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Pietro Ferraro

Researcher at National Research Council

Publications -  720
Citations -  14634

Pietro Ferraro is an academic researcher from National Research Council. The author has contributed to research in topics: Digital holography & Holography. The author has an hindex of 61, co-authored 653 publications receiving 12666 citations. Previous affiliations of Pietro Ferraro include Aeritalia & Centre national de la recherche scientifique.

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Proceedings ArticleDOI

Interferometric measurement of film thickness during bubble blowing

TL;DR: In this paper, the authors proposed a digital holography in transmission configuration as an effective method to measure the time-dependent thickness of polymeric films during bubble blowing and designed a complete set of experiments to measure bubble thickness, including the evaluation of the refractive index of the polymer solution.
Journal ArticleDOI

Identification of drug-resistant cancer cells in flow cytometry combining 3D holographic tomography with machine learning

TL;DR: In this paper , the authors used digital holographic flow cytometry to collect images of flowing cells and reconstructed their 3D tomographic phase and extracted meaningful morphometric features from the 3D and 2D phase maps through machine learning methods and finally compared their classification performance.
Journal ArticleDOI

One-step fabrication of free-standing flexible membranes reinforced with self-assembled arrays of carbon nanotubes

TL;DR: In this paper, a single step approach for fabricating free-standing polymer membranes reinforced with arrayed self-assembled carbon nanotubes (CNTs) is described, which self-assembles spontaneously by electrode-free DC dielectrophoresis based on surface charge templates.
Proceedings ArticleDOI

Simultaneous multiplane imaging in digital holographic microscopy

TL;DR: In this paper, a numerical quadratic deformed diffraction grating was introduced to obtain the image of an object at three planes at different depths simultaneously, in order to reconstruct a digital hologram.
Proceedings ArticleDOI

High-accuracy identification of micro-plastics by holographic microscopy enabled support vector machine

TL;DR: A very accurate classifier is obtained using a simple machine learning approach, which does not require a large amount of training data and identifies micro-plastics of various morphology and optical properties over a wide range of characteristic scales.