School of Science and Technology 科技學院
Computing Programmes 電腦學系
| Programme | Bachelor of Science with Honours in Web Technologies |
| Supervisor | Dr. Jeff Tang |
| Areas | Innovative Systems |
| Year of Completion | 2014 |
The aim of the intelligent work desk system (IWDS) is to enhance the efficiency and safety of human who working on a work desk. We are developing an intelligent system with the use of real-time monitoring 3D sensor. The system is expected to allow user to find their missed things and to preventing the things stolen from the work desk. We have concluded the concepts of the project into three core parts:
In order to decrease and enhance the safety of the working process, The IWDS can logs the date, times, coordinates, movements and other related information of different objects so users can locate the lost object easily. Also, IWDS is easy to use so users can interact with the system efficiently.
The structure of The Intelligent Work Desk System (IWDS) is divided into three sections, and each section has also divided into few components. We will describe each components of each section in this chapter, and to show their purposes, roles, and functions. More details will be described in the Component Implementation and Evaluation Document.
Image Visualization is the process of displaying the images captured by the Kinect sensor projected on the screen. It is used to keep extracting the current work desk environment captured by Kinect Sensor.
As for the calculation of later procedures, such as Object Segmentation, Object Recognition, Object Tracking, we need to remove useless points from the point cloud by using the Cartesian equation. If the depth value of a point is smaller than the z value, which is calculated by substituting the x, y coordinates of the point into the equation of the plane, we keep this point otherwise we discard it.
After the Table Points Removal procession, the result image gotten need to be converted into a grayscale image. The Blob Detection consists of three steps, Edge Detection, Contour Detection, and Drawing Bounding Box. The Edge Detection helps finding the edges of each blob. Second, Contour Detection helps finding the coordinates of the edge pixels. Finally, the IWDS draw a bounding box around each blob to tell users the IWDS how many objects have detected. The IWDS also save each blob detected into a database for later implementation of Object Recognition.
As for implementing the user interface, we consider that how to provide a user-friendly interface for users to use. There is a main goal of the interface design, which is that providing a user interface that users feel easy to use. Therefore, we do many references to other user interfaces. Finally, we implement the user interface shown as below:
After the fine tuning, the result can be good. Users can press the “Start Capturing” to start object logging. Each individual object is separated and saved into the database with 10s temporal. The default name of the first object will be “item0” and so on. The IWDS keep detect the movement of the object or even detect the new object, then save the latest object information into the database without flickering.
Object Recognition can help users find the stuff in real-time. When users select the particular item and search it in the recognition mode, the IWDS start the algorithm and match the pixel. As the matching process is required lots of resource, the object recognition mode has a little flickering but it is acceptable. The IWDS highlight the wallet with the bounding box. It only takes one second to match the object. When users move the wallet now, the bounding box will follow the new position of the wallet too.
To evaluate whether we have satisfied the aims of the project, we have conducted a survey to investigate the opinions from users. We invited 30 peoples to test our system and fill in the survey. Level 1 means strongly disagree, level 2 means disagree, level 3 means somewhat disagree, level 4 means neither agree or disagree, level 5 means somewhat agree, level 6 means agree, and level 7 means strongly agree.
In generally, users agree the IWDS can secure the stuff from being stolen. However, we can do better in this part. As the IWDS saved each objects status into the database with 10s temporal. If the certain stuff disappears with in this 10s, the information of this stuff will not be saved into the database.
We have got a very high mark in the second question. Everyone though the IWDS can really help them to remember the location of stuff. The object logging function is satisfied undoubtedly. Especially for the long-term usage, the IWDS log each object with information automatically. Unless the users delete the database, users can remember the location of stuff in long time ago easily.
Most of the respondents agree the user interface of the IWDS is easy to use. We have got some comments about the user interface. Some of the respondents love the UI design; they said it is modern and easy to use. Also, some of them mentioned that the voice command function is accuracy. However, some of the respondents said the initiation part of IWDS is complex; we may need to simplify this part.
All of the respondents thought the user interface of the IWDS is natural.
Most of the respondents agree the IWDS can help to enhance their productivity.
We have satisfied all the project aim including:
The IWDS is capable to recognize and track the locations of things on desk in real-time, such as stationaries, books, and cups. IWDS can log the date, times, and positions of different objects so users can locate the lost object easily. This function could help users to find the lost stuff easily. Also, IWDS can recognize voice so users can simply say the keyword to search the certain item quickly. After the evaluation part, we know that most users are identity and appreciate the IWDS. They agree the IWDS can help to enhance their productivity, help them to remember the location of stuff, help to secure their stuff on desk from being stolen, and they like the user interface.
