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Institution

Polytechnic University of Milan

EducationMilan, Italy
About: Polytechnic University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Computer science & Finite element method. The organization has 18231 authors who have published 58416 publications receiving 1229711 citations. The organization is also known as: PoliMi & L-NESS.


Papers
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Proceedings ArticleDOI
01 Jun 2000
TL;DR: The tutorial provides a conceptual framework for code mobility by illustrating a taxonomy of related technologies, architectural paradigms, and applications and applies the concepts developed to a quantitative assessment of the benefits of mobile code technologies and architectures in the network management application domain.
Abstract: The tutorial provides a conceptual framework for code mobility by illustrating a taxonomy of related technologies, architectural paradigms, and applications. As a final case study, the concepts developed in the taxonomy are then applied to a quantitative assessment of the benefits of mobile code technologies and architectures in the network management application domain.

486 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a review and systematize prior work on technological innovation in family firms and to open up an agenda to guide future research into this promising area.
Abstract: The purpose of this article is to review and systematize prior work on technological innovation in family firms and to open up an agenda to guide future research into this promising area. The study shows that family involvement has direct effects on innovation inputs (e.g., R&D expenditures), activities (e.g., leadership in new product development projects), and outputs (e.g., number of new products), as well as moderating effects on the relationships between these steps of technological innovation. The article uses theories applied in family business research (e.g., agency theory) to discuss opportunities for extending technological innovation frameworks by considering family involvement.

481 citations

Journal ArticleDOI
S. Amerio1, Salvatore Amoruso, M. Antonello, P. Aprili, Mario Armenante, F. Arneodo, A. Badertscher, B. Baiboussinov1, M. Baldo Ceolin1, G. Battistoni2, B. Bekman3, P. Benetti4, Elisa Bernardini, M. Bischofberger, A. Borio di Tigliole4, R. Brunetti4, Riccardo Bruzzese, Antonio Bueno5, E. Calligarich4, Mario Campanelli, F. Carbonara, C. Carpanese, D. Cavalli2, F. Cavanna, P. Cennini6, S. Centro1, A. Cesana7, A. Cesana2, Chang Chen8, D. Chen8, D.B. Chen1, Yi-Chun Chen8, Rosalía Cid5, David B. Cline9, K. Cieślik, A. G. Cocco, D. Corti1, Z. Dai, C. De Vecchi4, A. Dabrowska, A. Di Cicco, R. Dolfini4, Antonio Ereditato, Marta Felcini, A. D. Ferella, Arnaud Ferrari2, Arnaud Ferrari6, Federico Ferri, G. Fiorillo, S. Galli, D. García Gámez5, Y. Ge, D. Gibin1, A. Gigli Berzolari4, I. Gil-Botella, Krzysztof M. Graczyk, L. Grandi4, A. Guglielmi1, K. He8, J. Holeczek3, Xiaojing Huang8, Cezary Juszczak, D. Kielczewska10, Jan Kisiel3, T. Kozłowski, H. Kuna-Ciskal, M. Laffranchi, J. Łagoda10, Z. Li8, B. Lisowski9, F. Lu8, J. Ma8, Gianpiero Mangano, G. Mannocchi, M. Markiewicz, A. Martinez de la Ossa5, C. Matthey9, F. Mauri4, D. Mazza, A. Melgarejo5, Alessandro Menegolli4, G. Meng1, M. Messina, Jerzy W. Mietelski, C. Montanari4, Silvia Muraro2, S. Navas-Concha5, M. Nicoletto1, J. A. Nowak, G. Nurzia, C. Osuna5, S. Otwinowski9, Q. Ouyang8, O. Palamara, D. Pascoli1, L. Periale, G. Piano Mortari, A. Piazzoli4, P. Picchi11, F. Pietropaolo1, W. Półchłopek, M. C. Prata4, T. Rancati2, A. Rappoldi4, G.L. Raselli4, J. Rico, E. Rondio, Massimo Rossella4, André Rubbia, C. Rubbia4, Paola Sala2, R. Santorelli, D. A. Scannicchio4, E. Segreto, Youngho Seo9, F. Sergiampietri9, Jan T. Sobczyk, N. Spinelli, J. Stepaniak, R. Sulej12, M. Szeptycka, M. Szarska, M. Terrani2, M. Terrani7, G. C. Trinchero, Raffaele Velotta, Sandro Ventura1, C. Vignoli4, Hui Wang9, Xuan Wang, J. Woo9, G. Xu8, Z. Xu8, X. Yang9, A. Zalewska, J. Zalipska, Chao Zhang8, Q. Zhang8, S. Zhen8, W. Zipper3 
TL;DR: The ICARUS T600 liquid argon (LAr) time projection chamber (TPC) is the largest LAr TPC ever built, with a size of about 500 tons of fully imaging mass as mentioned in this paper.
Abstract: We have constructed and operated the ICARUS T600 liquid argon (LAr) time projection chamber (TPC). The ICARUS T600 detector is the largest LAr TPC ever built, with a size of about 500 tons of fully imaging mass. The design and assembly of the detector relied on industrial support and represents the applications of concepts matured in laboratory tests to the kton scale. The ICARUS T600 was commissioned for a technical run that lasted about 3 months. During this period all the detector features were extensively tested with an exposure to cosmic-rays at surface with a resulting data collection of about 30 000 events. The detector was developed as the first element of a modular design. Thanks to the concept of modularity, it will be possible to realize a detector with several ktons active mass, to act as an observatory for astroparticle and neutrino physics at the Gran Sasso Underground Laboratory and a second-generation nucleon decay experiment. In this paper a description of the ICARUS T600 is given, detailing its design specifications, assembly procedures and acceptance tests. Commissioning procedures and results of the technical run are also reported, as well as results from the off-line event reconstruction.

478 citations

Journal ArticleDOI
TL;DR: The system illustrated in this paper has been designed and developed particularly for automatic and reliable analysis of body movement in various conditions and environments and is based on real-time processing of the TV images to recognize multiple passive markers and compute their coordinates.
Abstract: The system illustrated in this paper has been designed and developed particularly for automatic and reliable analysis of body movement in various conditions and environments. It is based on real-time processing of the TV images to recognize multiple passive markers and compute their coordinates. This performance is achieved by using a special algorithm allowing the recognition of markers only if their shape matches a predetermined "mask." The main feature of the system is a two-level processing architecture, the first of which includes a dedicated peripheral fast processor for shape recognition (FPSR), designed and implemented by using fast VLSI chips. The second level consists of a general purpose computer and provides the overall system with high flexibility. The main characteristics are: no restriction on the number of markers, resolution of one part in 2500, and a 50 Hz sampling rate independent of the number of markers detected. The prototype has been fully developed, and preliminary results obtained from the analysis of several movements are illustrated.

477 citations


Authors

Showing all 18743 results

NameH-indexPapersCitations
Alex J. Barker132127384746
Pierluigi Zotto128119778259
Andrea C. Ferrari126636124533
Marco Dorigo10565791418
Marcello Giroletti10355841565
Luciano Gattinoni10361048055
Luca Benini101145347862
Alberto Sangiovanni-Vincentelli9993445201
Surendra P. Shah9971032832
X. Sunney Xie9822544104
Peter Nijkamp97240750826
Nicola Neri92112241986
Ursula Keller9293433229
A. Rizzi9165340038
Martin J. Blunt8948529225
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023302
2022813
20214,152
20204,301
20193,831
20183,767