Electrical and Electronic Engineering

inno show

Scavenger 01_Automatic ping pong collector

SCAVENGER-01 uses AI and computer vision to perform object detection and pick up ping pong balls, without interrupting athletes’ training.

It saves time, improves training efficiency, and prevents injuries.

It can be deployed in ping pong arenas, ideal for ping pong training centres, schools, colleges, clubs, and home settings. Where the robot will pick up the ping pong balls while athletes can focus on their training.

SCAVENGER-01 is a prototype, in the future, we plan to deploy a human avoidance algorithm to avoid colliding into the path of athletes, improve the routing algorithm so it can effectively go through the entire map/arena, and automatic wireless charging so the unmanned robot can head to the wireless charging station for automatic recharging.

SLAM robot with monocular depth estimation

This project aim to construct a robot using SLAM and a monocular camera for both SLAM and depth estimates. The system will be able to map the environment correctly and robustly, determining the robot’s location and orientation inside it. The robot can then estimate the depth using a deep learning model and pictures recorded by the monocular camera as input. This research intends to develop robotics and computer vision by investigating the potential of monocular cameras for SLAM and depth estimation, which might have practical applications in autonomous navigation and depth estimation through the use of a neural network model.

Quantum Physics Demonstrations @ 8th Inno Show

The project’s goal is to build a football robot using provided components and parts such as intelligent chips and motors, as well as other available technology such as a 3-D printer and a laser cutter.
Meanwhile, a comprehensive business plan is required. Based on the requirements, our robots develop a variety of capabilities such as song-playing, light control, self-assembly, programming potential, and power control. Furthermore, we apply the most adaptable remote control talent to provide individual wheel control. A complete and well-structured business plan, including all-sided market study, is tied to the robot’s many functions.

PERfECT Viral Sewage Monitoring System @ 7th Inno Show

Sewage monitoring is an effective preventive measure to detect possible outbreaks early due to viral sheds in sewage systems. However, the current sewage monitoring system is labour-intensive, time-consuming, and lacks scalability. Here, we present our PERfECT viral sewage monitoring system, which is scalable, digital, and can detect viral presence in real time.

PERfECT is a personalized electronic reader for electrochemical transistors that detects viruses when a DNA aptamer is attached to its sensor. Once the virus is detected, the data will be uploaded to the cloud and alert authorities. PERfECT viral detection is much faster than PCR and is reusable. With PERfECT being customisable, it can also monitor the disease progression of different viruses.

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Diabetes is one the most pressing chronic diseases in Hong Kong. There are about 0.7 million people in Hong Kong are suffering from diabetes. Realizing efficient blood glucose management is highly demanded to improve the patients’ healthcare. Currently, the blood glucose is mostly measured with the finger-stick method. However, the finger-stick method is painful and cannot continuously monitor the glucose level. This project develops a minimally invasive glucose biosensor by using a microneedle as sensor carrier and use HKU-developed “PERfECT wearable” platform for wireless data transmission. We hope this project could improve the health technology in Hong Kong to better serve the patients with diabetes.

PERfECT Wearables: for Minimally-Invasive and Continuous Blood-Glucose Monitoring @ 6th Inno Show

Diabetes is one the most pressing chronic diseases in Hong Kong. There are about 0.7 million people in Hong Kong are suffering from diabetes. Realizing efficient blood glucose management is highly demanded to improve the patients’ healthcare. Currently, the blood glucose is mostly measured with the finger-stick method. However, the finger-stick method is painful and cannot continuously monitor the glucose level. This project develops a minimally invasive glucose biosensor by using a microneedle as sensor carrier and use HKU-developed “PERfECT wearable” platform for wireless data transmission. We hope this project could improve the health technology in Hong Kong to better serve the patients with diabetes.

Machine Vision for Urban Farming Applications

The flowers of strawberries are insect-pollinated flowers that require insects to pollinate in the nature environment. However, insects will not be found in indoor hydroponic farms. Strawberry flowers require insects for the pollination process. Therefore, manual pollination is required since insects cannot be found in the indoor hydroponic lab.

In order to reduce the manpower and enhance the automation of urban indoor hydroponic farming, an autonomous pollination system will be developed for Vegetable Marketing Organization (VMO). This application applies YOLOv4 for detecting the strawberry flowers and for classifying the color of anthers. Also, this system includes the flower growing tracker, which can record the growing situation of the flower for the research fields related to planting.

Smart Water Auditing using IoT and Machine Learning

The Smart Water Auditing using IoT, and Machine Learning project aims to provide insights on how water is being in the households of Hong Kong to reduce the consumption of water and raise the awareness of people’s water consumption habits. To help increase participants’ water consumption awareness, a dashboard was designed to provide a simple yet interactive way for users to review their water usage. To create a dashboard, a graphical user interface and a communications system must be designed. However, the initial design of the dashboard suffered from connection issues and graphical user interface performance issues, both of which are essential functions of the dashboard. Therefore, extensive testing and optimisation was necessary to mitigate these problems. However, the dashboard still suffers from minor stuttering due to the microcontroller’s limited performance. Despite this, the design of the dashboard was a success, and it serves as a crucial medium for participants to review their water statistics.

Automated FireFighting Vehicle

Under the project, a fire-fighting AGV is developed. The idea is to save and protect firefighters’ lives by substituting human firefighters with AI robots in hazardous firefighting environments (e.g., toxic, extreme heat, radioactive, etc.). The AGV prototype has utilized the image recognition technologies, as well as a handful of sensors, including a ALS module, infrared array sensor as ultrasonic sensor. Modifications on the stock powertrain system of the given chassis were also made to accommodate the need to traverse in the difficult terrain of a firefighting scenario. Water is chosen to be the fire distinguishing agent.

PET Plastics Degradation

Our team is developing a self-sustaining system that digests PET plastic using the symbiotic relationship between the bacteria Escherichia coli and the cyanobacteria Synechococcus elongatus. We engineer E. coli to express PETase and MHETase enzymes and to absorb sucrose secreted by S. elongatus, which E. coli uses as its energy source. The PETase and MHETase enzymes facilitate the degradation of PET into its monomers – terephthalic acid (TPA) and ethylene glycol (EG), which can then be repurposed in the creation of new bioplastics to maintain a circular and greener supply chain.