A large class of dynamic sensors exhibit nonlinear input-output characteristics, often corresponding to a bistable potential energy function. Examples include: magnetic field sensors, e.g., fluxgate sensors, ferroelectric sensors, and mechanical sensors, e.g., acoustic transducers made with piezoelectric materials. Many of these sensors have assisted mankind in analyzing and controlling thousands of functions for many decades. Computer memory has increased over many years through the use of magnetic sensors embedded in storage devices. Airplanes fly with higher safety standards because of the high reliability of noncontact switching with magnetic sensors. As new technologies emerge, however, more powerful and more efficient sensors are required. In response to this need, we present preliminary results which demonstrate that higher sensitivity, lower power-consumption, and reduced costs, can all be achieved

through an integrative approach that combines a novel Intelligent Sensor Network (ISN) network architecture with a new sensing technique, the Residence Time Detection (RTD). By intelligent, we mean the following. We treat each sensor as a nonlinear dynamical system of the form dx/dt = -grad(U(x)), where x(t) is the state variable of the device, e.g., magnetization state, and U is the bistable potential function. Then the fundamental idea is to exploit the phenomenon of coupling-induced oscillations and the nonlinear characteristics of magnetic materials so that the ISN network can, intelligently, produce its own self-biasing signal and, simultaneously, achieve better sensitivity.