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.