A Bayesian wavelet-based multidimensional deconvolution with sub-band emphasis

This paper proposes a new algorithm for waveletbased multidimensional image deconvolution which employs subband-dependent minimization and the dual-tree complex wavelet transform in an iterative Bayesian framework. In addition, this algorithm employs a new prior instead of the popular ℓ1 norm, and is thus able to embed a learning scheme during the iteration which helps it to achieve better deconvolution results and faster convergence. © 2008 IEEE.

The role for low carbon electrification technologies in poverty reduction and climate change strategies: A focus on renewable energy mini-grids with case studies in Nepal, Peru and Kenya

As a potential poverty reduction and climate change strategy, this paper considers the advantages and disadvantages of using renewable energy technologies for rural electrification in developing countries. Although each case must be considered independently, given a reliable fuel source, renewable energy mini-grids powered by biomass gasifiers or micro-hydro plants appear to be the favoured option due to their lower levelised costs, provision of AC power, potential to provide a 24. h service and ability to host larger capacity systems that can power a wide range of electricity uses. Sustainability indicators are applied to three case studies in order to explore the extent to which sustainable welfare benefits can be created by renewable energy mini-grids. Policy work should focus on raising awareness about renewable energy mini-grids, improving institutional, technical and regulatory frameworks and developing innovative financing mechanisms to encourage private sector investments. Establishing joint technology and community engagement training centres should also be encouraged. © 2011 Elsevier Ltd.

Bayesian inference for multidimensional NMR image reconstruction

Reconstruction of an image from a set of projections has been adapted to generate multidimensional nuclear magnetic resonance (NMR) spectra, which have discrete features that are relatively sparsely distributed in space. For this reason, a reliable reconstruction can be made from a small number of projections. This new concept is called Projection Reconstruction NMR (PR-NMR). In this paper, multidimensional NMR spectra are reconstructed by Reversible Jump Markov Chain Monte Carlo (RJMCMC). This statistical method generates samples under the assumption that each peak consists of a small number of parameters: position of peak centres, peak amplitude, and peak width. In order to find the number of peaks and shape, RJMCMC has several moves: birth, death, merge, split, and invariant updating. The reconstruction schemes are tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA.