Acacia

Observation is essential to scientific discovery. In situ observation of natural environments is increasingly facilitated by new media and sensing technologies. These technologies not only enable the observation of complex---and at times sparse---ecological dynamics but provide new avenues for near real-time documentation, sharing, and discussion of these observations. Large scale interactive observations may perhaps spur (re)discovery of important environmental indicators or predictors.
This course samples emerging sensor, signal processing, and media technologies for non-intrusive in situ observation of representative aquatic, terrestrial, aerial, and plant species with particular emphasis on scalable technologies. The concept of observation is expanded from the activity of a single person in a habitat of interest to the collective observation of interconnected individuals at networked nodes.

Bounding ellipsoids approximate complex polytopes with well behaved and easy to manipulate algebraic representations. Many such polytopes emerge from constraint-based formulations of phenomena in engineering and science where linear models are accommodated with assumptions on worst case deviations. This short presentation derives a general expression for bounding ellipsoid polytopic supersets, discusses select optimization strategies, sequential convergence properties and potential for reducing computational complexity and power consumption. Sample applications are presented along with a discussion of parallels with Kalman filters and Support Vector Machines. Basic understanding of linear algebra is assumed.