Visualization of Complex Networks based on Dyadic Curvelet Transform

A visualization method is proposed for understanding the structure of complex networks based on an extended Curvelet transform named Dyadic Curvelet Transform (DClet). The proposed visualization method comes to answer specific questions about structures of complex networks by mapping data into orthogonal localized events with a directional component via the Cartesian sampling sets of detail coefficients. It behaves in the same matter as human visual system, seeing in terms of segments and distinguishing them by scale and orientation. Compressing the network is another fact. The performance of the proposed method is evaluated by two different networks with structural properties of small world networks with N = 16 vertices, and a globally coupled network with size N = 1024 and 523 776 edges. As the most large scale real networks are not fully connected, it is tested on the telecommunication network of Iran as a real extremely complex network with 92 intercity switching vertices, 706 350 E1 traffic channels and 315 525 transmission channels. It is shown that the proposed method performs as a simulation tool for successfully design of network and establishing the necessary group sizes. It can clue the network designer in on all structural properties that network has

Clasificación básica de neuroseñales

Uno de los planteamientos que se presentan a nivel tecnológico consiste en establecer una interfaz entre el ser humano y la máquina de una manera no invasiva, las investigaciones determinan que es muy difícil tomar este tipo de señal. Establecer a que señal corresponde y que información posee, puede proporcionar una herramienta útil para crear una interfaz cerebro computadora. El objetivo general de este trabajo es clasificar las señales neurológicas obtenidas a través de un equipo de electroencefalografía, para determinar la información correspondiente con una dirección, que indique intención de realizar un movimiento real o imaginado, para esto se han utilizando herramientas como las transformaciones matemáticas. Para esta investigación se implementó una interfaz de control mediante señales a partir del cerebro. A través de etapas de capturas de información utilizando una gran cantidad de electrodos y aplicando protocolos médicos para la adquisición de datos, con esto se creó una base de datos propietaria que se utilizó en el desarrollo de esta aplicación. La interfaz de aplicación, fue desarrollada con módulos interconectados entre sí, que adquieren la información proveniente de una base de datos; luego la descomponen utilizando transformadas matemáticas, para programar luego un clasificador de datos y verificar finalmente si la información es clasificada adecuadamente

Filter Scheduling Function Model In Internet Server: Resource Configuration, Performance Evaluation And Optimal Scheduling

ABSTRACT FILTER SCHEDULING FUNCTION MODEL IN INTERNET SERVER: RESOURCE CONFIGURATION, PERFORMANCE EVALUATION AND OPTIMAL SCHEDULING by MINGHUA XU August 2010 Advisor: Dr. Cheng-Zhong Xu Major: Computer Engineering Degree: Doctor of Philosophy Internet traffic often exhibits a structure with rich high-order statistical properties like selfsimilarity and long-range dependency (LRD). This greatly complicates the problem of server performance modeling and optimization. On the other hand, popularity of Internet has created numerous client-server or peer-to-peer applications, with most of them, such as online payment, purchasing, trading, searching, publishing and media streaming, being timing sensitive and/or financially critical. The scheduling policy in Internet servers is playing central role in satisfying service level agreement (SLA) and achieving savings and efficiency in operations. The increasing popularity of high-volume performance critical Internet applications is a challenge for servers to provide individual response-time guarantees. Existing tools like queuing models in most cases only hold in mean value analysis under the assumption of simplified traffic structures. Considering the fact that most Internet applications can tolerate a small percentage of deadline misses, we define a decay function model characterizes the relationship between the request delay constraint, deadline misses, and server capacity in a transfer function based filter system. The model is general for any time-series based or measurement based processes. Within the model framework, a relationship between server capacity, scheduling policy, and service deadline is established in formalism. Time-invariant (non-adaptive) resource allocation policies are design and analyzed in the time domain. For an important class of fixed-time allocation policies, optimality conditions with respect to the correlation of input traffic are established. The upper bound for server capacity and service level are derived with general Chebshev\u27s inequality, and extended to tighter boundaries for unimodal distributions by using VysochanskiPetunin\u27s inequality. For traffic with strong LRD, a design and analysis of the decay function model is done in the frequency domain. Most Internet traffic has monotonically decreasing strength of variation functions over frequency. For this type of input traffic, it is proved that optimal schedulers must have a convex structure. Uniform resource allocation is an extreme case of the convexity and is proved to be optimal for Poisson traffic. With an integration of the convex-structural principle, an enhance GPS policy improves the service quality significantly. Furthermore, it is shown that the presence of LRD in the input traffic results in shift of variation strength from high frequency to lower frequency bands, leading to a degradation of the service quality. The model is also extended to support server with different deadlines, and to derive an optimal time-variant (adaptive) resource allocation policy that minimizes server load variances and server resource demands. Simulation results show time-variant scheduling algorithm indeed outperforms time-invariant optimal decay function scheduler. Internet traffic has two major dynamic factors, the distribution of request size and the correlation of request arrival process. When applying decay function model as scheduler to random point process, corresponding two influences for server workload process is revealed as, first, sizing factor--interaction between request size distribution and scheduling functions, second, correlation factor--interaction between power spectrum of arrival process and scheduling function. For the second factor, it is known from this thesis that convex scheduling function will minimize its impact over server workload. Under the assumption of homogeneous scheduling function for all requests, it shows that uniform scheduling is optimal for the sizing factor. Further more, by analyzing the impact from queueing delay to scheduling function, it shows that queueing larger tasks vs. smaller ones leads to less reduction in sizing factor, but at the benefit of more decreasing in correlation factor in the server workload process. This shows the origin of optimality of shortest remain processing time (SRPT) scheduler

Cross-Layer QoE Improvement with Dynamic Spectrum Allocation in OFDM-Based Cognitive Radio.

