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Institution

Polytechnic University of Turin

EducationTurin, Piemonte, Italy
About: Polytechnic University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Finite element method & Nonlinear system. The organization has 11553 authors who have published 41395 publications receiving 789320 citations. The organization is also known as: POLITO & Politecnico di Torino.


Papers
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Journal ArticleDOI
TL;DR: A robust and physiologically relevant BBB microvascular model offers an innovative and valuable platform for drug discovery to predict neuro-therapeutic transport efficacy in pre-clinical applications as well as recapitulate patient-specific and pathological neurovascular functions in neurodegenerative disease.

420 citations

Journal ArticleDOI
TL;DR: A novel decomposition technique suitable for blind separation of linear mixtures of signals comprising finite-length symbols that approaches Bayesian optimal linear minimum mean square error estimator and is, hence, significantly noise resistant.
Abstract: This paper studies a novel decomposition technique, suitable for blind separation of linear mixtures of signals comprising finite-length symbols. The observed symbols are first modeled as channel responses in a multiple-input-multiple-output (MIMO) model, while the channel inputs are conceptually considered sparse positive pulse trains carrying the information about the symbol arising times. Our decomposition approach compensates channel responses and aims at reconstructing the input pulse trains directly. The algorithm is derived first for the overdetermined noiseless MIMO case. A generalized scheme is then provided for the underdetermined mixtures in noisy environments. Although blind, the proposed technique approaches Bayesian optimal linear minimum mean square error estimator and is, hence, significantly noise resistant. The results of simulation tests prove it can be applied to considerably underdetermined convolutive mixtures and even to the mixtures of moderately correlated input pulse trains, with their cross-correlation up to 10% of its maximum possible value.

417 citations

Journal ArticleDOI
TL;DR: This paper analyzes in detail the GN-model errors and derives a complete set of formulas accounting for all single, cross, and multi-channel effects that constitute the enhanced GN- model (EGN-model), which is found to be very good when assessing detailed span-by-span NLI accumulation and excellent when estimating realistic system maximum reach.
Abstract: The GN-model has been proposed as an approximate but sufficiently accurate tool for predicting uncompensated optical coherent transmission system performance, in realistic scenarios. For this specific use, the GN-model has enjoyed substantial validation, both simulative and experimental. Recently, however, it has been pointed out that its predictions, when used to obtain a detailed picture of non-linear interference (NLI) noise accumulation along a link, may be affected by a substantial NLI overestimation error, especially in the first spans of the link. In this paper we analyze in detail the GN-model errors. We discuss recently proposed formulas for correcting such errors and show that they neglect several contributions to NLI, so that they may substantially underestimate NLI in specific situations, especially over low-dispersion fibers. We derive a complete set of formulas accounting for all single, cross, and multi-channel effects, This set constitutes what we have called the enhanced GN-model (EGN-model). We extensively validate the EGN model by comparison with accurate simulations in several different system scenarios. The overall EGN model accuracy is found to be very good when assessing detailed span-by-span NLI accumulation and excellent when estimating realistic system maximum reach. The computational complexity vs. accuracy trade-offs of the various versions of the GN and EGN models are extensively discussed.

414 citations

Journal ArticleDOI
TL;DR: In this work the quadrature method of moments (QMOM) is tested for size-dependent growth and aggregation and is validated by comparison with both Monte Carlo simulations and analytical solutions using several functional forms for the aggregation kernel.
Abstract: Although use of computational fluid dynamics (CFD) for simulating precipitation (and particulate systems in general) is becoming a standard approach, a number of issues still need to be addressed. One major problem is the computational expense of coupling a standard discretized population balance (DPB) with a CFD code, as this approach requires the solution of an intractably large number of transport equations. In this work the quadrature method of moments (QMOM) is tested for size-dependent growth and aggregation. The QMOM is validated by comparison with both Monte Carlo simulations and analytical solutions using several functional forms for the aggregation kernel. Moreover, model predictions are compared with a DPB to compare accuracy, computational time, and the number of scalars involved. Analysis of the relative performance of various methods for treating aggregation provides readers with useful information about the range of application and possible limitations.

414 citations

Journal ArticleDOI
01 Jun 2012-Energy
TL;DR: In this paper, an overview of the clustering techniques used to establish suitable customer grouping, included in a general scheme for analysing electrical load pattern data, is provided, illustrated and discussed, providing links to relevant literature references.

414 citations


Authors

Showing all 11854 results

NameH-indexPapersCitations
Rodney S. Ruoff164666194902
Silvia Bordiga10749841413
Sergio Ferrara10572644507
Enrico Rossi10360641255
Stefano Passerini10277139119
James Barber10264242397
Markus J. Buehler9560933054
Dario Farina9483232786
Gabriel G. Katul9150634088
M. De Laurentis8427554727
Giuseppe Caire8282540344
Christophe Fraser7626429250
Erasmo Carrera7582923981
Andrea Califano7530531348
Massimo Inguscio7442721507
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023210
2022487
20212,789
20202,969
20192,779
20182,509