Enhanced multi-layer protection in multi-service GMPLS networks

This paper focuses on QoS routing with protection in an MPLS network over an optical layer. In this multi-layer scenario each layer deploys its own fault management methods. A partially protected optical layer is proposed and the rest of the network is protected at the MPLS layer. New protection schemes that avoid protection duplications are proposed. Moreover, this paper also introduces a new traffic classification based on the level of reliability. The failure impact is evaluated in terms of recovery time depending on the traffic class. The proposed schemes also include a novel variation of minimum interference routing and shared segment backup computation. A complete set of experiments proves that the proposed schemes are more efficient as compared to the previous ones, in terms of resources used to protect the network, failure impact and the request rejection ratio

Enhanced fault recovery methods for protected traffic services in GMPLS networks

Les noves tecnologies a la xarxa ens permeten transportar, cada cop més, grans volums d' informació i trànsit de xarxa amb diferents nivells de prioritat. En aquest escenari, on s'ofereix una millor qualitat de servei, les conseqüències d'una fallada en un enllaç o en un node esdevenen més importants. Multiprotocol Lavel Switching (MPLS), juntament amb l'extensió a MPLS generalitzat (GMPLS), proporcionen mecanismes ràpids de recuperació de fallada establint camins, Label Switch Path (LSPs), redundants per ser utilitzats com a camins alternatius. En cas de fallada podrem utilitzar aquests camins per redireccionar el trànsit. El principal objectiu d'aquesta tesi ha estat millorar alguns dels actuals mecanismes de recuperació de fallades MPLS/GMPLS, amb l'objectiu de suportar els requeriments de protecció dels serveis proporcionats per la nova Internet. Per tal de fer aquesta avaluació s'han tingut en compte alguns paràmetres de qualitat de protecció com els temps de recuperació de fallada, les pèrdues de paquets o el consum de recursos. En aquesta tesi presentem una completa revisió i comparació dels principals mètodes de recuperació de fallada basats en MPLS. Aquest anàlisi inclou els mètodes de protecció del camí (backups globals, backups inversos i protecció 1+1), els mètodes de protecció locals i els mètodes de protecció de segments. També s'ha tingut en compte l'extensió d'aquests mecanismes a les xarxes òptiques mitjançant el pla de control proporcionat per GMPLS. En una primera fase d'aquest treball, cada mètode de recuperació de fallades és analitzat sense tenir en compte restriccions de recursos o de topologia. Aquest anàlisi ens dóna una primera classificació dels millors mecanismes de protecció en termes de pèrdues de paquets i temps de recuperació. Aquest primer anàlisi no és aplicable a xarxes reals. Per tal de tenir en compte aquest nou escenari, en una segona fase, s'analitzen els algorismes d'encaminament on sí tindrem en compte aquestes limitacions i restriccions de la xarxa. Es presenten alguns dels principals algorismes d'encaminament amb qualitat de servei i alguna de les principals propostes d'encaminament per xarxes MPLS. La majoria dels actual algorismes d'encaminament no tenen en compte l'establiment de rutes alternatives o utilitzen els mateixos objectius per seleccionar els camins de treball i els de protecció. Per millorar el nivell de protecció introduïm i formalitzem dos nous conceptes: la Probabilitat de fallada de la xarxa i l'Impacte de fallada. Un anàlisi de la xarxa a nivell físic proporciona un primer element per avaluar el nivell de protecció en termes de fiabilitat i disponibilitat de la xarxa. Formalitzem l'impacte d'una fallada, quant a la degradació de la qualitat de servei (en termes de retard i pèrdues de paquets). Expliquem la nostra proposta per reduir la probabilitat de fallada i l'impacte de fallada. Per últim fem una nova definició i classificació dels serveis de xarxa segons els valors requerits de probabilitat de fallada i impacte. Un dels aspectes que destaquem dels resultats d'aquesta tesi és que els mecanismes de protecció global del camí maximitzen la fiabilitat de la xarxa, mentre que les tècniques de protecció local o de segments de xarxa minimitzen l'impacte de fallada. Per tant podem assolir mínim impacte i màxima fiabilitat aplicant protecció local a tota la xarxa, però no és una proposta escalable en termes de consum de recursos. Nosaltres proposem un mecanisme intermig, aplicant protecció de segments combinat amb el nostre model d'avaluació de la probabilitat de fallada. Resumint, aquesta tesi presenta diversos mecanismes per l'anàlisi del nivell de protecció de la xarxa. Els resultats dels models i mecanismes proposats milloren la fiabilitat i minimitzen l'impacte d'una fallada en la xarxa.

