Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.

Building distributed high availability system

Today’s commercial web sites are under heavy user load and they are expected to be operational and available at all times. Distributed system architectures have been developed to provide a scalable and failure tolerant high availability platform for these web based services. The focus on this thesis was to specify and implement resilient and scalable locally distributed high availability system architecture for a web based service. Theory part concentrates on the fundamental characteristics of distributed systems and presents common scalable high availability server architectures that are used in web based services. In the practical part of the thesis the implemented new system architecture is explained. Practical part also includes two different test cases that were done to test the system's performance capacity.

Exploring the relative DNA contribution of first and second object's users on mock touch DNA mixtures

Contact stains recovered at break-in crime scenes are frequently characterized by mixtures of DNA from several persons. Broad knowledge on the relative contribution of DNA left behind by different users overtime is of paramount importance. Such information might help crime investigators to robustly evaluate the possibility of detecting a specific (or known) individual's DNA profile based on the type and history of an object. To address this issue, a contact stain simulation-based protocol was designed. Fourteen volunteers either acting as first or second object's users were recruited. The first user was required to regularly handle/wear 9 different items during an 8-10-day period, whilst the second user for 5, 30 and 120 min, in three independent simulation sessions producing a total of 231 stains. Subsequently, the relative DNA profile contribution of each individual pair was investigated. Preliminary results showed a progressive increase of the percentage contribution of the second user compared to the first. Interestingly, the second user generally became the major DNA contributor when most objects were handled/worn for 120 min, Furthermore, the observation of unexpected additional alleles will then prompt the investigation of indirect DNA transfer events.

Disseny i implementació d'un clúster high performance computing

Clúster format per una màquina principal HEAD Node més 19 nodes de càlcul de la gama SGI13 Altix14 XE Servers and Clusters, unides en una topologia de màster subordinat, amb un total de 40 processadors Dual Core i aproximadament 160Gb de RAM.

Plataforma "Cloud Computing" per la comunicació entre pares i professors (Saas)

El projecte que es presenta a continuació és el resultat fruit de la detecció d’un problema de comunicació entre les escoles (sobretot de primària) i el pares dels alumnes que hi cursen els estudis, i la necessitat de trobar-hi una solució. Amb aquesta premissa, i tenint en compte que la tecnologia cada vegada ens ofereix més i millors eines per gestionar les nostres necessitats, es porta a terme la construcció d’un servei al núvol (Saas) capaç de cobrir aquestes necessitats i de fer-ho de la manera més senzilla i eficient possible. La plataforma Aula és un servei on tant professors com pares dels alumnes poden comunicar-se i consultar la informació referent als seus alumnes (en el cas del professors), i dels seus fills (en el cas dels pares/tutors). La solució adoptada ha de ser capaç de funcionar sobre qualsevol aparell (ordinador, tablet o mòbil) i en qualsevol lloc on hi hagi connexió a Internet. No s’ha de realitzar cap tipus d’instal·lació un cop el sistema estigui funcionant, i s’hi podran registrar tants centres com siguin necessaris, així com professionals i pares dels alumnes. També s’ha valorat el manteniment mínim del sistema i la seva escalabilitat, per poder fer front a diferents volums de dades. Així doncs, el projecte Aula es presenta com una solució per gestionar el dia a dia dels professors i alumnes, però que no pretén ser substitut de cap altre sistema de gestió que pugui tenir el centre.

Most and Least Preferred Colours Differ According to Object Context : New Insights from an Unrestricted Colour Range

Humans like some colours and dislike others, but which particular colours and why remains to be understood. Empirical studies on colour preferences generally targeted most preferred colours, but rarely least preferred (disliked) colours. In addition, findings are often based on general colour preferences leaving open the question whether results generalise to specific objects. Here, 88 participants selected the colours they preferred most and least for three context conditions (general, interior walls, t-shirt) using a high-precision colour picker. Participants also indicated whether they associated their colour choice to a valenced object or concept. The chosen colours varied widely between individuals and contexts and so did the reasons for their choices. Consistent patterns also emerged, as most preferred colours in general were more chromatic, while for walls they were lighter and for t-shirts they were darker and less chromatic compared to least preferred colours. This meant that general colour preferences could not explain object specific colour preferences. Measures of the selection process further revealed that, compared to most preferred colours, least preferred colours were chosen more quickly and were less often linked to valenced objects or concepts. The high intra- and inter-individual variability in this and previous reports furthers our understanding that colour preferences are determined by subjective experiences and that most and least preferred colours are not processed equally.

