George Washington s President Of The United States Essay

America has had many great leaders throughout its rich history. What is a president, is it not just another word for leader? George Washington was a military leader during the american revolution and later became the first president of the United States of America. He was a magnificent president and he set the bar for what the president should be like. Throughout history we have had many presidents some good some not so much. This upcoming election will decide our forty fifth president of the United States, and may be the single most important election we will ever have. All the presidents of the United States have had a problem that they had to deal with and that they would talk about in their campaigns to help them to be elected. The two candidates either Donald Trump or Hillary Clinton will have more than just one problem to deal with. Our nation is not in a very good position so the president we choose has to be able to think on his or her feet and be a strong leader we can trust . A lot of things come into effect when voting for the new president such as the way the battleground states vote. The battleground states are Ohio, Pennsylvania, Michigan, Florida, North Carolina, Virginia, and Wisconsin. These states have a major impact on who will be the next president for the reason that most of the time the candidate that wins the election wins the majority of these states. With this being such a huge election that is so close in the polls we need everyone who can vote toShow MoreRelatedGeorge Washington s President Of The United States2433 Words   |  10 PagesIn 1789, George Washington became the first elected President of the United States. President George Washington stayed in office for two terms (eight years), after which he decided to â€Å"step-down† or not to run again. His friends tried to convince him to run again, but he already had his mind made up. His successor John Adams continued to follow in George Washington’s footsteps and on ly served two terms. This started a tradition where Presidents generally only serve as Chief of State, Chief ExecutiveRead MoreGeorge Washington s President Of The United States961 Words   |  4 PagesGeneral George Washington, first president of the United States, was instrumental in establishing the procedures to govern an independent nation. The basic premise of Washington’s Farewell Address was to announce his decision to retire. Aside from defending his administration’s record, his message also encouraged and instructed future leaders to follow the principles necessary to successfully govern America as a unified, free nation in regards to domestic and foreign affairs. First and foremostRead MoreAnalysis Of George Washington s President Of The United States Essay2113 Words   |  9 PagesWhen George Washington was elected President in 1789 by members of the fledgling United States of America, he was setting into motion a tradition that has stood the test of over 225 years - the presidential election. Even as the United States has seen dozens of wars, made hundreds of scientific advances, and selected thousands of politicians to seats everywhere from small town councils to Congress, the principles of the election have remained the same; the people band together to determine who willRead MoreGeorge Washington s The War For American Independence1251 Words   |  6 PagesAmerican history, George Washington will always remain to be one of the brightest ones. This is not only because George Washington was the first president of the United States of America, but also because of his character. George Washington was more than just a politician, he was a national leader and an example for many of his followers. Many look to George Washington for the great things he accomplished in his lifetime. Winning the War for American Independence, being the first president, and being anRead MoreGeorge Washington Farewell Address Essay1663 Words   |  7 PagesGeorge Washington, a very famous man known as the first President of the United States of America, was born on the 22nd of February, 1732 in the colony of Bridges Creek, Virginia. (George Presidential Early Life sec.1 para.3) He was born into a wealthy, land-owning family and was a very political individual. Washington served as a Major in the Virginia militia and also fought in the French and Indian War from 1754 to 1763. In addition, Washington became a representative of the Virginia legislatureRead MoreGeorge Washington s Farewell Address Essay1258 Words   |  6 PagesChase Williams US History to 1865 Dr. Lisa Crutchfield October 14, 2015 George Washington s Farewell Address, Primary Source Analysis George Washington’s Presidential Farewell Address consisted of three critical elements that were considered vital for the functional survival of the country that had just won its independence. On September 19, 1796, President Washington advised the nation to stand together as one united country, warned the people about the dangers of political parties and he establishedRead MoreThe Farewell Address Essay1422 Words   |  6 PagesGeorge Washington, a very famous man known as the first president of The United States of America was born on the 22nd of February in 1732 in Bridges Creek, a colony in Virginia. He was born into a wealthy, land-owning family and was a very political individual. George served as a major in the Virginia militia and also fought in the French and Indian War that occurred from 1754 to 1763. In addition, Washington became a representative of the Virginia Legislature where he was titled a commissionerRead MoreAnalysis Of The Book Into The Wild By Jon Krak auer1686 Words   |  7 Pagesfreedom and exemption. Washington and McCandless are similar because they did something most people would not. They both stepped out of the ordinary society and decided to do what they think is best. For example, Washington led the Continental Army against the great British Empire, and Chris left his normal and traditional life, to seek a life of adventure and determined to go on a journey across the United States. Washington became the first president of the United States. Washington was also the onlyRead MoreThe First Five Presidents Of The United States Essay1596 Words   |  7 Pagesfirst five presidents of the United States impacted the United States greatly and their names were George Washington, John Adams, Thomas Jefferson, James Madison, and James Monroe. The president I think that impacted the U.S the most was George Washington who was in office for eight years (1789-1797). George Washington who was the commander in chief and led the army in the Revolutionary War and gained freedom from Great Britain at that time there was thirteen colonies in the United States. In 1783Read MoreGeorge Washington And The Era Of The American Revolution1569 Words   |  7 Pageshas certain secret rituals†. George Washington was one of the American elites to join the Freemasonry society, their intentions weren t to better themselves but to mimic the â€Å"English gentill behavior†, even though the organization actually ending up contributed to the development of the American Revolution. Through the start of this organization George Washington and many of the American elites policies were influenced to what we know them to stand for today. As president he advocated for many policies

Career Pl Professional Development Plan - 1638 Words

Week 5 Assignment 2: Professional Development Plan Name: Jennifer Jacobsen Date: 2/15/17 Overview: Professional Development Plan This course aims to help you utilize quality improvement processes and management tools to improve client care outcomes, partly by improving the nurse’s working environment as you make and implement good decisions. Now you will apply those processes and tools to yourself by creating a professional development plan. You will begin by completing some management graphic organizers or tools. Then you will use these tools as the basis of your plan. Objectives †¢ Explain how organizations function. †¢ Compare and contrast characteristics of leadership and management. †¢ Apply trends, issues, theories, and evidence as†¦show more content†¦st of at least four prioritized goals (3 points) List of at least two goals (0-1 point) All goals stated in measurable terms (5 points) Some goals stated in measureable terms (3 points) No goals stated in measurable terms (0 points) Dates identified for accomplishing each goal (5 points) Dates identified for accomplishing most goals (3 points) No dates identified (0 points) Specific Strategies (max 20 points) Identified specific strategies for accomplishing each goal (16-20 points total) Some strategies that are related to pursuance of goals (11-15 points total) Vague or no description of strategies (0-10 points) Priority of Goals (max 20 points) Priority of each goal consistent with statements of values (16-20 points total) Goals are related to values (11-15 points total) Priorities are not indicated and/or goals are not clearly related to values (0-10 points) References (max 10 points) At least five references (5 points) Four references (4 points) Fewer than four references (0-2 points) References formatted correctly (APA) (5 points) Fewer than three APA format errors (4 points) Three or more APA format errors (0-2 points) Format Organization (max 10 points) Professional, error-free APA formats, spelling, grammar, use of language, and organization of responses (9-10 points) Generally acceptable APA formats, spelling, grammar, use of language, and organization of responses (5-8 points) Error-laden APA formats, spelling, grammar, use of language, and/or lackShow MoreRelatedProfessional Development Pl Career Plan1889 Words   |  8 Pages10 Professional Development Plan LaKeshia Chaney Walden University Professional Development Plan Professional Development Plan (PDP) is a process of improving education and training opportunities in the community. This planning document will outline goals, steps to accomplish the