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

 Introduction  Requirements  Implementation  Results & Conclusions

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

 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.

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

ATM Machine WithdrawTransactionDeposit Vending Machine Used with Coin Used with Banknote

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

Existing Products

Existing Product

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

 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

Project Initialization

Project Planning

Project Implementation

Project Release and closing

 Functional Requirements  Non-functional Requirements  Hardware Requirements

 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: VND, VND etc  Recognize quality of the Banknote  Display information on the display screen

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

 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

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

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

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

 Banknote scanning and collecting

 NN data

 Slab and mask process

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

 Neural Network

 Project uses Back propagation NN

 Implement completed program from Linux to Embedded computer

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

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

 Test result :

 Results  Risks and Limitations  Conclusions

 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

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

 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

Thank you for listening