Integrated Project 3

The lab consists of two parts:

1. Two Python computer exercises, which continue the course labs of EE2S1 Signals and Systems:

• recording dual-microphone audio signals

• deconvolution (matched filter, frequency domain approach)

• application to estimating the distance between two microphones using an audio signal

2. Design of a complex system, with the choice of two projects:

1. KITT self-driving car

2. Electronic stethoscope

A In this project you team with your fellow students to remotely operate an electric toy car called "KITT". The car is equipped with ultrasonic parking sensors, a Bluetooth connection, an audio beacon and a micro-controller. You are in charge of this "enhanced" car and will design and implement algorithms so that the car can localize itself, sense obstacles and find its way autonomously; as well as to communicate with a base station at which calculations are performed. High tracking accuracy and speedy and tight control are your objectives. In a series of competitions you and your team will show your achievements in challenges of increasing difficulty.

B In this project you work with a 6-channel digital stethoscope array. The first objective is to capture heart signals, filter and analyse the signals (anomaly detection such as murmurs). The next objective is to localize the sources (e.g., the 4 valves) using deconvolution, TDOA estimation, and MUSIC-like (eigenvalue-based) algorithms. The algorithms are designed and tested on a dummy. After consideration of the ethical aspects, the signals could also be acquired on human subjects.

The projects contain a midterm report and are wrapped up by a demo session and a final report, followed by a presentation/discussion in front of a committee.

The final grade of the project consists of the following:

- Assignments (pass/fail)

- Midterm report (0.3)

- Demo/competition (0.2)

- Final report, oral presentation and defence (0.5)

The final grade is the weighted sum of these components. Individual grades can be adjusted via a peer-review system and staff observations, which can lead to a maximum 2 point variation (-1 to 1) with respect to the average group grade.

In case of insufficient results a repair option may exist in accordance with Article 2, Examination requirements, Clause 4, of the Implementation Regulations 2024-2025.

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

Students are allowed to collaborate, but not copy from other groups. Each student is responsible (being a sole author) for the content of their own deliverables. The documentation handed in needs to be self-written.  

- When it applies, the deliverable should specify the type of collaboration, and, whether and which AI-(writing)tools are used and how they are used. AI tools are not allowed to generate text, or code, or to interpret findings or results.  

- More course-specific information and guidelines will be provided on the course specific brightspace page.