We use the term HCI in a very broad sense to include communication between person and computer as well as communication between persons via computer. An example of the former is a person using a workstation, an example of the latter is tele-collaboration. Performance criteria will be based not only on compression factors, but also on scalability, interoperability, and ease of manipulation with compressed data. This project investigates how the learning process in adaptive digital filters is disturbed by hardware failures and how to design filters and adaptive algorithms that can continue operating in the presence of such failures.
Adaptive systems are capable of adjusting parameters to reduce a specified error criterion.
It has been shown that whenever a hardware failure occurs that increases the error, the system will attempt to compensate for this failure by further self-adjustment. The structure of the input autocorrelation matrix in Volterra second-order adaptive filters for general colored Gaussian input processes has been analyzed to determine how to best formulate a computationally efficient, fast adaptive algorithm. It was shown that when the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of quadratic filter inherits a block diagonal structure, with some of the subblocks also having diagonal structure. Some new results in developing and evaluating computationally efficient quasi-Newton adaptive algorithms have been obtained that take advantage of the sparsity and unique structure of the correlation matrix that results from this formulation. This research tries to shed light on this by investigating the fundamental rate-distortion performance bounds for realistic classes of image and video models.