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Your search keyword '"Magli, Enrico"' showing total 6 results
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6 results on '"Magli, Enrico"'

1. Exploring the Potential of Compressive Sensing Payloads for Earth Observation from Geostationary Platforms: An Instrumental Concept for Fire Monitoring

Author

Borrelli, Donato, primary, Baldi, Massimo, additional, Berndt, Dirk, additional, Bertoncini, Lucas, additional, Bianchi, Tiziano, additional, Bischof, Lionel, additional, Borque Gallego, Guzman, additional, Carlà, Roberto, additional, Coppo, Peter, additional, Corti, Chiara, additional, Corti, Francesco, additional, Corti, Marco, additional, Cox, Nick, additional, Dauderstädt, Ulrike A., additional, Dürr, Peter, additional, Franci, Enrico, additional, González, Sara Francés, additional, Gonnelli, Andrea, additional, Guerri, Irene, additional, Guzzi, Donatella, additional, Humbert, Stéphane, additional, Labate, Demetrio, additional, Lamquin, Nicolas, additional, Lastri, Cinzia, additional, Magli, Enrico, additional, Marzi, Emiliano, additional, Migliorati, Andrea, additional, Nardino, Vanni, additional, Pache, Christophe, additional, Palombi, Lorenzo, additional, Piccirillo, Alice Maria, additional, Pilato, Giuseppe, additional, Suetta, Enrico, additional, Taddei, Dario, additional, Valsesia, Diego, additional, Wagner, Michael, additional, and Raimondi, Valentina, additional

Published
2023
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2. Data Simulations of a Compressive Sensing Multispectral Imager in the Mid-Infrared Region and Its Performances for the Monitoring of High-Temperature Events

Author

Guzzi, Donatella, primary, Baldi, Massimo, additional, Bianchi, Tiziano, additional, Buongiorno, Fabrizia, additional, Lastri, Cinzia, additional, Magli, Enrico, additional, Nardino, Vanni, additional, Palombi, Lorenzo, additional, Romaniello, Vito, additional, Scopa, Tiziana, additional, Siciliani de Cumis, Mario, additional, Silvestri, Malvina, additional, Valsesia, Diego, additional, and Raimondi, Valentina, additional

Published
2023
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3. Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis.

Author

Nardino, Vanni, Guzzi, Donatella, Lastri, Cinzia, Palombi, Lorenzo, Coluccia, Giulio, Magli, Enrico, Labate, Demetrio, and Raimondi, Valentina

Subjects
ULTRAVIOLET-visible spectroscopy, SPECTROMETERS, MEDICAL microscopy, SPATIAL light modulators
Abstract

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV–Vis) stellar spectroscopy. The instrument—which is pixel-limited in the entire 300 nm–650 nm spectral range—features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system. [ABSTRACT FROM AUTHOR]

Published
2023
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4. SISSI Project: A Feasibility Study for a Super Resolved Compressive Sensing Multispectral Imager in the Medium Infrared

Author

Lastri, Cinzia, primary, Amato, Gabriele, additional, Baldi, Massimo, additional, Bianchi, Tiziano, additional, Buongiorno, Maria Fabrizia, additional, Corti, Chiara, additional, Corti, Francesco, additional, Corti, Marco, additional, Franci, Enrico, additional, Guzzi, Donatella, additional, Magli, Enrico, additional, Nardino, Vanni, additional, Palombi, Lorenzo, additional, Romaniello, Vito, additional, Scopa, Tiziana, additional, Siciliani De Cumis, Mario, additional, Silvestri, Malvina, additional, Valsesia, Diego, additional, and Raimondi, Valentina, additional

Published
2021
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5. Designing a Compressive Sensing Demonstrator of an Earth Observation Payload in the Visible and Medium Infrared: Instrumental Concept and Main Features

Author

Raimondi, Valentina, primary, Acampora, Luigi, additional, Baldi, Massimo, additional, Berndt, Dirk, additional, Bianchi, Tiziano, additional, Borrelli, Donato, additional, Corti, Chiara, additional, Corti, Francesco, additional, Corti, Marco, additional, Cox, Nick, additional, Dauderstädt, Ulrike A., additional, Dürr, Peter, additional, Fruchi, Alberto, additional, González, Sara Francés, additional, Guzzi, Donatella, additional, Kunze, Detlef, additional, Labate, Demetrio, additional, Lamquin, Nicolas, additional, Lastri, Cinzia, additional, Magli, Enrico, additional, Nardino, Vanni, additional, Pache, Christophe, additional, Palombi, Lorenzo, additional, Pilato, Giuseppe, additional, Pollini, Alexandre, additional, Suetta, Enrico, additional, Taddei, Dario, additional, Taricco, Davide, additional, Valsesia, Diego, additional, and Wagner, Michael, additional

Published
2021
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6. Convolutional Neural Networks for On-Board Cloud Screening.

Author

Ghassemi, Sina and Magli, Enrico

Subjects
*NETWORK performance, *ARTIFICIAL satellites, *ARTIFICIAL neural networks
Abstract

A cloud screening unit on a satellite platform for Earth observation can play an important role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art. [ABSTRACT FROM AUTHOR]

Published
2019
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