An adaptive tuning model for cognitive info-communication channels

Cognitive infocommunication channels are abstract channels which use sensory substitution to convey structured perceptual information via any number of sensory modalities. Our goal is to develop engineering systems for the remote teleoperation of robots using sensory substitution to convey feedback information in meaningful ways. Such applications could help reduce the cognitive load for the user on the one hand, and help alleviate the effect of control instabilities and hidden parameters on the other. In this paper, we present a tensor algebraic approach for the compact representation of complete sensory substitution channels. Using HOSVD, we transform our representations into canonical, granular forms. Through a combination of rank-reduction and error compensation techniques, we propose an adaptive tuning model that can be used for iterative parameter space exploration, thus enabling users to explore the generated sensory substituted signals in a more intuitive fashion.