PhDRapid development of devices and applications results in dramatic growth of wireless tra c, which leads to increasing demand on wire- less spectrum resources. Current spectrum resource allocation pol- icy causes low e ciency in licensed spectrum bands. Cognitive Ra- dio techniques are a promising solution to the problem of spectrum scarcity and low spectrum utilisation. Especially, OFDM based Cog- nitive Radio has received much research interest due to its exibility in enabling dynamic resource allocation. Extensive research has shown how to optimise Cognitive Radio networks in many ways, but there has been little consideration of the real-time packet level performance of the network. In such a situation, the Quality of Service metrics of the Secondary Network are di cult to guarantee due to uctuating resource availability; nevertheless QoS metric evaluation is actually a very important factor for the success of Cognitive Radio. Quality of Experience is also gaining interest due to its focus on the users' per- ceived quality, and this opens up a new perspective on evaluating and improving wireless networks performance. The main contributions of this thesis include: it focuses on the real-time packet level QoS (packet delay and loss) performance of Cognitive Radio networks, and eval- uates the e ects on QoS of several typical non-con gurable factors including secondary user service types, primary user activity patterns and user distance from base station. Furthermore, the evaluation results are uni ed and represented using QoE through existing map- ping techniques. Based on the QoE evaluation, a novel cross layer RA scheme is proposed to dynamically compensate user experience, and this is shown to signi cantly improve QoE in scenarios where traditional RA schemes fail to provide good user experience

Rate-distortion analysis and traffic modeling of scalable video coders

In this work, we focus on two important goals of the transmission of scalable video over the Internet. The first goal is to provide high quality video to end users and the second one is to properly design networks and predict network performance for video transmission based on the characteristics of existing video traffic. Rate-distortion (R-D) based schemes are often applied to improve and stabilize video quality; however, the lack of R-D modeling of scalable coders limits their applications in scalable streaming. Thus, in the first part of this work, we analyze R-D curves of scalable video coders and propose a novel operational R-D model. We evaluate and demonstrate the accuracy of our R-D function in various scalable coders, such as Fine Granular Scalable (FGS) and Progressive FGS coders. Furthermore, due to the time-constraint nature of Internet streaming, we propose another operational R-D model, which is accurate yet with low computational cost, and apply it to streaming applications for quality control purposes. The Internet is a changing environment; however, most quality control approaches only consider constant bit rate (CBR) channels and no specific studies have been conducted for quality control in variable bit rate (VBR) channels. To fill this void, we examine an asymptotically stable congestion control mechanism and combine it with our R-D model to present smooth visual quality to end users under various network conditions. Our second focus in this work concerns the modeling and analysis of video traffic, which is crucial to protocol design and efficient network utilization for video transmission. Although scalable video traffic is expected to be an important source for the Internet, we find that little work has been done on analyzing or modeling it. In this regard, we develop a frame-level hybrid framework for modeling multi-layer VBR video traffic. In the proposed framework, the base layer is modeled using a combination of wavelet and time-domain methods and the enhancement layer is linearly predicted from the base layer using the cross-layer correlation

Online QoS/Revenue Management for Third Generation Mobile Communication Networks

This thesis shows how online management of both quality of service (QoS) and provider revenue can be performed in third generation (3G) mobile networks by adaptive control of system parameters to changing traffic conditions. As a main result, this approach is based on a novel call admission control and bandwidth degradation scheme for real-time traffic. The admission controller considers real-time calls with two priority levels: calls of high priority have a guaranteed bit-rate, whereas calls of low priority can be temporarily degraded to a lower bit-rate in order to reduce forced termination of calls due to a handover failure. A second contribution constitutes the development of a Markov model for the admission controller that incorporates important features of 3G mobile networks, such as code division multiple access (CDMA) intra- and inter-cell interference and soft handover. Online evaluation of the Markov model enables a periodical adjustment of the threshold for maximal call degradation according to the currently measured traffic in the radio access network and a predefined goal for optimization. Using distinct optimization goals, this allows optimization of both QoS and provider revenue. Performance studies illustrate the effectiveness of the proposed approach and show that QoS and provider revenue can be increased significantly with a moderate degradation of low-priority calls. Compared with existing admission control policies, the overall utilization of cell capacity is significantly improved using the proposed degradation scheme, which can be considered as an 'on demand' reservation of cell capacity.To enable online QoS/revenue management of both real-time and non real-time services, accurate analytical traffic models for non real-time services are required. This thesis identifies the batch Markovian arrival process (BMAP) as the analytically tractable model of choice for the joint characterization of packet arrivals and packet lengths. As a key idea, the BMAP is customized such that different packet lengths are represented by batch sizes of arrivals. Thus, the BMAP enables the 'two-dimensional', i.e., joint, characterization of packet arrivals and packet lengths, and is able to capture correlations between the packet arrival process and the packet length process. A novel expectation maximization (EM) algorithm is developed, and it is shown how to utilize the randomization technique and a stable calculation of Poisson jump probabilities effectively for computing time-dependent conditional expectations of a continuous-time Markov chain required by the expectation step of the EM algorithm. This methodological work enables the EM algorithm to be both efficient and numerical robust and constitutes an important step towards effective, analytically/numerically tractable traffic models. Case studies of measured IP traffic with different degrees of traffic burstiness evidently demonstrate the advantages of the BMAP modeling approach over other widely used analytically tractable models and show that the joint characterization of packet arrivals and packet lengths is decisively for realistic traffic modeling at packet level