goals, social media preference, personal strengths and weakness, and timelines. The Strength, Weakness, Opportunity, Threat (SWOT), Personal Learning Network (PLN) and New Drivers of Leadership Assessment will be toolsRead MoreProfessional Development Pl Career Plan2028 Words   |  9 PagesPAGE 1 Professional Development Plan LaKeshia Chaney Walden University Professional Development Plan Professional Development Plan (PDP) is a process of improving education and training opportunities in the community. This planning document will outline goals, steps to accomplish goals, social media, personal strengths and weakness, and timelines. The Strength, Weakness, Opportunity, Threat (SWOT), Personal Learning Network (PLN) and New Drivers of Leadership Assessment Type will be tools toRead MoreThe Foundations Of Counseling And Guidance Essay1340 Words   |  6 Pageslaws: the Training of Professional Personnel Act of 1959 (PL 86-158) helped train leaders to educate children with mental retardation. The Captioned Films Acts of 1958 (PL 85-905) was the training provisions for teachers of students with mental retardation (PL 85-926), and 1961 (PL 87-715) supported the production and distribution of accessible films. The Teachers of the Deaf Act of 1961 (PL 87-276) trained instructional personnel for children who were deaf or hard of hearing. PL 88-164 expanded previousRead MoreT he Victims Of Children With Disabilities1331 Words   |  6 Pageslaws: the Training of Professional Personnel Act of 1959 (PL 86-158) helped train leaders to educate children with mental retardation. The Captioned Films Acts of 1958 (PL 85-905) was the training provisions for teachers of students with mental retardation (PL 85-926), and 1961 (PL 87-715) supported the production and distribution of accessible films. The Teachers of the Deaf Act of 1961 (PL 87-276) trained instructional personnel for children who were deaf or hard of hearing. PL 88-164 expanded previousRead MoreGes Talent Machine: the Making of a Ceo1305 Words   |  6 Pagespositions, is not done at a personnel level but rather on a professional level; this because all the other workers are also valuable for the company, therefore they cannot be disregard. The main difference is that engineers are the key for GE to outperform its competitors. Over the life of the company, with its different CEOs, GE had a lot of changes at the level of HR practices but all of them, since the beginning of the company, made the development of management talent a high priority, which was reflectedRead MoreThe Case For Intern As Oracle And Sql Developer1614 Words   |  7 PagesThis term, Sriven Technology has offered an unpaid employment for the position of Intern as Oracle PL/SQL Developer. THE COMPANY The Company I m working with is Sriven Technologies, located in Virginia. It is a leading information technology development and consulting firm serving clients throughout the United States. VISION OF SRIVEN TECHNOLOGIES The company key success is to provide deep domain expertise in technology solutions that differentiates our firm. The company use small, expert projectRead MoreThe Victims Of Children With Disabilities2087 Words   |  9 Pageslaws: the Training of Professional Personnel Act of 1959 (PL 86-158) helped train leaders to educate children with mental retardation. The Captioned Films Acts of 1958 (PL 85-905) was the training provisions for teachers of students with mental retardation (PL 85-926), and 1961 (PL 87-715) supported the production and distribution of accessible films. The Teachers of the Deaf Act of 1961 (PL 87-276) trained instructional personnel for children who were deaf or hard of hearing. PL 88-164 expanded previousRead MoreSelf Reflection Ppd Plan1486 Words   |  6 PagesThe following report is a personal and professional development plan that shows a self reflection of me using the various tool (Belbin team role analysis, Career survey guide, MBA skills audit etc) dis cussed in the PPD sessions in the class room. This also provides insights of various strengths and weakness I possess and the various things which I want to develop during the course of my MBA programme both in respect to my personal and professional development. It also gives out steps of how to achieveRead MoreCreating a Ppd Plan2391 Words   |  10 PagesMy Personal Development Plan Table of contents Introduction Areas that need development Your strengths Your action plan Key contacts Resources Related guidance on businesslink.gov.uk 2 2 5 7 8 9 10 Created by Business Link December 16, 2005 9:53 AM If you would like to come back and see how much you have improved, or update your current list of actions, please visit My Information at businesslink.gov.uk My Personal Development Plan | Created for Sample User on December 16, 2005 9:53Read MoreImpooving Employee Performance72019 Words   |  289 PagesGrote American Management Association New York †¢ Atlanta †¢ Brussels †¢ Chicago †¢ Mexico City †¢ San Francisco Shanghai †¢ Tokyo †¢ Toronto †¢ Washington, D.C. Special discounts on bulk quantities of AMACOM books are available to corporations, professional associations, and other organizations. For details, contact Special Sales Department, AMACOM, a division of American Management Association, 1601 Broadway, New York, NY 10019. Tel.: 212-903-8316. Fax: 212-903-8083. Web site: www.amacombooks.org

Attendance System Free Essays

Student Attendance System Based On Fingerprint Recognition and One-to-Many Matching A thesis submitted in partial ful? llment of the requirements for the degree of Bachelor of Computer Application in Computer Science by Sachin (Roll no. 107cs016) and Arun Sharma (Roll no. 107cs015) Under the guidance of : Prof. We will write a custom essay sample on Attendance System or any similar topic only for you Order Now R. C. Tripathi Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela-769 008, Orissa, India 2 . Dedicated to Our Parents and Indian Scienti? c Community . 3 National Institute of Technology Rourkela Certi? cate This is to certify that the project entitled, ‘Student Attendance System Based On Fingerprint Recognition and One-to-Many Matching’ submitted by Rishabh Mishra and Prashant Trivedi is an authentic work carried out by them under my supervision and guidance for the partial ful? llment of the requirements for the award of Bachelor of Technology Degree in Computer Science and Engineering at National Institute of Technology, Rourkela. To the best of my knowledge, the matter embodied in the project has not been submitted to any other University / Institute for the award of any Degree or Diploma. Date – 9/5/2011 Rourkela (Prof. B. Majhi) Dept. of Computer Science and Engineering 4 Abstract Our project aims at designing an student attendance system which could e? ectively manage attendance of students at institutes like NIT Rourkela. Attendance is marked after student identi? cation. For student identi? cation, a ? ngerprint recognition based identi? cation system is used. Fingerprints are considered to be the best and fastest method for biometric identi? cation. They are secure to use, unique for every person and does not change in one’s lifetime. Fingerprint recognition is a mature ? ld today, but still identifying individual from a set of enrolled ? ngerprints is a time taking process. It was our responsibility to improve the ? ngerprint identi? cation system for implementation on large databases e. g. of an institute or a country etc. In this project, many new algorithms have been used e. g. gender estimation, key based one to many matching, removing boundary minutiae. Using these new algorithms, we have developed an identi? cation system which is faster in implementation than any other available today in the market. Although we are using this ? ngerprint identi? cation system for student identi? ation purpose in our project, the matching results are so good that it could perform very well on large databases like that of a country like India (MNIC Project). This system was implemented in Matlab10, Intel Core2Duo processor and comparison of our one to many identi? cation was done with existing identi? cation technique i. e. one to one identi? cation on same platform. Our matching technique runs in O(n+N) time as compared to the existing O(Nn2 ). The ? ngerprint identi? cation system was tested on FVC2004 and Veri? nger databases. 5 Acknowledgments We express our profound gratitude and indebtedness to Prof. B. Majhi, Department of Computer Science and Engineering, NIT, Rourkela for introducing the present topic and for their inspiring intellectual guidance, constructive criticism and valuable suggestion throughout the project work. We are also thankful to Prof. Pankaj Kumar Sa , Ms. Hunny Mehrotra and other sta? s in Department of Computer Science and Engineering for motivating us in improving the algorithms. Finally we would like to thank our parents for their support and permitting us stay for more days to complete this project. Date – 9/5/2011 Rourkela Rishabh Mishra Prashant Trivedi Contents 1 Introduction 1. 1 1. 2 1. 3 1. 4 1. 1. 6 1. 7 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motivation and Challenges . . . . . . . . . . . . . . . . . . . . . . . . Using Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What is ? ngerprint? . . . . . . . . . . . . . . . . . . . . . . . . . . . Why use ? ngerprints? . . . . . . . . . . . . . . . . . . . . . . . . . . . Using ? ngerprint recognition system for attendance management . . . Organization of the thesis . . . . . . . . . . . . . . . . . . . . . . . . 17 17 17 18 18 19 19 19 21 21 22 23 24 24 30 30 33 33 33 35 35 36 36 2 Attendance Management Framework 2. 2. 2 2. 3 2. 4 2. 5 Hardware – Software Level Design . . . . . . . . . . . . . . . . . . . . Attendance Management Approach . . . . . . . . . . . . . . . . . . . On-Line Attendance Report Generation . . . . . . . . . . . . . . . . . Network and Database Management . . . . . . . . . . . . . . . . . . Using wireless network instead of LAN and bringing portability . . . 2. 5. 1 2. 