Convex approaches to model wavelet sparsity patterns

Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in de-convolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso. © 2011 IEEE.

Behaviour of cast-iron tunnel segmental joint from the 3D FE analyses and development of a new bolt-spring model

The behaviour of cast-iron tunnel segments used in London Underground tunnels was investigated using the 3-D finite element (FE) method. A numerical model of the structural details of cast-iron segmental joints such as bolts, panel and flanges was developed and its performance was validated against a set of full-scale tests. Using the verified model, the influence of structural features such as caulking groove and bolt pretension was examined for both rotational and shear loading conditions. Since such detailed modelling of bolts increases the computational time when a full scale segmental tunnel is analysed, it is proposed to replace the bolt model to a set of spring models. The parameters for the bolt-spring models, which consider the geometry and material properties of the bolt, are proposed. The performance of the combined bolt-spring and solid segmental models are evaluated against a more conventional shell-spring model. © 2014 Elsevier Ltd.