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Low-noise low-power CMOS instrumentation for wearable electrochemical sensor arrays in health hazard monitoring
Wearable senors play an important role in ambient environmental monitoring for human health protection. In this project, different microelectronic circuits have been developed to address circuit challenges in wearable gas sensor array development.
Lab-on-CMOS Microsystem
This lab-on-CMOS project is try to integrate biosensor array, CMOS electrochemical instrumentation and Microfluidic channel together into a single device to make a compact high-throughput microsystem to fully develop the miniaturized monolithic realization. Biosensor, CMOS instrumentation and Microchannel fabrication, include with the packaging and encapsulation research was performed in this project for the on-CMOS single device microsystem target.
This research project address the problem of how to identify changes in the neurons observed by wireless neural recorders to maintain a good spike sorting performance.
Implantable Optogentic Stimulator and Recording System
This project aims to implement a low power, hybrid optical/electrical implantable microsystem as the next generation of brain-machine interfaces. Optogenetic stimulation and simultaneously, electrical recording is targeted in this work.
This project aims to produce a portable, adaptable electrochemical sensor interface for malaria parasite detection in remote places. The aim is to diagnose harmful malaria parasites in blood without the use of sophisticated optical solutions. The proposed solution is embedded with a smart phone for power, processing and data analysis.
Ionic Liquid Based Miniaturized Electrochemical Gas Sensor
the goal of this project is to develop a wearable autonomous multi-gas sensor system capable of real-time individual environmental monitoring which could provide immediate feedback to warn the wearer of imminent environmental threats as well as a record of individual exposure that would aid the development of new treatment approaches
Automation and curation of multi-domain electrochemical experiments reseach automation electrochemical data provenance
A software infrastructure for automating research workflows, assembling and comparing metadata-rich data sets, and connecting lab equipment to the internet of things.
This research aims to develop a power-area efficient NSP capable of preserving useful neural data information while achieving a high compression rate. We have analyzed and applied techniques from signal detection theory and pattern recognition to neural signals, resulting in reliable spike-sorting algorithms which are robust to neural noise.