In future, this system can have lots of potential. We can learn more complicated algorithm or approach to achieve real 3D detection and recognition. Or other powerful function, For example, the system can recognize the degree of risk of the objects so the system can be used in the kindergarten or nursing home for enhance the safety, decrease the chance of accident.
Copyright Ng Wing Hoi, So Chung Hei, Tsui Chun Yi and Jeff Tang 2014
Jonathan handles all external affairs include business development, patents write up and public relations. He is frequently interviewed by media and is considered a pioneer in 3D printing products.
After graduating from OUHK, Krutz obtained an M.Sc. in Engineering Management from CityU. He is now completing his second master degree, M.Sc. in Biomedical Engineering, at CUHK. Krutz has a wide range of working experience. He has been with Siemens, VTech, and PCCW.
Hugo Leung Wai-yin, who graduated from his four-year programme in 2015, won the Best Paper Award for his ‘intelligent pill-dispenser’ design at the Institute of Electrical and Electronics Engineering’s International Conference on Consumer Electronics – China 2015.
The pill-dispenser alerts patients via sound and LED flashes to pre-set dosage and time intervals. Unlike units currently on the market, Hugo’s design connects to any mobile phone globally. In explaining how it works, he said: ‘There are three layers in the portable pillbox. The lowest level is a controller with various devices which can be connected to mobile phones in remote locations. Patients are alerted by a sound alarm and flashes. Should they fail to follow their prescribed regime, data can be sent via SMS to relatives and friends for follow up.’ The pill-dispenser has four medicine slots, plus a back-up with a LED alert, topped by a 500ml water bottle. It took Hugo three months of research and coding to complete his design, but he feels it was worth all his time and effort.
Hugo’s public examination results were disappointing and he was at a loss about his future before enrolling at the OUHK, which he now realizes was a major turning point in his life. He is grateful for the OUHK’s learning environment, its industry links and the positive guidance and encouragement from his teachers. The University is now exploring the commercial potential of his design with a pharmaceutical company. He hopes that this will benefit the elderly and chronically ill, as well as the society at large.
Soon after completing his studies, Hugo joined an automation technology company as an assistant engineer. He is responsible for the design and development of automation devices. The target is to minimize human labor and increase the quality of products. He is developing products which are used in various sections, including healthcare, manufacturing and consumer electronics.
| Course Code | Title | Credits | |
|---|---|---|---|
| COMP S321F | Advanced Database and Data Warehousing | 5 | |
| COMP S333F | Advanced Programming and AI Algorithms | 5 | |
| COMP S351F | Software Project Management | 5 | |
| COMP S362F | Concurrent and Network Programming | 5 | |
| COMP S363F | Distributed Systems and Parallel Computing | 5 | |
| COMP S382F | Data Mining and Analytics | 5 | |
| COMP S390F | Creative Programming for Games | 5 | |
| COMP S492F | Machine Learning | 5 | |
| ELEC S305F | Computer Networking | 5 | |
| ELEC S348F | IOT Security | 5 | |
| ELEC S371F | Digital Forensics | 5 | |
| ELEC S431F | Blockchain Technologies | 5 | |
| ELEC S425F | Computer and Network Security | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| ELEC S201F | Basic Electronics | 5 | |
| IT S290F | Human Computer Interaction & User Experience Design | 5 | |
| STAT S251F | Statistical Data Analysis | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| COMPS333F | Advanced Programming and AI Algorithms | 5 | |
| COMPS362F | Concurrent and Network Programming | 5 | |
| COMPS363F | Distributed Systems and Parallel Computing | 5 | |
| COMPS380F | Web Applications: Design and Development | 5 | |
| COMPS381F | Server-side Technologies and Cloud Computing | 5 | |
| COMPS382F | Data Mining and Analytics | 5 | |
| COMPS390F | Creative Programming for Games | 5 | |
| COMPS413F | Application Design and Development for Mobile Devices | 5 | |
| COMPS492F | Machine Learning | 5 | |
| ELECS305F | Computer Networking | 5 | |
| ELECS363F | Advanced Computer Design | 5 | |
| ELECS425F | Computer and Network Security | 5 |
