Team Members

Ahmed Hany

Team Leader

Ahmed Ehab

Team Member

Omar Sherif

Team Member

Omar Saeed

Team Member


Khaled Hussein


Haytham Metawie

Teaching Assistant


An E-nose is a collection of gas or chemical sensors that mimic the human nose. When compared to standard detection approaches, the e-nose has demonstrated tremendous potential and value in enhancing assessments of food quality parameters. Because of the COVID-19 outbreak, more light has been thrown on this device recently. The purpose of this paper is to suggest a new system that detects gas leaks, smoke or early fires, spoiled and rotten foods, dangerous chemicals and acts as an Air-Quality Control System. Using an array of gas sensors, as well as a motion sensor each of which distinguishes a certain fragrance or has distinct functionality. The analog pins of these sensors are linked to the Arduino-Uno pins, which convey the signals to an ADC module. Furthermore, in order to handle this data, it must go through two phases: a pre-processing phase and feature selection, followed by a processing phase to conclude the data processing, categorize odors and identify movement. Finally, the system is integrated with an Android application to send alerts whenever an abnormal gas behaviour is detected.

System Objectives

Our main objective is to construct an E-Nose that guards Anosmia-Suffering patients, along with other applications.

The system aims to encounter any kind of harmful gases that could put the subjected patient or one of his own at any kind of risk by:

• Detecting the presence of smoke or fire by detecting Natural Gas, LPG Gas, Carbon Monoxide,

Flammable Gas, and Hydrogen.

• Detecting spoiled or rotten food by detecting Volatile Organic Compounds, Methane, Alcohol, and Carbon Monoxide.

• Detecting In-house Gas Leaks by detecting LPG, Flammable Gas, and Natural Gases.

• Detecting Harmful Chemicals.

• Detecting the surrounding Air-Quality

System Scope

The project varies between 3 different scopes:


For Anosmia-Suffering Patients, elderly people, and homes that contain young children by detecting harmful gases.


Giving a hand within the Agriculture-field to minimize the percentage error, by Automating the decision time of the fruits and vegetables picking procedure to reduce the percentage error of unripped/over ripped fruits and vegetables.


Aiming to detect several diseases threatening people’s well-being, especially Anosmic people, by classifying dangerous diseases whether they are psychological as ’Schizophrenia’

or physical as ’Azotemia’.

Documents and Presentations


You will find here the documents and presentation for our proposal.




You will find here the documents and presentation for our SRS.




You will find here the documents and presentation for our SDD.




You will find here the documents and presentation for our Thesis