Welcome to ECE 420 Embedded DSP Laboratory

Course Description

The first seven weeks of the course will be structured labs based on fundamental digital signal processing (DSP) concepts from ECE 310. The next two weeks will be on the implementation and simulation of a fundamental DSP algorithm of a student's choosing from a set of seminal DSP papers (such as adaptive filtering, pitch detection, edge-aware filtering, motion tracking, pattern recognition, etc). The remaining six weeks in the course will revolve around the development, testing, and documentation of a DSP project of the student's choice (subject to instructor approval).

Course Goals

Students will learn to prototype, implement, and analyze real-time DSP systems. Students will both broaden and deepen their understanding of basic DSP theory and techniques and learn to relate this understanding to real-world observations and applications. Students will learn industrially-relevant skills such as rapid design prototyping in Python, and Android development of DSP applications in C++/Java for computationally-constrained mobile devices. Other significant educational experiences include open-ended design, oral, and written communication, and team projects.

Course Announcement

10/17/2022

8/15/2022

  • Lend-lease Tablet: Please read the following link carefully and request a tablet per student. An Android device is required for this course. If you are willing to use your own Android device, then you do not need to request. Please keep all packaging for the return shipment! https://ece.illinois.edu/academics/ugrad/lab-kits

Course Schedule

Schedule and Location

  • Location: ECEB 3015
  • Time: Monday, 2:00-2:50 PM.
 

The lecture topics are subject to change.

Week of Lecture Topic Lab Due (in lab)
08/22 Lec 1 - Course Overview [slides] Lab 1 - IMU Pedometer Mock Quiz (extra credit)
08/29 Lec 2 -Audio Processing [slides] Lab 2 - Real-time Audio Filtering Prelab 2, Demo(lab1) and Quiz 1
09/05 No Lecture (Labor day) Lab 3 - Spectrogram Prelab 3, Demo(lab2) and Quiz 2
09/12 Lec 3 -Correlation Analysis [slides] Lab 4 - Pitch Detection Prelab 4, Demo(lab3) and Quiz 3
09/19 Lec 4 -Pitch Modification[slides] Lab 5 - Pitch Synthesis Prelab 5, Demo(lab4) and Quiz 4
09/26 Lec 5 -Overview of 2D Image Processing [slides] Lab 6 - Image Processing Prelab 6, Demo(lab5) and Quiz 5
10/03 Lec 6 -Video Kalman Filter [slides] Lab 7 - Video Processing Prelab 7, Demo(lab6) and Quiz 6
10/10 Lec7 - Team Project Preview [slides] Assigned Project Lab [Latex template] Demo(lab7) and Quiz 7, Assigned Lab Project Proposal due by 10/17 @10AM
10/17 No lecture Assigned Project Lab N/A
10/24

Lec8 Special Topics

Design Review Presentation + Assigned Project Demo Presentation File, and Assigned Lab code due by 10/31 @10AM
10/31 Lec9 Extra Lab: Digit Recognition by Machine Learning [slides] Final Project - Start up Final Project Proposal due by 11/7 @10AM
11/07 Lec9 Extra Lab: Digit Recognition by Machine Learning -continued Final Project - Milestone 1 Work Milestone 1
11/14 Lec11 Special Topics: IoT and Signal Processing Final Project - Milestone 2 Work Milestone 2
11/21 No lecture - Thanksgiving Break N/A N/A
11/28 Extra office hour @ ECEB 3060 Final Project - Demo Final Project Demo and Presentation.
12/05 No lecture Final Week Final Project Report, Presentation File, Source Code and Video due by TBD

Labs

Schedule and Location

Labs are held by Zoom meeting (lab1-lab7) and in-person at ECEB 5072 (assigned lab and final demo).

[ECEB 5072 room schedule]

 

You can look up Zoom meeting links here (NetID log-in required).

  • Section AB1 meets Tuesday, 2:30-4:20 PM.
  • Section AB2 meets Wednesday, 2:00-3:50 PM.
  • Section AB3 meets Wednesday, 9:00-10:50 AM.
  • Section AB4 meets Friday, 3:00-4:50 PM.

