Page 104 - Undergraduate Research Opportunities Program Proceedings 2017-18

School of Engineering
Department of Mechanical & Aerospace Engineering

UROP2100, Summer

Gecko-inspired robotics gripper can operate on the most severe conditions due to its nature in adhesion application.
Base on the development of Spring semester 2018, the aim of this project in this semester is to design and build an
experiment platform, including the designing of overall installation of robotics gripper adhesion testing platform, the
control of motor and the feedback from the force sensor, for the testament of robotics gripper. Due to the
requirement of precision up-to micrometer movement, design and choice of the motor require more effort. A platform
for testing the product and the control program for motor has been built near the end of this semester. The overall
design of the platform and the data of the gecko-inspired gripper gleaned from the testament will be further discussed
in the following section.

Design of smart wearables for fall prevention of the elderly

Supervisor: YANG Jinglei / MAE
Student: TANG Yifei / ELEC Course: UROP1100, Fall

Falling of elderly is becoming a serious problem nowadays as it leads to great damages to the elderly, their family and
also the government. A smart wearable which can prevent falling-caused damages may help to solve such a problem.
This project mainly focuses on designing such a product from different aspects like availability, price, market needs and
so on. After making investigations on the existing similar products, a simple model is designed and we are still
progressing to optimze the design. Hopefully, we will continue this research project and may complete the design in the
near future.

Neural Nets for Mechanics

Supervisor: YE Wenjing / MAE
Student: JEYAKUMAR Aadhithya Vignesh / MAE Course: UROP1100, Spring

This UROP had the aim to apply machine learning techniques to accurately and quickly predict the mechanical properties
of various designs of materials. The current targeted problem is the mechanical property prediction of meta materials.
Meta materials are a new class of man-made materials of which the properties depend on the internal structure of their
building blocks instead of their chemical constituents. Accurate and fast prediction of their properties is vital in the
design of these materials. This project shall work on using a Variational Autoencoder (VAE) used for image
reconstruction and generation, and a Convolutional Neural Network (CNN) for prediction of material properties given a
certain design in an image.

Stretchable LED Display Driver Circuit Design
Supervisor: YU Hongyu / MAE
Student: CHOW Pok Man / MAE Course: UROP1100, Spring

The research of flexible display for wearable devices, based on LED/OLED display has been carried out extensively in
recent years. One idea of flexible LED display will be the concept of using origami-like designs incorporated into the LED
matrix, to allow folding and shape-changing capacities. In this progress report of UROP 1100, a preliminary testing circuit
based on passive drive, line scan methods, for the fabricated foldable LED display (size 16*16) is successfully developed,
with good potential of cascading, to allow larger LED display being tested. Several future improvements have also being
proposed.

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