6 Using Portable Device . . . . . . . . . . . . . . . . . . . . . . Comparison with other student attendance systems . . . . . . . . . . 3 Fingerprint Identi? cation System 3. 1 3. 2 How Fingerprint Recognition works? . . . . . . . . . . . . . . . . . Fingerprint Identi? cation System Flowchart . . . . . . . . . . . . . . 4 Finge rprint Enhancement 4. 1 4. 2 4. 3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 6 CONTENTS 4. 4 4. 5 4. 6 4. 7 Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . . . . . Gabor ? lter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 38 39 40 40 41 41 42 42 43 44 45 45 45 46 47 47 50 51 53 53 54 54 55 56 57 59 59 59 59 60 5 Feature Extraction 5. 1 5. 2 Finding the Reference Point . . . . . . . . . . . . . . . . . . . . . . . Minutiae Extraction and Post-Processing . . . . . . . . . . . . . . . . 5. 2. 1 5. 2. 2 5. 2. 3 5. 3 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . Post-Processing . . . . . . . . . . . . . . . . . . . . . . . . . Removing Boundary Minutiae . . . . . . . . . . . . . . . . . . Extraction of the key . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 1 What is key? . . . . . . . . . . . . . . . . . . . . . . . . . . Simple Key . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complex Key . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Partitioning of Database 6. 1 6. 2 6. 3 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation of Fingerprint . . . . . . . . . . . . . . . . . . . . . . . Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Matching 7. 1 7. 2 7. 3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Matching Techniques . . . . . . . . . . . . . . . . . . . . . One to Many matching . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 3. 1 7. 4 7. 5 Method of One to Many Matching . . . . . . . . . . . . . . . Performing key match and full ma tching . . . . . . . . . . . . . . . . Time Complexity of this matching technique . . . . . . . . . . . . . . 8 Experimental Analysis 8. 1 8. 2 Implementation Environment . . . . . . . . . . . . . . . . . . . . . . Fingerprint Enhancement . . . . . . . . . . . . . . . . . . . . . . . . 8. 2. 1 8. 2. 2 Segmentation and Normalization . . . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . 8 8. 2. 3 8. 2. 4 8. . 5 8. 3 CONTENTS Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation and Thinning . . . . . . . . . . . . . . . . . . . . 60 60 61 62 62 62 63 64 64 64 64 65 66 66 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 3. 1 Minutiae Extraction and Post Processing . . . . . . . . . . . . Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . After Removing Spurious and Boundary Minutiae . . . . . . . 8. 3. 2 Ref erence Point Detection . . . . . . . . . . . . . . . . . . . . 8. 4 Gender Estimation and Classi? ation . . . . . . . . . . . . . . . . . . 8. 4. 1 8. 4. 2 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 5 8. 6 Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 6. 1 8. 6. 2 Fingerprint Veri? cation Results . . . . . . . . . . . . . . . . . Identi? cation Results and Comparison with Other Matching techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 70 73 74 75 75 79 8. 7 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion 9. 1 Outcomes of this Project . . . . . . . . . . . . . . . . . . . . . . . . . 10 Future Work and Expectations 10. 1 Approach for Future Work A Matlab functions . . . . . . . . . . . . . . . . . . . . . . . List of Figur es 1. 1 2. 1 2. 2 2. 3 2. 4 2. 5 2. 6 2. 7 2. 8 3. 1 4. 1 4. 2 Example of a ridge ending and a bifurcation . . . . . . . . . . . . . . Hardware present in classrooms . . . . . . . . . . . . . . . . . . . . . Classroom Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 0 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 1 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 2 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Portable Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Identi? cation System Flowchart . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinned Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Row 1: ? lter response c1k , k = 3, 2, and 1. Row 2: ? lter response c2k , k = 3, 2, and 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2 5. 3 Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 42 43 40 18 22 23 25 26 27 27 28 29 34 37 Examples of typical false minutiae structures : (a)Spur, (b)Hole, (c)Triangle, (d)Spike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 44 44 45 48 5. 4 5. 5 5. 6 6. 1 Skeleton of window centered at boundary minutiae . . . . . . . . . . Matrix Representation of boundary minutiae . . . . . . . . . . . . . Key Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 10 6. 2 6. 3 LIST OF FIGURES 135o blocks of a ? ngerprint . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d1)Arch, (d2)Tented Arch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 4 7. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 8. 9 Partitioning Database . . . . . . . . . . . . . . . . . . . . . . . . . . One to Many Matching . . . . . . . . . . . . . . . . . . . . . . . . . . Normalized Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ridge Frequency Image . . . . . . . . . . . . . . . . . . . . . . . . . . Left-Original Image, Right-Enhanced Image . . . . . . . . . . . . . . Binarised Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinned Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . All Extracted Minutiae . . . . . . . . . . . . . . . . . . . . . . . . . . Composite Image with spurious and boundary minutiae . . . . . . . . Minutiae Image after post-processing . . . . . . . . . . . . . . . . . 51 52 57 59 60 60 61 61 62 62 63 63 64 65 50 8. 10 Composite Image after post-processing . . . . . . . . . . . . . . . . . 8. 11 Plotted Minutiae with Reference Point(Black Spot) . . . . . . . . . . 8. 12 Graph: Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . 8. 13 Graph: Time taken for Identi? cation vs Size of Database (n2 identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 14 Expected Graph for comparison : Time taken for Identi? cation vs Size of Database(1 million) . . . . . . . . . . . . . . . . . . . . . . . . . 68 69 71 List of Tables 2. 1 5. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 Estimated Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Properties of Crossing Number . . . . . . . . . . . . . . . . . . . . . 22 43 64 65 66 66 67 67 68 Average Number of Minutiae before and after post-processing . . . . Ridge Density Calculation Results . . . . . . . . . . . . . . . . . . . . Classi? cation Results on Original Image . . . . . . . . . . . . . . . . Classi? cation Results on Enhanced Image . . . . . . . . . . . . . . . Time taken for Classi? cation . . . . . . . . . . . . . . . . . . . . . . . Time taken for Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . Error Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance of ours and n2 matching based identi? cation techniques on a database of size 150 . . . . . . . . . . . . . . . . . . . . . . . . . 70 11 List of Algorithms 1 2 3 4 Key Extraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation Algorithm . . . . . . . . . . . . . . . . . . . . . . . Key Based One to Many Matching Algorithm . . . . . . . . . . . . . . Matching Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 49 55 56 12 Chapter 1 Introduction 1. 1 Problem Statement Designing a student attendance management system based on ? ngerprint recognition and faster one to many identi? cation that manages records for attendance in institutes like NIT Rourkela. 1. 2 Motivation and Challenges Every organization whether it be an educational institution or business organization, it has to maintain a proper record of attendance of students or employees for e? ective functioning of organization. Designing a better attendance management system for students so that records be maintained with ease and accuracy was an important key behind motivating this project. This would improve accuracy of attendance records because it will remove all the hassles of roll calling and will save valuable time of the students as well as teachers. Image processing and ? ngerprint recognition are very advanced today in terms of technology. It was our responsibility to improve ? ngerprint identi? cation system. We decreased matching time by partitioning the database to one-tenth and improved matching using key based one to many matching. 13 14 CHAPTER 1. INTRODUCTION 1. 3 Using Biometrics Biometric Identi? cation Systems are widely used for unique identi? cation of humans mainly for veri? cation and identi? ation. Biometrics is used as a form of identity access management and access control. So use of biometrics in student attendance management system is a secure approach. There are many types of biometric systems like ? ngerprint recognition, face recognition, voice recognition, iris recognition, palm recognition etc. In this project, we used ? ngerprint recogn ition system. 1. 4 What is ? ngerprint? A ? ngerprint is the pattern of ridges and valleys on the surface of a ? ngertip. The endpoints and crossing points of ridges are called minutiae. It is a widely accepted assumption that the minutiae pattern of each ? ger is unique and does not change during one’s life. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. Figure 1 illustrates an example of a ridge ending and a bifurcation. In this example, the black pixels correspond to the ridges, and the white pixels correspond to the valleys. Figure 1. 1: Example of a ridge ending and a bifurcation When human ? ngerprint experts determine if two ? ngerprints are from the same ? nger, the matching degree between two minutiae pattern is one of the most important factors. Thanks to the similarity to the way of human ? ngerprint experts and compactness of templates, the minutiae-based matching method is the most widely studied matching method. 1. 5. WHY USE FINGERPRINTS? 15 1. 