Course Objective

We expect that by the end of this course, students will be able to:
• Design a realistic smart system with the potential to benefit human
lives. The system acquires and processes audio and video data and
uses pattern recognition to take decisions that affect the environment
accordingly.
• Build a simplified version of this system, based on programmable
microcontrollers.
• Work together in a team, collaboratively identifying not only the
technical, but also the safety or ethical issues with their designs, and
then sharing their challenges and discoveries through reports,
presentations, and
in-class demonstrations.

Course Content

Pervasive (or ubiquitous) computing is a trend based on the Mark
Weiser's vision of computers available "always and everywhere", embedded
in everyday life. This course is an introduction to pervasive computing
systems that assist people in their daily life. Think about a
self-driving car, a fall-detection system, a speech-controlled
wheelchair or a navigation system for visually impaired pedestrians.
These systems:
1. sense the context (time, user's location, user's acceleration, road
scenery, etc),
2. recognize data patterns, reason and take intelligent decisions, and
3. act upon the environment, by controlling the wheels, suggesting the
best route, or just notifying a caretaker .
The main components of such a system are: sensors, controllers and
actuators. In this course, the students will learn different techniques
to acquire signals from the environment, to process these raw signals in
order to infer context by using machine learning, and to write software
agents for control. During the practical sessions, the students will
experience with these techniques and build their own
microcontroller-based smart system. Programming is done in MATLAB and
C++.
Guest lectures, given by researchers working in relevant fields are
planned as well.

Teaching Methods

Lectures, practical sessions

Method of Assessment

Compulsory practical assignments and written exam. The final grade
is calculated as Final grade = (0.5*PRAC) + (0.5*EXAM). A pass requires
both components to be >=5.5. It is possible to resit the exam, but not
the practical.

Literature

Silvis-Cividjian, N. (2017), Pervasive Computing - Engineering Smart
Systems, Springer International Publishing, ISBN 978-3-319-51654-7

Target Audience

1 CS