Problemas en la implementación de algoritmos de routing de alta complejidad en dispositivos móviles: el caso Itiner@

La aparición de terminales de telefonía móvil cada vez más potentes abre un nuevo abanico de posibilidades en cuanto a usos y aplicaciones. Sin embargo, y dadas las limitaciones tanto de memoria como de CPU que tienen estos dispositivos, algunas de las aplicaciones potenciales resultan muy difíciles o incluso imposibles de llevar a la práctica. Este es el caso, por ejemplo, de aplicaciones de cálculo de rutas. En el contexto del proyecto Itiner@, un asistente para rutas turísticas completamente autónomo que debe funcionar incluso sin conexión a Internet, todos los procesos deben ejecutarse íntegramente de forma local en el dispositivo móvil. Dado que es un proyecto orientado al ocio, es importante que la experiencia del usuario sea satisfactoria, por lo que además de poder ejecutar el algoritmo de cálculo de rutas, el sistema debe hacerlo de forma rápida. En este sentido, los algoritmos recursivos habituales son demasiado costosos o lentos para su uso en Itiner@ y ha sido necesario reinventar este tipo de algoritmos en función de las limitaciones que tienen estos dispositivos. En el presente trabajo se presenta el proceso seguido y las dificultades encontradas para implementar un algoritmo recursivo de cálculo de rutas que se ejecute íntegramente en un dispositivo móvil Android de forma eficiente. Así, finalmente se llega a un algoritmo recursivo de cálculo de rutas para dispositivos móviles que se ejecuta de forma más eficiente frente a algoritmos directamente portados a dispositivos móviles. La principal contribución del trabajo es doble: por un lado ofrece algunas guías útiles al desarrollo de algoritmos más eficientes para dispositivos móviles; y por el otro, muestra un algoritmo de cálculo de rutas que funciona con un tiempo de respuesta aceptable, en un entorno exigente, como es el de las aplicaciones de turismo en móviles

Multi-layer survivability: routing schemes for GMPLS-based networks

En les xarxes IP/MPLS sobre WDM on es transporta gran quantitat d'informacio, la capacitat de garantir que el trafic arriba al node de desti ha esdevingut un problema important, ja que la fallada d'un element de la xarxa pot resultar en una gran quantitat d'informacio perduda. Per garantir que el trafic afectat per una fallada arribi al node desti, s'han definit nous algoritmes d'encaminament que incorporen el coneixement de la proteccio en els dues capes: l'optica (WDM) i la basada en paquets (IP/MPLS). D'aquesta manera s'evita reservar recursos per protegir el trafic a les dues capes. Els nous algoritmes resulten en millor us dels recursos de la xarxa, ofereixen rapid temps de recuperacio, eviten la duplicacio de recursos i disminueixen el numero de conversions del trafic de senyal optica a electrica.

Congestion probability routing in virtual path ATM network

This dissertation focuses on the problem of providing mechanisms for routing point to point and multipoint connections in ATM networks. In general the notion of multipoint connection refers to connections that involve a group of users with more than two members. The main objective of this dissertation is to contribute to design efficient routing protocols with alterative routes in fully connected VP-based ATM Networks for call establishment of point to point and multipoint VC connections. An efficient route should be computed during this connection establishment phase.

Adaptive constraints for feature tracking

In this paper extensions to an existing tracking algorithm are described. These extensions implement adaptive tracking constraints in the form of regional upper-bound displacements and an adaptive track smoothness constraint. Together, these constraints make the tracking algorithm more flexible than the original algorithm (which used fixed tracking parameters) and provide greater confidence in the tracking results. The result of applying the new algorithm to high-resolution ECMWF reanalysis data is shown as an example of its effectiveness.

A cell by cell anisotropic adaptive mesh ALE scheme for the numerical solution of the Euler equations

In this paper a cell by cell anisotropic adaptive mesh technique is added to an existing staggered mesh Lagrange plus remap finite element ALE code for the solution of the Euler equations. The quadrilateral finite elements may be subdivided isotropically or anisotropically and a hierarchical data structure is employed. An efficient computational method is proposed, which only solves on the finest level of resolution that exists for each part of the domain with disjoint or hanging nodes being used at resolution transitions. The Lagrangian, equipotential mesh relaxation and advection (solution remapping) steps are generalised so that they may be applied on the dynamic mesh. It is shown that for a radial Sod problem and a two-dimensional Riemann problem the anisotropic adaptive mesh method runs over eight times faster.

