Femtosecond laser direct inscription is a technique especially useful for prototyping purposes due to its distinctive advantages such as high fabrication accuracy, true 3D processing flexibility, and no need for mold or photomask. In this paper, we demonstrate the design and fabrication of a planar lightwave circuit (PLC) power splitter encoded with waveguide Bragg gratings (WBG) using a femtosecond laser inscription technique for passive optical network (PON) fault localization application. Both the reflected wavelengths and intervals of WBGs can be conveniently tuned. In the experiment, we succeeded in directly inscribing WBGs in 1×4 PLC splitter chips with a wavelength interval of about 4 nm and an adjustable reflectivity of up to 70% in the C-band. The proposed method is suitable for the prototyping of a PLC splitter encoded with WBG for PON fault localization applications.

In recent years, explosively increasing data traffic has been boosting the continuous demand of high speed optical interconnection inside or among data centers, high performance computers and even consumer electronics. To pursue the improved interconnection performance of capacity, energy efficiency and simplicity, effective approaches are demonstrated including particularly advanced digital signal processing (DSP) methods. In this paper, we present a review about the enabling adaptive DSP methods for optical interconnection applications, and a detailed summary of our recent and ongoing works in this field. In brief, our works focus on dealing with the specific issues for short-reach interconnection scenarios with adaptive operation, including signal-to-noise-ratio (SNR) limitation, level nonlinearity distortion, energy efficiency consideration and the decision precision.

At present, the demand for perimeter security system is increasing greatly, especially for such system based on distributed optical fiber sensing. This paper proposes a perimeter security monitoring system based on phase-sensitive coherent optical time domain reflectometry(Ф-COTDR) with the practical pattern recognition function. We use fast Fourier transform (FFT) to exact features from intrusion events and a multi-class classification algorithm derived from support vector machine (SVM) to work as a pattern recognition technique. Five different types of events are classified by using a classification algorithm based on SVM through a three-dimensional feature vector. Moreover, the identification results of the pattern recognition system show that an identification accurate rate of 92.62% on average can be achieved.