Today, many people are more likely to check their gps-capable mobile phones than the stars in the sky for orientation. More than ever, we experience our world though sensor enabled gadgets. Sensors replace lost skills, introduce new ones and always remind us of other worlds we have no access to.

This course addresses synthetic sensing and data processing in the context of digital media arts. Understanding sensors and their limitations is an important prerequisite to building robust and satisfying information processing artifacts. The course will allow students to better understand both the concepts as well as the techniques underlying a variety of sensor typologies. While the course covers technical materials, the goal of the course is to uncover new possibilities with which students can investigate digital data of all kinds. The application domain we will focus on is ambient intelligence (AMI) and machine vision. AMI is an important domain of research in this context as sensor based information collection and control is slated to become, just as environmental control already has, a critical design problem within architectural and media arts practices. Readings will include technical texts on sensor systems, sociological studies of home appliances and critical texts on new wired cities.

Our lab is Ubuntu based and has a wide array of sensor types, an industry grade commercial machine vision library as well as an open source research grade vision library. We also have microprocessor based ccd cameras, ieee1394 compliant digital cameras, analogue video cameras with fast frame grabber cards and use Python under Wingware (both opensource) for this course. With this infrastructure and instructor guidance, students will be able to explore new ways of seeing, hearing and feeling and become familiar with software culture.

Prerequisites: MediaRobotics I or equivalent


Here are video documents of student work from previous MRII courses
2006
Brian Clark: underpants [.mpg], Bogdan Marion: flag [.mpg], Jesse Fabian: portaroad [.mpg]
2008
Steve Hibit: somewhere in France [.mp4], Chris Caprolingua: face [.wmv], Steve Korzelius: jack 1.0 [.wmv]


Here are the lecture notes, source code and tutorials for MRII