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Title: Applications Of Machine Learning Strategy For Wireless Power Transfer And Identification
Committee:
Dr. Tentzeris, Advisor
Dr. Peterson, Chair
Dr. Durgin
Abstract:
The objective of the proposed research is to propose and demonstrate machine learning (ML) applications into the wireless power transfer and identification technology. Specifically, this work describes the implementation of a ML strategy based on 1) the Neural Network for real-time range-adaptive automatic impedance matching of WPT applications, which can perform the effective prediction of the optimal parameters of the tunable matching network and classification range-adaptive transmitter coils to achieve an effective automatic impedance matching over a wide range of relative distances and 2) the Support Vector Machine (SVM) classification strategy for read/interrogation enhancement in chipless RFID applications, which can perform effective transponder readings for a wide variety of ranges ranges and contexts.
