System identification is widely employed for building mathematical models of manifold systems using statistical techniques. In this thesis, the application of system identification to atomic force microscopy using a real-time embedded solution has been reported. Atomic force microscopes are prevalent instruments utilized to explore material properties at the micro/nanometer level. A Field Programmable Gate Array has been chosen to harbor the design of the system identification module. The reported module has been successfully cascaded with an atomic force microscope to estimate local surface mechanical properties of materials. The design layout described in this thesis is not just applicable to commercially available atomic force microscopes, but to a large group of real-time signal processing units. Numerous simulations over multiple platforms and experimental results are presented to validate the accuracy and performance of the designed system identification module.
University of Minnesota M.S.E.E. thesis. 2017. Major: Electrical/Computer Engineering. Advisor: Murti Salapaka. 1 computer file (PDF); 66 pages.
Real time identification of local surface properties of material using atomic force microscope - An FPGA based implementation.
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