A survey of parallel algorithms for fractal image compression

This paper presents a short survey of the key research work that has been undertaken in the application of parallel algorithms for Fractal image compression. The interest in fractal image compression techniques stems from their ability to achieve high compression ratios whilst maintaining a very high quality in the reconstructed image. The main drawback of this compression method is the very high computational cost that is associated with the encoding phase. Consequently, there has been significant interest in exploiting parallel computing architectures in order to speed up this phase, whilst still maintaining the advantageous features of the approach. This paper presents a brief introduction to fractal image compression, including the iterated function system theory upon which it is based, and then reviews the different techniques that have been, and can be, applied in order to parallelize the compression algorithm

Business Intelligence Solution for an SME: A Case Study.

Business Intelligence (BI) leverages the usefulness of existing information. It equips business users with relevant information to perform various analyses to make key business decisions. Over the last two decades, BI has become a core strategy for the growth of many companies, in particular large corporations. However, studies show that small and medium-sized enterprises (SMEs) lag behind in implementation and exploitation of BI solutions. To stay ahead of the competition, SMEs must be able to monitor and effectively use all of their resources, in particular information resources, to assist them in making important business decisions. In this paper, we examine the challenges such as lack of technical expertise and limited budget when implementing a BI solution within an SME in the UK. In light of our experiences in tackling these issues, we discuss how these challenges can be overcome through applying various tools and strategies and the potential benefits

The North Wyke Farm Platform: Field Survey Data

The North Wyke Farm Platform (NWFP) was established in 2010 to study and improve grassland livestock production at the farm-scale. The NWFP uses a combination of environmental sensors, routine field and lab-based measurements, and detailed management records to monitor livestock and crop production, emissions to water, emissions to air, soil health, and biodiversity. The rich NWFP datasets help researchers to evaluate the effectiveness of different grassland (and arable) farming systems, which in turn, contributes to the development of sustainable, resilient and net zero land management strategies. This document serves as a user guide to the sub-catchment high-resolution (within-field) and low-resolution (field-level) surveys collected on the NWFP. Most surveys are site-wide across all the NWFP fields, while some are specific to a given triplet or small group of NWFP catchments. Low resolution surveys are site wide and are carried out quarterly and routinely as a management tool. This document is associated with other dedicated user guides that detail the design, establishment and development of the NWFP, field events, and the quality control process of datasets

Broad changes in body mass index between age 10 and adulthood are associated with type 2 diabetes risk independently of adult body mass index

 This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Diabetes Research and Wellness FoundationDiabetes UKEuropean Foundation for the Study of Diabete

Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.Comment: 26 pages, 2 figures, accepted in Journal of Computational and Graphical Statistics (http://www.amstat.org/publications/jcgs.cfm