Pervasive Computing

2019-2020

Course Objective

By the end of this course, students will be able to:
1) Design a realistic smart system with the potential to benefit human
lives. The system acquires and processes data from video, audio,
acceleration, or EEG sensors and uses pattern recognition to take
decisions that affect the environment accordingly. (Knowledge and
understanding) (Applying Knowledge and understanding)
2) Build a simplified version of the real system and program a software
agent to control it. (Applying Knowledge and understanding) (Making
judgements)
3) 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. (Making judgements)
(Communication) (Lifelong learning skills)

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
fall-detection system, a self-driving car, a brain-controlled wheelchair
or a navigation system for a blind pedestrian. These systems:
1. sense the context (e.g. time, user's location, emotions,
acceleration, environment)
2. recognise patterns, reason and take intelligent decisions
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, 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 lab the students will experiment with these
techniques and build their own smart system using robotic kits such as
Lego Mindstorms ev3, controlled by MATLAB. Guest lectures given by
researchers working in relevant fields are planned as well.

Teaching Methods

Lectures, practical lab sessions and mini-project.

Method of Assessment

Compulsory lab assignments and written exam.
The final grade is calculated as follows: 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

BSc Information Sciences (1st year)

Additional Information

This course is taught in English.

General Information

Course Code X_400552
Credits 6 EC
Period P4
Course Level 100
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. N. Silvis-Cividjian
Examiner dr. N. Silvis-Cividjian
Teaching Staff dr. N. Silvis-Cividjian

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture, Practical