Parallel and distributed computing with Java

The Java language first came to public attention in 1995. Within a year, it was being speculated that Java may be a good language for parallel and distributed computing. Its core features, including being objected oriented and platform independence, as well as having built-in network support and threads, has encouraged this view. Today, Java is being used in almost every type of computer-based system, ranging from sensor networks to high performance computing platforms, and from enterprise applications through to complex research-based.simulations. In this paper the key features that make Java a good language for parallel and distributed computing are first discussed. Two Java-based middleware systems, namely MPJ Express, an MPI-like Java messaging system, and Tycho, a wide-area asynchronous messaging framework with an integrated virtual registry are then discussed. The paper concludes by highlighting the advantages of using Java as middleware to support distributed applications.

Semantic organization to enhance active learning based on distributed multimedia educational resources

In order to organize distributed educational resources efficiently, to provide active learners an integrated, extendible and cohesive interface to share the dynamically growing multimedia learning materials on the Internet, this paper proposes a generic resource organization model with semantic structures to improve expressiveness, scalability and cohesiveness. We developed an active learning system with semantic support for learners to access and navigate through efficient and flexible manner. We learning resources in an efficient and flexible manner. We provide facilities for instructors to manipulate the structured educational resources via a convenient visual interface. We also developed a resource discovering and gathering engine based on complex semantic associations for several specific topics.

Optimal 3D surface reconstruction from multiview photographic images

This paper describes a new method for reconstructing 3D surface using a small number, e.g. 10, of 2D photographic images. The images are taken at different viewing directions by a perspective camera with full prior knowledge of the camera configurations. The reconstructed object's surface is represented a set of triangular facets. We empirically demonstrate that if the viewing directions are uniformly distributed around the object's viewing sphere, then the reconstructed 3D points optimally cluster closely on a highly curved part of the surface and are widely, spread on smooth or fat parts. The advantage of this property is that the reconstructed points along a surface or a contour generator are not undersampled or underrepresented because surfaces or contours should be sampled or represented with more densely points where their curvatures are high. The more complex the contour's shape, the greater is the number of points required, but the greater the number of points is automatically generated by the proposed method Given that the viewing directions are uniformly distributed, the number and distribution of the reconstructed points depend on the shape or the curvature of the surface regardless of the size of the surface or the size of the object.

A flexible monitoring and notification system for distributed resources

Resource monitoring in distributed systems is required to understand the 'health' of the overall system and to help identify particular problems, such as dysfunctional hardware, a faulty, system or application software. Desirable characteristics for monitoring systems are the ability to connect to any number of different types of monitoring agents and to provide different views of the system, based on a client's particular preferences. This paper outlines and discusses the ongoing activities within the GridRM wide-area resource-monitoring project.

Example uses of a learning object

A self study course for learning to program using the C programming language has been developed. A Learning Object approach was used in the design of the course. One of the benefits of the Learning Object approach is that the learning material can be reused for different purposes. 'Me course developed is designed so that learners can choose the pedagogical approach most suited to their personal learning requirements. For all learning approaches a set of common Assessment Learning Objects (ALOs or tests) have been created. The design of formative assessments with ALOs can be carried out by the Instructional Designer grouping ALOs to correspond to a specific assessment intention. The course is non-credit earning, so there is no summative assessment, all assessment is formative. In this paper examples of ALOs and their uses is presented together with their uses as decided by the Instructional Designer and learner. Personalisation of the formative assessment of skills can be decided by the Instructional Designer or the learner using a repository of pre-designed ALOs. The process of combining ALOs can be carried out manually or in a semi-automated way using metadata that describes the ALO and the skill it is designed to assess.

