Smart Embedded Systems, IoT & Edge AI Engineering

Embedded C Programming & Debugging

Topics

• Embedded C vs desktop C
• Memory architecture: flash, RAM, stack, heap
• Pointers, volatile, const, bitwise operations
• Register-level programming basics
• Bug types and debugging techniques

Hands-on

• Write Embedded C programs for GPIO control
• Bit manipulation using registers
• Debug a faulty C program using breakpoints
• Analyse stack and variable values using debugger

Embedded Protocol Fundamentals and applications

• UART: Frame format, Baud rate calculation, Polling vs Interrupt method, RS232 vs TTL level
• SPI: Synchronous communication, Master-Slave architecture, CPOL and CPHA modes, SPI timing diagram, Full duplex transfer, Multi-slave selection, SPI speed advantage
• I2C: Frame format, 7-bit vs 10-bit addressing, Multi-master arbitration, Clock stretching, I2C bus speed modes

Hands-on / Case Studies

• Debugging garbled data, PC terminal communication
• Interfacing SPI 7-segment slave module for data display
• Interfacing I2C serial EEPROM for data read/write

Embedded OS Concepts & Python for Embedded

Topics

• Bare-metal vs RTOS vs Embedded Linux
• Tasks, scheduling, interrupts, synchronization
• Common RTOS problems: deadlock, priority inversion
• Python basics for embedded engineers
• Python for automation and log analysis

Hands-on

• Analyse task execution using timing diagrams
• Identify race conditions from given scenarios
• Write Python scripts to parse embedded logs
• Automate a simple test case using Python

IoT Fundamentals & System Architecture

Topics

• IoT concepts and evolution
• IoT architecture: device, gateway, cloud, application
• Sensors and actuators in IoT
• Data acquisition and edge processing
• Embedded systems in IoT

Hands-on

• Identify sensors used in IoT applications
• Draw an end-to-end IoT architecture for a use case
• Map sensor data flow from device to cloud

IoT Communication & Protocols

Topics

• Review of UART, I2C, SPI
• IoT protocol stack
• MQTT and HTTP/HTTPS
• Device-to-cloud communication
• Modbus TCP (industrial IoT introduction)

Hands-on

• Analyse MQTT publish–subscribe flow
• Compare HTTP vs MQTT communication
• Simulate data transmission using Python
• Map Modbus registers to IoT data

IoT Software, Data Handling & Security

Topics

• IoT firmware role
• Data logging and telemetry
• Time-series data concepts
• Python for IoT data handling
• IoT security basics

Hands-on

• Log sensor data into files
• Write Python script to read and analyse IoT data
• Simulate secure data transmission flow
• Detect anomalies in sensor readings

IoT Applications & Case Studies

Topics

• Industrial IoT overview
• Smart monitoring and automation
• IoT in power and manufacturing systems
• End-to-end IoT design
• Interview-oriented system questions

Hands on

• Design IoT architecture for a smart system
• Case study discussion: industrial monitoring
• Identify failure points in IoT systems
• Group design exercise

Embedded AI & Edge Intelligence

AI & Machine Learning Fundamentals

Topics

• AI vs ML vs Deep Learning
• Types of ML (Supervised, Unsupervised, Reinforcement)
• ML Workflow (Data → Training → Evaluation → Deployment)
• Key Terminologies (Overfitting, Bias-Variance, Hyperparameters, Loss)

Hands on

• Build a simple classification model using Scikit-learn
• Data preprocessing (scaling, splitting)
• Evaluate using confusion matrix
• Explain ML workflow for an IoT use case

Classical ML Algorithms

Topics

• Regression (Linear Regression – core idea)
• Classification (Logistic Regression, KNN, Decision Tree)
• Model Evaluation Metrics (Accuracy, Precision, Recall, RMSE)
• Algorithm selection strategy

Hands on

• Implement Linear & Logistic Regression
• Compare two classifiers on sample dataset
• Perform basic hyperparameter tuning
• Interview Q&A on when to use which algorithm

Deep Learning & ANN

Topics

• Need for Deep Learning
• Artificial Neuron & Perceptron
• ANN Architecture (Input, Hidden, Output layers)
• Activation functions & Loss functions
• Backpropagation (conceptual understanding)

Hands on

• Build a simple ANN using TensorFlow/Keras
• Train & visualize loss vs epochs
• Modify activation functions and observe results
• Whiteboard explanation of backpropagation

Embedded AI & ESP32 Deployment

Topics

• Introduction to Embedded AI & Edge AI
• ESP32 architecture basics (relevant to AI deployment)
• Model conversion (TensorFlow Lite & Quantization)
• Deployment workflow on microcontroller
• Memory & latency constraints

Hands on

• Train lightweight model
• Convert to TensorFlow Lite
• Quantize model
• Deploy on ESP32
• Run real-time inference demo