Abstract
Invariants are constraints on software components which restrict their behavior in some desirable way, but whose maintenance entails some kind of proof obligation discharge. Such constraints may act not only over the input and output domains, as in a purely functional setting, but also over the underlying state space, as in the case of reactive components. This talk introduces an approach for reasoning about invariants which is both compositional and calculational: compositional because it is based on rules which break the complexity of such proof obligations across the structures involved; calculational because such rules are derived thanks to an algebra of invariants encoded in the language of binary relations. A main tool of this approach is the pointfree transform of the predicate calculus, which opens the possibility of changing the underlying mathematical space so as to enable agile algebraic calculation. The development of a theory of invariant preservation requires a broad, but uniform view of computational processes embodied in software components able to take into account data persistence and continued interaction. Such is the plan for this talk: we first introduce such processes as arrows, and then invariants as their types. © 2012 Springer-Verlag.

Abstract
The implementation of a renewed electrical grid is urgent and it must be flexible, reliable, efficient and manageable. In this context, sensors and actuators play an important part, thus being the "nerve cells" of the grid. The ZigBee standard is receiving an increased acceptance by the industry and is experiencing a fast implementation. Hence, several new field tests are presented in this paper in order to study the implementation challenges of different sensor systems, especially wireless ZigBee devices installation approaches in distributed generation sites for monitoring, management and control. Finally, conclusions are duly drawn.

Abstract

Abstract
We address the problem of dynamically switching the geometry of a formation of a number of undistinguishable agents. Given the current and the final desired geometries, there are several possible allocations between the initial and final positions of the agents as well as several combinations for each agent velocity. However, not all are of interest since collision avoidance is enforced. Collision avoidance is guaranteed through an appropriate choice of agent paths and agent velocities. Therefore, given the agent set of possible velocities and initial positions, we wish to find their final positions and traveling velocities such that agent trajectories are apart, by a specified value, at all times. Among all the possibilities we are interested in choosing the one that minimizes a predefined performance criteria, e.g. minimizes the maximum time required by all agents to reach the final geometry. We propose here a dynamic programming approach to solve optimally such problems. © Springer Science+Business Media New York 2012.

Abstract
Maximum-likelihood methods based on models of codon substitution have been widely used to infer positively selected amino acid sites that are responsible for adaptive changes. Nevertheless, in order to use such an approach, software applications are required to align protein and DNA sequences, infer a phylogenetic tree and run the maximum-likelihood models. Therefore, a significant effort is made in order to prepare input files for the different software applications and in the analysis of the output of every analysis. In this paper we present the ADOPS (Automatic Detection Of Positively Selected Sites) software. It was developed with the goal of providing an automatic and flexible tool for detecting positively selected sites given a set of unaligned nucleotide sequence data. An example of the usefulness of such a pipeline is given by showing, under different conditions, positively selected amino acid sites in a set of 54 Coffea putative S-RNase sequences. ADOPS software is freely available and can be downloaded from http://sing.ei.uvigo.es/ADOPS.

Abstract
We address the identification of discrete-time linear parameter varying systems in the state-space form with affine parameter dependence. In previous work, some of the authors have addressed this problem and an iterative algorithm that avoids the curse of dimensionality, inherent to this class of problems, was developed for the identification of multiple input multiple output systems. Although convergence of this algorithm has been assured for white noise sequences, it has also converged for other type of scheduling signals. Never less, its application is still not generalized to every class of scheduling parameters. In this paper, the algorithm is modified in order to identify multiple input single output systems with quasi-stationary scheduling signals. In every iteration, the system is modeled as a linear time invariant system driven by an extended input composed by the measured input, the Kronecker product between this signal and the scheduling parameter and the Kronecker product between the scheduling and the state estimated at the previous iteration. The remaining unknown signals are considered as "noise". Furthermore, the system is decomposed into a "deterministic" system driven by the known inputs and a "stochastic" subsystem driven by noise. The system is identified as a high order autoregressive exogeneous model. In order to whiten the noise, the input/output data is filtered by the inverse noise transfer function and a state-space model is estimated for the "deterministic" subsystem. Then, the output simulated by this system is subtracted from the measurements to obtain the output stochastic component. Finally, the state of the system is estimated using a Kalman filter and a deconvolution technique. Then, the state becomes an entry to the system for the next iteration, after being multiplied by the scheduling parameter. The whole process is repeated until convergence. The algorithm is tested using periodic scheduling signals and compared with other approaches developed by the same authors. © 2012 IFAC.