A comprehensive plan of work for designing and operating the next generation of self-powered magnetic sensors is presented. Using ideas and methods from nonlinear dynamics research in Engineering, Mathematics, and Physics, proof of concept is presented showing that higher sensitivity, lower power consumption, and reduced costs, can all be achieved through an integrative approach that combines a novel Intelligent Magnetic Sensor Network Architecture with a new sensing technique. The network architecture is intelligent in the sense that the nonlinear characteristic of each sensor can be exploited to make the network generate its own self-biasing signals, thus reducing power consumption and manufacturing costs. The goals of the proposed project are to advance knowledge and understanding of the field of sensors and student participation, especially minority, in research activities.
The project has a strong potential to directly impact science and industry in many areas where magnetic sensor devices are commonly used. Examples include: biomedical tracking of magnetic particles, e.g., MRI machines, which are commonly used for diagnosing multiple sclerosis, brain tumors, spinal infections, etc.; geological equipment, e.g., NASA explorers; homeland defense, e.g., detection of mines and explosives.
