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Physical Computing - Period 2
OVERVIEW
CEA CAPA Partner Institution: Vrije Universiteit Amsterdam
Location: Amsterdam, Netherlands
Primary Subject Area: Computer Sciences
Instruction in: English
Course Code: XB_40008
Transcript Source: Partner Institution
Course Details: Level 100
Recommended Semester Credits: 3
Contact Hours: 84
DESCRIPTION
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 (time, user's location, emotions, acceleration, environment, etc)
2. recognize data 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 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 lab the students will experiment with these techniques and build their own microcontroller-based smart system.
Guest lectures given by researchers working in relevant fields are planned as well.
Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course therefore, CEA's recommended credits are based on the ECTS credits assigned by VU Amsterdam. 1 ECTS equals 28 contact hours assigned by VU Amsterdam.
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 (time, user's location, emotions, acceleration, environment, etc)
2. recognize data 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 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 lab the students will experiment with these techniques and build their own microcontroller-based smart system.
Guest lectures given by researchers working in relevant fields are planned as well.
Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course therefore, CEA's recommended credits are based on the ECTS credits assigned by VU Amsterdam. 1 ECTS equals 28 contact hours assigned by VU Amsterdam.
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