely satisfies real-time constraints of supervision system. We propose a supervision system for telecommunication networks based on the offline generation of temporal sequences of alarms (scenarios or chronicles), and the on-line recognition of these sequences. We present here only the first part of this system: the off-line generation which is necessary in order to reduce the computing time during the on-line supervision. It relies on a structural and functional model of the network, and takes into account the temporal constraints linked to alarm propagation and the probability of loss of these alarms.     

Alarm driven supervision for telecommunication network: II - On-line chronicle recognition

We propose a supervision system for telecommunication networks based on the recognition of temporal sequences of alarms, called scenarios or chronicles. These chronicles can be described by an expert or by an automatic process (see previous article on the offline generation). The chronicle recognition system, to which this paper is devoted, receives as input a stream of time-stamped events; it performs recognition of instances of occurring chronicles, as they are developing, and it generates as output deduced events and actions to trigger It is mainly a temporal reasoning system. It is predictive in the sense that it predicts forthcoming events relevant to its task, it focuses its attention on them and it maintains their temporal windows of relevance. Its main functionality is to recognise efficiently complex temporal patterns on the fly, while they are taking place. This system has been used for the surveillance of a complex dynamic system and also for designing an experimental supervisor for telecommunication network.     

Wavelength domain optical network techniques

Optical network technology will soon allow wavelength-multiplexed optical channels to be carried to multiple terminals, giving rise to new broadband network architectures and protocols. Two stages of evolution are anticipated, with transmission-only structures appearing first and networks with optical switching appearing later. Some basic transmission considerations relating to system operation over wide optical bandwidths for both local and longer distance networks are reviewed, and the present status of experimental optical networks and the components they use are discussed     

家庭网络技术及相关协议

家庭网络技术是当前信息应用领域发展最为迅速的领域之一.罗列了目前8大典型的家庭网络联盟及主要研究状况,并列出了家庭网络核心技术--家庭网关的三类产品,最后提出了基于NGN的家庭网关的框架图.     

超高密度无线网络的自组织技术

网络密集化是应对未来5G无线网络容量提升1 000倍挑战的主要手段之一,且超高密度网络中单节点要配置和优化的参数超过2 000个,因此,只有通过新一代自组织技术来感知网络运行的态势,自主发现和调配网络资源来精准匹配网络中的业务流,才能有效提高网络的承载能力。分析了超高密度网络自组织技术面临的挑战和呈现的新属性,给出了超高密度网络中自组织技术的2个典型用例,展望了超高密度无线网络中自组织技术的发展前景和方向。     

基于XML的Web数据挖掘构建网络监督平台

以网络监督情报预警作为研究对象,提出并实现了基于XML的Web数据挖掘技术构建网络情报监督系统平台,该平台可以有效地从互联网上获取并分析相关舆情,达到预警和监督管理的目的.经系统性能分析表明其查准率高于通用搜索引擎.     

配网工程施工技术监管及安全措施

本文对配网工程施工技术管理的现状进行研究探讨,针对配网安全管理提出了一些有效的控制手段,望对配网工程施工技术监管及安全管理能有所帮助.     

Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.     

Methodologies for characterizing ultrasonic transducers usingneural network and pattern recognition techniques

System hardware for characterizing ultrasonic transducers and the associated data acquisition software and characterizing algorithms are considered. The hardware consists mainly of a workstation computer, a receiver/pulser with gated peak detector, various monitoring devices, a microcomputer-based 3D positioning controller, and an A/D converter. The characterization algorithms are based on neural network and pattern recognition techniques. It is found that artificial neural network techniques provide far better classification results than the pattern recognition techniques. A multilayer backpropagation neural network which provides a classification accuracy of 94% is developed. Two other multilayer neural networks-sum-of-products and a newly devised neural network called hybrid sum-of-products-have a classification accuracy of 90% and 93%, respectively. The most successful pattern recognition technique for this application is found to be the perceptron, which provides a classification accuracy of 77%     

实时监控技术的发展历程和发展趋势

实时监控系统以其直观、方便、信息内容丰富而被广泛应用于金融系统、交通系统、公安系统、教育系统和医疗系统等众多领域，在计算机技术、电子技术和通信技术迅猛发展的过程中，监控系统的技术水平也从初期的模拟信息传输与控制飞速发展到了数字化、网络化信息传输与控制。纵观监控技术的发展历程，大致经过了三个发展阶段。１ 模拟监控阶段在黑白摄像机、黑白电视机出现的初期，初期的黑白图像电视监控系统也诞生了。它被应用于保安、生产管理等场合，尽管当时图像质量低，控制功能少，但其直观方便的特性还是被人们所接受，之后投入了大…     

无线传感器网络安全技术综述

详细阐述了传感器网络安全现状、面临的安全挑战及其需要解决的安全问题。从密钥管理、安全路由、认证、入侵检测、DoS攻击、访问控制方面讨论传感器网络的各种安全技术，并进行综合对比。希望通过这些说明和对比，能够帮助研究者为特定传感器网络应用环境选择和设计安全解决方案。最后总结和讨论传感器网络安全技术的研究方向。     

SDH骨干网并行加扰/解扰技术研究

研究了SDH扰码序列的性质,在此基础上提出了两种实用化的并行加扰/解扰方案:基于矩阵推导的并行加扰/解扰和一种新的基于查找表结构的并行加扰/解扰,并总结了这两种方案所各自具有的优势,为解决任意位宽和阶数的并行扰码/解扰器的实现问题提供了新的思路.     

Efficient network coding techniques for time constrained multicast narrowband power line communications

Nowadays, power line communication (PLC) technology is receiving a renewed attention in many application fields. In particular, PLC is quite attractive for what concerns novel paradigms as smart microgrid systems and smart buildings. This paper deals with a proposal of a PLC scheme that efficiently allows multicast services based on the use of an efficient network coding (NC) scheme. Furthermore, an analytical approach is outlined to pursue the performance optimization of the proposed solution. Finally, the good behavior of the proposed NC approach is highlighted by presenting performance comparisons with the classical NC alternative.