Quizzes

There are seven 15-minute quizzes (plus one mock quiz) throughout the semester. They are open-book individual assessments taken at Prairielearn . Each quiz starts at the beginning of each lab section and ends after 15 minutes (e.g. AB1 quiz opens 2:30-2:45, every Tuesday). Students MUST take the quiz at their registered lab section. There is no makeup for missed quizzes. An absence letter from the Dean of Students is required to waive a missed quiz due to acute medical condition. Discussion of the quiz is NOT allowed until all sections have completed the quiz. The grading will be published every Friday evening. You will earn extra credit for the mock quiz (make-up for lost points in the quizzes).

Groups

Students will be working in groups to complete all labs and final project. Typically, groups of two are strongly prefered, group of more or less is allowed only on rare occasions.

For structured labs (lab 1 ~ lab 7), groups will be formed randomly and differently for each lab, so that students could have the chance to work with different partners.

For assigned project labs and final project labs, students are expected to form their own groups. Feel free to form groups as large as you want; however we do expect more work for larger groups. Feel free to form groups across different sections; if you plan to do so, make sure the entire group can attend one of the sections because you will need to do presentations and demos as a whole.

Submissions

Refer to the submission instructions page for more information.

Grades

Grades can be found on Gradescope (Entry Code: 4VVN2B).

Brief Grading Breakdown

  • Structured Labs - 40%
    • 10% for the prelab
    • 20% for the demo
    • 10% for the quiz
    • Extra credit Lab8 (5%)
  • Assigned Project Lab - 15%
    • 5% for the project proposal
    • 5% for the assigned project demo
    • 5% for design review presentation
  • Final Project - 40%
    • 10% for the final project proposal
    • 5% for the completion of two milestones
    • 15% for the final demo
    • 10% for the final report
    • Extra credit for the final video (1%)
  • Lecture participation - 5%

Detailed Grading Breakdown

The structured laboratory segment will count for 40% (10% for prelab, 10% for quiz, and 20% for demo and oral quiz) of the total grade, based on completion of, and oral examination over, the weekly laboratory assignments, including the underlying theory, details of the implementation and code, and the observed behavior of the system. We emphasize that your grade is based heavily on your understanding and demonstration of the course material, not just on submitting working code.

The assigned project lab (based on the student's chosen DSP paper) will account for 10% of the total grade.

The final project will count for 45% of the total grade, with 5% on the design review presentation, 10% on the project proposal, 5% for demonstrations of 2 project milestones, 15% for the final demo and oral presentations, and 10% on the final report.

The final 5% of the total course grade comes from lecture participation.

It is expected that each student will attend and participate in scheduled class and laboratory meetings, or will make prior alternate arrangements with the instructor. The final grade may be penalized if this does not occur.

A late penalty of 50% will be assessed for assignments less than a week late; assignments more than a week late will receive no credit. However, all graded assignments must be submitted to receive a passing grade in the course.

Project Details

Refer to the Assigned Project Lab and Final Project pages.

Academic Integrity Policy

Printed and online sources are allowed with proper citation. Please direct your question to Google or the course staff before you ask your classmates. Given the range of the material for this course, we encourage you to refer to any online source, but do not directly copy and paste.

We do not allow inter-group cooperation for the final project. If there is a sign of cooperation between groups, those groups will be treated as a big group, and the grade will be divided accordingly.

More information: Student Code.

Office Hours / Course Administrivia

  • Prof. Thomas Moon: After class
  • TA Dimitrios Gotsis: Tuesday 1:30-2:30 PM
  • TA Yuqi Li: Wednesday 1-2 PM
If you have questions, post them on Campuswire.
Invitation Link: link
Code: 5261

Instructor Contact Information

  • Prof. Thomas Moon: tmoon@illinois.edu
  • TA Dimitrios Gotsis: gotsis2@illinois.edu
  • TA Yuqi Li: yuqil3@illinois.edu