ENABLE: a wrist worn device with integrated accessible services to support old people living independently and safely at home

This RTD project, 2007-2009, is partly funded by the European Commission, in Framework Programme 6. It aims to assist elderly people for living well, independently and at case. ENABLE will provide a number of services for elderly people based on the new technology provided by mobile phones. The project is developing a Wrist unit with both integrated and external sensors, and with a radio frequency link to a mobile phone. Dedicated ENABLE software running on the wrist unit and mobile phone makes these services fully accessible for the elderly users. This paper outlines the fundamental motivation and the approach which currently is undertaken in order to collect the more detailed user needs and requirements. The general architecture and the design of the ENABLE system are outlined.

Tycho: a wide-area messaging framework with an integrated virtual registry

In a distributed environment remote entities, usually the producers or consumers of services, need a means to publish their existence so that clients, needing their services, can search and find the appropriate ones that they can then interact with directly. The publication of information is via a registry service, and the interaction is via a high-level messaging service. Typically, separate libraries provide these two services. Tycho is an implementation of a wide-area asynchronous messaging framework with an integrated distributed registry. This will free developers from the need to assemble their applications from a range of potentially diverse middleware offerings, which should simplify and speed application development and more importantly allow developers to concentrate on their own domain of expertise. In the first part of the paper we outline our motivation for producing Tycho and then review a number of registry and messaging systems popular with the Grid community. In the second part of the paper we describe the architecture and implementation of Tycho. In the third part of the paper we present and discuss various performance tests that were undertaken to compare Tycho with alternative similar systems. Finally, we summarise and conclude the paper and outline future work.

Collaborative grid infrastructure for molecular simulations: The eMinerals minigrid as a prototype integrated computer and data grid

This paper describes a prototype grid infrastructure, called the eMinerals minigrid, for molecular simulation scientists. which is based on an integration of shared compute and data resources. We describe the key components, namely the use of Condor pools, Linux/Unix clusters with PBS and IBM's LoadLeveller job handling tools, the use of Globus for security handling, the use of Condor-G tools for wrapping globus job submit commands, Condor's DAGman tool for handling workflow, the Storage Resource Broker for handling data, and the CCLRC dataportal and associated tools for both archiving data with metadata and making data available to other workers.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

Hammerstein model identification algorithm using Bezier-Bernstein approximation

A new identification algorithm is introduced for the Hammerstein model consisting of a nonlinear static function followed by a linear dynamical model. The nonlinear static function is characterised by using the Bezier-Bernstein approximation. The identification method is based on a hybrid scheme including the applications of the inverse of de Casteljau's algorithm, the least squares algorithm and the Gauss-Newton algorithm subject to constraints. The related work and the extension of the proposed algorithm to multi-input multi-output systems are discussed. Numerical examples including systems with some hard nonlinearities are used to illustrate the efficacy of the proposed approach through comparisons with other approaches.