Successful use of a single-section quantum well (QW) passively mode-locked laser (MLL) as a comb source for optical interconnects is demonstrated for the first time. Sixteen comb lines spaced by 37.6 GHz are modulated using 25 Gb/s compatible single sideband orthogonal frequency division multiplexed (SSB-OFDM) signals and transmitted over 50 km of standard single-mode fiber with bit error ratio below the 7% forward error correction limit. The system performance, analyzed on the basis of the relative intensity noise of the device, reveal the suitability of single-section QW MLLs as inexpensive comb sources for inter- and intra-data center communication scenarios.

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In this paper we propose an efficient technique for micromachining lithium niobate that is used in Ti-diffused waveguides. The use of Focused Ion Beam (FIB) etching allows obtaining homogeneous periodic microstructures. Bragg gratings with a period of 1.05 m and an aspect ratio of 6:1 (depth-to-half period ratio) have been achieved. A reflectivity greater than 95% associated with a bandwidth at half maximum of about 100 nm within a window centered at 1550 nm, is demonstrated for a Bragg grating of period 3=18 m and a length of 144 m, in good agreement with theoretical predictions.

Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients—Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7–15% improvement). This result was observed for both recording types (high-quality microphone and telephone).