Raspberry Pi from zero to hero 

الدبلومة مكونة من عدة كورسات:

• كورس أساسيات الراسبيري باي

• كورس Object detection and AI مع الراسبيري باي

• كورس ال ROS and SLAM Robotics  

كورس أساسيات الراسبيري باي

Module 1: Introduction to Raspberry Pi

1. Getting Started with Raspberry Pi

   • What is Raspberry Pi? Overview and features

   • Installing Raspberry Pi OS

   • Initial setup: Display, keyboard, mouse, and Wi-Fi

2. Basic Linux Commands

   • Introduction to Linux terminal

   • File system navigation and management

   • Installing software via apt and pip

Module 2: Introduction to Linux on Raspberry Pi

1. Getting Started with Linux

   • What is Linux? Overview and key features

   • Understanding the Raspberry Pi OS (Debian-based)

   • Practical: Explore the Raspberry Pi desktop and terminal

2. Basic Linux Commands

   • Navigating the file system

   • Creating, moving, and deleting files and directories 

   • Viewing file contents

Module 3: Introduction to Python and Raspberry Pi

1. Setting Up Python on Raspberry Pi

   • Installing Python 3 and pip

   • Using Python IDEs: Thonny, VS Code, or the terminal

2. Python Basics

   • Data types, variables, and operators

   • Control flow and loops

   • Functions and modules

Module 4: Interfacing with Hardware using Python

5. GPIO Programming with Python

   • Using the RPi.GPIO library for pin control

   • Input and output with buttons and LEDs

6. Interfacing Sensors and Actuators

   • Using libraries for sensors

   • Controlling motors with Python

7. I2C, SPI, and UART Communication

   • Using libraries for I2C and SPI devices

   • Serial communication 

كورس Object detection and AI مع الراسبيري باي

Course 2: Object Detection and ADAS Systems

Module 1: Computer Vision Fundamentals (Week 1)

Theoretical Concepts

• Image representation and processing

• Color spaces (RGB, HSV, Grayscale)

• Image filtering techniques

• Histogram of oriented gradients (HOG)

Practical Skills

• OpenCV installation on Raspberry Pi

• Image reading and manipulation

• Basic image processing techniques

• Edge detection algorithms

Module 2: Object Detection Techniques (Week 2)

Detection Algorithms

1. Haar Cascades

   • Viola-Jones object detection framework

   • Training custom cascades

   • Face and feature detection

2. YOLO (You Only Look Once)

   • Real-time object detection architecture

   • Different YOLO versions (v3, v4, v5)

   • Performance optimization

3. SSD (Single Shot Detector)

   • Multi-scale feature mapping

   • Anchor box concepts

   • Implementation strategies

Hands-on Implementation

• TensorFlow Lite integration

• Model conversion techniques

• Performance benchmarking

• Raspberry Pi optimization

Module 3: ADAS Practical Applications (Week 3)

Specific Detection Systems

1. Lane Detection

   • Perspective transformation

   • Color thresholding

   • Hough transform

   • Curve fitting algorithms

2. Pedestrian Detection

   • Machine learning models

   • Real-time tracking

   • Safety distance calculation

3. Obstacle Avoidance

   • Depth estimation techniques

   • Sensor fusion

   • Decision-making algorithms

Module 4: Concept behind advanced projects (Week 4)

Project 1: Smart Surveillance System

• Multi-object tracking

• Event logging

• Notification systems

• Cloud integration

Project 2: Autonomous Vehicle Simulation

• Navigation algorithms

• Sensor data fusion

• Path planning

• Collision prediction

Project 3: V2V

• Implement vehicle-to-vehicle communication

• Develop real-time data exchange protocols

• Create cooperative safety applications

Project 4: V2X

• Develop vehicle-to-infrastructure communication

• Create smart city transportation solutions

• Implement multi-layer communication strategies

Course 3: ROS and SLAM with Raspberry Pi

Course Overview

Comprehensive robotics course focusing on Robot Operating System (ROS) and Simultaneous Localization and Mapping (SLAM) technologies using Raspberry Pi as the primary computational platform.

Module 1: ROS Fundamentals (Weeks 1)

ROS Architecture and Configuration

1. ROS Installation and Setup

• Raspberry Pi OS preparation

• ROS Noetic/ROS2 installation

• Development environment configuration

• Cross-compilation techniques

2. ROS Core Concepts

• Nodes and communication

• Topics, services, and actions

• Message passing

• Package management

• Launch file creation

3. Practical Networking

• Master-slave configuration

• Distributed computing

• Network configuration

• Multi-machine ROS setup

Hands-on Exercises

• Creating custom ROS packages

• Implementing basic publisher/subscriber nodes

• Writing service and action servers

• Message type definition

Module 2: SLAM Techniques (Weeks 2)

Mapping and Localization

1. SLAM Algorithms

• Gmapping

• Cartographer

• RTAB-Map

• Hector SLAM

• Visual SLAM approaches

2. Sensor Integration

• LiDAR configuration

• Depth camera setup

• IMU sensor fusion

• Odometry estimation

3. Mapping Strategies

• 2D and 3D mapping techniques

• Loop closure mechanisms

• Map optimization

• Feature extraction and matching

Practical Projects

• Creating 2D occupancy grid maps

• 3D environment reconstruction

• Real-time mapping demonstrations

• Sensor calibration techniques

Module 3: Navigation and Path Planning (Weeks 3)

Navigation Stack

1. Movement Planning

• Global and local planners

• Cost map generation

• Obstacle detection

• Trajectory optimization

2. Navigation Algorithms

• A* algorithm

• Dijkstra's algorithm

• RRT (Rapidly-exploring Random Tree)

• Dynamic window approach

3. Autonomous Navigation

• Waypoint navigation

• Obstacle avoidance

• Dynamic path recalculation

• Multi-robot coordination

Module 4: Advanced Sensor Fusion and Machine Learning for Robotics (Weeks 4)

Sensor Fusion Fundamentals

1. Sensor Integration Techniques

• Kalman Filter fundamentals

• Extended Kalman Filter (EKF)

• Particle Filter

• Sensor data alignment and synchronization

2.  Multi-Sensor Fusion Architectures

• IMU and GPS integration

• LiDAR and camera fusion

• Proprioceptive and exteroceptive sensor combination

• Uncertainty modeling and management