A middleware independent grid workflow builder for scientific applications

Grid workflow authoring tools are typically specific to particular workflow engines built into Grid middleware, or are application specific and are designed to interact with specific software implementations. g-Eclipse is a middleware independent Grid workbench that aims to provide a unified abstraction of the Grid and includes a Grid workflow builder to allow users to author and deploy workflows to the Grid. This paper describes the g-Eclipse Workflow Builder and its implementations for two Grid middlewares, gLite and GRIA, and a case study utilizing the Workflow Builder in a Grid user's scientific workflow deployment.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Security, trust and privacy supported by context-aware middleware

Driven by new network and middleware technologies such as mobile broadband, near-field communication, and context awareness the so-called ambient lifestyle will foster innovative use cases in building automation, healthcare and agriculture. In the EU project Hydra1 highlevel security, trust and privacy concerns such as loss of control, profiling and surveillance are considered at the outset. At the end of this project the Hydra middleware development platform will have been designed so as to enable developers to realise secure ambient scenarios especially in the user domains of building automation, healthcare, and agriculture. This paper gives a short introduction to the Hydra project, its user domains and its approach to ensure security by design. Based on the results of a focus group analysis of the building automation domain typical threats are evaluated and their risks are assessed. Then, specific security requirements with respect to security, privacy, and trust are derived in order to incorporate them into the Hydra Security Meta Model. How concepts such as context security, semantic security, and virtualisation support the overall Hydra approach will be introduced and illustrated on the basis of a technical building automation scenario.

Application of auto regressive models of wavelet sub-bands for classifying Terahertz pulse measurements

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined - the classi. cation of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classi. cation, a simple Mahalanobis distance classi. er is used. After feature extraction, classi. cation accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.

Styx grid services: lightweight middleware for efficient scientific workflows

The service-oriented approach to performing distributed scientific research is potentially very powerful but is not yet widely used in many scientific fields. This is partly due to the technical difficulties involved in creating services and workflows and the inefficiency of many workflow systems with regard to handling large datasets. We present the Styx Grid Service, a simple system that wraps command-line programs and allows them to be run over the Internet exactly as if they were local programs. Styx Grid Services are very easy to create and use and can be composed into powerful workflows with simple shell scripts or more sophisticated graphical tools. An important feature of the system is that data can be streamed directly from service to service, significantly increasing the efficiency of workflows that use large data volumes. The status and progress of Styx Grid Services can be monitored asynchronously using a mechanism that places very few demands on firewalls. We show how Styx Grid Services can interoperate with with Web Services and WS-Resources using suitable adapters.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

Auto-associative memory using n-tuple techniques

The use of n-tuple or weightless neural networks as pattern recognition devices has been well documented. They have a significant advantages over more common networks paradigms, such as the multilayer perceptron in that they can be easily implemented in digital hardware using standard random access memories. To date, n-tuple networks have predominantly been used as fast pattern classification devices. The paper describes how n-tuple techniques can be used in the hardware implementation of a general auto-associative network.

Communication mechanisms and middleware for distributed video surveillance

A new generation of advanced surveillance systems is being conceived as a collection of multi-sensor components such as video, audio and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are: the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers) and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronisation. The paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of Real Time Networks/Data Oriented Requirements Implementation Scheme (RTN/DORIS). It also proposes the implementation of these components using the well-known middleware technology Common Object Request Broker Architecture (CORBA). Results using this architecture for video surveillance are presented through an implemented prototype.

Catch-up strategies in the Indian auto components industry: domestic firms’ responses to market liberalization

Market liberalization in emerging-market economies and the entry of multinational firms spur significant changes to the industry/institutional environment faced by domestic firms. Prior studies have described how such changes tend to be disruptive to the relatively backward domestic firms, and negatively affect their performance and survival prospects. In this paper, we study how domestic supplier firms may adapt and continue to perform, as market liberalization progresses, through catch-up strategies aimed at integrating with the industry's global value chain. Drawing on internalization theory and the literatures on upgrading and catch-up processes, learning and relational networks, we hypothesize that, for continued performance, domestic supplier firms need to adapt their strategies from catching up initially through technology licensing/collaborations and joint ventures with multinational enterprises (MNEs) to also developing strong customer relationships with downstream firms (especially MNEs). Further, we propose that successful catch-up through these two strategies lays the foundation for a strategy of knowledge creation during the integration of domestic industry with the global value chain. Our analysis of data from the auto components industry in India during the period 1992–2002, that is, the decade since liberalization began in 1991, offers support for our hypotheses.

Auto ID-Bridging the physical and the digital on construction projects

This book looks at how auto-ID has evolved and how it can be used in the construction industry and across projects from the perspective of all the stakeholders, from owners to design consultants, contractors and the supply chain. It could help to improve efficiency, reduce costs, ensure quality, protect the environment, and enhance safety.