Efficient Peer Selection in P2P JXTA-Based Platforms

Peer-reviewed

Combining Generic Programming and Service-Oriented Architectures for the Effective and Timely Development of Complex e-Learning Systems

Peer-reviewed

An Experimental Study on Peer Selection in a P2P Network over PlanetLab

Peer-reviewed

Computing the diameter of Coxeter groups

This thesis addresses the problem of computing the minimal and maximal diameter of the Cayley graph of Coxeter groups. We first present and assert relevant parts of polytope theory and related Coxeter theory. After this, a method of contracting the orthogonal projections of a polytope from Rd onto R2 and R3, d ¸ 3 is presented. This method is the Equality Set Projection algorithm that requires a constant number of linearprogramming problems per facet of the projection in the absence of degeneracy. The ESP algorithm allows us to compute also projected geometric diameters of high-dimensional polytopes. A representation set of projected polytopes is presented to illustrate the methods adopted in this thesis.

Visual SLAM for underwater vehicles using video velocity log and natural landmarks

A visual SLAM system has been implemented and optimised for real-time deployment on an AUV equipped with calibrated stereo cameras. The system incorporates a novel approach to landmark description in which landmarks are local sub maps that consist of a cloud of 3D points and their associated SIFT/SURF descriptors. Landmarks are also sparsely distributed which simplifies and accelerates data association and map updates. In addition to landmark-based localisation the system utilises visual odometry to estimate the pose of the vehicle in 6 degrees of freedom by identifying temporal matches between consecutive local sub maps and computing the motion. Both the extended Kalman filter and unscented Kalman filter have been considered for filtering the observations. The output of the filter is also smoothed using the Rauch-Tung-Striebel (RTS) method to obtain a better alignment of the sequence of local sub maps and to deliver a large-scale 3D acquisition of the surveyed area. Synthetic experiments have been performed using a simulation environment in which ray tracing is used to generate synthetic images for the stereo system

DAI-DEPUR: an integrated and distributed architecture for wastewater treatment plants supervision

The activated sludge process - the main biological technology usually applied towastewater treatment plants (WWTP) - directly depends on live beings (microorganisms), and therefore on unforeseen changes produced by them. It could be possible to get a good plant operation if the supervisory control system is able to react to the changes and deviations in the system and can take thenecessary actions to restore the system’s performance. These decisions are oftenbased both on physical, chemical, microbiological principles (suitable to bemodelled by conventional control algorithms) and on some knowledge (suitable to be modelled by knowledge-based systems). But one of the key problems in knowledge-based control systems design is the development of an architecture able to manage efficiently the different elements of the process (integrated architecture), to learn from previous cases (spec@c experimental knowledge) and to acquire the domain knowledge (general expert knowledge). These problems increase when the process belongs to an ill-structured domain and is composed of several complex operational units. Therefore, an integrated and distributed AIarchitecture seems to be a good choice. This paper proposes an integrated and distributed supervisory multi-level architecture for the supervision of WWTP, that overcomes some of the main troubles of classical control techniques and those of knowledge-based systems applied to real world systems

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

Unsupervised visual object categorization

The number of digital images has been increasing exponentially in the last few years. People have problems managing their image collections and finding a specific image. An automatic image categorization system could help them to manage images and find specific images. In this thesis, an unsupervised visual object categorization system was implemented to categorize a set of unknown images. The system is unsupervised, and hence, it does not need known images to train the system which needs to be manually obtained. Therefore, the number of possible categories and images can be huge. The system implemented in the thesis extracts local features from the images. These local features are used to build a codebook. The local features and the codebook are then used to generate a feature vector for an image. Images are categorized based on the feature vectors. The system is able to categorize any given set of images based on the visual appearance of the images. Images that have similar image regions are grouped together in the same category. Thus, for example, images which contain cars are assigned to the same cluster. The unsupervised visual object categorization system can be used in many situations, e.g., in an Internet search engine. The system can categorize images for a user, and the user can then easily find a specific type of image.