Distributed System for Cognitive Stimulation Over Interactive TV.

Descripción y evaluación de sistema de estimulación cognitiva a través de la TDT orientada a personas con enfermedad de Parkinson, con supervisión por parte de sus terapeutas de forma remota. Abstract: This paper details the full design, implementation, and validation of an e-health service in order to improve the community health care services for patients with cognitive disorders. Specifically, the new service allows Parkinson’s disease patients benefit from the possibility of doing cognitive stimulation therapy (CST) at home by using a familiar device such as a TV set. Its use instead of a PC could be a major advantage for some patients whose lack of familiarity with the use of a PC means that they can do therapy only in the presence of a therapist. For these patients this solution could bring about a great improvement in their autonomy. At the same time, this service provides therapists with the ability to conduct follow-up of therapy sessions via the web,benefiting from greater and easier control of the therapy exercises performed by patients and allowing them to customize new exercises in accordance with the particular needs of each patient. As a result, this kind of CST is considered to be a complement of other therapies oriented to the Parkinson patients. Furthermore, with small changes, the system could be useful for patients with a different cognitive disease such as Alzheimer’s or mild cognitive impairment.

Remarks on methods for the computation of boundary-element integrals by co-ordinate transformation

It is well known that the evaluation of the influence matrices in the boundary-element method requires the computation of singular integrals. Quadrature formulae exist which are especially tailored to the specific nature of the singularity, i.e. log(*- x0)9 Ijx- JC0), etc. Clearly the nodes and weights of these formulae vary with the location Xo of the singular point. A drawback of this approach is that a given problem usually includes different types of singularities, and therefore a general-purpose code would have to include many alternative formulae to cater for all possible cases. Recently, several authors1"3 have suggested a type independent alternative technique based on the combination of standard Gaussian rules with non-linear co-ordinate transformations. The transformation approach is particularly appealing in connection with the p.adaptive version, where the location of the collocation points varies at each step of the refinement process. The purpose of this paper is to analyse the technique in eference 3. We show that this technique is asymptotically correct as the number of Gauss points increases. However, the method possesses a 'hidden' source of error that is analysed and can easily be removed.

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

Distributed orientation agreement in a group of robots

In this article, a method for the agreement of a set of robots on a common reference orientation based on a distributed consensus algorithm is described. It only needs that robots detect the relative positions of their neighbors and communicate with them. Two different consensus algorithms based on the exchange of information are proposed, tested and analyzed. Systematic experiments were carried out in simulation and with real robots in order to test the method. Experimental results show that the robots are able to agree on the reference orientation under certain conditions. Scalability with an increasing number of robots was tested successfully in simulation with up to 49 robots. Experiments with real robots succeeded proving that the proposed method works in reality.

Optoelectronic Properties of InAlN/GaN Distributed Bragg Reflector Heterostructure Examined by Valence

High-resolution monochromated electron energy loss spectroscopy (EELS) at subnanometric spatial resolution and <200 meV energy resolution has been used to assess the valence band properties of a distributed Bragg reflector multilayer heterostructure composed of InAlN lattice matched to GaN. This work thoroughly presents the collection of methods and computational tools put together for this task. Among these are zero-loss-peak subtraction and nonlinear fitting tools, and theoretical modeling of the electron scattering distribution. EELS analysis allows retrieval of a great amount of information: indium concentration in the InAlN layers is monitored through the local plasmon energy position and calculated using a bowing parameter version of Vegard Law. Also a dielectric characterization of the InAlN and GaN layers has been performed through Kramers-Kronig analysis of the Valence-EELS data, allowing band gap energy to be measured and an insight on the polytypism of the GaN layers.

Distributed Orientation Agreement in a Group of Robots

In this article, a method for the agreement of a set of robots on a common reference orientation based on a distributed consensus algorithm is described. It only needs that robots detect the relative positions of their neighbors and communicate with them. Two different consensus algorithms based on the exchange of information are proposed, tested and analyzed. Systematic experiments were carried out in simulation and with real robots in order to test the method. Experimental results show that the robots are able to agree on the reference orientation under certain conditions. Scalability with an increasing number of robots was tested successfully in simulation with up to 49 robots. Experiments with real robots succeeded proving that the proposed method works in reality.

Distributed bees algorithm for task allocation in swarm of robots

In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.

An efficient nonlinear transformation for the numerical computation of the singular integrals appearing in the 2-D Boundary Element Method

We discuss several methods, based on coordinate transformations, for the evaluation of singular and quasisingular integrals in the direct Boundary Element Method. An intrinsec error of some of these methods is detected. Two new transformations are suggested which improve on those currently available.

