The indoor localization system using vision for multiple mobile robots

This paper presents the development of an indoor localization system using camera vision. The localization system has a capability to determine 2D coordinate (x, y) for a team of mobile robots, Miabot. The experimental results show that the system outperforms our existing sonar localizer both in accuracy and a precision.

Opportunities for improvements in flocking models

Flocking is the capacity of coherent movement between multiple animals, including birds. Prominent research into flocking is presented. Particle Swarm Optimisation (PSO) has been the prominent result from research into flocking. It is considered that opportunities for further research in flocking exist. With the potential for automated traffic systems, it is concluded that flocking should be reinvestigated for this purpose.

Networks of contextualised ontologies for the Semantic Web

Currently many ontologies are available for addressing different domains. However, it is not always possible to deploy such ontologies to support collaborative working, so that their full potential can be exploited to implement intelligent cooperative applications capable of reasoning over a network of context-specific ontologies. The main problem arises from the fact that presently ontologies are created in an isolated way to address specific needs. However we foresee the need for a network of ontologies which will support the next generation of intelligent applications/devices, and, the vision of Ambient Intelligence. The main objective of this paper is to motivate the design of a networked ontology (Meta) model which formalises ways of connecting available ontologies so that they are easy to search, to characterise and to maintain. The aim is to make explicit the virtual and implicit network of ontologies serving the Semantic Web.

Multi-modal fingerprinting for multimedia assets: optimisation of media security protection in online distribution environments

Since the advent of the internet in every day life in the 1990s, the barriers to producing, distributing and consuming multimedia data such as videos, music, ebooks, etc. have steadily been lowered for most computer users so that almost everyone with internet access can join the online communities who both produce, consume and of course also share media artefacts. Along with this trend, the violation of personal data privacy and copyright has increased with illegal file sharing being rampant across many online communities particularly for certain music genres and amongst the younger age groups. This has had a devastating effect on the traditional media distribution market; in most cases leaving the distribution companies and the content owner with huge financial losses. To prove that a copyright violation has occurred one can deploy fingerprinting mechanisms to uniquely identify the property. However this is currently based on only uni-modal approaches. In this paper we describe some of the design challenges and architectural approaches to multi-modal fingerprinting currently being examined for evaluation studies within a PhD research programme on optimisation of multi-modal fingerprinting architectures. Accordingly we outline the available modalities that are being integrated through this research programme which aims to establish the optimal architecture for multi-modal media security protection over the internet as the online distribution environment for both legal and illegal distribution of media products.

Multi-level Input Fusion in multi-modal systems

Context-aware multimodal interactive systems aim to adapt to the needs and behavioural patterns of users and offer a way forward for enhancing the efficacy and quality of experience (QoE) in human-computer interaction. The various modalities that constribute to such systems each provide a specific uni-modal response that is integratively presented as a multi-modal interface capable of interpretation of multi-modal user input and appropriately responding to it through dynamically adapted multi-modal interactive flow management , This paper presents an initial background study in the context of the first phase of a PhD research programme in the area of optimisation of data fusion techniques to serve multimodal interactivite systems, their applications and requirements.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

Supporting multi-organisational collaborative groups in the public sector

In this paper, the issues that arise in multi-organisational collaborative groups (MOCGs) in the public sector are discussed and how a technology-based group support system (GSS) could assist individuals within these groups. MOCGs are commonly used in the public sector to find solutions to multifaceted social problems. Finding solutions for such problems is difficult because their scope is outside the boundary of a single government agency. The standard approach to solving such problems is collaborative involving a diverse range of stakeholders. Collaborative working can be advantageous but it also introduces its own pressures. Conflicts can arise due to the multiple contexts and goals of group members and the organisations that they represent. Trust, communication and a shared interface are crucial to making any significant progress. A GSS could support these elements.

Running climate models on grids using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them also involves complicated workflows implemented as shell scripts. A new grid middleware system that is well suited to climate modelling applications is presented in this paper. Grid Remote Execution (G-Rex) allows climate models to be deployed as Web services on remote computer systems and then launched and controlled as if they were running on the user's own computer. Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model. G-Rex has a REST architectural style, featuring a Java client program that can easily be incorporated into existing scientific workflow scripts. Some technical details of G-Rex are presented, with examples of its use by climate modellers.

Grid Remote Execution (G-Rex) of Climate Models

G-Rex is light-weight Java middleware that allows scientific applications deployed on remote computer systems to be launched and controlled as if they are running on the user's own computer. G-Rex is particularly suited to ocean and climate modelling applications because output from the model is transferred back to the user while the run is in progress, which prevents the accumulation of large amounts of data on the remote cluster. The G-Rex server is a RESTful Web application that runs inside a servlet container on the remote system, and the client component is a Java command line program that can easily be incorporated into existing scientific work-flow scripts. The NEMO and POLCOMS ocean models have been deployed as G-Rex services in the NERC Cluster Grid, and G-Rex is the core grid middleware in the GCEP and GCOMS e-science projects.

