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This study proposes the combination of fractional-order edge detection (FOED) and a chaos synchronisation classifier for fingerprint identification. Fingerprints have various morphologies and exhibit singular points, which result in fingerprint individuality. Thumbprint images are captured from subjects using an optical fingerprint reader. The identification procedure consists of three stages: image enhancement, feature extraction and pattern identification. The adjustment of grey-scale values is used to enhance the contrast of the image. In order to overcome the limitations of the integral-order method, FOED is used to improve the clarity of the ridge and valley structures in fingerprint images. Using a reference point, it provides a stable sampling window for fingerprint extraction. Multiple CS-based detectors are used to track the differences as dynamic errors between heterogeneous fingerprints, on a one-to-one basis. The maximum-likelihood method performs a comparison of these different dynamic errors to identify individuals. Using 30 laboratory subjects, the proposed hybrid methods have a faster processing time and provide more accurate fingerprint identification.

This study proposes a maximum power tracking (MPT) controller based on chaos synchronisation (CS) for a windenergy-conversion system. The output power conversion of a wind generator depends on the wind speed, and therefore the optimal conversion of wind energy can be obtained by a variable-speed variable-frequency model. Based on a sensorless controller, CS can express dynamic behaviours by using an incremental conductance to adjust the terminal voltage to the maximum power point. A voltage detector based on the Sprott system is used to track the desired voltage and to control the duty cycle of a boost converter. For a permanent-magnet synchronous generator, the simulation results demonstrate the effectiveness of the proposed MPT controller.

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