High linearity silicon DC Kerr modulator enhanced by slow light for 112 Gbit/s PAM4 over 2 km single mode fiber transmission.

We demonstrate a high efficiency, high linearity and high-speed silicon Mach-Zehnder modulator based on the DC Kerr effect enhanced by slow light. The two modulation arms based on 500-µm-long grating waveguides are embedded with PN and PIN junctions, respectively. A comprehensive comparison between the two modulation arms reveals that insertion loss, bandwidth and modulation linearity are improved significantly after employing the DC Kerr effect. The complementary advantages of the slow light and the DC Kerr effect enable a modulation efficiency of 0.85 V·cm, a linearity of 115 dB·Hz2/3, and a bandwidth of 30 GHz when the group index of slow light is set to 10. Furthermore, 112 Gbit/s PAM4 transmission over 2 km standard single mode fiber (SSMF) with bit error ratio (BER) below the soft decision forward error correction (SD-FEC) threshold is also demonstrated.

Highly integrated 3×3 silicon thermo-optical switch using a single combined phase shifter for optical interconnects.

We report on an experimental 3×3 thermo-optical switch on silicon on insulator. By controlling a single combined phase shifter, light from any input waveguide can be directed to any output waveguide, showing a simple control method and highly integrated structure as compared to the conventional multiway optical switches. Furthermore, the proposed optical switch can be generalized to be a 1×N and N×N optical switch without an extra phase shifter. The switch is fabricated by complementary metal oxide semiconductor technology. By experiment, full 3×3 switching functionality is demonstrated at a wavelength of 1.55 µm, with an average cross talk of -11.1 dB and a power consumption of 97.5 mW.

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

In this paper, we intend to predict protein structural classes (alpha, beta, alpha+beta, or alpha/beta) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.