THE PN2I NETWORK： A SCALABLE BANYAN NETWORK

To design a Banyan network with an arbitrary even-sized port number, the PN2I network is proposed. The PN2I network can be divided into two classes： the complete and incomplete versions. A simple routing algorithm is given, but in the incomplete PN2I networks, this routing algorithm fails to make the traffic in links even, which deteriorates the performance badly. Thus a new routing algorithm is proposed, which makes the incomplete PN2I networks behave almost the same as the PN2I networks with respect to the performance issues.     

The Batcher-banyan self-routing network: universality andsimplification

It is shown that the Batcher-banyan network performs as a universal self-routing switch when inputs with unassigned destinations are present. This is demonstrated by first proving that banyan networks can realize permutations represented by bitonic sequences, and then noting that the sorted output of the Batcher network can be viewed as a bitonic sequence. Two methods are proposed for reducing the complexity of the Batcher-banyan network. In the first method, one stage of the banyan network is eliminated by assigning proper destination tags to the unassigned inputs. In the second, a self-routing switch based on the binary-radix sorting scheme is shown to be more economical for a small number of lines     

A fail-safe self-routing fiber-optic ring network using multitailedreceiver-transmitter units as nodes

A fiber-optic component consisting of N receivers, each with one fiber input port, and one transmitter with n fiber output ports, with a circuit in between, form a multitailed node. This is the basic building block for a self-routing `filled-ring network' with many stations, which can be disconnected without affecting the ring integrity. The token-passing medium-access protocol is found to have a several times better performance, in terms of the average delay-traffic intensity characteristic, in a filled-ring network than in a conventional ring network with the same number of stations. In a realistic example with 64 stations the improvement is by a factor of two to three. Using the multitailed nodes and self-routing, the network can easily be expanded to more complex configurations without introducing a routing overhead to keep the network under control     

适合大规模网络的一种新型智能网管模型及性能分析

提出了一种适合大规模网络的新型智能网管模型——MADINM(multi-agent based distributed intelligent network management)模型。该模型采用基于域的管理策略,在管理功能的实现上集移动agent和智能agent的优点于一身,既能够对网络管理提供更多的智能支持,又能够解决系统的性能瓶颈问题。理论分析和实验结果均表明,在管理大规模网络时,MADINM模型在性能上优于基于SNMP的网管模型和基于移动agent的网管模型。     

A large-scale AWG-based single-hop WDM network using couplers with collision avoidance

Single-hop wavelength division multiplexing (WDM) networks based on a central arrayed waveguide grating (AWG) have attracted a great deal of attention as a solution for metropolitan area network applications because they can achieve high throughput with reduced cost due to the periodic wavelength-routing property of the AWG. Unfortunately, scalability is a significant problem in an AWG-based single-hop WDM network because the number of transceivers required at each node is equal to the total number of nodes. This problem can be solved by providing optical couplers between the AWG and the nodes and by aggregating multiple nodes before connecting to the AWG. In this case, however, packet collisions at the couplers will seriously increase the packet network delay. Therefore, we propose a novel AWG-based single-hop WDM network in which an autonomic collision avoidance mechanism is introduced in the couplers. We derive the optimum number of couplers for this architecture. Through numerical study, we clarify that the proposed network architecture can reduce the total network cost dramatically.     

SAMURAI: A general and efficient simulated-annealing schedule with fully adaptive annealing parameters

This paper describes a novel simulated annealing procedure with a general scope. New concepts have been derived for dynamically and efficiently updating the essential annealing parameters. These have allowed to speed up the convergence and to save a lot of objective function evaluations compared to other methods which have been published. Our most important contribution is the introduction of a generally applicable and fully adaptive ‘inner-loop criterion’. The quality of the results obtained with this general optimisation routine has first been demonstrated for a challenging application, namely the accurate characterisation of worst-case limit-cycle behaviour and other finite word-length effects in digital filters. In this paper, the general applicability of our approach is further substantiated with some promising results for the solution of clustered TSP problems with many cities. Finally, hierarchial partitioning is established as an important as an important property for distingushing applications which are very well-suited for simulated annealing from those which are not.     

