On sparsity averaging

Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regularization method for compressive imaging in the context of compressed sensing with coherent redundant dictionaries. The approach relies on the observation that natural images exhibit strong average sparsity over multiple coherent frames. The associated reconstruction algorithm, based on an analysis prior and a reweighted $\ell_1$ scheme, is dubbed Sparsity Averaging Reweighted Analysis (SARA). We review these advances and extend associated simulations establishing the superiority of SARA to regularization methods based on sparsity in a single frame, for a generic spread spectrum acquisition and for a Fourier acquisition of particular interest in radio astronomy.Comment: 4 pages, 3 figures, Proceedings of 10th International Conference on Sampling Theory and Applications (SampTA), Code available at https://github.com/basp-group/sopt, Full journal letter available at http://arxiv.org/abs/arXiv:1208.233

Planning and implementation of effective collaboration in construction projects

The 21st century is now seen as the time for the construction industry to embrace new ways of working if it is to continue to be competitive and meet the needs of its ever demanding clients. Collaborative working is considered by many to be essential if design and construction teams are to consider the whole lifecycle of the construction product. Much of the recent work on collaborative working has focused on the delivery of technological solutions with a focus on web (extranets), CAD (visualisation), and knowledge management technologies. However, it is now recognised that good collaboration does not result from the implementation of information technology solutions alone. The organisational and people issues, which are not readily solved by pure technical systems, need to be resolved. However, approaches that exclusively focus on organisational and people issues will not reap the benefits derived from the use of technology, especially in the context of distributed teams which are the norm in construction. Work currently being undertaken at Loughborough University aims to bring together the benefits enabled by the technology, with the organisational, and its people issues to provide a framework enabling high level strategic decisions to be made to implement effective collaboration. This paper reports on the initial stages of the project: the background to the project, the methodology used, and findings from the literature survey and the requirements capture survey conducted as part of the project

PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging

In recent works, compressed sensing (CS) and convex opti- mization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest contributions [1, 2, 3], which leverage the versatility of convex optimization to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to significant acceleration of the reconstruction and high-dimensional data scalability. The new algorithmic structure promoted in a new software PURIFY (beta version) relies on the simultaneous-direction method of multipliers (SDMM). The performance of various sparsity priors is evaluated through simulations in the continuous visibility setting, confirming the superiority of our recent average sparsity approach SARA