The teigen R package is introduced and utilized for model-based clustering and classification. The tEIGEN family of mixtures of multivariate t distributions is formed via an eigen-decomposition of the component covariance matrices and subsequent componentwise constraints. The teigen package implements all previously published tEIGEN family members as well as eight additional models: four multivariate and four univariate. The resulting family of 32 mixture models is implemented in both serial and parallel, with useful dedicated functions. Methodology and examples that illustrate teigen's functionality are presented.

Providing affordable, high-quality healthcare to the elderly while enabling them to live independently longer is of critical importance. In our Smart Condo project, we have deployed a wireless sensor network in an 850-square-foot condominium for assisted living. The sensor network records a variety of events and environmental parameters and feeds the related data into our web-based system. This system is responsible for inferring higher-order information about the activities of the condo's occupant and visualizing the collected information in both a 2D Geographic Information System (GIS) and a 3D virtual world. The architecture is flexible in terms of supported sensor types, analyses, and visualizations through which it communicates this information to its users, including the condo's occupant, their family, and their healthcare providers.

Providing affordable, high-quality healthcare to the elderly while enabling them to live independently longer is of critical importance, as this is an increasing and expensive demographic to treat. Sensor-network technologies are essential to developing assisted living environments. In our Smart Condo project, we have deployed a sensor network with a variety of sensor types in an 850 square-foot condominium. The sensor network records a variety of events and environmental parameters and feeds the related data into our web-based system. This system is responsible for inferring higher-order information about the activities of the condo's occupant and supporting the visualization of the collected information in a 2D Geographic Information System (GIS) and a 3D virtual world, namely Second Life (SL).

The low-powered transmissions in a wireless sensor network (WSN) are highly susceptible to interference from external sources. Our work is a step towards enabling WSN devices to better understand the interference in their environment so that they can adapt to it and communicate more efficiently. We extend our previous work in which we collected received signal strength traces using mote-class synchronized receivers at sample rates that are, to the best of our knowledge, higher than previously described in the literature. These traces contain distinct interference patterns, each with a different potential for being exploited by cognitive radio strategies. In order to exploit a pattern, however, a node must first recognize it. Given the energy and space constraints of a node, we explore succinct decision tree classifiers for the two most disruptive patterns. We expand on a basic feature set to incorporate attributes based on the dip statistic and the Lomb periodogram, both of which address specific, empirically observed behaviour, and we show their positive impact on both the decision tree structure and the overall classification performance. Moreover, we present an approximation of the periodogram that makes its construction feasible for mote-class devices, and we describe the simplification's impact on classification performance.