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Your search keyword '"Picariello, Francesco"' showing total 137 results
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137 results on '"Picariello, Francesco"'

2. ST-Segment Anomalies Detection from Compressed Sensing Based ECG Data by Means of Machine Learning

Author

Rosa, Giovanni, Russodivito, Marco, Laudato, Gennaro, Colavita, Angela Rita, De Vito, Luca, Picariello, Francesco, Scalabrino, Simone, Tudosa, Ioan, Oliveto, Rocco, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Roque, Ana Cecília A., editor, Gracanin, Denis, editor, Lorenz, Ronny, editor, Tsanas, Athanasios, editor, Bier, Nathalie, editor, Fred, Ana, editor, and Gamboa, Hugo, editor

Published
2023
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5. Multi-class Detection of Arrhythmia Conditions Through the Combination of Compressed Sensing and Machine Learning

Author

Rosa, Giovanni, Russodivito, Marco, Laudato, Gennaro, Colavita, Angela Rita, Vito, Luca De, Picariello, Francesco, Scalabrino, Simone, Tudosa, Ioan, Oliveto, Rocco, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gehin, Claudine, editor, Wacogne, Bruno, editor, Douplik, Alexandre, editor, Lorenz, Ronny, editor, Bracken, Bethany, editor, Pesquita, Cátia, editor, Fred, Ana, editor, and Gamboa, Hugo, editor

Published
2022
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8. Automatic Classification of Anomalous ECG Heartbeats from Samples Acquired by Compressed Sensing.

Author

Picariello, Enrico, Picariello, Francesco, Tudosa, Ioan, Rajan, Sreeraman, and De Vito, Luca

Subjects
*DISCRETE cosine transforms, *SIGNAL classification, *COMPRESSED sensing, *K-nearest neighbor classification, *AUTOMATIC classification
Abstract

In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Morphometric Measurement of Fish Blood Cell: An Image Processing and Ellipse Fitting Technique

Author

Ahmed, Imran, Balestrieri, Eulalia, Daponte, Pasquale, Imperatore, Roberta, Lamonaca, Francesco, Paolucci, Marina, and Picariello, Francesco

Abstract

The measurement of morphometric parameters of fish erythrocytes (MFEs) has been recognized as a valuable tool for ecological biomonitoring. Conventional methods for measuring MFE greatly depend on manual measurements, leading to a labor-intensive and time-consuming process that relies on human expertise. To overcome these issues, in this article, a solution based on image processing and ellipse fitting to provide an automated framework for the measurement of MFE is presented. The performance of the proposed method is assessed quantitatively by comparing its results with manual measurements performed by an expert biologist. In addition, a comparison of the proposed method with three state-of-the-art methods is presented by providing the statistical analysis of their mean absolute errors. The analysis of results demonstrates the proposed method significantly outperforms existing methods for both cell and nucleus measurement. It notably achieves a lower absolute mean error value of 78 pixels for cell area and 36 pixels for nucleus area, confirming the better performance of the proposed method compared with the others. The experimental results demonstrate that the proposed method can correctly fit the ellipse on both fish blood cells and their nuclei.

Published
2024
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20. A Compressed-Sensing System for Radio Spectrum Monitoring and Localization of Noncooperative Sources

Author

Daponte, Pasquale, De Vito, Luca, Picariello, Francesco, Rapuano, Sergio, Remondini, Enrico, Schettini, Clemente, and Tudosa, Ioan

Abstract

This article presents a prototype system for the monitoring of radio frequency (RF) emissions and their localization. The system is composed of a network of RF spectrum measurement nodes and a command and control center (CCC) which is in charge of collecting the sensed data (i.e., the RF field signature) by the measurement nodes. To reduce the amount of data sent by each measurement node toward the CCC, a compressed-sensing-based algorithm is implemented for this task. A description of: 1) the architecture of the realized distributed measurement system; 2) the hardware implementation of the RF spectrum measurement nodes; and 3) the CCC is given. Evaluation tests that were carried out in laboratory and on-field environments will also be presented showing the capability of the system of online detecting and localizing noncooperative RF emitters.

Published
2024
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34. ATTICUS: Ambient-Intelligent Tele-monitoring and Telemetry for Incepting and Catering Over hUman Sustainability

Author

Laudato, Gennaro, primary, Scalabrino, Simone, additional, Colavita, Angela Rita, additional, Chiacchiari, Quintiliano, additional, D'Orazio, Romolo, additional, Donadelli, Roberto, additional, De Vito, Luca, additional, Picariello, Francesco, additional, Tudosa, Ioan, additional, Malatesta, Raffaele, additional, Gallo, Lolita, additional, and Oliveto, Rocco, additional

Published
2021
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