A Comparison of AVIRIS and Synthetic Landsat Data for Land Use Classification at the Urban Fringe

In this study I tested whether AVIRIS data allowed for improved classification over synthetic Landsat TM data for a location on the urban-rural fringe of Colorado. After processing the AVIRIS image and creating a synthetic Landsat image, I used standard classification and post-classification procedures to compare the data sources for land use mapping. I found that, for this location, AVIRIS holds modest but real advantages over Landsat for the classification of heterogeneous and vegetated land uses. Furthermore, this advantage comes almost entirely from the high spectral resolution of the sensor rather than the high radiometric resolution

Mutual synchronization and clustering in randomly coupled chaotic dynamical networks

We introduce and study systems of randomly coupled maps (RCM) where the relevant parameter is the degree of connectivity in the system. Global (almost-) synchronized states are found (equivalent to the synchronization observed in globally coupled maps) until a certain critical threshold for the connectivity is reached. We further show that not only the average connectivity, but also the architecture of the couplings is responsible for the cluster structure observed. We analyse the different phases of the system and use various correlation measures in order to detect ordered non-synchronized states. Finally, it is shown that the system displays a dynamical hierarchical clustering which allows the definition of emerging graphs.Comment: 13 pages, to appear in Phys. Rev.

Gaussian process person identifier based on simple floor sensors

Abstract. This paper describes methods and sensor technology used to identify persons from their walking characteristics. We use an array of simple binary switch floor sensors to detect footsteps. Feature analysis and recognition are performed with a fully discriminative Bayesian approach using a Gaussian Process (GP) classifier. We show the usefulness of our probabilistic approach on a large data set consisting of walking sequences of nine different subjects. In addition, we extract novel features and analyse practical issues such as the use of different shoes and walking speeds, which are usually missed in this kind of experiment. Using simple binary sensors and the large nine-person data set, we were able to achieve promising identification results: a 64 % total recognition rate for single footstep profiles and an 84 % total success rate using longer walking sequences (including 5- 7-footstep profiles). Finally, we present a context-aware prototype application. It uses person identification and footstep location information to provide reminders to a user. Key words: Person identification, machine learning, floor sensors, context-awareness