A new algorithm for spectral and spatial reconstruction of proton beams from dosimetric measurements

We report on a new algorithm developed for the dosimetric analysis of broad-spectrum, multi-MeV laser-accelerated proton beams. The algorithm allows the reconstruction of the proton beam spectrum from radiochromic film data. This processing technique makes dosimetry measurements a viable alternative to the use of track detectors for spatially and spectrally resolved proton beam analysis. (C) 2003 Elsevier B.V. All rights reserved.

Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually network dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsomous network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise.

A topological algorithm for identification of structural domains of proteins

Background: Identification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure.

A Frequency Domain Algorithm for Wave Separation

We propose a frequency domain adaptive algorithm for
wave separation in wind instruments. Forward and backward travelling waves are obtained from the signals acquired by two microphones placed along the tube, while the
separation ?lter is adapted from the information given by a
third microphone. Working in the frequency domain has a
series of advantages, among which are the ease of design of
the propagation ?lter and its differentiation with respect to
its parameters.
Although the adaptive algorithm was developed as a ?rst
step for the estimation of playing parameters in wind instruments it can also be used, without any modi?cations, for
other applications such as in-air direction of arrival (DOA)
estimation. Preliminary results on these applications will
also be presented.

Convergence of the iterative algorithm for a general Hammerstein system identification

The convergence of the iterative identification algorithm for a general Hammerstein system has been an open problem for a long time. In this paper, it is shown that the convergence can be achieved by incorporating a regularization procedure on the nonlinearity in addition to a normalization step on the parameters.