Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Marco Cococcioni"'
Publikováno v:
Applied Sciences, Vol 14, Iss 10, p 3992 (2024)
The hole-drilling method (HDM) is a common technique used for the determination of residual stresses, especially for metal alloy components, though also for polymers. This technique is usually implemented with strain gages, though other methods for d
Externí odkaz:
https://doaj.org/article/99dd367f6c2f4edbb0d85b17e11e1b2a
Publikováno v:
Mathematics, Vol 11, Iss 7, p 1758 (2023)
One-sided heavy tailed distributions have been used in many engineering applications, ranging from teletraffic modelling to financial engineering. In practice, the most interesting heavy tailed distributions are those having a finite mean and a diver
Externí odkaz:
https://doaj.org/article/aa0a09433dc246ddaecc5ab2bb81ba85
Autor:
Lorenzo Fiaschi, Marco Cococcioni
Publikováno v:
Mathematics, Vol 10, Iss 23, p 4536 (2022)
This work presents a generalized implementation of the infeasible primal-dual interior point method (IPM) achieved by the use of non-Archimedean values, i.e., infinite and infinitesimal numbers. The extended version, called here the non-Archimedean I
Externí odkaz:
https://doaj.org/article/4364e931928a48d98010c5f2c16e4194
Publikováno v:
IEEE Access, Vol 7, Pp 175827-175841 (2019)
Photonic solutions are today a mature industrial reality concerning high speed, high throughput data communication and switching infrastructures. It is still a matter of investigation to what extent photonics will play a role in next-generation compu
Externí odkaz:
https://doaj.org/article/5edb06b8d65844b3b12676e5ea93a8ba
Publikováno v:
Journal of Marine Science and Engineering, Vol 9, Iss 11, p 1175 (2021)
Thanks to the advent of new technologies and higher real-time computational capabilities, the use of unmanned vehicles in the marine domain has received a significant boost in the last decade. Ocean and seabed sampling, missions in dangerous areas, a
Externí odkaz:
https://doaj.org/article/5b2036455c474c399ef62374d9c226aa
Autor:
Lorenzo De Marinis, Marco Cococcioni, Odile Liboiron-Ladouceur, Giampiero Contestabile, Piero Castoldi, Nicola Andriolli
Publikováno v:
Applied Sciences, Vol 11, Iss 13, p 6232 (2021)
Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two
Externí odkaz:
https://doaj.org/article/4a36f20b1e484774ad1feeb477fe9775
Publikováno v:
Sensors, Vol 20, Iss 5, p 1515 (2020)
With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs) by real-time scenarios, there is the need to review information representation. A very challenging path is to employ an encoding that allows a fast processing a
Externí odkaz:
https://doaj.org/article/7f3328bdcd4246bd9f8f9adceba9cf69
Publikováno v:
Sensors, Vol 16, Iss 1, p 28 (2015)
This paper describes an optimal sampling approach to support glider fleet operators and marine scientists during the complex task of planning the missions of fleets of underwater gliders. Optimal sampling, which has gained considerable attention in t
Externí odkaz:
https://doaj.org/article/324b971271024275afb0484edafba19d
Publikováno v:
IEEE Transactions on Emerging Topics in Computing. 10:1898-1908
Nowadays, two groundbreaking factors are emerging in neural networks. Firstly, there is the RISC-V open instruction set architecture (ISA) that allows a seamless implementation of custom instruction sets. Secondly, there are several novel formats for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::440b804db05eea3614c64c6ca0636105
https://doi.org/10.1109/tetc.2021.3120538
https://doi.org/10.1109/tetc.2021.3120538
Autor:
Emilio Paolini, Lorenzo De Marinis, Marco Cococcioni, Luca Valcarenghi, Luca Maggiani, Nicola Andriolli
Publikováno v:
Neural Computing and Applications. 34:15589-15601
Photonics-based neural networks promise to outperform electronic counterparts, accelerating neural network computations while reducing power consumption and footprint. However, these solutions suffer from physical layer constraints arising from the u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::849c447d165c3773be2938d6d84fe493
https://doi.org/10.1007/s00521-022-07243-z
https://doi.org/10.1007/s00521-022-07243-z