Banknote Detect Module BDM Capstone Project team Hung

Banknote Detect Module BDM Capstone Project team : Hung. PD - Supervisor Thanh. LN – Leader Manh. DC Bien. VT Ninh. VH

Contents Introduction Requirements Implementation Results & Conclusions

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

Idea’s Origin 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.

Idea’s Origin Module is used as personal device Module is used in Banknote counter

Idea’s Origin Withdraw ATM Machine Transaction Deposit Vending Machine Used with Coin Used with Banknote

Idea’s Origin Banknote Counter Counts Banknote Classify Banknote Counter Vending machine ATM Need Banknote Detecting Module

Existing Products

Existing Product

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

Members’ Roles & Responsibilities Thanh. LN – Leader + QA + dev • Neural Network • Detect object Manh. DC – PTL + dev + test • Linux GUI development • Image process Bien. VT – Hardware Implementer + test • Build ARM 9 embedded Linux Ninh. VH – Dev + test • Image process • Neural Network

Project Plan Project Initialization

Project Planning

Project Plan Project Implementation

Project Plan Project Release and closing

Requirements Functional Requirements Non-functional Requirements Hardware Requirements

Functional Requirement 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

Non-functional Requirement Response time User interface Maintainability Reliability Low cost

Hardware Requirement Small size and low weight Touch screen Camera : Minimum configuration : • 1. 3 Mp • Zoom 10 x Embedded machine : Minimum configuration : • • Chip speed : Above 300 Mhz RAM : above 64 Mb, 32 bit data bus Have touch screen Have a USB gate to connect with Camera

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

Analysis & Selection of Devices, Tools Software: • • • Platform: QT SDK IDE : QT creator Document : Microsoft office 2007 Design : Microsoft Visio 2007 OS : Windows, Ubuntu Language : C, C++

Analysis & Selection of Devices, Tools Hardware : • Camera : Colorvis CVC ND 40. • Embedded machine : mini 2440 ARM 9.

Design

Design

Design Banknote scanning and collecting

Design Grayscale Banknote

Design NN data

Mask and Slab value

Design Slab and mask process

Design

Design

Design

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

Design Neural Network

Design Project uses Back propagation NN

NN training flowchart Project uses Back propagation NN

NN recognition flowchart Project uses Back propagation NN

Design Implement completed program from Linux to Embedded computer

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

Testing Hardware • ARM 9 machine • Webcam Graphic User Interfaces • Button • Output view System Process • Banknote detect • Non-banknote detect

Testing Test result :

Results & Conclusions Results Risks and Limitations Conclusions

Results 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

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

Conclusions 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 Hung. PD, who guide us to this success • Thanks to all staff and students, who went with us from 1 st year

Q&A

Thank you for listening