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Classification of Buried Objects Using Acoustic Waves Minoufiya University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Classification of Buried Objects Using Acoustic Waves A Thesis Submitted for the Degree of M. Sc. in Electronic Engineering, Communications Engineering, Department of Electronics and Communications Engineering By Eng. Emad Abd Elhalim Elsayed Elshazly B. Sc. in Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Minoufiya University, Menouf 2004 Supervisors Prof. Mohamed F. El-Kordy Prof. in Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Minoufiya University Prof. Sayed M. El-Araby Chairman of Atomic Energy Authority Dr. Osama F. Zahran Lecturer in Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Minoufiya University 2012 Minoufiya University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Classification of Buried Objects Using Acoustic Waves A Thesis Submitted for the Degree of M. Sc. in Electronic Engineering, Communications Engineering, Department of Electronics and Communications Engineering By Eng. Emad Abd Elhalim Elsayed Elshazly B. Sc. in Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Minoufiya University, Menouf, 2004 Supervisors Prof. Mohamed F. El-Kordy ( ) Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Minoufiya University Prof. Sayed M. El-Araby ( ) Chairman of Atomic Energy Authority Dr. Osama F. Zahran ( ) Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Minoufiya University 2012 Minoufiya University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Classification of Buried Objects Using Acoustic Waves A Thesis Submitted for the Degree of M. Sc. in Electronic Engineering, Communications Engineering, Department of Electronics and Communications Engineering By Eng. Emad Abd Elhalim Elsayed Elshazly B. Sc. in Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Minoufiya University, Menouf, 2004 Approved by Prof. Ali Hassan Moustafa ( ) Emeritus Professor Faculty of Electronic Engineering Minoufiya University Prof. Hesham Fathy Aly Hamed ( ) Professor Faculty of Engineering Minia University Prof. Mohamed F. El-Kordy ( ) Professor Faculty of Electronic Engineering Minoufiya University 2012 ﺑِﺴﻢِ ﺍﻟﹼﻠﻪِ ﺍﻟﺮﺣﻤـَﻦِ ﺍﻟﺮﺣِﻴﻢِ (((ﻳﺮﻓﹶﻊِ ﺍﻟﱠﻠﻪ ﺍﱠﻟﺬِﻳﻦ ﺁﻣﻨﻮﺍ ﻣِﻨﻜﹸﻢ ﻭﺍﱠﻟﺬِﻳﻦ ﺃﹸﻭﺗﻮﺍ ﺍﹾﻟﻌِﹾﻠﻢ ﺩﺭﺟﺎﺕٍ ﻭﺍﻟﱠﻠﻪ ﺑِﻤﺎ ﺗﻌﻤﻠﹸﻮﻥﹶ ﺧﺒِﻴﺮ ))) ﺻﺪﻕ ﺍﷲ ﺍﻟﻌﻈﻴﻢ ﺳﻮﺭﺓ ﺍﻟﻤﺠﺎﺩﻟﺔ : ﺍﻵﻳﺔ ١١١١١١ ACKNOWLEDGEMENTS First and foremost, I thank ALLAH , the most gracious, the ever merciful for helping me finishing this work. I want to thank all those, who helped me by their knowledge and experience. I will always appreciate their efforts. I wish to express my sincere thanks to my supervisors Prof. Mohamed El-Kordy, Prof. Sayed El-Araby and Dr. Osama Zahran. I am deeply indebted to them for valuable supervision, continuous encouragement, useful suggestions, and active help during the course of this work. Special thanks to Dr. Fathi Abd El-Samie for his valuable suggestions and continuous support. My sincere appreciation and gratitude to my parents, my brothers, my father in law, my mother in law, my brothers in law, my wife, and my daughter Mariam for their help and patience during the preparation of this work. I LIST OF PUBLICATIONS [1] E. A. El-shazly, O. Zahran, Sayed M. S. Elaraby, M. El-Kordy and F. E. Abd El-Samie, “ Automatic Detection of Buried Landmines using Cepstral Approach ”, 1st International Conference on Electrical and Computer Systems Engineering (ECSE 2010), 6th October City, Egypt, 1-3 November, 2010. [2] E. A. El-shazly, O. Zahran, Sayed M. S. Elaraby, M. El-Kordy and F. E. Abd El-Samie, “ Cepstral Detection of Buried Landmines from Acoustic Images with a Spiral Scan ”, 6th International Computer Engineering Conference (ICENCO 2010), IEEE, pp. 97-102, Cairo, Egypt, 28-29 December, 2010. [3] E. A. El-shazly, O. Zahran, Sayed M. S. Elaraby, M. El-Kordy, S. El-Rabie and F. E. Abd El-Samie, “ Identification of Buried Landmines Using Mel Frequency Cepstral Coefficients and Support Vector Machines ”, 8th International Conference on Informatics and Systems (INFOS 2012), IEEE, pp. MM60-MM66, Cairo, Egypt, 14-16 May 2012. II ABSTRACT There is no doubt that the problem of landmines is one of the most important problems that concerns the whole world. Egypt is one of the countries that suffer from this problem. There are more than 23 million landmines that are subject to explode at any time and these landmines occupy a large area estimated by 3910 square kilometers. The source of these landmines is the wars; World War II and the Arab-Israeli wars of 1956, 1967, and 1973 in the eastern