Information Theory is traditionally concerned with data transmission and compression and has not recieved as much intention for the description of signel processing systems. While traditional signal processing measures are related to signal energy and correlation, an information processing view should emphasize the amount of entropy generated by a signal model or the amount of entropy reduction resulting from an input-output system operation. While most linears systems fall in the class od information allpasses which do not increase or decrease the entropy rate of the processed signals, nonlinear systems do. We study the impact of nonlinear systems on the information content
of signals, exploit nonlinear models for signal compression and signal generation, including the recovery of lost information, and study the distributed analysis of sensor data under total capacity constraints.