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Embedded robotics : From mobile robots to autonomous vehicles with Raspberry Pi and Arduino
Presents a unique examination of mobile robots and autonomous vehicles using embedded systems, from introductory to advanced level. It is structured in four parts, dealing with Embedded Systems (processors, sensors, actuators, control, multitasking and communication), Robot Hardware (driving and walking robots, autonomous boats and planes, as well as robot manipulators), Robot Software (localization, navigation, image processing and automotive systems), and Artificial Intelligence (neural networks, genetic algorithms and deep learning). The book is organized for ease of use, with numerous figures, photographs, and worked example programs. The book is written as a text for courses in computer science, computer engineering, IT, electronics engineering, and mechatronics, as well as a guide for robot hobbyists and researchers.
Crime detection camera
This paper presents a comprehensive crime detection system that uses a combination of hardware and software to monitor homes and communities in real time. The system consists of a Raspberry Pi 4B, a Raspberry Pi Camera V2, a flame sensor, an MQ-6 gas sensor, and a microphone, which are all connected to a database management system powered by MySQL. The data collected from these devices is analyzed by machine learning algorithms to detect crimes, such as theft or robbery, as well as fires and gas leaks. The system also includes a mobile app, ‘Safe Home’ which provides live video monitoring and real-time notifications to users, and an employee dashboard to monitor all statistics and manage all implemented systems.
Blind smart helmet
The Smart Helmet for the Blind is a project aimed at providing solutions for the challenges faced by blind individuals in their daily lives. The problem of detecting objects, identifying obstacles and distances, knowing the current location, and using a mobile application is a common issue for blind people. To address these problems, the Smart Helmet project was created, utilizing advanced technology and artificial intelligence to provide real-time assistance to the wearer. The helmet is connected to a Raspberry Pi 4, which processes information from the helmet's cameras and AI algorithms to analyze and predict the surrounding environment for a blind person.
Attendnce system during covid-19
Authentication system has become a hot topic in the field of security, one of the most interested methods of authentication systems is the radio frequency identity (RFID) which is used in this project to build a smart record attendance system that contains many features, one of it to determine whether the student is wearing a mask or not by using Deep Learning algorithms, another feature is the student's temperature measurement through an electronic sensor. The results obtained are processed and stored by the processing unit which is the Raspberry pi then display the data on a mobile application.