Adaptive project planning and management based on best practices - COBRA system

Planning a project with proper considerations of all necessary factors and managing a project to ensure its successful implementation will face a lot of challenges. Initial stage in planning a project for bidding a project is costly, time consuming and usually with poor accuracy on cost and effort predictions. On the other hand, detailed information for previous projects may be buried in piles of archived documents which can be increasingly difficult to learn from the previous experiences. Project portfolio has been brought into this field aiming to improve the information sharing and management among different projects. However, the amount of information that could be shared is still limited to generic information. This paper, we report a recently developed software system COBRA to automatically generate a project plan with effort estimation of time and cost based on data collected from previous completed projects. To maximise the data sharing and management among different projects, we proposed a method of using product based planning from PRINCE2 methodology. (Automated Project Information Sharing and Management System -�COBRA) Keywords: project management, product based planning, best practice, PRINCE2

A cell by cell anisotropic adaptive mesh ALE method

A cell by cell anisotropic adaptive mesh Arbitrary Lagrangian Eulerian (ALE) method for the solution of the Euler equations is described. An efficient approach to equipotential mesh relaxation on anisotropically refined meshes is developed. Results for two test problems are presented.

Predicting mesh density for adaptive modelling of the global atmosphere

The shallow water equations are solved using a mesh of polygons on the sphere, which adapts infrequently to the predicted future solution. Infrequent mesh adaptation reduces the cost of adaptation and load-balancing and will thus allow for more accurate mapping on adaptation. We simulate the growth of a barotropically unstable jet adapting the mesh every 12 h. Using an adaptation criterion based largely on the gradient of the vorticity leads to a mesh with around 20 per cent of the cells of a uniform mesh that gives equivalent results. This is a similar proportion to previous studies of the same test case with mesh adaptation every 1–20 min. The prediction of the mesh density involves solving the shallow water equations on a coarse mesh in advance of the locally refined mesh in order to estimate where features requiring higher resolution will grow, decay or move to. The adaptation criterion consists of two parts: that resolved on the coarse mesh, and that which is not resolved and so is passively advected on the coarse mesh. This combination leads to a balance between resolving features controlled by the large-scale dynamics and maintaining fine-scale features.

Right ventromedial and dorsolateral prefrontal cortices mediate adaptive decisions under ambiguity by integrating choice utility and outcome evaluation.

The Iowa gambling task (IGT) is one of the most influential behavioral paradigms in reward-related decision making and has been, most notably, associated with ventromedial prefrontal cortex function. However, performance in the IGT relies on a complex set of cognitive subprocesses, in particular integrating information about the outcome of choices into a continuously updated decision strategy under ambiguous conditions. The complexity of the task has made it difficult for neuroimaging studies to disentangle the underlying neurocognitive processes. In this study, we used functional magnetic resonance imaging in combination with a novel adaptation of the task, which allowed us to examine separately activation associated with the moment of decision or the evaluation of decision outcomes. Importantly, using whole-brain regression analyses with individual performance, in combination with the choice/outcome history of individual subjects, we aimed to identify the neural overlap between areas that are involved in the evaluation of outcomes and in the progressive discrimination of the relative value of available choice options, thus mapping the two fundamental cognitive processes that lead to adaptive decision making. We show that activation in right ventromedial and dorsolateral prefrontal cortex was predictive of adaptive performance, in both discriminating disadvantageous from advantageous decisions and confirming negative decision outcomes. We propose that these two prefrontal areas mediate shifting away from disadvantageous choices through their sensitivity to accumulating negative outcomes. These findings provide functional evidence of the underlying processes by which these prefrontal subregions drive adaptive choice in the task, namely through contingency-sensitive outcome evaluation.

Mediating between hearer's and speaker's views in the generation of adaptive explanations

In this article, we examine the case of a system that cooperates with a “direct” user to plan an activity that some “indirect” user, not interacting with the system, should perform. The specific application we consider is the prescription of drugs. In this case, the direct user is the prescriber and the indirect user is the person who is responsible for performing the therapy. Relevant characteristics of the two users are represented in two user models. Explanation strategies are represented in planning operators whose preconditions encode the cognitive state of the indirect user; this allows tailoring the message to the indirect user's characteristics. Expansion of optional subgoals and selection among candidate operators is made by applying decision criteria represented as metarules, that negotiate between direct and indirect users' views also taking into account the context where explanation is provided. After the message has been generated, the direct user may ask to add or remove some items, or change the message style. The system defends the indirect user's needs as far as possible by mentioning the rationale behind the generated message. If needed, the plan is repaired and the direct user model is revised accordingly, so that the system learns progressively to generate messages suited to the preferences of people with whom it interacts.