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Dr. Milin Zhang's research interests are in designing of traditional and various non-traditional sensing system, devices and circuit, such as brain-machine-interface and relative biomedical sensing applications, polarization imaging sensors and focal-plane compressive acquisition image sensors. The intellectual merit of the work in her research group is threefold:

  1. To bridge the gap between life science and silicon electronics by designing portable and/or implantable devices and interface circuits for various applications, including, but is not limited to, bio/healthcare area, robotics, computer vision and artificial intelligence.
  2. To explore new sensing technologies by introducing nano- to macro-structured materials into standard CMOS technology to realize wearable and/or implantable sensory system.
  3. To improve the power consumption and robustness of integrated sensory circuits and systems as well as wireless sensor networks, by investigating trade-offs in the circuit design. Introduce compressive and/or feature extractive sensory information acquisition methodologies into standard CMOS circuit and system to improve the efficiency.

The broader impact of the work in her research group will be to link silicon electronics system design methodology to emerging frontier life science researches, to enhance and extend scientific theory through real world situations and practices. The research in Dr. Zhang's research group will provide 1) a platform for neuroscientist to better understand the relationship between brain activities and object behavior, as well as new therapy to neural injury; and 2) low power, compact smart wireless sensory system resolutions for various consumer electronics to improve the customer experience.

Brain Machine Interface

Project description

A Brain Machine Interface (BMI) creates artificial pathway between the brain and external hardware, which has shown promise in replacing sensory and motor pathways lost due to neurological injury or disease. This program explores device implementation, signal processing, as well as custom circuit design. This work focuses on

  1. key technology on high performance, super compact, wireless BMI system design;
  2. key methodology on hardware implementation of the on-chip, real-time general purpose neural signal processing;
  3. key technology on fully customized, implantable neural IC chip design.

Potential research opportunity

1. Design of a user-friendly graphic interface for remote BMI control
Project Type Undergraduate SRT
Project Description In-vivo test plays an important role in the brain-machine-interface experiment. A graphic user interface will greatly simplify the procedure in in-vivo experiments while using BMI devices customized in this research lab. This project seeks to design improve an Android based GUI, enabling wireless control of existing prototype devices.
  • Improve the design of the graphic user interface
  • Implement neural signal recording in the APP
  • Implement basic signal processing in the APP
  • Experience in Android coding is required
  • Fundamental understanding of embedded system is helpful.

Implantable/Wearable Sensing Devices and/or Networks

Project description

The HMI Lab's interests in wearable smart sensor networks focuses on i) the interface circuit design to various sensory devices; ii) the implementation of smart sensor nodes, integrated with sensory information acquisition, on-line data processing, data transmission; iii) custom communication protocol and transceiver module design and iv) system level implementation.

Potential research opportunity

Polarization Imaging Sensors & Signal Processing

Project description

The human visual system as well as traditional imagers only detect and encode two characteristics of light: intensity and wavelength, interpreted as perceptual qualities of brightness and color. A third important characteristic of light, namely, polarization, has not been effectively exploited as the other two characteristics have. Dr. Zhang's research explores new sensor technology by introducing nano- to macroscales patterns into standard CMOS circuit design at the focal plane. This resulted in the successful implementation of a monolithic CMOS polarization image sensor.

Potential research opportunity