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Resource dimensioning aspect of heterogeneous traffic with different service requirements: Integration versus segregation

In this paper, we consider the ATM networks in which the virtual path concept is implemented. The question of how to multiplex two or more diverse traffic classes while providing different quality of service requirements is a very complicated open problem. Two distinct options are available: integration and segregation. In an integration approach all the traffic from different connections are multiplexed onto one VP. This implies that the most restrictive QOS requirements must be applied to all services. Therefore, link utilization will be decreased because unnecessarily stringent QOS is provided to all connections. With the segregation approach the problem can be much simplified if different types of traffic are separated by assigning a VP with dedicated resources (buffers and links). Therefore, resources may not be efficiently utilized because no sharing of bandwidth can take place across the VP. The probability that the bandwidth required by the accepted connections exceeds the capacity of the link is evaluated with the probability of congestion (PC). Since the PC can be expressed as the CLP, we shall simply carry out bandwidth allocation using the PC. We first focus on the influence of some parameters (CLP, bit rate and burstiness) on the capacity required by a VP supporting a single traffic class using the new convolution approach. Numerical results are presented both to compare the required capacity and to observe which conditions under each approach are preferred

Optimal Traffic Grooming in WDM using Lighttours

Recent developments in optical communications have allowed simpler optical devices to improve network resource utilization. As such, we propose adding a lambda-monitoring device to a wavelength-routing switch (WRS) allowing better performance when traffic is routed and groomed. This device may allow a WRS to aggregate traffic over optical routes without incurring in optical-electrical-optical conversion for the existing traffic. In other words, optical routes can be taken partially to route demands creating a sort of "lighttours". In this paper, we compare the number of OEO conversions needed to route a complete given traffic matrix using either lighttours or lightpaths

Classification of textures seen from different distances and under varying illumination direction

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

Automatic classification of breast density

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

A new approach to real time traffic sign recognition based on colour information

The automatic interpretation of conventional traffic signs is very complex and time consuming. The paper concerns an automatic warning system for driving assistance. It does not interpret the standard traffic signs on the roadside; the proposal is to incorporate into the existing signs another type of traffic sign whose information will be more easily interpreted by a processor. The type of information to be added is profuse and therefore the most important object is the robustness of the system. The basic proposal of this new philosophy is that the co-pilot system for automatic warning and driving assistance can interpret with greater ease the information contained in the new sign, whilst the human driver only has to interpret the "classic" sign. One of the codings that has been tested with good results and which seems to us easy to implement is that which has a rectangular shape and 4 vertical bars of different colours. The size of these signs is equivalent to the size of the conventional signs (approximately 0.4 m2). The colour information from the sign can be easily interpreted by the proposed processor and the interpretation is much easier and quicker than the information shown by the pictographs of the classic signs

G+: Enhanced Traffic Grooming in WDM Mesh Networks using Lighttours

In this article, a new technique for grooming low-speed traffic demands into high-speed optical routes is proposed. This enhancement allows a transparent wavelength-routing switch (WRS) to aggregate traffic en route over existing optical routes without incurring expensive optical-electrical-optical (OEO) conversions. This implies that: a) an optical route may be considered as having more than one ingress node (all inline) and, b) traffic demands can partially use optical routes to reach their destination. The proposed optical routes are named "lighttours" since the traffic originating from different sources can be forwarded together in a single optical route, i.e., as taking a "tour" over different sources towards the same destination. The possibility of creating lighttours is the consequence of a novel WRS architecture proposed in this article, named "enhanced grooming" (G+). The ability to groom more traffic in the middle of a lighttour is achieved with the support of a simple optical device named lambda-monitor (previously introduced in the RingO project). In this article, we present the new WRS architecture and its advantages. To compare the advantages of lighttours with respect to classical lightpaths, an integer linear programming (ILP) model is proposed for the well-known multilayer problem: traffic grooming, routing and wavelength assignment The ILP model may be used for several objectives. However, this article focuses on two objectives: maximizing the network throughput, and minimizing the number of optical-electro-optical conversions used. Experiments show that G+ can route all the traffic using only half of the total OEO conversions needed by classical grooming. An heuristic is also proposed, aiming at achieving near optimal results in polynomial time

Classification of sags according to their origin based on the waveform similarity

A statistical method for classification of sags their origin downstream or upstream from the recording point is proposed in this work. The goal is to obtain a statistical model using the sag waveforms useful to characterise one type of sags and to discriminate them from the other type. This model is built on the basis of multi-way principal component analysis an later used to project the available registers in a new space with lower dimension. Thus, a case base of diagnosed sags is built in the projection space. Finally classification is done by comparing new sags against the existing in the case base. Similarity is defined in the projection space using a combination of distances to recover the nearest neighbours to the new sag. Finally the method assigns the origin of the new sag according to the origin of their neighbours

Admission control schemes for TCP elastic traffic in class-based networks

En aquesta tesi proposem dos esquemes de xarxa amb control d'admissió per al trànsit elàstic TCP amb mecanismes senzills. Ambdós esquemes són capaços de proporcionar throughputs diferents i aïllament entre fluxos, on un "flux" es defineix com una seqüència de paquets relacionats dins d'una connexió TCP. Quant a l'arquitectura, ambdós fan servir classes de paquets amb diferents prioritats de descart, i un control d'admissió implícit, edge-to-edge i basat en mesures. En el primer esquema, les mesures són per flux, mentre que en el segon, les mesures són per agregat. El primer esquema aconsegueix un bon rendiment fent servir una modificació especial de les fonts TCP, mentre que el segon aconsegueix un bon rendiment amb fonts TCP estàndard. Ambdós esquemes han estat avaluats satisfactòriament a través de simulació en diferents topologies de xarxa i càrregues de trànsit.