8,756 research outputs found
A short curriculum of the robotics and technology of computer lab
Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23
researchers that carry out their teaching and researching labor at the Escuela
Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería
Informática (Computer Engineering School). The main research fields are: a)
Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes,
c) Embedded and real-time systems, d) Parallel and massive processing computer
architecture, d) Information Technologies for rehabilitation, handicapped and elder
people, e) Web accessibility and usability
In this paper, the Lab history is presented and its main publications and research
projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo
multidisciplinar de 23 investigadores que realizan su labor docente e investigadora
en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las
principales líneas de investigaciones son: a) Robótica industrial y móvil. b)
Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas
empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo.
e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las
personas mayores. f) Usabilidad y accesibilidad Web.
En este artículo se reseña la historia del grupo y se resumen las principales
publicaciones y proyectos que ha conseguido en los últimos años
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
Statistical analysis of chemical computational systems with MULTIVESTA and ALCHEMIST
The chemical-oriented approach is an emerging paradigm for programming the behaviour of densely distributed and context-aware devices (e.g. in ecosystems of displays tailored to crowd steering, or to obtain profile-based coordinated visualization). Typically, the evolution of such systems cannot be easily predicted, thus making of paramount importance the availability of techniques and tools supporting prior-to-deployment analysis. Exact analysis techniques do not scale well when the complexity of systems grows: as a consequence, approximated techniques based on simulation assumed a relevant role. This work presents a new simulation-based distributed tool addressing the statistical analysis of such a kind of systems, which has been obtained by chaining two existing tools: MultiVeStA and Alchemist. The former is a recently proposed lightweight tool which allows to enrich existing discrete event simulators with distributed statistical analysis capabilities, while the latter is an efficient simulator for chemical-oriented computational systems. The tool is validated against a crowd steering scenario, and insights on the performance are provided by discussing how these scale distributing the analysis tasks on a multi-core architecture
GUBS, a Behavior-based Language for Open System Dedicated to Synthetic Biology
In this article, we propose a domain specific language, GUBS (Genomic Unified
Behavior Specification), dedicated to the behavioral specification of synthetic
biological devices, viewed as discrete open dynamical systems. GUBS is a
rule-based declarative language. By contrast to a closed system, a program is
always a partial description of the behavior of the system. The semantics of
the language accounts the existence of some hidden non-specified actions
possibly altering the behavior of the programmed device. The compilation
framework follows a scheme similar to automatic theorem proving, aiming at
improving synthetic biological design safety.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
On the Dynamic Evolution of Distributed Computational Aggregates
Engineering and programming approaches for collective adaptive systems often leverage ensemble-like abstractions
to characterise a subset of devices as a domain for a given
task or computation. In this paper, we address the problem of
programming the dynamic evolution of distributed computational
aggregates, through neighbour-based coordination. This is a
problem of interest, since several situated activities (especially
in large-scale settings) require decentralised collaboration, and
need to be sustained by limited subsets of devices. These subsets
may vary dynamically due to delegation, completion of local
contributions, exhaustion of resources, failure, or change in the
device set induced by the openness of system boundaries.
In order to study and develop how distributed aggregates
progressively take form by local coordination, we build on the
field-based framework of aggregate processes, and extend it
with techniques to support more expressive evolution dynamics.
We propose novel algorithms for more effective propagation
and closure of the boundaries of dynamic aggregates, based on
statistics on the information speed and a notion of progressive
closure through wave-like propagation. We verify the proposed
techniques by simulation of a paradigmatic case study of multihop message delivery in mobile settings, and show increased
performance and success rate with respect to previous work
Analysis of Routing Algorithms based on the Natural Inspiration
Nature is a great and immense source of inspiration for solving hard and complex problems in computer science since it exhibits extremely diverse, dynamic, robust, complex and fascinating phenomenon. Nature inspired algorithms are metaheuristics that mimics the nature for solving optimisation problems opening a new era in computation. A new agent-based routing algorithm using optimisation techniques is implemented in this paper. The different optimisation techniques are warty frog fish, artificial ant, ant, ant lion, grey wolf, genetic algorithm (GA) are the combinations used in the packet delivery between the networks. The routing is a process of carrying the data from source to destination in the network. The output of these algorithms is determined by the simulation time. The experiments are implemented with the NS2 software platform, which is based on the basics of C, C++ and TCL scripting language. The results of the algorithm showed that the grey wolf optimiser (GWO) is much better than the other algorithms in the packet delivery between the networks
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