Passive wavelength selective polarization rotator in a hybrid waveguide platform.

Polarization rotation and wavelength filtering are key functionalities used to build complex photonic integrated circuits. Both these functionalities have been demonstrated in various material and device platforms. We propose, for the first time, a fully passive wavelength selective polarization rotation in silicon nitride/amorphous silicon hybrid waveguide. We demonstrate TE0 → TM0 and TM0 → TE0 wavelength selective polarization rotator-cum-filter with a measured 3dB bandwidth of 14.8 nm. Further, we experimentally demonstrate a proof of concept for simultaneous coarse wavelength division multiplexing and polarization rotation for the first time in a passive configuration. We also show the feasibility of bandwidth engineering from 0.59 nm to 81 nm, enabled by the unique flexibility of the proposed hybrid waveguide.

Detection of Common Cold from Speech Signals using Deep Neural Network.

This paper presents a deep learning-based analysis and classification of cold speech observed when a person is diagnosed with the common cold. The common cold is a viral infectious disease that affects the throat and the nose. Since speech is produced by the vocal tract after linear filtering of excitation source information, during a common cold, its attributes are impacted by the throat and the nose. The proposed study attempts to develop a deep learning-based classification model that can accurately predict whether a person has a cold or not based on their speech. The common cold-related information is captured using Mel-frequency cepstral coefficients (MFCC) and linear predictive coding (LPC) from the speech signal. The data imbalance is handled using the sampling strategy, SMOTE-Tomek links. Then, utilizing MFCC and LPC features, a deep learning-based model is trained and then used to categorize cold speech. The performance of a deep learning-based method is compared to logistic regression, random forest, and gradient boosted tree classifiers. The proposed model is less complex and uses a smaller feature set while giving comparable results to other state-of-the-art methods. The proposed method gives an UAR of 67.71 % , higher than the benchmark OpenSMILE SVM result of 64 % . The study's success will yield a noninvasive method for cold detection, which can further be extended to detect other speech-affecting pathologies.