desert and Sinai. The problem of landmines is not human casualties only, but there are also serious economic losses. There are several obstacles that are faced in removing the landmines such as the loss or absence of maps as well as the high costs needed to remove them. The work presented in t his thesis provides an introduction to land mines ; definition, their components and types, a summary of the techniques and the different methods used for detecting and clearing them, and the operating principles of each method. This thesis proposes efficient landmine identification techniques, which help in identifying the several types of landmines with different dimensions, shapes, and types. These techniques use Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) and they are based on the MFCCs to identify the different types of landmines. The performance of these techniques is evaluated in the presence of different types of noise with and without blurring. The thesis also proposes a classification technique using ANNs to classify the different types of landmines into different categories based on MFCCs features. III TABLE OF CONTENTS List of Abbreviations . …… . … . …. VIII List of Symbols . ... …….. …. X List of Figures . …... .. …….. …. XII List of Tables . .... ……….. XVII CHAPTER (1) INTRODUCTION ……………………………...…………… 1 1.1 Introduction………………………………...…………...…..…… 1 1.2 Thesis Objectives and Contributions ….………..………….……. 3 1.3 Thesis Outlines……………………….………...…………....…... 4 CHAPTER (2) A SURVEY STUDY ON LANDMINES PROBLEM ….….. 5 2.1 Introduction………..………...….………….……………..…...… 5 2.2 Short Overview of Buried Landmines.....…….…….…......…..… 5 2.3 Landmine Components …...……..…..…….…………..………… 6 2.3.1 Firing Mechanism ……….………...………...……...….. 7 2.3.2 Detonator or Igniter……….…………………..…...…… 7 2.3.3 The Booster Charge……….………………….…..….…. 7 2.3.4 The Main Charge……….……………...……...…..……. 7 2.3.5 The Case……….……………………..………......…….. 7 2.4 Types of Landmines ………………….…………..……...….…... 8 2.4.1 AT Landmines…………………...……..……….……… 10 2.4.2 AP Landmines…..…………………….....……………... 11 2.5 Countries and Regions Affected by Landmines……..…………... 13 2.6 Landmines in Egypt………..………………………………...…... 15 2.7 Demining Approaches and Technologies……………..…………. 18 2.7.1 Manual Demining…………..……………………...…. 18 2.7.2 The Use of Animals, Insects and Bacteria...…..….…….. 18 2.7.3 Mechanical Demining….……….………..………..……. 20 2.7.4 Robots and Humanitarian Demining..……….…………. 21 IV 2.7.5 Landmine Detection and Sensing Technologies…….….. 22 2.8 Summary....…...……………………………………….....………. 26 CHAPTER (3) REVIEW STUDY ON LANDMINE DETECTION SYSTEMS AND ARTIFICIAL NEURAL NETWORKS …….…………… 27 3.1 Introduction....………………………………….………..………. 27 3.2 Literature Review on LDV Based A/S Landmine Detection....…. 28 3.3 Principles of A/S Coupling ……………………………………... 31 3.3.1 Body Waves…………..…………………...…................. 31 3.3.1.1 Primary Waves……...…….……...………….. 32 3.3.1.2 Secondary Waves…...……………………….. 32 3.3.2 Surface Waves………………………………………….. 32 3.3.2.1 Loove Waves……………………………….. 32 3.3.2.2 Rayleigh Waves……………………………... 32 3.4 A/S Transmission System with Acoustic Loudspeakers...………. 33 3.5 A/S Receiving System using LDV………………………………. 33 3.6 Segmentation of Acoustic Landmine Images……………………. 35 3.6.1 Erosion…………………………………………………. 36 3.6.2 Dilation…………………………………………………. 36 3.6.3 Opening…………………………………………………. 37 3.6.4 Closing………………………………………………….. 37 3.7 Artificial Neural Networks (ANNs).………...………………..…. 38 3.7.1 Neural Network Concepts………………….…………… 39 3.7.1.1 Cells………………………...……………….. 39 3.7.1.2 Layers………………………………………... 39 3.7.1.3 Arcs……………………………………...…... 40 3.7.1.4 Weights………………………………...……. 40 3.7.2 The Learning Rules of Neural Networks…………….…. 40 3.7.2.1 Supervised Learning………………...……….. 40 3.7.2.2 Unsupervised Learning…..………………….. 41 3.7.2.3 Reinforcement Learning……...……………… 41 V 3.7.3 Neuron Model…………….…………………………….. 41 3.7.3.1 Simple Neuron………………...…………….. 41 3.7.3.2 Neuron with Vector Input……………...…..... 42 3.7.4 Activation Rules………………………………………... 43 3.7.4.1 Identity Function…...………...……………… 43 3.7.4.2 Step Function..…………………………...….. 43 3.7.4.3 Sigmoid Function……………………………. 43 3.7.5 Network Architectures………………………………….. 44 3.7.5.1 Single Layer Network……………………….. 44 3.7.5.2 Multi Layer Network………………………… 45 3.7.6 Back-propagation Neural Network…………..…………. 46 3.8 Types of Noise……………………...……………………………. 49 3.8.1 The AWGN……………………………………………... 49 3.8.2 Impulsive Noise……………………………………….... 50 3.8.3 Speckle Noise…………………………………………... 50 3.8.4 Poisson Noise…...…………………………………….… 50 3.8.5 Image Blurring...………………………………………..
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