MOX Reports
The preprint collection of the Laboratory for Modeling and Scientific Computation MOX. It mainly contains works on numerical
analysis and mathematical modeling applied to engineering problems. MOX web site is mox.polimi.it
Found 1249 products
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49/2013 - 10/24/2013
Micheletti, S.
Fast simulations in Matlab for Scientific Computing | Abstract | | We show how the numerical simulation of typical problems found in Scientific Computing can be run efficiently even under the serial Matlab environment. This is made possible by a strong employment of vectorization
and sparse matrix manipulation. Numerical examples based on FEMs on
2D unstructured triangular grids assess the flexibility and efficiency of the
simulation tool, both on simple elliptic problems as well as on the steady
and unsteady incompressible Navier-Stokes equations. Any type of finite
elements, and 1D and 2D quadrature rules can be easily accommodated
within our framework. Emphasis is focused on vectorization programming
and sparse matrix storage and operations, which allow one to obtain very
efficient programs which run in a few minutes on a common notebook.
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48/2013 - 10/23/2013
Simone Palamara, Christian Vergara, Elena Faggiano, Fabio Nobile
An effective algorithm for the generation of patient-specific Purkinje networks in computational electrocardiology | Abstract | | The Purkinje network is responsible for the fast and coordinated distribution
of the electrical impulse in the ventricle that triggers its contraction.
Therefore, it is necessary to model its presence to obtain an accurate
patient-specific model of the ventricular electrical activation. In this paper,
we present an efficient algorithm for the generation of a patient-specific
Purkinje network, driven by measures of the electrical activation acquired
on the endocardium. The proposed method provides a correction of an
initial network, generated by means of a fractal law, and it is based on
the solution of Eikonal problems both in the muscle and in the Purkinje
network. We present several numerical results both in an ideal geometry
with synthetic data and in a real geometry with patient-specific clinical
measures. These results highlight an improvement of the accuracy of the patient-specific Purkinje network with respect to the initial one, also in the
cases of a cross-validation test and of noisy data. |
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46/2013 - 10/08/2013
Marron, J.S.; Ramsay, J.O.; Sangalli, L.M.; Srivastava, A.
Statistics of Time Warpings and Phase Variations | Abstract | | Many methods exist for one dimensional curve registration, and how methods compare has not been made clear in the literature. This special section is a summary of a detailed comparison of a number of major methods, done during a recent workshop. The basis of the comparison was simultaneous analysis of a set of four real data sets, which engendered a high level of informative discussion. Most research groups in this area were represented, and many insights were gained, which are discussed here. The format of this special section is four papers introducing the data, each accompanied by a number of analyses by di erent groups, plus a discussion summary of the lessons learned. |
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45/2013 - 10/08/2013
Sangalli, L.M.; Secchi, P.; Vantini, S.
Analysis of AneuRisk65 data: K-mean Alignment | Abstract | | We describe the k-mean alignment procedure, for the joint alignment and clustering of functional data and we apply it to the analysis of
AneuRisk65 data. Thanks to the efficient separation of the variability in phase variability and within/between clusters amplitude variability, we are able to discriminate subjects having aneurysms in di fferent cerebral districts and identifying diff erent morphological shapes of Inner Carotid Arteries, unveiling a strong association between vessel morphologies and the aneurysmal pathology. |
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44/2013 - 10/07/2013
Sangalli, L.M.; Secchi, P.; Vantini, S.
AneuRisk65: a dataset of three-dimensional cerebral vascular geometries | Abstract | | We describe AneuRisk65 data, obtained from image reconstruction of three-dimensional cerebral angiographies. This dataset was collected
for the study of the aneurysmal pathology, within the AneuRisk Project. It includes the geometrical reconstructions of one of the main cerebral vessels, the Inner Carotid Artery, described in terms of the vessel centreline and of the vessel radius profile. We briefly illustrate the data derivation and processing, explaining various aspects that are of interest for this applied problem, while also discussing the peculiarities
and critical issues concerning the definition of phase and amplitude variabilities for these three-dimensional functional data. |
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43/2013 - 10/06/2013
Patriarca, M.; Sangalli, L.M.; Secchi, P.; Vantini, S.
Analysis of Spike Train Data: an Application of K-mean Alignment | Abstract | | We analyze the spike train data by means of the k-mean alignment algorithm in a double perspective: data as non periodic and data as periodic. In the first analysis, we show that alignment is not needed to identify paths. Indeed, without allowing for warping, we
detect four clusters strongly associated to the four possible paths. In the second analysis, by exploiting the circular nature of data and allowing for shifts, we detect two clusters distinguishing between spike trains presenting higher or lower neuronal activity during the bottom-left/bottom-right movement respectively. In this latter case, the alignment procedure is able to match the four movements across paths. |
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42/2013 - 10/05/2013
Bernardi, M.; Sangalli, L.M.; Secchi, P.; Vantini, S.
Analysis of Juggling Data: an Application of K-mean Alignment | Abstract | | We analyze the juggling data by means of the k-mean alignment algorithm using cycles as the experimental units of the analysis. Allowing for affine warping, we detect two clusters distinguishing between mainly-planar trajectories and trajectories tilted toward the body of the juggler in the lower part of the cycle. In particular we detect an anomalous presence of tilted trajectories among the record third cycles. We also find warping functions to be clustered according to records suggesting that each record is performed at a di fferent pace and thus associated to a diff erent typical cycle-duration. |
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41/2013 - 10/04/2013
Bernardi, M.; Sangalli, L.M.; Secchi, P.; Vantini, S.
Analysis of Proteomics data: Block K-mean Alignment | Abstract | | We analyze the proteomics data introducing a block k-mean alignment procedure. This technique is able to jointly align and cluster the data, accounting appropriately for the block structure of these data, that includes measurement repetitions for each patient. An analysis of area-under-peaks, following the alignment, separates patients who respond and those who do not respond to treatment. |
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