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In this paper we describe our proposal for humanoid robotic system to develop a football team that compete in the Robocup, describing hardware components used and software components developed.

This paper covers a new approach on Cloud Computing for Scientific Data Storage and Processing using a hybrid system with both on-site and on-the-cloud systems. The system analyzes use cases which are not resolved by either one of the single systems themselves.

The interoperability is a general requirement of the public transport information systems. This property permits them the integration with others systems that operate in different contexts. The interoperability is achieved by system components with the capacity to work autonomously in different contexts and using a common conceptual model in order to the components can collaborate together. In this work, a complete conceptual model for public information system is explained; this model permits us to develop intelligent environments where information services are provided for the actors involved in a public transport network. The main goal is provide an interoperability kernel formed by a conceptual model and system resources in order that the information systems that run in these environments have a high level of functional scalability, environmental adaptability and accessibility.

A case of practical use of ubiquitous data management is presented in this paper; specifically the intensive-data management of a ubiquitous environment working in a public transport context. The description of the working principles of the system is made addressing the specific characteristics of the pervasive environment that are: autonomy, mobility, heterogeneity, distribution and lack of a common catalogue and schema, describing how these aspects affect the data management.

The interoperability is the capacity of a system to work autonomously in different contexts, permitting the collaboration with other systems. To achieve this challenge it is necessary for the system to use a common context model. Therefore the context model is a key aspect of the ubiquitous system. In this work, a context model for public transport information system is explained; this model permits us to develop ubiquitous information services in a context of intermodal public transport networks. The main aspects of this context model are described: the ontology, the representation of the context entities and the main data involved. Using this model, ubiquitous information services, with a high level of functional scalability, context adaptability and accessibility, can be developed.

Industrial control applications are developed as ad hoc solutions. These solutions require specific programming and installation, and very often it takes large economic investments. We have developed a system that makes a special effort trying to offer versatility and a solution to a widespread of industrial installations. Our system understands an industrial plant as a compound of sensors, actuators and processes. The user will define its own processes, sensors and actuators. It will also be in his hand the design of the plants visualization diagram. In order to achieve this generalness it is of essence to offer a user interface as friendly as possible. Via a very intuitive user interface the implemented functionalities are far more powerful because the user understand them and makes use of them.

This paper covers some of the most advanced optimization techniques for web servers and web applications applied to a Modular Object Oriented Distance Learning Environment based on PHP 5 and Apache 2.

The main goal of this project is to monitor and control remote sensors from one or more desalination plants. These plants can be found and distributed from each other miles away. All critical parameters must be controlled. In a desalination plant, the installed sensors are the key elements. These sensors feed the system providing the needed information so it could work properly. The plant state is defined by the sensors readings. From this state, we can identify possible anomalies in the normal behavior of the plant, which could cause the activation of different alarms. In order to accomplish these goals, a modular and flexible system has been developed not only for desalination plants but also for any other systems that require controlling remote sensors. The system allows direct access to this remote information through a variety of interfaces (web, SMS). The tier architectural design plays an important role in providing a basic characteristic: its adaptability to different environments. Therefore, adding or changing specific modules can be easily introduced into the modular structure of the system without interfering the core application modules. The application has been develop under Java technologies (J2EE) using Open Source Toolkits.

For a, b > 0, we consider a temporally homogeneous, one-dimensional diffusion process X(t) defined over I = (-b, a), with infinitesimal parameters depending on the sign of X(t). We suppose that, when X(t) reaches the position 0, it is reflected rightward to delta with probability p > 0 and leftward to -delta with probability 1 - p, where delta > 0. It is presented a method to find approximate formulae for the mean exit time from the interval (-b,a), and for the probability of exit through the right end a, generalizing the results of Lefebvre ([1]) holding, in the limit delta -> 0, for asymmetric Brownian motion with drift.

Motion is a key, basic descriptor of our visual experience of the outside world. The lack of motion perception is a devastating illness that leads to death in animals and seriously impaired behavior in humans. Thus, the study of biological basis of motion detection and analysis and the modelling and artificial implementation of those mechanisms has been a fruitful path of science in the last 60 years. Along this paper, the authors make a review of the main models of motion perception that have emerged since the decade of the 60's stress-ing the underlying biological concepts that have inspired most of them and the traditional architectural concepts imprinted in their functionality and design: formal mathematical analysis, strict geometric patterns of neuron-like processors, selectivity of stimulate etc. Traditional approaches are, then, questioned to include “messy” characteristics of real biological systems such as random distribution of neuron-like processors, non homogeneity of neural architecture, sudden failure of processing units and, in general, non deterministic behavior of the system. As a result is interesting to show that reliability of motion analysis, computational cost and extraction of pure geometrical visual descriptors (size and position of moving objects) besides motion are improved in an implemented model.

In this paper we present a working vision system for estimating size, location and motion of an object by using a set of randomly distributed receptive fields on a retina. The approach used here differs from more conventional ones in which the receptive fields are arranged in a geometric pattern. From the input level, computations are performed in parallel in two different channels: one for purely spatial properties, the other for time-space analysis, and are then used at a subsequent level to yield estimates of the size and center of gravity of an object and the speed and direction of motion. Movement analysis refining is implemented by a lateral interaction (spatial) and memory (temporal) schemes in which direction and speed are used to build a trajectory. The different parameters involved (receptive field size, memory weighting function, number of cells) are tested for different speeds and the results compared, yielding new insights on the functioning of the living retina and suggesting ideas for improving the artificial system.

Presynaptic Inhibition (PI) basically consists of the strong suppression of a neuron’s response before the stimulus reaches the synaptic terminals mediated by a second, inhibitory, neuron. It has a long lasting effect, greatly potentiated by the action of anaesthetics, that has been observed in motorneurons and in several other places of nervous systems, mainly in sensory processing. In this paper we will focus on several different ways of modelling the effect of Presynaptic Inhibition(PI) in the visual pathway as well as the different artificial counterparts derived from such modelling, mainly in two directions: the possibility of computing invariant representations against general changes in illumination of the input image impinging the retina (which is equivalent to a low-level non linear information processing filter) and the role of PI as selector of sets of stimulae that have to be derived to higher brain areas, which, in turn, is equivalent to a “higher-level filter” of information, in the sense of “filtering” the possible semantic content of the information that is allowed to reach later stages of processing.

In the present paper attention will be focus on a special class of one-dimensional Markov processes sharing the features of being both Gauss and diffusion. Contributions in such direction have recently made significant progress as proved for instance in [Lo and Hi, 2006] and [Taillefumier and Magnasco, 2010]. In particular, in [Lo and Hi, 2006] a method is provided for determining the FPT probability mass at any preassigned state value, whereas in [Taillefumier and Magnasco, 2010] a sophisticated, subtle and largely time saving procedure is worked out to simulate the sample paths of the process. Our contribution towards the solution of such problems rests on the following circumstances: the proved existence of an integral equation for the FPT pdf ([Buonocore et al., 1987], [Buonocore et al., 2011], [Di Nardo et al., 2001]), the possibility of evaluating FPT density and distribution function at any given time without need to start afresh the computation as time is changed and the implementation of numerical methods, all this working jointly in an integrated fashion, with consequent significantly increased efficiency and reliability.