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In this work, five people were invited repeatedly to slide a z-type password on the touch panel (Figure 49a left) for 50 times each. The LED array were lighted accordingly, as illustrated in Figure 49a right. Figure 49b show the detected intensity-time plots of one user. Two factors of user biometrics can be extracted from these plots, e.g., the signal magnitude (denoted as A, reflecting the touching force) and the sliding latency (denoted as T, reflecting the sliding speed). For the z-type sliding across the “1-2-3-4-7-10-13-14-15- 16” taxels, a total of 19 features can be obtained accordingly, i.e. 10 signal magnitudes and 9 sliding latencies. The radar plot of the normalized mean feature values of five users is shown in Figure 49c, which shows the distinctive “slide to unlock” behaviors. To improve the accuracy and robustness of the authentication and identification, we adopted the supervised machine learning to build up the user profiles for classification.185, 207 The working process is illustrated in Figure 49d. The 50 sets of data from each user were randomly split into two halves, one for training and the other for testing. The classifier performance was optimized by tuning the decision threshold value and an accuracy of 95.2% was achieved. 129PDF Image | HIGH PERFORMANCE TRIBOELECTRIC NANOGENERATOR AND ITS APPLICATIONS
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