The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Multifractal Internet Traffic Model and Active Queue Management

We propose a multilevel (hierarchical) ON/OFF model to simultaneously capture the mono/multifractal behavior of Internet traffic. Parameter estimation methods are developed and applied to estimate the model parameters from real traces. Wavelet analysis and simulation results show that the synthetic traffic (using this new model with estimated parameters) and real traffic share the same statistical properties and queuing behaviors. Based on this model and its statistical properties, as described by the Logscale diagram of traces, we propose an efficient method to predict the queuing behavior of FIFO and RED queues. In order to satisfy a given delay and jitter requirement for real time connections, and to provide high goodput and low packet loss for non-real time connections, we also propose a parallel virtual queue control structure to offer differential quality of services. This new queue control structure is modeled and analyzed as a regular nonlinear dynamic system. The conditions for system stability and optimization are found (under certain simplifying assumptions) and discussed. The theoretical stationary distribution of queue length is validated by simulation

Taajuusvastemenetelmät pyörivien koneiden diagnostiikassa

Condition monitoring of machines is very important in maintenance of factories. By monitoring the condition of machines in real time, maintenances can be planned better and unnecessary loss of production and break down of machines can be avoided. Frequency-domain methods provide efficient means for such monitoring. The methods can be used in recognizing periodic signals from data thus providing valuable information of the condition and possible error sources. The aim of the thesis is to review the frequency-domain methods used in literature, and then investigate their performance in recognizing periodic signals from data. Five frequency-domain methods were chosen from the literature and applied in studies. The chosen methods were autocorrelation, fast Fourier transform (FFT), short time Fourier transform (STFT) and continuous wavelet transform (CWT). The methods were applied to real-world data obtained from an earth crushing facility. Two experiment setups were implemented to compare the different methods with each other. The results showed that autocorrelation and FFT were very good at finding the periodic signal from the data. The noise and the length of the data affected FFT more than autocorrelation, but the results were still good. The problem with autocorrelation and FFT was that information on the nature of the periodic signal was lost. FFT also had no time information of the periodic signal. STFT and CWT provided very good information on the nature of the periodic signal. The results showed that CWT was a bit weaker than autocorrelation and FFT at finding the periodic signal, but the noise did not affect the results so much. STFT was always the weakest of the methods at finding the periodic signal, but the results were still good. The experiments showed that STFT has very good frequency resolution. The frequency resoultion of CWT was worse and the stronger frequencies drowned the other frequencies, but all the frequencies could still be found from the data. Autocorrelation and FFT are very easy and fast methods to use compared to STFT and CWT, but STFT and CWT provide better information on the periodic content of the data. All the methods have pros and cons. STFT is the best method to use at finding continuous periodic signals from data because the time domain content is preserved in the transformation and the frequency resolution is good. CWT is the best method to use at finding impulsive periodic signals from data because the time domain content is preserved and the impulsive signals are clear and easy to find from the transform. The results of the thesis can be used to select a frequency-domain method for a specific application to perform frequency-domain analysis and condition monitoring

Variational models for color image processing in the RGB space inspired by human vision Mémoire d'Habilitation a Diriger des Recherches dans la spécialité Mathématiques

La recherche que j'ai développée jusqu'à maintenant peut être divisée en quatre catégories principales : les modèles variationnels pourla correction de la couleur basée sur la perception humaine, le transfert d'histogrammes, le traitement d'images à haute gammedynamique et les statistiques d'images naturelles en couleur. Les sujets ci-dessus sont très inter-connectés car la couleur est un sujetfortement inter-disciplinaire

