Titre :	SIMULATION AND CONCEPTION OF MIMO BIOSENSOR FOR DAMAGE LUNGS RUNG CLASSIFICATION.
Type de document :	document multimédia
Auteurs :	Rania Derar, Auteur ; Fatima Djerfaf, Directeur de thèse
Editeur :	Laghouat : Université Amar Telidji - Département de génie électrique
Année de publication :	2024
Importance :	85p.
Accompagnement :	1 CD ROM Optique Némérique
Note générale :	Instrumentation
Langues :	Anglais
Mots-clés :	Biosensors  MIMO device  Metamaterials  Neural networks  COVID-19  Lungs.
Résumé :	In the aftermath of the COVID-19 pandemic, many individuals have suffered from severe health repercussions, including both short and long-term lung damage. While X-rays have traditionally been employed for detecting lung issues, the need for faster and more efficient diagnostic methods is evident. In response to this need, this study introduces a novel split ring resonator (SRR) as Multiple-Input Multiple-Output (MIMO) biosensor. It is designed for the millimeter range, to swiftly and safely detect pneumonia associated to the COVID-19. Operating within the 5G frequency bands (36 GHz to 38 GHz) and leveraging metamaterial technology, this biosensor offers a compact solution for identifying lung abnormalities. By analyzing the water percentage in the lungs, the MIMO biosensor distinguishes the lung damage’s levels. Through extensive neural network classification and MIMO biosensor’s S parameters, a robust model for accurately classifying lung damage is developed. The proposed MIMO biosensor device demonstrates precise detection of affected lung level.
note de thèses :	Memoire de Master en Electronique

SIMULATION AND CONCEPTION OF MIMO BIOSENSOR FOR DAMAGE LUNGS RUNG CLASSIFICATION. [document multimédia] / Rania Derar, Auteur ; Fatima Djerfaf, Directeur de thèse . - Laghouat : Université Amar Telidji - Département de génie électrique, 2024 . - 85p. + 1 CD ROM Optique Némérique.
Instrumentation
Langues : Anglais

Mots-clés :	Biosensors  MIMO device  Metamaterials  Neural networks  COVID-19  Lungs.
Résumé :	In the aftermath of the COVID-19 pandemic, many individuals have suffered from severe health repercussions, including both short and long-term lung damage. While X-rays have traditionally been employed for detecting lung issues, the need for faster and more efficient diagnostic methods is evident. In response to this need, this study introduces a novel split ring resonator (SRR) as Multiple-Input Multiple-Output (MIMO) biosensor. It is designed for the millimeter range, to swiftly and safely detect pneumonia associated to the COVID-19. Operating within the 5G frequency bands (36 GHz to 38 GHz) and leveraging metamaterial technology, this biosensor offers a compact solution for identifying lung abnormalities. By analyzing the water percentage in the lungs, the MIMO biosensor distinguishes the lung damage’s levels. Through extensive neural network classification and MIMO biosensor’s S parameters, a robust model for accurately classifying lung damage is developed. The proposed MIMO biosensor device demonstrates precise detection of affected lung level.
note de thèses :	Memoire de Master en Electronique