Extended description of tunnel junctions for distributed modeling of concentrator multi-junction solar cells

One of the key components of highly efficient multi-junction concentrator solar cells is the tunnel junction interconnection. In this paper, an improved 3D distributed model is presented that considers real operation regimes in a tunnel junction. This advanced model is able to accurately simulate the operation of the solar cell at high concentraions at which the photogenerated current surpasses the peak current of the tunnel junctionl Simulations of dual-junction solar cells were carried out with the improved model to illustrate its capabilities and the results have been correlated with experimental data reported in the literature. These simulations show that under certain circumstances, the solar cells short circuit current may be slightly higher than the tunnel junction peak current without showing the characteristic dip in the J-V curve. This behavior is caused by the lateral current spreading toward dark regions, which occurs through the anode/p-barrier of the tunnel junction.

Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff.

A practical approach for outdoors distributed target localization in wireless sensor networks

Wireless sensor networks are posed as the new communication paradigm where the use of small, low-complexity, and low-power devices is preferred over costly centralized systems. The spectra of potential applications of sensor networks is very wide, ranging from monitoring, surveillance, and localization, among others. Localization is a key application in sensor networks and the use of simple, efficient, and distributed algorithms is of paramount practical importance. Combining convex optimization tools with consensus algorithms we propose a distributed localization algorithm for scenarios where received signal strength indicator readings are used. We approach the localization problem by formulating an alternative problem that uses distance estimates locally computed at each node. The formulated problem is solved by a relaxed version using semidefinite relaxation technique. Conditions under which the relaxed problem yields to the same solution as the original problem are given and a distributed consensusbased implementation of the algorithm is proposed based on an augmented Lagrangian approach and primaldual decomposition methods. Although suboptimal, the proposed approach is very suitable for its implementation in real sensor networks, i.e., it is scalable, robust against node failures and requires only local communication among neighboring nodes. Simulation results show that running an additional local search around the found solution can yield performance close to the maximum likelihood estimate.

A distributed end-to-end overload control mechanism for networks of SIP servers.

The Session Initiation Protocol (SIP) is an application-layer control protocol standardized by the IETF for creating, modifying and terminating multimedia sessions. With the increasing use of SIP in large deployments, the current SIP design cannot handle overload effectively, which may cause SIP networks to suffer from congestion collapse under heavy offered load. This paper introduces a distributed end-to-end overload control (DEOC) mechanism, which is deployed at the edge servers of SIP networks and is easy to implement. By applying overload control closest to the source of traf?c, DEOC can keep high throughput for SIP networks even when the offered load exceeds the capacity of the network. Besides, it responds quickly to the sudden variations of the offered load and achieves good fairness. Theoretic analysis and extensive simulations verify that DEOC is effective in controlling overload of SIP networks.

Uniform Distributed Pushdown Automata Systems.

We consider here uniform distributed pushdown automata systems (UDPAS), namely distributed pushdown automata systems having all components identical pushdown automata. We consider here just a single protocol for activating/deactivating components, namely a component stays active as long as it can perform moves, as well as two ways of accepting the input word: by empty stacks (all components have empty stacks) or by final states (all components are in final states), when the input word is completely read. We mainly investigate the computational power of UDPAS accepting by empty stacks and a few decidability and closure properties of the families of languages they define. Some directions for further work and open problems are also discussed.

A multi-agent system with distributed bayesian reasoning for network fault diagnosis

In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented. The final goal is dealing with uncertainty in network diagnosis, but the solution can be of applied in other fields. The validation testbed has been a P2P streaming video service. An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems.

Failure Detectors in Homonymous Distributed Systems (with an Application to Consensus).

This paper is on homonymous distributed systems where processes are prone to crash failures and have no initial knowledge of the system membership (?homonymous? means that several processes may have the same identi?er). New classes of failure detectors suited to these systems are ?rst de?ned. Among them, the classes H? and H? are introduced that are the homonymous counterparts of the classes ? and ?, respectively. (Recall that the pair h?,?i de?nes the weakest failure detector to solve consensus.) Then, the paper shows how H? and H? can be implemented in homonymous systems without membership knowledge (under different synchrony requirements). Finally, two algorithms are presented that use these failure detectors to solve consensus in homonymous asynchronous systems where there is no initial knowledge ofthe membership. One algorithm solves consensus with hH?, H?i, while the other uses only H?, but needs a majority of correct processes. Observe that the systems with unique identi?ers and anonymous systems are extreme cases of homonymous systems from which follows that all these results also apply to these systems. Interestingly, the new failure detector class H? can be implemented with partial synchrony, while the analogous class A? de?ned for anonymous systems can not be implemented (even in synchronous systems). Hence, the paper provides us with the ?rst proof showing that consensus can be solved in anonymous systems with only partial synchrony (and a majority of correct processes).