Improving subject transfer in EEG classification with divergence estimation.

. Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test subjects. We improve performance using new regularization techniques during model training.

Inverse regular perturbation with ML-assisted phasor correction for fiber nonlinearity compensation.

We improve an inverse regular perturbation (RP) model using a machine learning (ML) technique. The proposed learned RP (LRP) model jointly optimizes step-size, gain and phase rotation for individual RP branches. We demonstrate that the proposed LRP can outperform the corresponding learned digital back-propagation (DBP) method based on a split-step Fourier method (SSFM), with up to 0.

DNN-assisted phase distance tuned PSK modulation for PAM4-to-QPSK format conversion gateway node.

This paper proposes an optical gateway that converts pulse amplitude modulation (PAM) format to phase shift keying (PSK) modulation format, enabling flexible intensity-to-phase mapping without relying on optical-electrical-optical data conversion at heterogenous network connections. A proof-of-principle experiment shows that optically converted PSK signals from regular PAM signals will induce non-uniform irregular phase noise distortion. The proposed optical gateway is designed to provide an optimized phase distance for PSK signals such that an achievable information rate is maximized by a deep learning-based decision on the receiver side.

Spectrally sparse optical coherence tomography.

Swept-source optical coherence tomography (OCT) typically relies on expensive and complex swept-source lasers, the cost of which currently limits the suitability of OCT for new applications. In this work, we demonstrate spectrally sparse OCT utilizing randomly spaced low-bandwidth optical chirps, suitable for low-cost implementation with telecommunications grade devices. Micron scale distance estimation accuracy with a resolution of 40 μm at a standoff imaging distance greater than 10 cm is demonstrated using a stepped chirp approach with approximately 23% occupancy of 4 THz bandwidth.

Deep Neural Network Inverse Design of Integrated Photonic Power Splitters.

Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response.

High-dimensional modulation for coherent optical communications systems.

In this paper, we examine the performance of several modulation formats in more than four dimensions for coherent optical communications systems. We compare two high-dimensional modulation design methodologies based on spherical cutting of lattices and block coding of a 'base constellation' of binary phase shift keying (BPSK) on each dimension. The performances of modulation formats generated with these methodologies is analyzed in the asymptotic signal-to-noise ratio regime and for an additive white Gaussian noise (AWGN) channel.

An MMI-based wavelength combiner employing non-uniform refractive index distribution.

A novel wavelength combiner using non-uniform refractive index distribution within a multimode interference device is proposed and simulated. The refractive index step creates separate localized modes with different effective refractive indices and two modes are strongly excited which form the basis of an interferometer. We applied the concept to 1.

An MMI-based polarization splitter using patterned metal and tilted joint.

A novel polarization splitter on an InP substrate utilizing an MMI coupler loaded with a dielectric and gold layer pad is proposed and simulated. A tilted joint is used for adjusting the phases of TE and TM modes. The MMI section is less than 540 μm.

High-order statistical equalizer for nonlinearity compensation in dispersion-managed coherent optical communications.

Fiber nonlinearity has become a major limiting factor to realize ultra-high-speed optical communications. We propose a fractionally-spaced equalizer which exploits a trained high-order statistics to deal with data-pattern dependent nonlinear impairments in fiber-optic communications. The computer simulation reveals that the proposed 3-tap equalizer improves Q-factor by more than 2 dB for long-haul transmissions of 5,230 km distance and 40 Gbps data rate.

Mode-evolution-based polarization rotator-splitter design via simple fabrication process.

A mode-evolution-based polarization rotator-splitter built on InP substrate is proposed by combining a mode converter and an adiabatic asymmetric Y-coupler. The mode converter, consisting of a bi-level taper and a width taper, effectively converts the fundamental TM mode into the second order TE mode without changing the polarization of the fundamental TE mode. The following adiabatic asymmetric Y-coupler splits the fundamental and the second order TE modes and also converts the second order TE mode into the fundamental TE mode.