5 Why use ? ngerprints? Fingerprints are considered to be the best and fastest method for biometric identi? cation. They are secure to use, unique for every person and does not change in one’s lifetime. Besides these, implementation of ? ngerprint recognition system is cheap, easy and accurate up to satis? ability. Fingerprint recognition has been widely used in both forensic and civilian applications. Compared with other biometrics features , ? ngerprint-based biometrics is the most proven technique and has the largest market shares . Not only it is faster than other techniques but also the energy consumption by such systems is too less. 1. 6 Using ? ngerprint recognition system for attendance management Managing attendance records of students of an institute is a tedious task. It consumes time and paper both. To make all the attendance related work automatic and on-line, we have designed an attendance management system which could be implemented in NIT Rourkela. It uses a ? ngerprint identi? cation system developed in this project. This ? ngerprint identi? cation system uses existing as well as new techniques in ? ngerprint recognition and matching. A new one to many matching algorithm for large databases has been introduced in this identi? cation system. 1. 7 Organization of the thesis This thesis has been organized into ten chapters. Chapter 1 introduces with our project. Chapter 2 explains the proposed design of attendance management system. Chapter 3 explains the ? ngerprint identi? cation system used in this project. Chapter 4 explains enhancement techniques, Chapter 5 explains feature extraction methods, Chapter 6 explains our database partitioning approach . Chapter 7 explains matching technique. Chapter 8 explains experimental work done and performance analysis. Chapter 9 includes conclusions and Chapter 10 introduces proposed future work. Chapter 2 Attendance Management Framework Manual attendance taking and report generation has its limitations. It is well enough for 30-60 students but when it comes to taking attendance of students large in number, it is di? cult. For taking attendance for a lecture, a conference, etc. oll calling and manual attendance system is a failure. Time waste over responses of students, waste of paper etc. are the disadvantages of manual attendance system. Moreover, the attendance report is also not generated on time. Attendance report which is circulated over NITR webmail is two months old. To overcome these non-optimal situations, it is necessary that we should use an automatic on-line attendance management system. So we present an implementable attendance management framework. Student attendance system framework is divided into three parts : Hardware/Software Design, Attendance Management Approach and On-line Report Generation. Each of these is explained below. 2. 1 Hardware – Software Level Design Required hardware used should be easy to maintain, implement and easily available. Proposed hardware consists following parts: (1)Fingerprint Scanner, (2)LCD/Display Module (optional), (3)Computer 16 2. 2. ATTENDANCE MANAGEMENT APPROACH Table 2. 1: Estimated Budget Device Cost of Number of Total Name One Unit Units Required Unit Budget Scanner 500 100 50000 PC 21000 100 2100000 Total 21,50,000 (4)LAN connection 17 Fingerprint scanner will be used to input ? ngerprint of teachers/students into the computer software. LCD display will be displaying rolls of those whose attendance is marked. Computer Software will be interfacing ? ngerprint scanner and LCD and will be connected to the network. It will input ? ngerprint, will process it and extract features for matching. After matching, it will update database attendance records of the students. Figure 2. 1: Hardware present in classrooms Estimated Budget Estimated cost of the hardware for implementation of this system is shown in the table 2. 1. Total number of classrooms in NIT Rourkela is around 100. So number of units required will be 100. 2. 2 Attendance Management Approach This part explains how students and teachers will use this attendance management system. Following points will make sure that attendance is marked correctly, without any problem: (1)All the hardware will be inside classroom. So outside interference will be absent. (2)To remove unauthorized access and unwanted attempt to corrupt the hardware by students, all the hardware except ? ngerprint scanner could be put inside a small 18 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK cabin. As an alternate solution, we can install CCTV cameras to prevent unprivileged activities. (3)When teacher enters the classroom, the attendance marking will start. Computer software will start the process after inputting ? ngerprint of teacher. It will ? nd the Subject ID, and Current Semester using the ID of the teacher or could be set manually on the software. If teacher doesn’t enter classroom, attendance marking will not start. (4)After some time, say 20 minutes of this process, no attendance will be given because of late entrance. This time period can be increased or decreased as per requirements. Figure 2. 2: Classroom Scenario 2. 3 On-Line Attendance Report Generation Database for attendance would be a table having following ? elds as a combination for primary ? ld: (1)Day,(2)Roll,(3)Subject and following non-primary ? elds: (1)Attendance,(2)Semester. Using this table, all the attendance can be managed for a student. For on-line report generation, a simple website can be hosted on NIT Rourkela servers, 2. 4. NETWORK AND DATABASE MANAGEMENT 19 which will access this table for showing attendance of students. The sql queries will be used for report generation. Following query will give total numbers of classes held in subject CS423: SELECT COUNT(DISTINCT Day) FROM AttendanceTable WHERE SUBJECT = CS423 AND Attendance = 1 For attendance of oll 107CS016, against this subject, following query will be used: SELECT COUNT(Day) FROM AttendanceTable WHERE Roll = 107CS016 AND SUBJECT = CS423 AND Attendance = 1 Now the attendance percent can easily be calculated : ClassesAttended ? 100 ClassesHeld Attendance = (2. 1) 2. 4 Network and Database Management This attendance system will be spread over a wide network from classrooms via intranet to internet. Network diagram is shown in ? g. 2. 3. Using this network, attendance reports will be made available over internet and e-mail. A monthly report will be sent to each student via email and website will show the updated attendance. Entity relationship diagram for database of students and attendance records is shown in ? g. 2. 4. In ER diagram, primary ? elds are Roll, Date, SubjectID and TeacherID. Four tables are Student, Attendance, Subject and Teacher. Using this database, attendance could easily be maintained for students. Data? ow is shown in data ? ow diagrams (DFD) shown in ? gures 2. 5, 2. 6 and 2. 7. 2. 5 Using wireless network instead of LAN and bringing portability We are using LAN for communication among servers and hardwares in the classrooms. We can instead use wireless LAN with portable devices. Portable device will have an embedded ? ngerprint scanner, wireless connection, a microprocessor loaded with a software, memory and a display terminal, see ? gure 2. 5. Size of device could be small like a mobile phone depending upon how well the device is manufactured. 20 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK Figure 2. 3: Network Diagram 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY21 Figure 2. 4: ER Diagram 22 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK Figure 2. 5: Level 0 DFD Figure 2. 6: Level 1 DFD 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY23 Figure 2. : Level 2 DFD 24 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK This device should have a wireless connection. Using this wireless connection, Figure 2. 8: Portable Device attendance taken would be updated automatically when device is in network of the nodes which are storing the attendance records. Database of enrolled ? ngerprints will be in this portable device. Size of enrolled database was 12. 1 MB when 150 ? ngerprints were enrolled in this project. So for 10000 students, atleast 807 MB or more space would be required for storing enrolled database. For this purpose, a removable memory chip could be used. We cannot use wireless LAN here because fetching data using wireless LAN will not be possible because of less range of wireless devices. So enrolled data would be on chip itself. Attendance results will be updated when portable device will be in the range of nodes which are storing attendance reports. We may update all the records online via the mobile network provided by di? erent companies. Today 3G network provides su? cient throughput which can be used for updating attendance records automatically without going near nodes. In such case, 2. 6. COMPARISON WITH OTHER STUDENT ATTENDANCE SYSTEMS 25 he need of database inside memory chip will not be mandatory. It will be fetched by using 3G mobile network from central database repository. The design of such a portable device is the task of embedded system engineers. 2. 5. 1 Using Portable Device In this section, we suggest the working of portable device and the method of using it for marking attendance. The device may either be having touchscreen input/display or buttons with lcd display. A software specially designed for the device will be running on it. Teachers will verify his/her ? ngerprint on the device before giving it to students for marking attendance. After verifying the teacher’s identity, software will ask for course and and other required information about the class which he or she is going to teach. Software will ask teacher the time after which device will not mark any attendance. This time can vary depending on the teacher’s mood but our suggested value is 25 minutes. This is done to prevent late entrance of students. This step will hardly take few seconds. Then students will be given device for their ? ngerprint identi? cation and attendance marking. In the continuation, teacher will start his/her lecture. Students will hand over the device to other students whose attendance is not marked. After 25 minutes or the time decided by teacher, device will not input any attendance. After the class is over, teacher will take device and will end the lecture. The main function of software running on the device will be ? ngerprint identi? cation of students followed by report generation and sending reports to servers using 3G network. Other functions will be downloading and updating the database available on the device from central database repository. 