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Your search keyword '"Giulietti, Nicola"' showing total 16 results
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16 results on '"Giulietti, Nicola"'

5. Vision Measurement System for Gender-Based Counting of Acheta domesticus.

Author

Giulietti, Nicola, Castellini, Paolo, Truzzi, Cristina, Ajdini, Behixhe, and Martarelli, Milena

Subjects
*SUSTAINABLE agriculture, *NUTRITIONAL value, *SYSTEMS availability, *IMAGE processing, *SYSTEMS design
Abstract

The exploitation of insects as protein sources in the food industry has had a strong impact in recent decades for many reasons. The emphasis for this phenomenon has its primary basis on sustainability and also to the nutritional value provided. The gender of the insects, specifically Acheta domesticus, is strictly related to their nutritional value and therefore the availability of an automatic system capable of counting the number of Acheta in an insect farm based on their gender will have a strong impact on the sustainability of the farm itself. This paper presents a non-contact measurement system designed for gender counting and recognition in Acheta domesticus farms. A specific test bench was designed and realized to force the crickets to travel inside a transparent duct, across which they were framed by means of a high-resolution camera able to capture the ovipositor, the distinction element between male and female. All possible sources of uncertainty affecting the identification and counting of individuals were considered, and methods to mitigate their effect were described. The proposed method, which achieves 2.6 percent error in counting and 8.6 percent error in gender estimation, can be of significant impact in the sustainable food industry. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Approximating swimming trajectories with RBFs.

Author

De Santis, Giulia, Giulietti, Nicola, Caputo, Alessia, Castellini, Paolo, and Maponi, Pierluigi

Subjects
*DEEP learning, *RADIAL basis functions, *POINT set theory, *SWIMMING, *VIDEO processing, *FISH locomotion
Abstract

We present how to profitably approximate swimming trajectories leveraging Radial Basis Functions (RBFs). The data of these trajectories were obtained by recording athletes of the Deaf Olympic Italian National Team while swimming. In particular, collected videos were processed by U-NET, a deep learning model architecture, resulting in some sets of two-coordinates points of virtual targets. The obtained sets of points describe trajectories that are approximated with RBFs. [ABSTRACT FROM AUTHOR]

Published
2024
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