10,293 research outputs found
AltURI: a thin middleware for simulated robot vision applications
Fast software performance is often the focus when developing real-time vision-based control applications for robot simulators. In this paper we have developed a thin, high performance middleware for USARSim and other simulators designed for real-time vision-based control applications. It includes a fast image server providing images in OpenCV, Matlab or web formats and a simple command/sensor processor. The interface has been tested in USARSim with an Unmanned Aerial Vehicle using two control applications; landing using a reinforcement learning algorithm and altitude control using elementary motion detection. The middleware has been found to be fast enough to control the flying robot as well as very easy to set up and use
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A survey of simulation techniques in commerce and defence
Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches
Mesmerizer: A Effective Tool for a Complete Peer-to-Peer Software Development Life-cycle
In this paper we present what are, in our experience, the best
practices in Peer-To-Peer(P2P) application development and
how we combined them in a middleware platform called Mesmerizer. We explain how simulation is an integral part of
the development process and not just an assessment tool.
We then present our component-based event-driven framework for P2P application development, which can be used
to execute multiple instances of the same application in a
strictly controlled manner over an emulated network layer
for simulation/testing, or a single application in a concurrent
environment for deployment purpose. We highlight modeling aspects that are of critical importance for designing and
testing P2P applications, e.g. the emulation of Network Address Translation and bandwidth dynamics. We show how
our simulator scales when emulating low-level bandwidth
characteristics of thousands of concurrent peers while preserving a good degree of accuracy compared to a packet-level
simulator
On properties of modeling control software for embedded control applications with CSP/CT framework
This PROGRESS project (TES.5224) traces a design framework for implementing embedded real-time software for control applications by exploiting its natural concurrency. The paper illustrates the stage of yielded automation in the process of structuring complex control software architectures, modeling controlled mechatronic systems and designing corresponding control laws, simulating them, generating control code out of simulated control strategy and implementing the software system on a (embedded) computer. The gap between the development of control strategies and the procedures of implementing them on chosen hardware targets is going to be overcome
A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce
Nowadays many companies have available large amounts of raw, unstructured
data. Among Big Data enabling technologies, a central place is held by the
MapReduce framework and, in particular, by its open source implementation,
Apache Hadoop. For cost effectiveness considerations, a common approach entails
sharing server clusters among multiple users. The underlying infrastructure
should provide every user with a fair share of computational resources,
ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In
this paper we consider two mathematical programming problems that model the
optimal allocation of computational resources in a Hadoop 2.x cluster with the
aim to develop new capacity allocation techniques that guarantee better
performance in shared data centers. Our goal is to get a substantial reduction
of power consumption while respecting the deadlines stated in the SLAs and
avoiding penalties associated with job rejections. The core of this approach is
a distributed algorithm for runtime capacity allocation, based on Game Theory
models and techniques, that mimics the MapReduce dynamics by means of
interacting players, namely the central Resource Manager and Class Managers
OpenPING: A Reflective Middleware for the Construction of Adaptive Networked Game Applications
The emergence of distributed Virtual Reality (VR) applications
that run over the Internet has presented networked game
application designers with new challenges. In an environment
where the public internet streams multimedia data and is
constantly under pressure to deliver over widely heterogeneous
user-platforms, there has been a growing need that distributed VR
applications be aware of and adapt to frequent variations in their
context of execution. In this paper, we argue that in contrast to
research efforts targeted at improvement of scalability, persistence
and responsiveness capabilities, much less attempts have been
aimed at addressing the flexibility, maintainability and
extensibility requirements in contemporary distributed VR
platforms. We propose the use of structural reflection as an
approach that not only addresses these requirements but also
offers added value in the form of providing a framework for
scalability, persistence and responsiveness that is itself flexible,
maintainable and extensible. We also present an adaptive
middleware platform implementation called OpenPING1 that
supports our proposal in addressing these requirements
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
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