2. 6 Comparison with other student attendance systems There are various other kind of student attendance management systems available like RFID based student attendance system and GSM-GPRS based student attendance system. These systems have their own pros and cons. Our system is better because ? rst it saves time that could be used for teaching. Second is portability. Portability 26 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK has its own advantage because the device could be taken to any class wherever it is scheduled. While GSM-GPRS based systems use position of class for attendance marking which is not dynamic and if schedule or location of the class changes, wrong attendance might be marked. Problem with RFID based systems is that students have to carry RFID cards and also the RFID detectors are needed to be installed. Nonetheless, students may give proxies easily using friend’s RFID card. These problems are not in our system. We used ? ngerprints as recognition criteria so proxies cannot be given. If portable devices are used, attendance marking will be done at any place and any time. So our student attendance system is far better to be implemented at NITR. Chapter 3 Fingerprint Identi? cation System An identi? cation system is one which helps in identifying an individual among any people when detailed information is not available. It may involve matching available features of candidate like ? ngerprints with those already enrolled in database. 3. 1 How Fingerprint Recognition works? Fingerprint images that are found or scanned are not of optimum quality. So we remove noises and enhance their quality. We extract features like minutiae and others for matching. If the sets of minutiae are matched with those in the database, we call it an identi? ed ? ngerprint. After matching, we perform post-matching steps which may include showing details of identi? ed candidate, marking attendance etc. A brief ? owchart is shown in next section. 3. 2 Fingerprint Identi? cation System Flowchart A brief methodology of our Fingerprint Identi? cation System is shown here in following ? owchart. Each of these are explained in the later chapters. 27 28 CHAPTER 3. FINGERPRINT IDENTIFICATION SYSTEM Figure 3. 1: Fingerprint Identi? cation System Flowchart Chapter 4 Fingerprint Enhancement The image acquired from scanner is sometimes not of perfect quality . It gets corrupted due to irregularities and non-uniformity in the impression taken and due to variations in the skin and the presence of the scars, humidity, irt etc. To overcome these problems , to reduce noise and enhance the de? nition of ridges against valleys, various techniques are applied as following. 4. 1 Segmentation Image segmentation [1] separates the foreground regions and the background regions in the image. The foreground regions refers to the clear ? ngerprint area which contains the ridges and valleys. This is the area o f interest. The background regions refers to the regions which is outside the borders of the main ? ngerprint area, which does not contain any important or valid ? ngerprint information. The extraction of noisy and false minutiae can be done by applying minutiae extraction algorithm to the background regions of the image. Thus, segmentation is a process by which we can discard these background regions, which results in more reliable extraction of minutiae points. We are going to use a method based on variance thresholding . The background regions exhibit a very low grey – scale variance value , whereas the foreground regions have a very high variance . Firstly , the image is divided into blocks and the grey-scale variance is calculated for each block in the image . If the variance is less than the global threshold , then the block is assigned to be part of background region or else 29 30 CHAPTER 4. FINGERPRINT ENHANCEMENT it is part of foreground . The grey – level variance for a block of size S x S can be calculated as : 1 V ar(k) = 2 S S? 1 S? 1 (G(i, j) ? M (k))2 i=0 j=0 (4. 1) where Var(k) is the grey – level variance for the block k , G(i,j) is the grey – level value at pixel (i,j) , and M(k) denotes the mean grey – level value for the corresponding block k . 4. 2 Normalization Image normalization is the next step in ? ngerprint enhancement process. Normalization [1] is a process of standardizing the intensity values in an image so that these intensity values lies within a certain desired range. It can be done by adjusting the range of grey-level values in the image. Let G(i, j) denotes the grey-level value at pixel (i, j), and N(i, j) represent the normalized grey-level value at pixel (i, j). Then the normalized image can de? ned as: ? ? M + 0 N (i, j) = ? M ? 0 V0 (G(i,j)? M )2 V V0 (G(i,j)? M )2 V , if I(i, j) M , otherwise where M0 and V0 are the estimated mean and variance of I(i, j), respectively . 4. 3 Orientation estimation The orientation ? eld of a ? ngerprint image de? es the local orientation of the ridges contained in the ? ngerprint . The orientation estimation is a fundamental step in the enhancement process as the subsequent Gabor ? ltering stage relies on the local orientation in order to e? ectively enhance the ? ngerprint image. The least mean square estimation method used by Raymond Thai [1] is used to compu te the orientation image. However, instead of estimating the orientation block-wise, we have chosen to extend their method into a pixel-wise scheme, which produces a ? ner and more accurate estimation of the orientation ? eld. The steps for calculating the orientation at pixel i, j) are as follows: 4. 3. ORIENTATION ESTIMATION 31 1. Firstly , a block of size W x W is centered at pixel (i, j) in the normalized ? ngerprint image. 2. For each pixel in the block, compute the gradients dx (i, j) and dy (i, j), which are the gradient magnitudes in the x and y directions, respectively. The horizontal Sobel operator[6] is used to compute dx(i, j) : [1 0 -1; 2 0 -2;1 0 -1] Figure 4. 1: Orientation Estimation 3. The local orientation at pixel (i; j) can then be estimated using the following equations: i+ W 2 j+ W 2 Vx (i, j) = u=i? W 2 i+ W 2 v=j? W 2 j+ W 2 2? x (u, v)? y (u, v) (4. 2) Vy (i, j) = u=i? W v=j? W 2 2 2 2 ? (u, v) ? ?y (u, v), (4. 3) ?(i, j) = 1 Vy (i, j) tan? 1 , 2 Vx (i, j) ( 4. 4) where ? (i, j) is the least square estimate of the local orientation at the block centered at pixel (i, j). 4. Smooth the orientation ? eld in a local neighborhood using a Gaussian ? lter. The orientation image is ? rstly converted into a continuous vector ? eld, which is de? ned as: ? x (i, j) = cos 2? (i, j), ? y (i, j) = sin 2? (i, j), (4. 5) (4. 6) where ? x and ? y are the x and y components of the vector ? eld, respectively. After 32 CHAPTER 4. FINGERPRINT ENHANCEMENT the vector ? eld has been computed, Gaussian smoothing is then performed as follows: w? w? 2 ?x (i, j) = w? u=? 2 w? v=? 2 G(u, v)? x (i ? uw, j ? vw), (4. 7) w? 2 w? 2 ?y (i, j) = w? u=? 2 w? v=? 2 G(u, v)? y (i ? uw, j ? vw), (4. 8) where G is a Gaussian low-pass ? lter of size w? x w? . 5. The ? nal smoothed orientation ? eld O at pixel (i, j) is de? ned as: O(i, j) = ? y (i, j) 1 tan? 1 2 ? x (i, j) (4. 9) 4. 4 Ridge Frequency Estimation Another important parameter,in addition to the orientation image, that can be used in the construction of the Gabor ? lter is the local ridge frequency. The local frequency of the ridges in a ? ngerprint is represented by the frequency image. The ? st step is to divide the image into blocks of size W x W. In the next step we project the greylevel values of each pixels located inside each block along a direction perpendicular to the local ridge orientation. This projection results in an almost sinusoidal-shape wave with the local minimum points denoting the ridges in the ? ngerprint. It involves smoothing the projected waveform using a Gaussian lowpass ? lter of size W x W which helps in reducing the e? ect of noise in the projection. The ridge spacing S(i, j) is then calculated by counting the median number of pixels between the consecutive minima points in the projected waveform. The ridge frequency F(i, j) for a block centered at pixel (i, j) is de? ned as: F (i, j) = 1 S(i, j) (4. 10) 4. 5. GABOR FILTER 33 4. 5 Gabor ? lter Gabor ? lters [1] are used because they have orientation-selective and frequencyselective properties. Gabor ? lters are called the mother of all other ? lters as other ? lter can be derived using this ? lter. Therefore, applying a properly tuned Gabor ? lter can preserve the ridge structures while reducing noise. An even-symmetric Gabor ? lter in the spatial domain is de? ned as : 1 x2 y2 G(x, y, ? , f ) = exp{? [ ? + ? ]} cos 2? f x? , 2 2 2 ? x ? y (4. 11) x? = x cos ? + y sin ? , (4. 12) y? ? x sin ? + y cos ? , (4. 13) where ? is the orientation of the Gabor ? lter, f is the frequency of the cosine wave, ? x and ? y are the standard deviations of the Gaussian envelope along the x and y axes, respectively, and x? and y? de? ne the x and y axes of the ? lter coordinate frame respectively. The Gabor Filter is applied to the ? ngerprint image by spatially convolving the image with the ? lter. The convolution of a pixel (i,j) in the image requires the corresponding orientation value O(i,j) and the ridge frequency value F(i,j) of that pixel . wy 2 wx 2 E(i, j) = u=? wx 2 w v=? 2y G(u, v, O(i, j), F (i, j))N (i ? u, j ? v), (4. 4) where O is the orientation image, F is the ridge frequency image, N is the normalized ? ngerprint image, and wx and wy are the width and height of the Gabor ? lter mask, respectively. 