Prototyping low-cost and flexible vehicle diagnostic systems

Diagnostic systems are software and hardware-based equipment that interoperate with an external monitored system. Traditionally, they have been expensive equipment running test algorithms to monitor physical properties of, e.g., vehicles, or civil infrastructure equipment, among others. As computer hardware is increasingly powerful (whereas its cost and size is decreasing) and communication software becomes easier to program and more run-time efficient, new scenarios are enabled that yield to lower cost monitoring solutions. This paper presents a low cost approach towards the development of a diagnostic systems relying on a modular component-based approach and running on a resource limited embedded computer. Results on a prototype implementation are shown that validate the presented design, its flexibility, performance, and communication latency.This work has been partly funded by the project REM4VSS (TIN2011-28339) and M2C2 (TIN2014-56158-C4-3-P), funded by the Spanish Ministry of Economy and Competitivenes

Message from the General Chairs

REACTION 2013. 2nd International Workshop on Real-time and distributed computing in emerging applications. December 3rd, 2013, Vancouver, Canada

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring

A practical solution for functional reconfiguration of real-time service based applications through partial schedulability

REACTION 2012. 1st International workshop on Real-time and distributed computing in emerging applications. December 4th, 2012, San Juan, Puerto Rico.Timely reconfiguration in distributed real-time systems is a complex problem with many sides to it ranging from system-wide concerns down to the intrinsic non-robust nature of the specific middleware software and the used programming techniques. In an completely open distributed system, it is not possible to achieve time-deterministic functional reconfiguration; the set of possible target configurations that the system can transition to could be extremely large threatening the temporal predictability of the reconfiguration process. Therefore, a set of bounds and limitations to the structure of systems and to their open nature need to be imposed. In this paper, we present the different sides of the problem of reconfiguration. We provide a solution for timely reconfiguration based on reducing the solution space of solutions of partially closed applications; we have enhanced the logic of a middleware for distributed soft real-time applications with the proposed technique. As a result, applications require a limited number of schedulability tests to search for the valid target configuration. We present some results on the actual reduction of the configuration space achieved by our middleware.This work has been partly supported by the iLAND project (ARTEMISJU 100026) funded by the ARTEMIS JTU Call 1 and the Spanish Ministry of Industry (www.iland-artemis.org), ARTISTDesign NoE (IST-2007- 214373) of the EU 7th Framework Programme, and by the Spanish national project REM4VSS (TIN 2011-28339)

Towards a Reconfiguration Service for Distributed Real-Time Java

REACTION 2012. 1st International workshop on Real-time and distributed computing in emerging applications. December 4th, 2012, San Juan, Puerto Rico.Ancient monolithic distributed systems were attached to well-known development practices and offline analysis. Current scenarios are more dynamic, and open, plenty of applications and services which appear and disappear dynamically at runtime. Likewise, these scenarios require taking into account actions that were traditionally addressed offline, this time in an online scenario. This paper contributes a reconfiguration service in the context of distributed real-time Java application as a means to include real-time reconfiguration into next generation real-time Java systems. The paper addresses the integration taking into account changes required in the API and the cost of some reconfiguration strategies.This research was partially supported by the European Commission (ARTIST2 NoE, ST-2004-004527; iLAND ARTEMIS-JU Call 1) and by the Spanish national project REM4VSS (TIN-2011-28339)

Correcting and improving imitation models of humans for Robosoccer agents

Proceeding of: 2005 IEEE Congress on Evolutionary Computation (CEC'05),Edimburgo, 2-5 Sept. 2005The Robosoccer simulator is a challenging environment, where a human introduces a team of agents into a football virtual environment. Typically, agents are programmed by hand, but it would be a great advantage to transfer human experience into football agents. The first aim of this paper is to use machine learning techniques to obtain models of humans playing Robosoccer. These models can be used later to control a Robosoccer agent. However, models did not play as smoothly and optimally as the human. To solve this problem, the second goal of this paper is to incrementally correct models by means of evolutionary techniques, and to adapt them against more difficult opponents than the ones beatable by the human.Publicad