Linear hashtable motion estimation algorithm for distributed video processing

This paper presents a parallel Linear Hashtable Motion Estimation Algorithm (LHMEA). Most parallel video compression algorithms focus on Group of Picture (GOP). Based on LHMEA we proposed earlier [1][2], we developed a parallel motion estimation algorithm focus inside of frame. We divide each reference frames into equally sized regions. These regions are going to be processed in parallel to increase the encoding speed significantly. The theory and practice speed up of parallel LHMEA according to the number of PCs in the cluster are compared and discussed. Motion Vectors (MV) are generated from the first-pass LHMEA and used as predictors for second-pass Hexagonal Search (HEXBS) motion estimation, which only searches a small number of Macroblocks (MBs). We evaluated distributed parallel implementation of LHMEA of TPA for real time video compression.

Impact of transmission errors in enhanced relay-enabled distributed coordination function

This paper analyzes the performance of Enhanced relay-enabled Distributed Coordination Function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 Distributed Coordination Function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

Performance evaluation of enhanced relay-enabled distributed coordination function

In this paper we evaluate the performance of our earlier proposed enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduced energy consumption compared to IEEE 802.11 distributed coordination function (DCF). This is a result of. 1) using relay which helps to increase the throughput and lower overall blocking time of nodes due to faster dual-hop transmission, 2) using dynamic preamble (i.e. using short preamble for the relay transmission) which further increases the throughput and lower overall blocking time and also by 3) reducing unnecessary overhearing (by other nodes not involved in transmission). We evaluate the throughput and energy performance of the ErDCF with different rate combinations. ErDCF (11,11) (ie. R1=R2=11 Mbps) yields a throughput improvement of 92.9% (at the packet length of 1000 bytes) and an energy saving of 72.2% at 50 nodes.

Analysis of enhanced relay-enabled distributed coordination function under transmission errors

This paper analyzes the performance of enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 distributed coordination function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

Using a RESTful messaging and registry system to support a range a distributed applications

Tycho was conceived in 2003 in response to a need by the GridRM [1] resource-monitoring project for a ldquolight-weightrdquo, scalable and easy to use wide-area distributed registry and messaging system. Since Tycho's first release in 2006 a number of modifications have been made to the system to make it easier to use and more flexible. Since its inception, Tycho has been utilised across a number of application domains including widearea resource monitoring, distributed queries across archival databases, providing services for the nodes of a Cray supercomputer, and as a system for transferring multi-terabyte scientific datasets across the Internet. This paper provides an overview of the initial Tycho system, describes a number of applications that utilise Tycho, discusses a number of new utilities, and how the Tycho infrastructure has evolved in response to experience of building applications with it.

Case study: object and ground point separation on a river bank using airborne based LIDAR data

In the past decade, airborne based LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sectors as a reliable and accurate source for land surveying in environmental, engineering and civil applications. Commonly, the first task to investigate LIDAR point clouds is to separate ground and object points. Skewness Balancing has been proven to be an efficient non-parametric unsupervised classification algorithm to address this challenge. Initially developed for moderate terrain, this algorithm needs to be adapted to handle sloped terrain. This paper addresses the difficulty of object and ground point separation in LIDAR data in hilly terrain. A case study on a diverse LIDAR data set in terms of data provider, resolution and LIDAR echo has been carried out. Several sites in urban and rural areas with man-made structure and vegetation in moderate and hilly terrain have been investigated and three categories have been identified. A deeper investigation on an urban scene with a river bank has been selected to extend the existing algorithm. The results show that an iterative use of Skewness Balancing is suitable for sloped terrain.

An asynchronous distributed dynamic channel assignment scheme for dense WLANs

Wireless local area networks (WLANs) have changed the way many of us communicate, work, play and live. Due to its popularity, dense deployments are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable if an effective channel assignment scheme is not used. In this paper, we propose an enhanced asynchronous distributed and dynamic channel assignment scheme that is simple to implement, does not require any knowledge of the throughput function, allows asynchronous channel switching by each access point (AP) and is superior in performance. Simulation results show that our proposed scheme converges much faster than previously reported synchronous schemes, with a reduction in convergence time and channel switches by tip to 73.8% and 30.0% respectively.