A
Alessandro Massaro
Researcher at Istituto Italiano di Tecnologia
Publications - 227
Citations - 1579
Alessandro Massaro is an academic researcher from Istituto Italiano di Tecnologia. The author has contributed to research in topics: Computer science & Antenna (radio). The author has an hindex of 19, co-authored 199 publications receiving 1178 citations. Previous affiliations of Alessandro Massaro include National Research Council & Polytechnic University of Bari.
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Journal ArticleDOI
Design and Characterization of a Nanocomposite Pressure Sensor Implemented in a Tactile Robotic System
Alessandro Massaro,Fabrizio Spano,Aime Lay-Ekuakille,P. Cazzato,Roberto Cingolani,Athanassia Athanassiou +5 more
TL;DR: A new class of optical pressure sensors in a robotic tactile-sensing system based on polydimethylsiloxane (PDMS) and a detailed algorithm for the detection of roughness and shapes by means of a robotic finger is proposed.
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In situ formation and size control of gold nanoparticles into chitosan for nanocomposite surfaces with tailored wettability.
Fabrizio Spano,Alessandro Massaro,Laura Blasi,Mario Malerba,Roberto Cingolani,Athanassia Athanassiou +5 more
TL;DR: The capability of tailoring the hydrophilicity of nanocomposite materials based on natural polymer and biocompatible gold nanoparticles provides new potentialities in microfluidics or lab on chip devices for blood analysis or drugs transport, as well as in scaffold development for preferential cells growth.
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A Portable Optical Sensor for Sea Quality Monitoring
Filippo Attivissimo,Carlo Guarnieri Calo Carducci,Anna Maria Lucia Lanzolla,Alessandro Massaro,Maria Rosaria Vadrucci +4 more
TL;DR: In this article, the authors proposed a low-cost optical sensor for the measurement of chlorophyll fluorescence and turbidity due to scattering for in situ monitoring of trophic status of seawater.
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Accurate Time-Domain Modeling of Reconfigurable Antenna Sensors for Non-Invasive Melanoma Skin Cancer Detection
TL;DR: An enhanced locally conformal finite-difference time-domain procedure, based on the definition of effective material parameters and a suitable normalization of the electromagnetic field-related quantities, is adopted and an insightful understanding of the physical processes responsible for the performance of considered class of devices is achieved.
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LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction
TL;DR: The paper goal is mainly to provide guidelines for the application of L STM neural network in type I and II diabetes prediction adopting automatic procedures, and a percentage improvement of test set accuracy of 6.5% has been observed by applying the LSTM-AR- approach.