Image splicing localization using residual image and residual-based fully convolutional network

Fully convolutional networks (FCNs) have been efficiently applied in splicing localization. However, the existing FCN-based methods still have three drawbacks: (a) their performance in detecting image details is unsatisfactory; (b) deep FCNs are difficult to train; (c) results of multiple FCNs are merged using fixed parameters to weigh their contributions. So, an improved method is proposed. Firstly, both the original spliced image and its corresponding residual image are regarded as the inputs of the network. Secondly, the residual block is introduced into FCN as residual-based FCN (RFCN) to make the network easier to optimize. Thirdly, three different RFCNs are merged to enhance locating maps with two learnable weight parameters. Besides, condition random field is introduced into the whole network to improve the results further. Experimental results on five datasets show that the proposed method performs better than some existing methods in localization ability, generalization ability, and robustness against additional operations.     

A multilayer complex neural network training algorithm and its application in adaptive equalization

In this paper, the layer-by-layer optimizing algorithm for training multilayer neural network is extended for the case of a multilayer neural network whose inputs, weights, and activation functions are all complex. The updating of the weights of each layer in the network is based on the recursive least squares method. The performance of the proposed algorithm is demonstrated with application in adaptive complex communication channel equalization.     

Kinematic control of a three-fingered and fully adaptive end-effector using a Jacobian matrix

A three-fingered end-effector has been designed for use in an unstructured environment. The design has three driven inputs per finger to move the tip to the desired position in order to make contact with an object. A theoretical analysis of the three-dimensional workspace of the three-fingered gripper has been used to form a Jacobian matrix based on two inputs per finger, to provide a numerical inverse kinematics solution. Simulation of the theoretical model has shown that this approach is capable of highly accurate predictions of tip position. Given a priori knowledge of an object (e.g. the diameter of a sphere), the proposed controller will be able to determine the finger control inputs from a predefined quadratic function that will result in the fast execution of control algorithms.     

大规模网络拓扑测量中的关键技术

Internet的测量与分析为加强网络管理、提高网络利用率、防范大规模网络攻击提供技术平台,已成为学术界、企业界和国家政府部门所普遍关心的重要问题之一。本文首先介绍面向Internet的网络拓扑测量的研究背景,然后探讨大规模网络拓扑测量的总体方案,提出存在的主要难点问题及采取的关键技术,最后给出实验结果和下一步工作计划。     

Performance analysis of narrowband beamforming using fully and partial adaptive beamformers with a spherical array

This research proposes an improved narrowband partial adaptive beamformer analysis using a proposed spherical array. Comparison between the fully and the partial adaptive beamformers is given. The study is performed by investigating performance parameters like the beamformer output signal-to-noise ratio and the beamformer output signal-to-interference-plus-noise ratio both in the steady state and along adaptation. Furthermore, computational complexity and convergence speed of the proposed sensor arrangement are also analyzed and examples are given. The results demonstrate that this beamformer considerably reduces the number of complex operations and features faster convergence speed.     

A packet header recognition assigning the position of a signal inthe time axis and its application to all-optical self-routing

We describe an all-optical circuit that recognizes by a packet the header [virtual channel identifier (VCI): 16 bits and virtual path identifier (VPI): 8 bits] extracted from a cell for self-routing. The circuit's operation is confirmed by an experiment in the recognition of four header bits, and an all-optical routing experiment is carried out with a sequence of four cells, which are composed of two header bits. The output from the header recognition circuit appears in a signal assigned In the time axis according to the header information. The signal controls the switch in the switching system. The proposed header recognition and self-routing circuits are successfully demonstrated by the experiments     

ARTEMIS: 40-gb/s all-optical self-routing node and network architecture employing asynchronous bit and packet-level optical signal processing

A 40-Gb/s asynchronous self-routing network and node architecture that exploits bit and packet level optical signal processing to perform synchronization, forwarding, and switching in the optical domain is presented. Optical packets are self-routed on a hop-by-hop basis through the network by using stacked optical tags, each representing a specific optical node. Each tag contains necessary control signals for configuring the node-switching matrix and forwarding each packet to the appropriate outgoing link and onto the next hop. In order to investigate the feasibility of their approach physical-layer simulations are performed, modeling each optical subsystem of the node showing acceptable signal quality and end-to-end bit error rates. In the All-optical self-RouTer EMploying bIt and packet-level procesSing (ARTEMIS) control plane, a timed/delayed resource reservation-based signaling scheme is employed combined with a load-balancing feedback-based contention-avoidance mechanism that can guarantee a high performance in terms of blocking probability and end-to-end delay.     

