Yousef Osama

IOT Home Light Control:

• Developed a home light control system using NodeMCU and IoT technology
• Utilized Blynk, a software company that provides infrastructure for the Internet of Things, to connect to the NodeMCU via Wi-Fi
• Programmed the system using Arduino IDE and C programming language
• Implemented features such as manual control of lights through a mobile app and automatic feedback of light state in the room using an LDR sensor
• Created a user-friendly interface for controlling the lights through the Blynk mobile app, improving accessibility and convenience for users

Elevator using PIC16f877a microcontroller:

• Developed an elevator system controlled by a PIC16F877A microcontroller and simulated using Proteus software and programmed with MikroC software.
• Features a 3-level elevator with IR sensor and button to call elevator to a floor.
• Includes a 7-segment display for door level and LCD screen for elevator status when moving up or down.
• Uses PWM for motor control and interrupt for firing alarm and stopping motor.
• Programmed using mikroC pro for PIC microcontrollers software.
• Simulated using Proteus software before hardware implementation.

Office Automation System

• Developed an automated office system using ATMEGA32 microcontroller, Proteus simulation software, and Eclipse IDE.
• Implemented password protection to ensure maximum security and prevent unauthorized access.
• Established reliable communication between two microcontrollers using UART protocol for seamless data transfer.
• Created a responsive user interface for password entry and system feedback using LEDs and an alarm.
• Successfully achieved the primary project goal of saving electricity by automating office lighting, AC, and curtains.
• Successfully integrated multiple hardware components and software modules to create a fully functional automation system.

Face detection and recording attendance 

• Developed a face detection system using Python, OpenCV, and Haar Cascade algorithm.
• Designed to automate attendance tracking and minimize physical contact during COVID-19 pandemic.
• Captured images of individuals entering the area, and detected faces using the algorithm.
• Compared the detected face with a database of known faces to record attendance.
• Eliminated the need for physical interaction with shared surfaces, reducing the risk of COVID-19 transmission.
• The system was highly accurate and efficient due to the Haar Cascade algorithm's speed and accuracy in real-time face detection.