Biblio

Optical time domain reflectometry (OTDR) plays an important role in optical fiber communications. To improve the performance of OTDR, we propose a method based on the Variational Mode Decomposition (VMD) and Hilbert transform (HT) for fiber events detection. Firstly, the variational mode decomposition is applied to decompose OTDR data into some intrinsic mode functions (imfs). To determine the decomposition mode number in VMD, an adaptive estimation method is introduced. Secondly, the Hilbert transform is utilized to obtain the instantaneous amplitude of the imf for events localization. Finally, the Dynamic Time Warping (DTW) is used for identifying the type of event. Experimental results show that the proposed method can locate events accurately. Compared with the Short-Time Fourier Transform (STFT) method, the VMD-HT method presents a higher accuracy in events localization, which indicates that the method is effective and applicable.

One of the primary motivations for using ultrasound reflectometry for fingerprint imaging is the promise of increased spoof resistance over conventional optical or capacitive sensing approaches due to the ability for ultrasound to determine the elastic impedance of the imaged material. A fake 3D printed plastic finger can therefore be easily distinguished from a real finger. However, ultrasonic sensors are still vulnerable to materials that are similar in impedance to tissue, such as water or rubber. Previously we demonstrated an ultrasonic fingerprint reader operating with 1.3GHz ultrasound based on pulse echo impedance imaging on the backside silicon interface. In this work, we utilize the large bandwidth of these sensors to differentiate between a finger and materials with similar impedances using the frequency response of elastic impedance obtained by transducer excitation with a wideband RF chirp signal. The reflected signal is a strong function of impedance mismatch and absorption [Hoople 2015].