How can multiple different display and interaction devices be used together to create an effective augmented reality environment? We explore the design of several prototype hybrid user interfaces that combine a 2D multi-touch tabletop display with a 3D head-tracked video-see-through display. We describe a simple modeling application and an urban visualization tool in which the information presented on the head-worn display supplements the information displayed on the tabletop, using a variety of approaches to track the head-worn display relative to the tabletop. In all cases, our goal is to allow users who can see only the tabletop to interact effectively with users wearing head-worn displays.

This paper describes how echo state networks (ESN) can be used in conjunction with minimum average correlation energy (MACE) filters in order to create a system that can identify spikes in neural recordings. Various experiments using real-world data were used to compare the performance of the ESN-MACE against threshold and matched filter detectors to ascertain the capabilities of such a system in detecting neural action potentials. The experiments demonstrate that the ESN-MACE can correctly detect spikes with lower false alarm rates than established detection techniques since it captures the inherent variability and the covariance information in spike shapes by training.