Running Climate Models On The NERC Cluster Grid Using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them usually involves complicated workflows implemented as shell scripts. For example, NEMO (Smith et al. 2008) is a state-of-the-art ocean model that is used currently for operational ocean forecasting in France, and will soon be used in the UK for both ocean forecasting and climate modelling. On a typical modern cluster, a particular one year global ocean simulation at 1-degree resolution takes about three hours when running on 40 processors, and produces roughly 20 GB of output as 50000 separate files. 50-year simulations are common, during which the model is resubmitted as a new job after each year. Running NEMO relies on a set of complicated shell scripts and command utilities for data pre-processing and post-processing prior to job resubmission. Grid Remote Execution (G-Rex) is a pure Java grid middleware system that allows scientific applications to be deployed as Web services on remote computer systems, and then launched and controlled as if they are running on the user's own computer. Although G-Rex is general purpose middleware it has two key features that make it particularly suitable for remote execution of climate models: (1) Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model; (2) The client component is a command-line program that can easily be incorporated into existing model work-flow scripts. G-Rex has a REST (Fielding, 2000) architectural style, which allows client programs to be very simple and lightweight and allows users to interact with model runs using only a basic HTTP client (such as a Web browser or the curl utility) if they wish. This design also allows for new client interfaces to be developed in other programming languages with relatively little effort. The G-Rex server is a standard Web application that runs inside a servlet container such as Apache Tomcat and is therefore easy to install and maintain by system administrators. G-Rex is employed as the middleware for the NERC1 Cluster Grid, a small grid of HPC2 clusters belonging to collaborating NERC research institutes. Currently the NEMO (Smith et al. 2008) and POLCOMS (Holt et al, 2008) ocean models are installed, and there are plans to install the Hadley Centre’s HadCM3 model for use in the decadal climate prediction project GCEP (Haines et al., 2008). The science projects involving NEMO on the Grid have a particular focus on data assimilation (Smith et al. 2008), a technique that involves constraining model simulations with observations. The POLCOMS model will play an important part in the GCOMS project (Holt et al, 2008), which aims to simulate the world’s coastal oceans. A typical use of G-Rex by a scientist to run a climate model on the NERC Cluster Grid proceeds as follows :(1) The scientist prepares input files on his or her local machine. (2) Using information provided by the Grid’s Ganglia3 monitoring system, the scientist selects an appropriate compute resource. (3) The scientist runs the relevant workflow script on his or her local machine. This is unmodified except that calls to run the model (e.g. with “mpirun”) are simply replaced with calls to "GRexRun" (4) The G-Rex middleware automatically handles the uploading of input files to the remote resource, and the downloading of output files back to the user, including their deletion from the remote system, during the run. (5) The scientist monitors the output files, using familiar analysis and visualization tools on his or her own local machine. G-Rex is well suited to climate modelling because it addresses many of the middleware usability issues that have led to limited uptake of grid computing by climate scientists. It is a lightweight, low-impact and easy-to-install solution that is currently designed for use in relatively small grids such as the NERC Cluster Grid. A current topic of research is the use of G-Rex as an easy-to-use front-end to larger-scale Grid resources such as the UK National Grid service.

A Conceptual Model for Group Support Systems in Local Councils

This paper proposes a conceptual model of a context-aware group support system (GSS) to assist local council employees to perform collaborative tasks in conjunction with inter- and intra-organisational stakeholders. Most discussions about e-government focus on the use of ICT to improve the relationship between government and citizen, not on the relationship between government and employees. This paper seeks to expose the unique culture of UK local councils and to show how a GSS could support local government employer and employee needs.

Storm prediction research and its application to the oil/gas industry

The accurate prediction of storms is vital to the oil and gas sector for the management of their operations. An overview of research exploring the prediction of storms by ensemble prediction systems is presented and its application to the oil and gas sector is discussed. The analysis method used requires larger amounts of data storage and computer processing time than other more conventional analysis methods. To overcome these difficulties eScience techniques have been utilised. These techniques potentially have applications to the oil and gas sector to help incorporate environmental data into their information systems

The Geostationary Earth Radiation Budget Project

This paper reports on a new satellite sensor, the Geostationary Earth Radiation Budget (GERB) experiment. GERB is designed to make the first measurements of the Earth's radiation budget from geostationary orbit. Measurements at high absolute accuracy of the reflected sunlight from the Earth, and the thermal radiation emitted by the Earth are made every 15 min, with a spatial resolution at the subsatellite point of 44.6 km (north–south) by 39.3 km (east–west). With knowledge of the incoming solar constant, this gives the primary forcing and response components of the top-of-atmosphere radiation. The first GERB instrument is an instrument of opportunity on Meteosat-8, a new spin-stabilized spacecraft platform also carrying the Spinning Enhanced Visible and Infrared (SEVIRI) sensor, which is currently positioned over the equator at 3.5°W. This overview of the project includes a description of the instrument design and its preflight and in-flight calibration. An evaluation of the instrument performance after its first year in orbit, including comparisons with data from the Clouds and the Earth's Radiant Energy System (CERES) satellite sensors and with output from numerical models, are also presented. After a brief summary of the data processing system and data products, some of the scientific studies that are being undertaken using these early data are described. This marks the beginning of a decade or more of observations from GERB, as subsequent models will fly on each of the four Meteosat Second Generation satellites.