1. BDM Capstone Project team : HungPD - Supervisor ThanhLN – Leader ManhDC BienVT NinhVH

2.  Introduction  Requirements  Implementation  Results & Conclusions

3.  Idea’s Origin  Existing Methods  Objective System  Members’ Roles & Responsibilities  Project Plan

4.  Nowadays, many vending machines are used widely in life but it seems that they are not suitable with the habit of buying in Vietnam.  In the real world, this module will be used widely in automation vending machines.

5.  Module is used as personal device  Module is used in Banknote counter

6. ATM Machine WithdrawTransactionDeposit Vending Machine Used with Coin Used with Banknote

7. Banknote Counter Counts Banknote Banknote Classify Need Banknote Detecting Module ATM Banknote Counter Vending machine

8. Existing Products

9. Existing Product

10.  Module is suitable for a vending machine with its small size, low weight, accuracy, and flexibility

11.  ThanhLN – Leader + QA + dev Neural Network Detect object  ManhDC – PTL + dev + test Linux GUI development Image process  BienVT – Hardware Implementer + test Build ARM9 embedded Linux  NinhVH – Dev + test Image process Neural Network

12. Project Initialization

13. Project Planning

14. Project Implementation

15. Project Release and closing

16.  Functional Requirements  Non-functional Requirements  Hardware Requirements

17.  Turn on/turn off the module  Recognize the input which customers put in is Banknote or not Banknote  Recognize type of the Banknote such as:10.000 VND, 20.000 VND etc  Recognize quality of the Banknote  Display information on the display screen

18.  Response time  User interface  Maintainability  Reliability  Low cost

19.  Small size and low weight  Touch screen  Camera : Minimum configuration : 1.3 Mp Zoom 10x  Embedded machine : Minimum configuration : Chip speed : Above 300Mhz RAM : above 64Mb, 32 bit data bus Have touch screen Have a USB gate to connect with Camera

20.  Analysis & Selection of Devices, Tools  Design  Implemented Technical Problems  Testing

21.  Software: Platform: QT SDK IDE : QT creator Document : Microsoft office 2007 Design : Microsoft Visio 2007 OS : Windows, Ubuntu Language : C, C++

22.  Hardware : Camera : Colorvis CVC ND40. Embedded machine : mini 2440 ARM9.

25.  Banknote scanning and collecting

27.  NN data

29.  Slab and mask process

33.  Algorithm uses axis-symmetry mask  Program has 50 different masks to calculate slab values.  Slab value is calculated by sum of non- masked pixels

34.  Neural Network

36.  Project uses Back propagation NN

39.  Implement completed program from Linux to Embedded computer

40.  Learn how to control a webcam in linux  Learn how to use Neural Network  Configure Embedded System to use in this module

41.  Hardware ARM9 machine Webcam  Graphic User Interfaces Button Output view  System Process Banknote detect Non-banknote detect

42.  Test result :

43.  Results  Risks and Limitations  Conclusions

44.  Over the world, especially in embedded system, the perfect does not exist. Every system has trouble. Around the system as a capstone project, we try best to create and develop an acceptable and stable system. The system cannot quick, easy to use cannot good, but the stable of system can be believable  Our module can change function or requirement easily. With a full database and a developer of team, we can add USD or EURO to detect. This is increase value of the module. Module can use in international transaction, and in vending machine, in every where

45.  Risk Hardware may not stable Aspect of environment Camera is not very good  Limitation Not include fake money detect

46.  This product is our last project in FPT University as students. So we want to send our appreciation to : Teachers, who gave us knowledge through 4 years in this university. Specially to Dr HungPD, who guide us to this success Thanks to all staff and students, who went with us from 1 st year

48. Thank you for listening