34 CHAPTER 4. FINGERPRINT ENHANCEMENT 4. 6 Binarisation Most minutiae extraction algorithms operate on basically binary images where there are only two levels of interest: the black pixels represent ridges, and the white pixels represent valleys. Binarisation [1] converts a greylevel image into a binary image. This helps in improving the contrast between the ridges and valleys in a ? ngerprint image, and consequently facilitates the extraction of minutiae. One very useful property of the Gabor ? lter is that it contains a DC component of zero, which indicates that the resulting ? ltered image has a zero mean pixel value. Hence, binarisation of the image can be done by using a global threshold of zero. Binarisation involves examining the grey-level value of every pixel in the enhanced image, and, if the grey-level value is greater than the prede? ned global threshold, then the pixel value is set to value one; else, it is set to zero. The outcome of binarisation is a binary image which contains two levels of information, the background valleys and the foreground ridges. . 7 Thinning Thinning is a morphological operation which is used to remove selected foreground pixels from the binary images. A standard thinning algorithm from [1] is used, which performs this operation using two subiterations. The algorithm can be accessed by a software MATLAB via the ‘thin’ operation of the bwmorph function. Each subiteration starts by exa mining the neighborhood of every pixel in the binary image, and on the basis of a particular set of pixel-deletion criteria, it decides whether the pixel can be removed or not. These subiterations goes on until no more pixels can be removed. Figure 4. 2: (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinned Image Chapter 5 Feature Extraction After improving quality of the ? ngerprint image we extract features from binarised and thinned images. We extract reference point, minutiae and key(used for one to many matching). 5. 1 Finding the Reference Point Reference point is very important feature in advanced matching algorithms because it provides the location of origin for marking minutiae. We ? nd the reference point using the algorithm as in [2]. Then we ? nd the relative position of minutiae and estimate the orientation ? ld of the reference point or the singular point. The technique is to extract core and delta points using Poincare Index. The value of Poincare index is 180o , ? 180o and 0o for a core, a delta and an ordinary point respectively. Complex ? lters are used to produce blur at di? erent resolutions. Singular point (SP) or reference point is the point of maximum ? lter response of these ? lters applied on image. Complex ? lters , exp(im? ) , of order m (= 1 and -1) are used to produce ? lter response. Four level resolutions are used here:level 0, level 1, level 2, level 3. Level 3 is lowest resolution and level 0 is highest resolution. Only ? lters of ? rst order are used : h = (x + iy)m g(x, y) where g(x,y) is a gaussian de? ned as g(x, y) = exp? ((x2 + y 2 )/2? 2 ) and m = 1, ? 1. Filters are applied to the complex valued orientation tensor ? eld image z(x, y) = (fx + ify )2 and not directly to the image. Here f x is the derivative of the original image in the x-direction and f y is the derivative in the y-direction. To ? nd the position of a possible 35 36 CHAPTER 5. FEATURE EXTRACTION Figure 5. 1: Row 1: ? lter response c1k , k = 3, 2, and 1. Row 2: ? ter response c2k , k = 3, 2, and 1. SP in a ? ngerprint the maximum ? lter response is extracted in image c13 and in c23 (i. e. ?lter response at m = 1 and level 3 (c13 ) and at m = ? 1 and level 3 (c23 )). The search is done in a window computed in the previous higher level (low resolution). The ? lter response at lower level (high resolution) is used for ? nding response at higher level (low resolut ion). At a certain resolution (level k), if cnk (xj , yj ) is higher than a threshold an SP is found and its position (xj , yj ) and the complex ? lter response cnk (xj , yj ) are noted. 5. 2 5. 2. 1 Minutiae Extraction and Post-Processing Minutiae Extraction The most commonly employed method of minutiae extraction is the Crossing Number (CN) concept [1] . This method involves the use of the skeleton image where the ridge ? ow pattern is eight-connected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3 x 3 window. The CN value is then computed, which is de? ned as half the sum of the di? erences between pairs of adjacent pixels in the eight-neighborhood. Using the properties of the CN as shown in ? gure 5, the ridge pixel can then be classi? d as a ridge ending, bifurcation or non-minutiae point. For example, a ridge pixel with a CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation. 5. 2. MINUTIAE EXTRACTION AND POST-PROCESSING Table 5. 1: Properties of Crossing Number CN Property 0 Isolated Point 1 Ridge Ending Point 2 Continuing Ridge Point 3 Bifurcation Point 4 Crossing Point 37 Figure 5 . 2: Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 5. 2. 2 Post-Processing False minutiae may be introduced into the image due to factors such as noisy images, and image artefacts created by the thinning process. Hence, after the minutiae are extracted, it is necessary to employ a post-processing [1] stage in order to validate the minutiae. Figure 5. 3 illustrates some examples of false minutiae structures, which include the spur, hole, triangle and spike structures . It can be seen that the spur structure generates false ridge endings, where as both the hole and triangle structures generate false bifurcations. The spike structure creates a false bifurcation and a false ridge ending point. Figure 5. 3: Examples of typical false minutiae structures : (c)Triangle, (d)Spike (a)Spur, (b)Hole, 38 CHAPTER 5. FEATURE EXTRACTION 5. 2. 3 Removing Boundary Minutiae For removing boundary minutiae, we used pixel-density approach. Any point on the boundary will have less white pixel density in a window centered at it, as compared to inner minutiae. We calculated the limit, which indicated that pixel density less than that means it is a boundary minutiae. We calculated it according to following formula: limit = ( w w ? (ridgedensity)) ? Wf req 2 (5. 1) where w is the window size, Wf req is the window size used to compute ridge density. Figure 5. 4: Skeleton of window centered at boundary minutiae Figure 5. 5: Matrix Representation of boundary minutiae Now, in thinned image, we sum all the pixels in the window of size w centered at the boundary minutiae. If sum is less than limit, the minutiae is considered as boundary minutiae and is discarded. 5. 3. EXTRACTION OF THE KEY 39 5. 3 5. 3. 1 Extraction of the key What is key? Key is used as a hashing tool in this project. Key is small set of few minutiae closest to reference point. We match minutiae sets, if the keys of sample and query ? ngerprints matches. Keys are stored along with minutiae sets in the database. Advantage of using key is that, we do not perform full matching every time for non-matching minutiae sets, as it would be time consuming. For large databases, if we go on matching full minutiae set for every enrolled ? ngerprint, it would waste time unnecessarily. Two types of keys are proposed – simple and complex. Simple key has been used in this project. Figure 5. 6: Key Representation Simple Key This type of key has been used in this project. Minutiae which constitute this key are ten minutiae closest to the reference point or centroid of all minutiae, in sorted 40 CHAPTER 5. FEATURE EXTRACTION order. Five ? lds are stored for each key value i. e. (x, y, ? , t, r). (x, y) is the location of minutiae, ? is the value of orientation of ridge related to minutia with respect to orientation of reference point, t is type of minutiae, and r is distance of minutiae from origin. Due to inaccuracy and imperfection of reference point detection algorithm, we used centroid of all minuti ae for construction of key. Complex Key The complex key stores more information and is structurally more complex. It stores vector of minutiae in which next minutiae is closest to previous minutiae, starting with reference point or centroid of all minutiae. It stores x, y, ? , t, r, d, ? . Here x,y,t,r,? are same, d is distance from previous minutiae entry and ? is di? erence in ridge orientation from previous minutiae. Data: minutiaelist = Minutiae Set, refx = x-cordinate of centroid, refy = y-cordinate of centroid Result: Key d(10)=null; for j = 1 to 10 do for i = 1 to rows(minutiaelist) do d(i) Chapter 6 Partitioning of Database Before we partition the database, we perform gender estimation and classi? cation. 6. 1 Gender Estimation In [3], study on 100 males and 100 females revealed that signi? cant sex di? erences occur in the ? ngerprint ridge density. Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Based on this estimation, searching for a record in the database can be made faster. Method for ? nding mean ridge density and estimated gender: The highest and lowest values for male and female ridge densities will be searched. If ridge density of query ? ngerprint is less than the lowest ridge density value of females, the query ? ngerprint is obviously of a male. Similarly, if it is higher than highest ridge density value of males, the query ? gerprint is of a female. So the searching will be carried out in male or female domains. If the value is between these values, we search on the basis of whether the mean of these values is less than the density of query image or higher. 41 42 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 1: Gender Estimation 6. 1. GENDER ESTIMATION Data: Size of Database = N; Ridge Density of query ? ngerprint = s Result: Estimated Gender i. e. male or female maleupperlimit=0; femalelowerlimit=20; mean=0; for image femalelowerlimit then femalelowerlimit 43 if s maleupperlimit then estimatedgender 44 CHAPTER 6. PARTITIONING OF DATABASE 6. 