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Figure 44. The two-factor authentication and identification system built upon the triboelectric keystroke device. (a) The hardware schematic of the experimental system. (b) The keystroke features defined to construct user profile models. Typing latency, hold time, and signal magnitude are denoted as L, H, and M respectively. (c) The radar plot of the normalized mean feature values of five users after they typed the number sequence “8- 0-7-3-4-5” for 150 times each. A total of 17 features, 5 typing latencies, 6 hold time and 6 signal magnitudes, can be extracted from the 6-digits sequence. (d) The difference score matrices across user inputs with varying combinations of feature types. Reproduced with permission.151 Copyright 2018, Elsevier. 4.2.3 Classification Results The 150 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 comparing the resulting false rejection rate (FRR) and false acceptance rate (FAR). With the optimal decision threshold of 0.75, the tradeoff between a low FRR and a low FAR can be well satisfied so that an equal error rate (EER) as low as 117PDF Image | HIGH PERFORMANCE TRIBOELECTRIC NANOGENERATOR AND ITS APPLICATIONS
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