Figure 1: Our method captures and renders existing trees from photographs, by estimating opacity in a volume, then generating and displaying view-dependent textures attached to cells of the volume. (a) One of the original photographs of an oak. (b) The α mask used for the opacity estimation. Two cross slices of the resulting opacity are shown in (c). A synthetic image of the original view, using our view-dependent rendering, is shown in (d). Textures are attached to billboards in cells of the volume and are generated based on estimated opacity.

Reconstructing and rendering trees is a challenging problem due to the geometric complexity involved, and the inherent difficulties of capture. In this paper we propose a volumetric approach to capture and render trees with relatively sparse foliage. Photographs of such trees typically have single pixels containing the blended projection of numerous leaves/branches and background. We show how we estimate opacity values on a recursive grid, based on alphamattes extracted from a small number of calibrated photographs of a tree. This data structure is then used to render billboards attached to the centers of the grid cells. Each billboard is assigned a set of view-dependent textures corresponding to each input view. These textures are generated by approximating coverage masks based on opacity and depth from the camera. Rendering is performed using a view-dependent texturing algorithm. The resulting volumetric tree structure has low polygon count, permitting interactive rendering of realistic 3D trees. We illustrate the implementation of our system on several different real trees, and show that we can insert the resulting model in virtual scenes.

We demonstrate a Machine-Learning-based routing module for software-defined networks. By training with the optimal routing solutions of historical traffic traces, the module can classify traffic matrices to provide real-time routing decisions.

Lately, Artificial Intelligence and Machine Learning (ML) have become game-changing technologies due to their ability to generalize from data and infer algorithmic behaviors that consider larger casuistic that humans are able to. In short, these technologies pursue the installation of human-like intelligence to computer tasks so they can overtake different functions. Despite, their implantation and development in many fields is still too early stage, not to mention the requirements and needs they entail.Therefore, the aim of this thesis is to advance in the application of these technologies and for that we will consider an specific field: The Internet Infrastructure. To this aim, contributions focus on two main specific areas, namely cybersecurity and optical WDM networks.On the security side, we propose a new approach for malware detection and application quality assessment that relies in application meta-information, that is, the data describing the application (such as description, category, permissions...) instead of application code. This approach is detailed and validated in two specific applications: ML-based detection of malware and scalable repackaging detection through meta-data semantic clustering.The first application consists on the usage of meta-data as Machine Learning features with a labeled collection of malware applications to detect whether they are malware or not. Resulting algorithms are capable of detecting malware to a good extent in certain conditions, reaching F-score values of nearly 0.9.Arising from the observations from Machine Learning analysis, Antivirus (AV) engines coming from multi-scanner tools are inspected using data analytics and AI technologies aiming at the understanding of their lack of consensus at the detection and categorization levels. The main aim for this study is twofold: advancing on the understanding of AV detection patterns and policies and the improvement multiengine detection by proposing different aggregation and cleaning tools.Initially, AV engine detections are inspected, showing that most engines disagree when detecting malware to the extent of not completely agreeing in the detection of a single application. Moreover, different detection patterns are observed, namely leader, follower and eccentric engines. At the end, an estimation of the risk of malware per application based on Structural Equation models is proposed.On the family side, we propose a lightweight categorization scheme that achieves comparable scores to other alternatives in the literature at a smaller train cost: Sig-natureMiner. Using such system, we normalize and categorize AV signatures into 41 distinct families and three broader categories, namely adware, harmful and unknown. Then, an ML classifier to assign and specific category to unknown malware is proposed with high performance.Another application explored for meta-data is that of repackaging detection. Using similarity clustering, a large collection of unlabeled applications from Google xiv Play are inspected and compared to detect p...

Traffic demand in the access has grown in the last years, and service providers need to upgrade their infrastructure to the latest access standards. While fiber has become the preferred technology of choice in access networks, there are many fibre access technologies available in the market. This poses a challenging question to operators not always easy to answer: how to upgrade? what technology and for how long it will cope with the demands? In this paper we model the traffic forecast in the access for the next decade and analyze possible upgrade scenarios of fibre access networks, concluding which of the NG-PON flavors could better fit the demand.