Signal Processing

Digital signal processing is used in many modern computer sciences systems and applications. Examples are machine learning, artificial intelligence, and big data analysis; content recommenders in information systems; speech, music and image content based retrieval and searching; music and video compression; sensor data processing in embedded systems; bioinformatics and medical data analysis. This course deals with the foundations and principles of digital signal processing. The first part concentrates on acquiring digital signals (sampling) and the basic linear filtering operations and convolution. The second part of the course introduces the concept of frequency or Fourier description of signals and systems. This concept is the foundation of many of today's computer science-based systems and applications, and has found wide applications in the processing of a variety of sound, music, sensor, image, video and other multimedia information

The first partial examination is in week 5, and deals with the materials covered in plenary lectures, working groups and Python assignments in week 1-4. The result of this partial exam counts for 30%.

The second partial examination is scheduled at the end of the quarter, and covers all course material with emphasis on the materials covered in plenary lectures, working groups and Python assignments in the second half of the course. The result of this partial exam counts for 70%.

A resit exam, covering all materials of the plenary lectures, working groups and Python assignments, is scheduled at the end of the second quarter.

Permitted Materials during Test: The lecturers have prepared one page with key equations (formuleblad). This page is permitted during the examination. It is also allowed to make notes on this one printed page.

The Python hands-on assignments are integral part of the course and must be submitted for approval within the weekly-indicated deadlines. Students can solve their assignments at home (using an own computer) or during the lab sessions. Completion of the hands-on assignments are a prerequisite for entering the second partial exam or the resit exam. Students can re-submit at most one failed Python hands-on assignment during the course to still be eligible for the second partial exam. This must be done within the sessions of the spare labs in weeks six and ten. Completion of the hands-on assignments are a prerequisite for entering the second partial exam or the resit exam.

The final grade at the end of the quarter in which the course is lectured is determined as follows.

1. The grade (rounded to one decimal) of the first partial examination counts for 30%.

2. The grade (rounded to one decimal) of the second partial examination counts for 70%.

   • Both exam grades need to be at least 5.0. The final course grade needs to be at least 5.8. Standard TU Delft rounding rules apply.

3. The Python hands-on assignments have to be completed and signed off. The approval of each assignment must be done within the week set for that assignment. In case of unforeseen circumstances (like illness) their are repair opportunities in the spare lab sessions.

   Validity of partial examination results and completed Python (Jupyter Notebook) hands-on assignments.

* The completed Python hands-on assignments and midterm/endterm results are valid for an entire academic (study) year, but expire at the end of the academic (study) year.

The grade of the resit examination is determined as follows.

1. The grade of the resit examination.

2. The Python hands-on assignments completed before the resit examination. Students who had not yet completed the hands-on assignments during the regular sessions will be given the opportunity to do so during the two spare labs to verify and approve all (remaining) assignments. These appointments take place in the weeks before midterm and the endterm.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4)