Mon | Tue | Wed | Thu | Fri |
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8/24 | 8/25 | 8/26 | 8/27 | 8/28 |
8/31 | 9/1 | 9/2 | 9/3 | 9/4 |
9/7 | 9/8 | 9/9 | 9/10 | 9/11 |
9/14 | 9/15 | 9/16 | 9/17 | 9/18 |
9/21 | 9/22 | 9/23 | 9/24 | 9/25 |
9/28 | 9/29 | 9/30 | 10/1 | 10/2 |
10/5 | 10/6 | 10/7 | 10/8 | 10/9 |
10/12 | 10/13 | 10/14 | 10/15 | 10/16 |
10/19 | 10/20 | 10/21 | 10/22 | 10/23 |
10/26 | 10/27 | 10/28 | 10/29 | 10/30 |
11/2 | 11/3 | 11/4 | 11/5 | 11/6 |
11/9 | 11/10 | 11/11 | 11/12 | 11/13 |
11/16 | 11/17 | 11/18 | 11/19 | 11/20 |
11/23 | 11/24 | 11/25 | 11/26 | 11/27 |
11/30 | 12/1 | 12/2 | 12/3 | 12/4 |
12/7 | 12/8 | 12/9 | 12/10 | 12/11 |
Syllabus: ECE 401
An introduction to signal analysis and processing methods for advanced undergraduates or graduate students in the biological, physical, social, engineering and computer sciences. Signal analysis methods and their capabilities, weaknesses, and artifacts with an emphasis on their practical application. Significant hands-on processing and interpretation of real data using python. 4 undergraduate hours. 4 graduate hours. Credit is not given for both ECE 310 and ECE 401. Prerequisite: MATH 220. Assignments | Staff | Resources
Grading Scheme
- 15%: Written homework
- 45%: Machine problems
- 10% each Midterm
- 20% Final Exam
- Up to 1% extra credit for answering the questions of other students on piazza.
Grade cutoffs are approximately as follows, where mu=class average, sigma=standard deviation.
- min(90%, mu+0.25sigma): A-, min(94%, mu+0.5sigma): A, min(99%, mu+1.5sigma): A+
- min(75%, mu-sigma): B-, min(80%, mu-0.75sigma): B, min(85%, mu): B+
- min(60%, mu-2.25sigma): C-, min(65%, mu-2sigma): C, min(70%, mu-1.25sigma): C+
- min(45%, mu-3sigma): D-, min(50%, mu-2.75sigma): D, min(55%, mu-2.5sigma): D+
Assignments
HomeWork
Written homework will be due every two weeks; write your answers by hand, photograph, and submit to Gradescope.
Directory of homework assignments: HW1 | HW2 | HW3 | HW4 | HW5 | HW6
Machine Problems
Machine problems will be in python, once every two weeks, and will be autograded on Gradescope.
Directory of machine problems: MP1 | MP2 | MP3 | MP4 | MP5 | MP6
Late Policy
HW and MPs are accepted up to 7 days late on Gradescope, with only a 5% penalty. If you're more than 7 days late, you'll have to submit by e-mail; in general, credit of up to 50% is possible for any submission, at any time before the end of the semester.
Extensions will not be given for software that didn't work; you should have checked that in advance. Extensions are possible in case of illness, on a case-by-case basis.
Academic Integrity
You are encouraged to consult with other students in your attempts to solve any of the MPs. The only thing that’s expressly forbidden is sharing code.
- Do not: share code. Don’t send lines of python by text message or e-mail, don’t post it on your blog, don’t share it on github, don’t post it in piazza.
- Do: consult with other students about how to solve a problem. You can discuss code on a teleconference, as long as nobody's copying the code verbatim. You are encouraged to share pseudo-code and natural language descriptions of algorithms. You are allowed to send chat messages etc. with pseudo-code – just don’t send actual lines of python.
Exams
Exams will be open-book. They will be taken on Compass. They will be timed, and scheduled at the regular lecture time, unless you notify me in advance of a conflict. Sample exams will be available, in advance, for study.
Directory of exams: Midterm 1 | Midterm 2 | Final
Staff
Instructor
Lectures and Office Hours
Lectures are Tuesdays and Thursdays, 12:30-2:00pm. Office hours are Thursdays, 5-6pm. URLs for both are provided on the course Compass page, and on piazza.
Lectures will be recorded, and posted to MediaSpace after class. If you have trouble accessing the Mediaspace video, please send me e-mail.
Resources
Text
DSP First by McClellan, Schafer and Yoder. I've bought this book (twice), and consider it the best introductory text on signal processing for people who've never studied signal processing before. I will not require problems from the text, so you're not required to buy it, but I will probably recommend study problems from the text.
