A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals.

The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis.

Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals.

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts' experience and their prior knowledge about different physiological signals and conditions of the subjects.

An Improved Passivity-based Control of Electrostatic MEMS Device.

It is commonly known that the performance of an electrostatic microelectromechanical system (MEMS) device is limited to a specific range of the full gap distance due to the inherited "pull-in instability" phenomenon. In this work, we design a controller to extend the stabilization range of an electrostatic MEMS device and to enhance its performance. The interconnection and damping assignment-passivity based control (IDA-PBC) method is used and the controller design involves coordinate transformations and a coupling between the mechanical and electrical subsystems of the device.