Schedule

Due Soon

😎 Nothing~

Wk Date Type Topic Due
W1 9/28 Lecture
L1: Introduction and Course Overview
9/30 Lecture
L2: Input & Output
10/2 Discussion
D1: Intro to Hardware
W2 10/5 Lecture
L2 (cont'd)
L3: Input Devices
10/7 Lecture
L3 (cont'd)
10/9 Discussion
D2: Arduino-to-Frontend Workshop
W3 10/12 Lecture
L3 (cont'd)
L5: Wireless Technologies for Embedded Systems
10/14 Lecture
L4: Output Devices
10/16 Discussion
D3: M1 Demo
M1
W4 10/19 Lecture
L6: Introduction to IoT
10/21 Exam/Quiz
Quiz #1 (Lectures 2–5)
L6 (cont'd)
10/23 Discussion
D4: M2 Demo
M2Team sign-up
W5 10/26 Lecture
L7: Handling Sensor Data
10/28 Lecture
L7 (cont'd)
10/30 Discussion
D5: Quiz #1 Walkthrough
proposal
W6 11/2 Lecture
L7 (cont'd)
11/4 Lecture
L8: Power Management
L9: Timing Constraints & Scheduling
11/6 Discussion
D6: M3 Demo
M3
W7 11/9 Lecture
L9 (cont'd)
Project Mid-Term Presentations
Peer grading
11/11 Holiday
Veterans Day – No Class
11/13 Discussion
D7: Intro to M4 & M5
W8 11/16 Lecture
Project Mid-Term Presentations (cont'd)
11/18 Lecture
L10: History of Embedded Systems
New IoT Development
11/20 Discussion
D8: M4 & M5 Demo
M4M5
W9 11/23 Exam/Quiz
Quiz #2 (Lectures 6–9)
New IoT Development (cont'd)
11/25 Lecture
Guest Lecture: Dr. Richard Li (UW)
Project Help Desk
11/27 Holiday
Thanksgiving Holiday – No Class
W10 11/30 Exam/Quiz
Final Project Demo
Lightning round, walk-around showcase, peer grading. Upload demo video before class.
Peer grading
12/2 Lecture
TBD
12/4 Discussion
D9: Quiz #2 Walkthrough
12/6 Deadline
M6 Due (Extra Credit)
M6

Components

Component Weight Due† Notes
Mini Projects 45% M1–M5; M6 is extra credit
↳ M1: Hello, IMU! 5% 10/16 Peer graded
↳ M2: Connect the Dots… 10% 10/23 Peer graded
↳ M3: Show Me the Data 5% 11/6 Peer graded
↳ M4: Pong (Part 1) 15% 11/20 Peer graded
↳ M5: Pong (Part 2) 10% 11/20 Peer graded
↳ M6: Hungry for Power +5% 12/6 Extra credit
Quizzes 16% Open-book, open-internet, no collaboration.
↳ Quiz #1 · Not yet open 8% 10/21 Lectures 2–5: I/O, Input Devices, Wireless Technologies
↳ Quiz #2 · Not yet open 8% 11/23 Lectures 6–9: IoT, Sensor Data, Power Management, Timing
Course Project 35% Teams of 2–3 people · Theme: Innovative, Interactive, and Intelligent Internet of Things
↳ Project Proposal 5% 10/30 Graded by instructor
↳ Mid-Term Presentation
Details

~15 min per team including Q&A

  • (9%) Project idea addressing innovative/interactive/intelligent criteria
  • (3%) Review of 3–5 relevant research papers or technical articles
  • (3%) Progress delta compared to proposal
15% 11/9 Graded by peers
↳ Final Demo (instructor)
Details
  • System demo achieves innovative/interactive/intelligent criteria (equal weights)
10% 11/30 Graded by instructor
↳ Final Demo (peers)
Details

Submit 1-min demo video before class. Mingle and grade live demos during class.

5% 11/30 Graded by peers
Participation 4% In-class Q&A and online discussion
Participation Extra Credit +1% Final teaching evaluation

† Late penalty. G_late = G_original × max(1 − n × 10%, 50%). Only applies to submittable work. In-class presentations are grade-or-zero; no lateness permitted.

Course Info

Units

4

Course Objectives

  1. Explain the organization of embedded systems and how they interface with input and output devices
  2. Sample, filter, and process sensor data — using an IMU as a case study — including applied machine learning
  3. Connect embedded devices to networks and front-end applications using wireless technologies and IoT architectures
  4. Reason about power and energy management, timing constraints, and scheduling in embedded systems
  5. Design and build an innovative, interactive, intelligent IoT system in a team project

Requirements

  • Software programming and computer organization
  • Proficiency in C/C++ and Python
  • Unix/Linux command line
  • Basic computer networking
Required Hardware
Arduino Nano RP2040 Connect with Headers — Amazon · DigiKey (With header pins — avoid the version without headers)

Due to the combined theoretical and hands-on nature of this course, attendance in both lecture and discussion/lab sections is essential.

Policies

Code Citation

Code not written by yourself must be commented and cited using the course citation format. See the citation format.

Academic Integrity

  • Individual assignments must be completed individually; team assignments by team members only.
  • You may ask peers to clarify what a question means, but never share solutions.
  • Stealing ideas but presenting them in a different style is still plagiarism.
  • Aiding cheating carries the same penalty as cheating.

Accessibility & Accommodations

Students needing academic accommodations based on a disability should contact the UCLA Center for Accessible Education (CAE) and provide the instructor with a letter of accommodation as early in the quarter as possible.

Student Wellbeing

Your wellbeing matters. If you are feeling overwhelmed, UCLA Counseling & Psychological Services (CAPS) offers free, confidential support for students.