Digital Signal Processing Laboratory

ECE 432 is a Digital Signal Processing Laboratory focusing on implementing DSP algorithms like filters and FFT on hardware using assembly and high-level languages.

offering recorded3 credits
Recorded instructors · Fall 2026 Paul Milenkovic2.6/5

Summary

1 / 3

Recent recorded grades — Spring 2022: 3.55 GPA, 81.8% A/AB (n=22 letter grades); Spring 2023: 3.57 GPA, 78.6% A/AB (n=28 letter grades); Fall 2025: 3.46 GPA, 69.6% A/AB (n=23 letter grades).

Grade history

average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

E C E 330 and COMP SCI 300, graduate/professional standing, or member of Engineering Guest Students

  • take one
ECE 432

This is a best-effort interpretation; check the catalog requirements above.

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  • Any of

Professors

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 2.27/5 from 63 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

Recent recorded grades — Spring 2022: 3.55 GPA, 81.8% A/AB (n=22 letter grades); Spring 2023: 3.57 GPA, 78.6% A/AB (n=28 letter grades); Fall 2025: 3.46 GPA, 69.6% A/AB (n=23 letter grades).

Historical instructors & teaching patterns

PAUL MILENKOVIC is recorded teaching in Spring 2007, Spring 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2020, Spring 2022, Spring 2023, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction18 / 480

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

Student experience

the class

Recent recorded grades — Spring 2022: 3.55 GPA, 81.8% A/AB (n=22 letter grades); Spring 2023: 3.57 GPA, 78.6% A/AB (n=28 letter grades); Fall 2025: 3.46 GPA, 69.6% A/AB (n=23 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Digital signal processing algorithms, IIR and FIR filters, FFT, fixed-point arithmetic, and DSP applications in communication systems.

Skills

  • Implementing DSP algorithms (IIR, FIR, FFT) on hardware using assembly and high-level languages, including fixed-point scaling and real-time system design.

Grades

Latest available · Fall 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Selected offering source records
ECE 432 · Fall 2026

Digital Signal Processing Laboratory

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:320:006372",
    "course_id": "ECE 432",
    "course_uid": "course_c5e749f187ec7e465cd05f49",
    "term_id": "1272",
    "source_course_id": "006372",
    "source_subject_id": "320",
    "title": "Digital Signal Processing Laboratory",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Spring"
  }
]
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "abdca4414ddd2b5e5626f658e3c2999853c063fbef7e870863b988e12a8b521b",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}