Software
- Python and NumPy:
Campus Resources
- COVID19
- Statements from the undergraduate advising office about sexual misconduct, academic integrity, religious observances, disability-related accommodations, and FERPA
- Home page of the undergraduate advising office
Lectures, Homework, MPs and Exams
Week 1
- Lecture 1, T8/25 12:30
- Slides: Integration, Summation, and Complex Numbers
- Ad for a related course that some of you may find interesting
- Lecture 2, T8/27 12:30
- Slides: Convolution
Week 2
- Homework 1, M8/31 23:59
- PDF: Review of integration, summation, and complex numbers (a solutions)
- Lecture 3, T9/1 12:30
- Slides: Sines, Cosines, and Complex Exponentials
- Lecture 4, R9/3 12:30
- Slides: Spectrum
Week 3
- Machine Problem 1, M9/7 23:59
- Web page: Image smoothing and edge detection
- Lecture 5, T9/8 12:30
- Slides: Fourier Series and DFT
- Lecture 6, R9/10 12:30
- Slides: Music
Week 4
- Homework 2, M9/14 23:59
- Cosines, Phasors and Spectrum: homework, solution.
- Lecture 7, T9/15 12:30
- Slides: Frequency Response
- Lecture 8, R9/17 12:30
- Slides: Filtering Periodic Signals
Week 5
- Monday 9/21: Machine Problem 2, due 23:59
- Web page: Music analysis and synthesis
- Extra credit: group assignment
- Tuesday 9/22: Exam 1 Review
- We will do some of the examples on Compass.
- Wednesday 9/23: Extra Office Hours
- I will hold extra office hours 5-6pm, in case of any last-minute questions before the exam.
- Thursday 9/24: Midterm 1, R9/24 12:30
- The midterm exam will be a timed, open-book, open-notes, open-internet exam, held on Compass. It will appear in your Compass folder at 12:15PM on Thursday 9/24, and will be available until 2:45PM; you may choose any 90-minute period during that time in which to take the exam. You may type your answers in any mixture of plaintext pseudo-math or pseudo-python syntax; as long as I can understand what you mean, you will get the points. Your answer should contain no integrals or infinite-length sums, but otherwise, you do not need to simplify explicit numerical expressions. Examples are available in the two sample exams that are currently available on Compass; you may also find it useful to look at exams from past semesters, though they are in a different format. Reference solutions to all practice exam problems are available on Compass after you submit your answers, and will also be posted on the course web page on Tuesday 9/22 after lecture. Piazza is open now for questions about the practice exam, and will be open, during the real exam, for private questions to the instructor.
- Thursday 9/24: No office hours
Week 6
- Lecture 9, T9/29 12:30
- Slides: Discrete-Time Fourier Transform
- Lecture 10, R10/1 12:30
- Slides: Ideal Filters and Useful Filters
Week 7
- Homework 3, M10/5 23:59
- Frequency response. assignment, solutions.
- Lecture 11, T10/6 12:30
- Slides: Z Transform
- Lecture 12, R10/8 12:30
- Slides: Autoregressive Filters
Week 8
- Machine Problem 3, M10/12 23:59
- Assignment Page: Designing FIR filters to extract alpha, beta, low-gamma, and high-gamma bands from EEG.
- Lecture 13, T10/13 12:30
- Slides: Block Diagrams and Inverse Z Transform
- Lecture 14, R10/15 12:30
- Slides: Notch Filters
Week 9
- Homework 4, M10/19 23:59
- Z transform. assignment, solutions.
- Lecture 15, T10/20 12:30
- Slides: Second-Order All-Pole Filters
- Lecture 16, R10/22 12:30
- Slides: Linear Prediction
Week 10
- Machine Problem 4, M10/26 23:59
- Overview page: Removing 60Hz hum using notch filters.
- Midterm Review, T10/27 12:30
- See the sample exams on Compass.
- Wednesday 10/28: Extra Office Hours
- I will hold extra office hours 5-6pm, in case of any last-minute questions before the exam.
- Midterm 2, R10/29 12:30
- The midterm exam will be a timed, open-book, open-notes, open-internet exam, held on Compass. It will appear in your Compass folder at 12:15PM on Thursday 10/29, and will be available until 3:15PM; you may choose any 90-minute period during that time in which to take the exam. You may type your answers in any mixture of plaintext pseudo-math or pseudo-python syntax. Your answer should contain no integrals or infinite-length sums, but otherwise, you do not need to simplify explicit numerical expressions. Examples are available in the two sample exams that are currently available on Compass; you may also find it useful to look at exams from past semesters, though they are in a different format. Reference solutions to all practice exam problems are available on Compass after you submit your answers. Piazza will be open during the exam for private questions to the instructor.
Week 11
- T11/03
- No Lecture!
- R11/05
- Lecture 17: LPC-10 Speech Synthesis
Week 12
- Homework 5, M11/09 23:59
- Linear prediction and second-order filters: PDF.
- T 11/10
- Lecture 18: Power Spectrum
- R 11/12
- Lecture 19: Autocorrelation
Week 13
- Machine Problem 5, M11/16 23:59
- Linear prediction: mp5overview page.
- T 11/17
- Lecture 20: Wiener Filter
- R 11/19
- Lecture 21: Wiener Filter, Part 2
Week 14
- Homework 6, M11/30 23:59
- Autocorrelation and Power Spectrum: PDF
- T 12/1
- Lecture 22: Aliasing in Time: the DFT
- R 12/3
- Lecture 23: Aliasing in Frequency: the Sampling Theorem
Week 15
- Machine Problem 6, M12/7 23:59
- Electrocardiogram: Assignment page
- Final Exam F12/11 13:30-16:30
- Details will show up here.