Imaging Brain Activity
Optical instrumentation, In vivo experiments, Computational analysis
Recording brain activity is one of the key technologies to understand how a brain circuit works. By genetically engineering the neurons, they can be modified to emit fluorescent light where the light intensity changes as a function of the neuronal activity. This combined with high-speed 3-D fluorescent microscopy allows us to optically record the brain activity in 3-D.
Nature Methods (2014), Nature Chemical Biology (2018), SfN (2014, 2015)
Imaging Brain Structure
Optical tissue imaging, Image analysis, Deconvolution
Imaging brain structure with a high resolution is the first step to understand how a brain circuit is connected. By physically expanding a brain tissue, it can be imaged with a much higher effective resolution (which we call Expansion Microscopy). This allows us to image the nanostructures of a brain like the synapses and the dendrites.
Nature Methods (2017), SfN (2016)
Machine learning &
Analyzing Brain Structure
Neural network, Image segmentation, Simulation study
Expansion Microscopy (ExM) allows us to optically image a brain tissue with ~20nm resolution which is fine enough to see the synapses. This combined with computational image analysis via artificial intelligence will potentially allow us to reconstruct the map of a brain.
Front. Comp. Neurosci. (2017), SfN (2016)
Monitoring Depth of Anesthesia
Bio-signal processing, In vivo experiment, Biomedical engineering
Electroencephalogram (EEG) is an electrical signal from an ensemble of neurons. By analyzing the signal, it is possible to monitor the depth of anesthesia of a subject to make sure that just the right amount of anesthetics is being applied during a surgical operation.
IEEE EMBC (2011, 2013)
Biosensor
Biomedical engineering
Pulse wave velocity (PWV) is the speed at which the arterial pulse propagates through the vessels. By designing a sensor that can simultaneously measure the ECG and the bio-impedance of a subject, the PWV can be monitored in real time.
IEEE BioCAS (2009)
Direct RF Sampling ADC
CMOS circuit design, ADC, RF
By embedding a discrete-time signal processing theory into analog circuit design, it is possible to implement a tunable direct RF sampling ADC without using large passive components. Such direct RF sampling ADCs can pave the ways to fully digital and tunable RF radios.
IEEE Symp. VLSI (2009), IEEE T-CAS (2008)
Time-based ADC
CMOS circuit design, ADC
With CMOS scaling, designing an analog circuit is becoming increasingly difficult due to the reduced supply voltage, large leakage current, low output impedance and so forth. Time-based ADC is a novel way of implementing an analog-to-digital converter that circumvents such issues.
IEEE T-CAS (2010), IEEE T-VLSI (2012), IEEE IMWS (2011), IEEE ISOCC (2009)