Effects of aggregation on estimators of long-range dependence

Modern technologies have made available huge amount of data from phenomena that exhibit long-range dependence (LRD). For example, network traffic data can be sampled at intervals as small as one nanosecond. The analysis of such a big amount of data can pose practical challenges. Temporal aggregation has been employed to cope with the limitations in the storage capacity and analysis tools. One question that arises immediately is whether the aggregated process has the same long-range dependence characteristics of the underlying process. Under mild local assumptions on the spectral density of the LRD process, we show that the fractional order d of the processes is invariant under temporal aggregation. A second related question is whether and how the estimators of long-range dependence are affected by aggregation. We focus our attention on the log-periodogram regression estimator of Geweke and Porter-Hudak (GPH) and on the local Whittle (LW) estimator. For such estimators, a trimming parameter m needs to chosen by the user so as to balance the trade-off between bias and variance. One optimal choice of m is the value that minimizes the asymptotic mean squared error (MSE) of the estimator. We derive the asymptotic MSE of the GPH estimator for the model in consideration. We show how the MSE-optimal choice of m varies under aggregation for both the GPH and LW estimators. We consider also the case when the sum of the LRD process and a white noise process is observed. This model emerges from long-memory stochastic volatility models (LMSV). We derive an expression for the asymptotic MSE of LW estimator. A LW-type estimator (ELW) which accounts for the presence of noise is also considered. We derive a representation of the Hessian matrix of the ELW estimator as functionals of incomplete beta functions. We evaluate numerically the effect of aggregation on the LW and ELW estimators when the observed process is composed by a LRD process plus noise. We perform an empirical analysis of the effects of aggregation on the UNC network data

North Atlantic winter surface extratropical cyclone track variability on interannual-to-decadal time-scales.

The variability of cyclone activity at interannual and decadal-to-multidecadal time-scales is examined. First, interannual cyclone variability is classified into three major modes based on a VARIMAX Rotated Principal Component Analysis (VRPC) of CDF anomalies. Differences between the new CDF modes are emphasized in several respects (e.g., time-frequency decomposition of VRPC scores). Relationships of these unique modes to various atmospheric/oceanic circulation anomalies (in addition to NAO and ENSO) are then documented; the different physical mechanisms involved are elucidated. Further, decadal ocean-atmosphere interactions are explored via a new lead/lag CCA-based procedure. CDF also is used to re-examine the predictability of Moroccan precipitation anomalies by accounting for VRPC scores (besides NAO). Finally, new ensemble simulation of trends in LF NAO for 21st century climate changes using coupled ocean-atmosphere GCMs is analyzed.The CDF and related cyclone attributes are used to compile a new climatology (including persistence) of cyclone activity. This climatology will be arguably the most accurate and representative set yet compiled. Moreover, use of a novel aspect of wavelet analysis of CDF permitted (1) separation of the high-frequency and low-frequency (LF) components of cyclone activity; and (2) full 2-D grid analyses that document spatial heterogeneity of cyclone behavior, rather than areal averaging.Most recent automated methods for detecting and tracking cyclones suffer mainly from spatio-temporal inhomogeneities. To overcome these weaknesses, a new approach was developed to construct a hybrid space- and time-smoothed surface Cyclone track Density Function (CDF) over the North Atlantic Basin on a 2° x 2° grid with 1-day time resolution (Oct--Mar, 1948/49--1998/99). The CDF field is designed to provide a better description of storm tracks rather than tracking individual storms. Development of other related fields of cyclone characteristics (e.g., intensity, moving speed, duration) is enlightening because of additional physical insight/consistency provided. Thus, the aim of this analysis is to give new insight into the North Atlantic winter cyclone track organization and behavior. State-of-the-art techniques, models, and data sets were used to achieve this goal

Recent patents in bionanotechnologies: nanolithography, bionanocomposites, cell-based computing and entropy production

Recent Patents on Nanotechnology, 2: pp. 1-7This article reviews recent disclosures of bio-inspired, bio-mimicked and bionanotechnologies. Among the patents discussed is a nanoscale porous structure for use in nanocomposites and nanoscale processing. Patents disclosing methods for printing biological materials using nanolithography techniques such as dip-pen technology are discussed, as are patents for optimizing drug design. The relevance of these technologies to disease prevention, disease treatment and disease resistance is discussed. The paper closes with a review of cell-based computing and a brief examination of how information technology has enabled the development of these technologies. Finally a forecast of the how these technologies are likely to accelerate global entropization is discussed as well as a new classification of machine types

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference