Contemporary medicine suffers from impactful shortcomings in terms of successful disease diagnosis and treatment. Diagnostic delays and/or inaccuracies can cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or result in psychological burden or financial repercussions. Our objective is to develop an AI-based smart health monitoring system for non-intrusive, continuous, real-time and personalized detection of physical and (bio)chemical markers that are linked with overall health of the human body.
Our goal is to develop multi-modal neural network architectures for the tasks of guided super-resolution (SR) of dynamic elevation models (DEM). Current DEMs for most of the earth surface is still low resolution (sometimes 2 meters per pixel, but more often 10, 15, or 30 meters per pixel) and thus cannot accurately represent the morphology of the terrain. High resolution DEMs, however, have many uses, including precision agriculture, urban mapping, high-definition maps for autonomous navigation, line-of-sight analysis, and more.
We address the problem of residual echo suppression (RES) in real-life acoustic environments that often include low signal-to-noise ratios, reverberations, and degraded audio measurements. We propose a low power, low-resources, on-device system that receives a dual-channel audio streaming as waveform and applies deep learning-based echo cancellation to it. This solution can benefit many practical speech-based hands-free communication platforms such as smart-phones, conference room speakerphones, and smart speakers like Amazon Alexa and Google Home.
