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corresponding channel, while the induced noise in other channels are negligible, demonstrating the successful implementation of the triboelectric device as a numeric keypad. 4.2.2 SVM-Algorithm Based Software Platform A customized SVM-based software platform (Figure 43) was developed and integrated with the triboelectric numeric keypad to construct a two-factor authentication/identification system (Figure 44a). Analog voltage signals transduced from keystrokes is firstly converted to digital signals by the hardware circuits consisting of voltage dividers and an ADC. Subsequently, the keystroke features (defined as in Figure 44b), such as typing latencies (L), hold time (H) and signal magnitudes (M), are extracted using specific signal processing techniques, e.g., denoising, baseline elimination and peak detection. For an exemplary number sequence consisting of six digits, “8-0-7-3-4-5”, a total of 17 features can be obtained accordingly and the radar plot of their normalized mean values shows distinctive typing behaviors among five different users (Figure 44c). In the training process, these normalized feature vectors are then used to build user profile models via supervised learning with the help of principle component analysis (PCA)184 and SVM. For the implementation of the authentication and identification, a similar process is carried out and the test user profile is cross-referenced with the existing profile database for decision making through a pre-trained classifier. The classifier is built upon a customized classification algorithm modified from the LibSVM toolbox185, 186, which achieves a satisfactory tradeoff between computational complexity and prediction accuracy, and fits the scenario of a large number of users well. Moreover, it can be analyzed theoretically with a precise probability model and avoid the over-fitting issue of the training sets. The 115PDF Image | HIGH PERFORMANCE TRIBOELECTRIC NANOGENERATOR AND ITS APPLICATIONS
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