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Figure 49. Self-powered wireless touch panel for user authentication/identification. (a) The schematic diagram of a z-type “slide to unlock” action on the TENG based touch panel (left) and the corresponding response of the connected LED array (right). (b) The intensity-time plot of User 1 and the sliding features defined to construct user profile models. Signal magnitude (LED intensity or touching force) and sliding latency are denoted as A and T respectively. (c) The radar plot of the normalized mean feature values of five users after they repeat the z-type “slide to unlock” action for 50 times each. A total of 19 features, 10 signal magnitudes and 9 sliding latencies, can be extracted from the z- type “slide to unlock” action going through 10 taxels. (d) The process flow of the proposed algorithm for user authentication/identification based on the supervised machine learning scheme. Reproduced with permission.130 Copyright 2018, Elsevier. In this work, the concept of the self-powered OWC driven by TENG is proposed. By powering the LED array with the TENG sensing device, the TENG can work as both the mechanical event trigger and the power source of the LED, and they can constitute a simplest OWC transmitter with no additional power supply or complicated circuits. With 130PDF Image | HIGH PERFORMANCE TRIBOELECTRIC NANOGENERATOR AND ITS APPLICATIONS
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