Estimating the number of nodes in wireless sensor networks

We propose an efficient algorithm to estimate the number of live computer nodes in a network. This algorithm is fully distributed, and has a time-complexity which is independent of the number of computer nodes. The algorithm is designed to take advantage of a medium access control (MAC) protocol which is prioritized; that is, if two or more messages on different nodes contend for the medium, then the node contending with the highest priority will win, and all nodes will know the priority of the winner.

F-LQE: a Fuzzy Link Quality Estimator for wireless sensor networks

Radio Link Quality Estimation (LQE) is a fundamental building block for Wireless Sensor Networks, namely for a reliable deployment, resource management and routing. Existing LQEs (e.g. PRR, ETX, Fourbit, and LQI ) are based on a single link property, thus leading to inaccurate estimation. In this paper, we propose F-LQE, that estimates link quality on the basis of four link quality properties: packet delivery, asymmetry, stability, and channel quality. Each of these properties is defined in linguistic terms, the natural language of Fuzzy Logic. The overall quality of the link is specified as a fuzzy rule whose evaluation returns the membership of the link in the fuzzy subset of good links. Values of the membership function are smoothed using EWMA filter to improve stability. An extensive experimental analysis shows that F-LQE outperforms existing estimators.

Engineering PROFIBUS networks with heterogeneous transmission media

A significant number of process control and factory automation systems use PROFIBUS as the underlying fieldbus communication network. The process of properly setting up a PROFIBUS network is not a straightforward task. In fact, a number of network parameters must be set for guaranteeing the required levels of timeliness and dependability. Engineering PROFIBUS networks is even more subtle when the network includes various physical segments exhibiting heterogeneous specifications, such as bus speed or frame formats, just to mention a few. In this paper we provide underlying theory and a methodology to guarantee the proper operation of such type of heterogeneous PROFIBUS networks. We additionally show how the methodology can be applied to the practical case of PROFIBUS networks containing simultaneously DP (Decentralised Periphery) and PA (Process Automation) segments, two of the most used commercial-off-the-shelf (COTS) PROFIBUS solutions. The importance of the findings is however not limited to this case. The proposed methodology can be generalised to cover other heterogeneous infrastructures. Hybrid wired/wireless solutions are just an example for which an enormous eagerness exists.

Model morphisms (MoMo) to enable language independent information models and interoperable business networks

MSc. Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer Engineering

Distributed Forest Fire Monitoring Using Wireless Sensor Networks

Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire’s shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.

Augmenting spaces and creating interactive experiences using video camera networks

This research addresses the problem of creating interactive experiences to encourage people to explore spaces. Besides the obvious spaces to visit, such as museums or art galleries, spaces that people visit can be, for example, a supermarket or a restaurant. As technology evolves, people become more demanding in the way they use it and expect better forms of interaction with the space that surrounds them. Interaction with the space allows information to be transmitted to the visitors in a friendly way, leading visitors to explore it and gain knowledge. Systems to provide better experiences while exploring spaces demand hardware and software that is not in the reach of every space owner either because of the cost or inconvenience of the installation, that can damage artefacts or the space environment. We propose a system adaptable to the spaces, that uses a video camera network and a wi-fi network present at the space (or that can be installed) to provide means to support interactive experiences using the visitor’s mobile device. The system is composed of an infrastructure (called vuSpot), a language grammar used to describe interactions at a space (called XploreDescription), a visual tool used to design interactive experiences (called XploreBuilder) and a tool used to create interactive experiences (called urSpace). By using XploreBuilder, a tool built of top of vuSpot, a user with little or no experience in programming can define a space and design interactive experiences. This tool generates a description of the space and of the interactions at that space (that complies with the XploreDescription grammar). These descriptions can be given to urSpace, another tool built of top of vuSpot, that creates the interactive experience application. With this system we explore new forms of interaction and use mobile devices and pico projectors to deliver additional information to the users leading to the creation of interactive experiences. The several components are presented as well as the results of the respective user tests, which were positive. The design and implementation becomes cheaper, faster, more flexible and, since it does not depend on the knowledge of a programming language, accessible for the general public.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Structural Analysis of Networks

Network analysis naturally relies on graph theory and, more particularly, on the use of node and edge metrics to identify the salient properties in graphs. When building visual maps of networks, these metrics are turned into useful visual cues or are used interactively to filter out parts of a graph while querying it, for instance. Over the years, analysts from different application domains have designed metrics to serve specific needs. Network science is an inherently cross-disciplinary field, which leads to the publication of metrics with similar goals; different names and descriptions of their analytics often mask the similarity between two metrics that originated in different fields. Here, we study a set of graph metrics and compare their relative values and behaviors in an effort to survey their potential contributions to the spatial analysis of networks.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

TFC : disseny d'aplicacions per a iOS Viral Zombies TD

Joc d'estrategia per a iPhone desenvolupat amb xCode.

TFC Redes de computadores : filtro de páginas web

Aquest treball final de carrera consisteix en la creació d'un complement que afegeix noves característiques a un navegador conegut com Firefox produït i proporcionat pel Projecte Mozilla. Aquest projecte desenvolupa, implementa i promou el programari lliure. El complement consisteix en un filtre de pàgines web a nivell de contingut.

TFC JEE-Gestión de Incidencias

El projecte consisteix en una aplicació per a la recollida i gestió d'incidències informàtiques i manteniment d'una base de dades en la qual puguem guardar les dades de cada equip i les seves respectives avaries.