2 Classi? cation of Fingerprint We divide ? ngerprint into ? ve classes – arch or tented arch, left loop, right loop, whorl and unclassi? ed. The algorithm for classi? cation [4] is used in this project. They used a ridge classi? cation algorithm that involves three categories of ridge structures:nonrecurring ridges, type I recurring ridges and type II recurring ridges. N1 and N2 represent number of type I recurring ridges and type II recurring ridges respectively. Nc and Nd are number of core and delta in the ? ngerprint. To ? nd core and delta, separate 135o blocks from orientation image. 35o blocks are shown in following ? gures. Figure 6. 2: 135o blocks of a ? ngerprint Based on number of such blocks and their relative positions, the core and delta are found using Poincare index method. After these, classi? cation is done as following: 1. If (N2 0) and (Nc = 2) and (Nd = 2), then a whorl is identi? ed. 2. If (N1 = 0) and (N2 = 0) and (Nc = 0) a nd (Nd = 0), then an arch is identi? ed. 3. If (N1 0) and (N2 = 0) and (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm[4]. 4. If (N2 T2) and (Nc 0), then a whorl is identi? ed. 5. If (N1 T1) and (N2 = 0) and (Nc = 1) then classify the input using the core and delta assessment algorithm[4]. 6. If (Nc = 2), then a whorl is identi? ed. 7. If (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm[4]. 8. If (N1 0) and (Nc = 1), then classify the input using the core and delta assessment algorithm. 6. 3. PARTITIONING 9. If (Nc = 0) and (Nd = 0), then an arch is identi? ed. 10. If none of the above conditions is satis? ed, then reject the ? ngerprint. 45 Figure 6. 3: Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d1)Arch, (d2)Tented Arch . 3 Partitioning After we estimate gender and ? nd the class of ? ngerprint, we know which ? ngerprints to be searched in the database. We roughly divide database into one-tenth using the above parameters. This would roughly reduce identi? cation time to one-tenth. 46 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 4: Partitioning Database Chapter 7 Matching Matching means ? nding mo st appropriate similar ? ngerprint to query ? ngerprint. Fingerprints are matched by matching set of minutiae extracted. Minutiae sets never match completely, so we compute match score of matching. If match score satis? s accuracy needs, we call it successful matching. We used a new key based one to many matching intended for large databases. 7. 1 Alignment Before we go for matching, minutiae set need to be aligned(registered) with each other. For alignment problems, we used hough transform based registration technique similar to one used by Ratha et al[5]. Minutiae alignment is done in two steps minutiae registration and pairing. Minutiae registration involves aligning minutiae using parameters ? x, ? y, ? which range within speci? ed limits. (? x, ? y) are translational parameters and ? is rotational parameter. Using these parameters, minutiae sets are rotated and translated within parameters limits. Then we ? nd pairing scores of each transformation and transformation giving maximum score is registered as alignment transformation. Using this transformation ? x, ? y, ? , we align query minutiae set with the database minutiae set. Algorithm is same as in [5] but we have excluded factor ? s i. e. the scaling parameter because it does not a? ect much the alignment process. ? lies from -20 degrees to 20 degrees in steps of 1 or 2 generalized as ? 1 , ? 2 , ? 3 †¦? k where k is number of rotations applied. For every query minutiae i we check if ? k + ? i = ? j where ? i and ? j are orientation 47 48 CHAPTER 7. MATCHING parameters of ith minutia of query minutiae set and j th minutia of database minutiae set. If condition is satis? ed, A(i,j,k) is ? agged as 1 else 0. For all these ? agged values, (? x, ? y) is calculated using following formula: ? (? x , ? y ) = qj ? ? cos? sin? ? ? ? pi , (7. 1) ?sin? cos? where qj and pi are the coordinates of j th minutiae of database minutiae set and ith minutiae of query minutiae set respectively. Using these ? x, ?y, ? k values, whole query minutiae set is aligned. This aligned minutiae set is used to compute pairing score. Two minutiae are said to be paired only when they lie in same bounding box and have same orientation. Pairing score is (number of paired minutiae)/(total number of minutiae). The i,j,k values which have highest pairing score are ? nally used to align minutiae set. Co-ordinates of aligned minutiae are found using the formula: ? qj = ? cos? sin? ? ? ? pi + (? x , ? y ), (7. 2) ?sin? cos? After alignment, minutiae are stored in sorted order of their distance from their centroid or core. 7. 2 Existing Matching Techniques Most popular matching technique of today is the simple minded n2 matching where n is number of minutiae. In this matching each minutiae of query ? ngerprint is matched with n minutiae of sample ? ngerprint giving total number of n2 comparisons. This matching is very orthodox and gives headache when identi? cation is done on large databases. 7. 3 One to Many matching Few algorithms are proposed by many researchers around the world which are better than normal n2 matching. But all of them are one to one veri? cation or one to one identi? cation matching types. We developed a one to many matching technique which uses key as the hashing tool. Initially, we do not match minutiae sets instead we per- 7. 3. ONE TO MANY MATCHING 49 form key matching with many keys of database. Those database ? ngerprints whose keys match with key of query ? ngerprint, are allowed for full minutiae matching. Key matching and full matching are performed using k*n matching algorithm discussed in later section. Following section gives method for one to many matching. Data: Query Fingerprint; Result: Matching Results; Acquire Fingerprint, Perform Enhancement, Find Fingerprint Class, Extract Minutiae, Remove Spurious and Boundary Minutiae, Extract Key,Estimate Gender; M . 3. 1 Method of One to Many Matching The matching algorithm will be involving matching the key of the query ? ngerprint with the many(M) keys of the database. Those which matches ,their full matching will be processed, else the query key will be matched with next M keys and so on. 50 Data: Gender, Class, i; Result: Matching Results; egender CHAPTER 7. MATCHING if keymatchstatus = s uccess then eminutiae 7. 4 Performing key match and full matching Both key matching and full matching are performed using our k*n matching technique. Here k is a constant(recommended value is 15) chosen by us. In this method, we match ith minutiae of query set with k unmatched minutiae of sample set. Both the query sets and sample sets must be in sorted order of distance from reference point or centroid. ith minutia of query minutiae list is matched with top k unmatched minutiae of database minutiae set. This type of matching reduces matching time of n2 to k*n. If minutiae are 80 in number and we chose k to be 15, the total number of comparisons will reduce from 80*80=6400 to 80*15=1200. And this means our matching will be k/n times faster than n2 matching. 7. 5. TIME COMPLEXITY OF THIS MATCHING TECHNIQUE 51 Figure 7. : One to Many Matching 7. 5 Time Complexity of this matching technique Let s = size of the key, n = number of minutiae, N = number of ? ngerprints matched till successful identi? cation, k = constant (see previous section). There would be N-1 unsuccessful key matches, one successful key match, one successful full match. Time for N-1 unsuccessful key matches is (N-1)*s*k (in w orst case), for successful full match is s*k and for full match is n*k. Total time is (N-1)*s*k+n*k+s*k = k(s*N+n). Here s=10 and we have reduced database to be searched to 1/10th ,so N matching technique, it would have been O(Nn2 ). For large databases, our matching technique is best to use. Averaging for every ? ngerprint, we have O(1+n/N) in this identi? cation process which comes to O(1) when N n. So we can say that our identi? cation system has constant average matching time when database size is millions. Chapter 8 Experimental Analysis 8. 1 Implementation Environment We tested our algorithm on several databases like FVC2004, FVC2000 and Veri? nger databases. We used a computer with 2GB RAM and 1. 83 GHz Intel Core2Duo processor and softwares like Matlab10 and MSAccess10. 8. 2 8. 2. 1 Fingerprint Enhancement Segmentation and Normalization Segmentation was performed and it generated a mask matrix which has values as 1 for ridges and 0 for background . Normalization was done with mean = 0 and variance = 1 (? g 8. 1). Figure 8. 1: Normalized Image 52 8. 2. FINGERPRINT ENHANCEMENT 53 8. 2. 2 Orientation Estimation In orientation estimation, we used block size = 3*3. Orientations are shown in ? gure 8. 2. Figure 8. 2: Orientation Image 8. 2. 3 Ridge Frequency Estimation Ridge density and mean ridge density were calculated. Darker blocks indicated low ridge density and vice-versa. Ridge frequencies are shown in ? gure 8. 3. Figure 8. 3: Ridge Frequency Image 8. 2. 4 Gabor Filters Gabor ? lters were employed to enhance quality of image. Orientation estimation and ridge frequency images are requirements for implementing gabor ? lters. ?x and ? y are taken 0. 5 in Raymond Thai, but we used ? x = 0. 7 and ? y = 0. 7. Based on these values , we got results which were satis? able and are shown in ? gure 8. 4. 54 CHAPTER 8. EXPERIMENTAL ANALYSIS Figure 8. 4: Left-Original Image, Right-Enhanced Image 8. 2. 5 Binarisation and Thinning After the ? ngerprint image is enhanced, it is then converted to binary form, and submitted to the thinning algorithm which reduces the ridge thickness to one pixel wide. Results of binarisation are shown in ? gure 8. 5 and of thinning are shown in ? gure 8. 6. Figure 8. 5: Binarised Image 8. 3. FEATURE EXTRACTION 55 Figure 8. 6: Thinned Image 8. 3 8. 3. 1 Feature Extraction Minutiae Extraction and Post Processing Minutiae Extraction Using the crossing number method, we extracted minutiae. For this we used skeleton image or the thinned image. Due to low quality of ? ngerprint, a lot of false and boundary minutiae were found. So we moved forward for post-processing step. Results are shown in ? gure 8. 