Ultrafast all-optical pattern matching using differential spin excitation and its application to bypass/drop self-routing for asynchronous optical packets

This paper presents a novel scheme for ultrafast all-optical pattern matching using the differential spin excitation in semiconductor multiple quantum wells (MQWs). In a demonstration of an all-optical pattern matching between two 100-Gb/s 16-bit optical packets, the contrast ratio of the photodiode (PD) output from the pattern matcher, between the pattern matched and the pattern-unmatched cases, was more than four for packets with a 2-dB power fluctuation. As an application of the pattern matcher to optical-packet-switched ring networks, bypass/drop self-routing is demonstrated for asynchronous 100-Gb/s 32-bit optical packets with 8-bit labels. In the experiment, a label of an incoming packet was compared to a local address (LA) given to a node in the optical domain. By changing the pattern of the LA packet instead of that of the incoming packet, the pattern matching was carried out for packets with various kinds of patterns. The contrast ratio of the PD output was more than six for all patterns.     

Tamper video detection and localization using an adaptive segmentation and deep network technique

In this work we have explored the hybrid deep learning architecture for recognizing the tampering from the videos. This hybrid architecture explores the features from the authentic videos to categorize the tampered portions from the forged videos. Initially, the process begins by compressing the input video using the Discrete cosine transform (DCT) based double compression approach. Then, the filtering process is carried out to improve the quality of compressed frame using the bilateral filtering. Then, the modified segmentation approach is applied to segment the frames into different regions. The features from these segmented portions are extracted and fed into hybrid DNN-AGSO (deep neural network- Adaptivf RELATED WORKSe Galactic Swarm Optimization) using Gabor wavelet transform (GWT) technique. Three different datasets are used to evaluate the overall performance they are, VTD, MFC-18, and VIRAT by MATLAB platform. The recognition rate achieved by VTD, MFC-18, and VIRAT datasets are 96%, 95.2%, and 93.47% respectively.     

基于自适应结构的径向基神经网络模式识别

为了提高神经网络模式识别的泛化能力,运用梯度下降、扩展卡尔曼滤波、无先导卡尔曼滤波和一种基于遗传算法与扩展卡尔曼滤波组合的新方法,对径向基神经网络的中心节点和权重进行了优化,建立了自适应结构的径向基神经网络模型,实现了对 IRIS 数据集的识别。通过仿真实验,对基于不同算法的径向基神经网络,从逼近能力、输出误差、学习效率与识别精确度等方面进行了分析比较。本文方法具有很强的非线性处理能力和自适应能力及较快的学习速度。     

Virtual fully connected WDM network: A high-performance single-hop architecture for metropolitan area networks

With the widespread use of broadband access technologies and the development of high-speed Internet backbones, the requirement for high-performance metropolitan area networks (MANs) is increasing. Traditional ring- or star-based metro networks are costly to scale up to high speed and cannot recover from multiple failures, while backbone solutions are too expensive to fit into the cost-sensitive metro market. This paper proposes a virtual fully connected (VFC) architecture for metro networks to provide high-performance node-to-node all-optical transportation. The architecture emulates a fully connected network by providing optical channels between node pairs without intermediate buffering, and thus realizes single-hop transportation and avoids expensive packet routers. In addition, a scheduling algorithm is developed for medium access control and dynamic bandwidth allocation, which achieves 100% throughput and provides a fairness guarantee. Simulations show that the VFC network achieves good performance under both uniform and non-uniform loads.     

A fully analytical AC large-signal model of the GaAs MESFET fornonlinear network analysis and design

A fully analytical version of an AC large-signal model for the GaAs MESFET is presented. The source model is based on basic principles and the actual physics and geometry of the device. The analytical version was developed by curve fitting the analytical expressions to the source model. The accuracy of the model for microwave circuits is demonstrated using simulation examples of a power amplifier and a mixer