Research
Living cell monitoring using electrochemical impedance method
Status: Active
Period: 2015 - present
Description:
In this project, we aim to realize non-invasive, label free, real time, massively-parallel living cell monitoring down to single cell level. This might be used for cell-based assay, drug discovery/development, and regenerative medicine. Our activities involve the electrochemical impedance measurement of living cells on sensor electrodes, design of micro-scale sensor electrode arrays on complementary metal-oxide-semiconductor (CMOS) large scale integrated (LSI) circuit chip, analog CMOS electronic circuit design for on-chip impedance sensing and 2D imaging, mathematical modeling and computer simulation of impedance from living cells on sensor electrodes, data analysis based on statistical methods and machine learning frameworks.
Tools:
Keysight E4990A Impedance Analyzer for impedance measurement, Virtuoso, HSPICE, Calibre for CMOS custom IC design, COMSOL Multiphysics for computer simulation, Python and other machine-learning frameworks for data analytics.
Collaboration:
Hiroshima University, Ritsumeikan University (Pharmaceutical Science).
Human activity monitoring by wearable and ambient sensors
Status: Active
Period: 2020 - present
Description:
In this project, we aim to build a system which monitors human activity by wearable/ambient sensors, analyzes such data to identify physical/physiological/psychological state, and give recommendations for better performance in work, sleep, and health. Multi-modal data are collected using commercially-available smart watches, smart phones, web cameras, and designated sensor systems built based on single-board computers with ambient sensors. Data are uploaded on a cloud storage, undergo data analysis based on statistical methods and machine learning frameworks. Possible use-case will include, but not limited to, exercise, sleep, and study quality improvement.
Tools:
Commercially-available wearable monitor devices from Apple, Fit-bit, Garmin, etc. for activity data acquisition, single-board micro-controllers/computers such as ArduinoUno/RaspberryPi for ambient data acquisition, Python and various machine-learning frameworks for data analytics.
Collaboration:
Ritsumeikan University (Robotics).
Quantum computing and its applications to real-world problems
Status: in preparation
Period: 2021-
Description:
In this project, we will explore new quantum computing algorithms and their circuit implementation based on both quantum annealing and universal gate quantum computing. As this project is still in preparation phase, more details will come up soon.