Compute intensive applications featured as workflows necessitate Heterogeneous Processing Systems (HPS) for attaining high performance to minimize the turnaround time. Efficient scheduling of the workflow tasks is paramount to attain higher potentials of HPS and is a challenging NP-Complete...

"This book provides comprehensive discussions on strategies and techniques for optimizing microprocessor-based electronic system development and examines methods for acquiring improved software and hardware skills. Highlighting innovative concepts across a range of topics, such as serial...

The adoption of cloud computing for engineering and scientific applications is still lagging behind, although many cloud providers today offer powerful computing infrastructure as a service, and enterprises are already making routine use of it. Reasons for this slow adoption are many: complex...

As a typical unsupervised learning method, the TextRank algorithm performs well for large-scale text mining, especially for automatic summarization or keyword extraction. However, TextRank only considers the similarities between sentences in the processes of automatic summarization and neglects...

The Grid has been recognised as the next-generation distributed computing paradigm by seamlessly integrating heterogeneous resources across administrative domains as a single virtual system. There are an increasing number of scientific and business projects that employ Grid computing...

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for...

Flood inundation modelling in developing countries is severely limited by the lack of high resolution terrain data and suitable imagery to map flood extents. This study assessed the predictive uncertainty of modelled flood extents generated from TELEMAC2D model using low-cost, sparse input data...