Network protocol design, spatio-temporal compression and data mining from various application domains, machine learning based data collection and processing schemes. Our current activities include the study of biometric signals from wearable IoT devices and wireless sensing.
Research on 5G cellular nets is ongoing to achieve unprecendently experienced bitrates, exploiting massive MIMO technology, machine learning for self-adapting audio/video streaming and intelligent network policies for energy harvesting base stations and devices.
We are involved in several research activities for underwater mobile networking. There, our focus is centered around the design of networking protocol (channel access, routing, data gathering, etc). Various experimental activities are also on the way, including simulation, lab and field test of network protocols and at-sea testing.
Droplet microfluidics refers to manipulation and control of little amount of fluids flowing into channels of micro-size scale. We are concerned with understanding microfluidic flow dynamics and their propagation characteristics. These concepts are then used to devise networking algorithms to route droplets in a controlled manner through complex microfluidic networks.
Human sensing technology is being massively introduced in our everyday life. Example are smartphone applications gathering inertial signals, assisted living products and intelligent white goods. The general idea is to measure human data / posture / vital signs or gestures to feed applications. Our group is especially interested in data mining in the contexts of physiological signals and motion tracking.
The millimeter wave bands offer orders of magnitude more spectrum than conventional wireless frequencies but, on the other hand, suffer from increased path loss, severe channel intermittency and inability to penetrate through solid materials. To overcome these issues, further studies are required in a way to perform the transmission in future mobile networks.