*Course Description* 

This course covers the design and implementation of embedded systems, focusing on real-time operating systems (RTOS), hardware-software co-design, low-power optimization, and IoT applications. Students will gain hands-on experience with microcontrollers, sensors, and RTOS scheduling. 

 

*Learning Objectives* 

By the end of this course, students will: 

1. Design embedded systems using microcontrollers (e.g., ARM Cortex-M, Raspberry Pi). 

2. Implement real-time scheduling algorithms (e.g., rate-monotonic, earliest deadline first). 

3. Optimize systems for power efficiency and memory constraints. 

4. Integrate IoT protocols (MQTT, Bluetooth Low Energy) into embedded applications. 

 

*Required Materials* 

- Textbook: Real-Time Embedded Systems by Xiaocong Fan. 

- Hardware: STM32 Nucleo/ESP32 development boards (provided). 

- Software: FreeRTOS, Zephyr RTOS, PlatformIO. 

 

*Course Schedule* 

| Week | Topics                                      | Assessments | 

|------|---------------------------------------------|-------------| 

| 1–2  | Introduction to RTOS: Tasks, semaphores, queues | Lab 1 (Blinking LED with FreeRTOS) | 

| 3–4  | Real-time scheduling algorithms             | Homework 1  | 

| 5–6  | Hardware-software co-design (FPGA + MCU)    | Midterm Exam | 

| 7–8  | Low-power design (sleep modes, energy harvesting) | Lab 2 (Battery Optimization) | 

| 9–10 | IoT protocols & edge computing              | Project Proposal | 

| 11–14| Final project: Smart sensor network with RTOS | Final Demo & Report | 

 

*Assessment* 

- Labs (30%) 

- Homework (20%) 

- Midterm Exam (20%) 

- Final Project (30%) 

 

*Policies* 

- Late submissions: 10% penalty per day (up to 3 days). 

- Collaboration: Group work allowed for labs, but individual code submissions. 

- Lab safety: No food/drinks near hardware stations. 

 

*Graduate Program Curriculum Overview* 

A *Master’s or Ph.D. in Computer Engineering* typically includes *core courses, **electives*, and a thesis/dissertation. Below is a general structure: 

 

*Core Courses* 

1. *Advanced Digital Systems Design* 

   - FPGA/ASIC design, RTL coding (Verilog/VHDL), synthesis, and verification. 

2. *Computer Architecture* 

   - Pipeline optimization, memory hierarchies, GPUs, and heterogeneous computing. 

3. *Embedded & Cyber-Physical Systems* 

   - RTOS, IoT, sensor networks, and robotics integration. 

4. *Hardware Security* 

   - Side-channel attacks, secure enclaves, and cryptographic accelerators. 

 

*Electives* 

- *AI Hardware Acceleration* 

  - Design of TPUs, neuromorphic chips, and FPGA-based AI inference. 

- *Quantum Computing Architectures* 

  - Qubit control, quantum error correction, and hybrid classical-quantum systems. 

- *Autonomous Systems* 

  - Self-driving car architectures, real-time perception, and sensor fusion. 

- *Advanced Computer Networks* 

  - 5G/6G protocols, SDN, and network-on-chip (NoC) design. 

 

*Research/Thesis* 

- *M.S.*: 1–2 years of research (e.g., optimizing edge AI systems, secure embedded devices). 

- *Ph.D.*: 3–5 years of original research (e.g., novel architectures for quantum computing, energy-efficient IoT networks). 

- Defence and peer-reviewed publication(s) required. 

 

*Specializations* 

1. *Hardware Design*: VLSI, FPGA, ASIC, and low-power circuits. 

2. *Embedded Systems & IoT*: Real-time systems, edge computing, robotics. 

3. *AI & Machine Learning Hardware*: Accelerators, neuromorphic engineering. 

4. *Cybersecurity*: Secure hardware design, cryptographic architectures. 

 

 *Program Policies* 

- *Credits*: 30–36 credits (M.S.), 60+ credits (Ph.D.). 

- *Comprehensive Exams*: Required for Ph.D. candidacy (written + oral). 

- *Internships*: Optional partnerships with companies like NVIDIA, Intel, or Bosch. 

- *Teaching/Research Assistantships*: Available for lab/course support.