7 and 8. 8. Figure 8. 7: All Extracted Minutiae 56 CHAPTER 8. EXPERIMENTAL ANALYSIS Figure 8. 8: Composite Image with spurious and boundary minutiae After Removing Spurious and Boundary Minutiae False minutiae were removed using method described in earlier section. For removing boundary minutiae, we employed our algorithm which worked ? ne and minutiae extraction results are shown in table 8. 2. Results are shown in ? gure 8. 9 and 8. 10. Figure 8. 9: Minutiae Image after post-processing As we can see from table 8. 2 that removing boundary minutiae considerably reduced the number of false minutiae from minutiae extraction results. 8. 4. GENDER ESTIMATION AND CLASSIFICATION 57 Figure 8. 0: Composite Image after post-processing Table 8. 1: Average Number of Minutiae before and after post-processing DB After After Removing After Removing Used Extraction Spurious Ones Boundary Minutiae FVC2004DB4 218 186 93 FVC2004DB3 222 196 55 8. 3. 2 Reference Point Detection For reference point extraction we used complex ? lters as described earlier. For a database size of 300, refe rence point was found with success rate of 67. 66 percent. 8. 4 8. 4. 1 Gender Estimation and Classi? cation Gender Estimation Average ridge density was calculated along with minimum and maximum ridge densities shown in table 8. . Mean ridge density was used to divide the database into two parts. This reduced database size to be searched by half. Based on the information available about the gender of enrolled student, we can apply our gender estimation algorithm which will further increase the speed of identi? cation. 8. 4. 2 Classi? cation Fingerprint classi? cation was performed on both original and enhanced images. Results were more accurate on the enhanced image. We used same algorithm as in sec 6. 2 to classify the ? ngerprint into ? ve classes – arch, left loop, right loop, whorl and 58 CHAPTER 8. EXPERIMENTAL ANALYSIS Figure 8. 11: Plotted Minutiae with Reference Point(Black Spot) Table 8. 2: Ridge Density Calculation Results Window Minimum Maximum Mean Total Average Size Ridge Ridge Ridge Time Time Taken Density Density Density Taken Taken 36 6. 25 9. 50 7. 87 193. 76 sec 1. 46 sec unclassi? ed. This classi? cation was used to divide the database into ? ve parts which would reduce the database to be searched to one-? fth and ultimately making this identi? cation process ? ve times faster. Results of classi? cation are shown in table 8. 4, 8. 5 and 8. 6. 8. 5 Enrolling At the time of enrolling personal details like name, semester, gender, age, roll number etc. were asked to input by the user and following features of ? ngerprint were saved in the database (1)Minutiae Set (2)Key (3)Ridge Density (4)Class Total and average time taken for enrolling ? ngerprints in database is shown in table 8. 6. MATCHING Table 8. 3: Classi? cation Results on Original Image Class No. of (1-5) Images 1 2 2 2 3 3 4 4 5 121 Table 8. 4: Classi? cation Results on Enhanced Image Class No. of (1-5) Images 1 8 2 3 3 3 4 6 5 112 59 8. 7. All the personal details were stored in the MS Access database and were modi? d by running sql queries inside matlab. Fingerprint features were stored in txt format inside a separate folder. When txt ? le were used, the process of enrolling was faster as compared to storing the values in MS Access DB. It was due to the overhead of connections, running sql queries for MS Access DB. 8. 6 Matching Fingerprint matching is required by both veri? ca tion and identi? cation processes. 8. 6. 1 Fingerprint Veri? cation Results Fingerprint veri? cation is the process of matching two ? ngerprints against each other to verify whether they belong to same person or not. When a ? gerprint matches with the ? ngerprint of same individual, we call it true accept or if it doesn’t, we call it false reject. In the same way if the ? ngerprint of di? erent individuals match, we call it a false accept or if it rejects them, it is true reject. False Accept Rate (FAR) and False Reject Rate (FRR) are the error rates which are used to express matching trustability. FAR is de? ned by the formula : 60 CHAPTER 8. EXPERIMENTAL ANALYSIS Table 8. 5: Time taken for Classi? cation Image Average Total Taken Time(sec) Time(sec) Original 0. 5233 69. 07 Enhanced 0. 8891 117. 36 Table 8. : Time taken for Enrolling No. of Storage Average Total Images Type Time(sec) Time(hrs) 294 MS Access DB 24. 55 2. 046 60 MS Access DB 29. 37 0. 49 150 TXT ? les 15. 06 1 . 255 F AR = FA ? 100, N (8. 1) FA = Number of False Accepts, N = Total number of veri? cations FRR is de? ned by the formula : FR ? 100, N F RR = (8. 2) FR = Number of False Rejects. FAR and FRR calculated over six templates of Veri? nger DB are shown in table 8. 8. This process took approximately 7 hours. 8. 6. 2 Identi? cation Results and Comparison with Other Matching techniques Fingerprint identi? cation is the process of identifying a query ? gerprint from a set of enrolled ? ngerprints. Identi? cation is usually a slower process because we have to search over a large database. Currently we match minutiae set of query ? ngerprint with the minutiae sets of enrolled ? ngerprints. In this project, we store key in the database at the time of enrolling. This key as explained in sec 5. 3 helps in 8. 6. MATCHING Table 8. 7: Error Rates FAR FRR 4. 56 12. 5 14. 72 4. 02 61 Figure 8. 12: Graph: Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) reduc ing matching time over non-matching ? ngerprints. For non-matching enrolled ? gerprints, we don’t perform full matching, instead a key matching. Among one or many keys which matched in one iteration of one to many matching, we allow full minutiae set matching. Then if any full matching succeeds, we perform post matching steps. This identi? cation scheme has lesser time complexity as compared to conventional n2 one to one identi? cation. Identi? cation results are shown in table 8. 9. The graph of time versus N is shown in ? gure 8. 13. Here N is the index of ? ngerprint to be identi? ed from a set of enrolled ? ngerprints. Size of database of enrolled ? ngerprints was 150. So N can vary from How to cite Attendance System, Essay examples

Strategic Information System Finance and Accounting

Question: Discuss about the Strategic Information System for Finance and Accounting. Answer: Introduction Software accounting which goes under the broad meaning of Accounting Data Systems (AIS) is a PC programming that records and procedures bookkeeping exchanges inside practical modules, for example, creditor liabilities, records of sales, finance, and trial parity. It might be created in-house by the association utilizing it, might be obtained from an outsider (off-the-rack bundled programming, for example, MYOB and QuickBooks), or might be a blend of an outsider application programming package with nearby adjustments. It shifts incredibly in its multifaceted nature and expense. Today's bundled bookkeeping programming not just records money related exchanges and deliver accounting reports, however, they incorporate usefulness for core administrative leadership went for increasing upper hand. Agreeing to Collins, "small business accounting software has made enormous Technological Process Technological leaps in power, speed, sophistication and flexibility in recent years." Mohamed noticed that "throughout the years, programming suppliers have included more creative components to their fund bundles, for example, web interfaces and better joining with inventory network and different applications, and they have likewise modified items to make them more useable for non-bookkeepers." A hefty portion of the bookkeeping programming items has additional elements that can be utilized to coordinate the product with other programming or web/the web (Osman, 2010). For instance, Intuit, one of the US-based SBA programming, offers more than 450 additional items that an outsider can coordinate with QuickBooks. Bookkeeping programming for particular business sector fragments, for example, retail industry, are likewise accessible in the business sector. The fundamental form of a standalone SBA programming costs as low as $US100. As indicated by one assessment, in Australia, more than 18,000 new duplicates of off-the-rack bookkeeping programming bundles are sold each year. MYOB is the primary SBA programming in Australia and Australia and has an overall after of 500,000 clients. Accounting and finance application programming bundles are utilized packages among little organizations. In a review of Finland, small organizations did by Heikkila, 85 percent of the respondents were using a bookkeeping package (Grefenstette and Wilber, 2010). Additionally, in an overview of IT use in little organizations completed Bookkeeping Business and the Public Interest, Vol. 9, 2010 101 in the UK in 1998, 86 percent of the 800 respondents reported that they had electronic their bookkeeping frameworks. In 2001, in Australia, 77 percent utilized IT structures to finish accounts (Leonard Higson, 2014). Research on IT appropriation in little and medium undertakings (SMEs) is quickly expanding especially amid the most recent decade (Laughlin, 2014). There are additionally various rese arch concentrates on execution and reception of particular IT anticipates, for example, the performance of e-trade and Electronic Data Interchange (EDI) in SMEs. Be that as it may, distributed exploration on the usage of bookkeeping programming in little organizations is meager, despite the fact that it is one of the utilized programmings among little organizations. Evidence of research A 'little business' is characterized distinctively by various scientists (Table 1) and distinctive nations. In this study, a little business is unified with nineteen or fewer workers as characterized by Australia Ministry of Economic Development [42]. The SME division assumes a critical part in the national economies (Fuller and Cummings, 2003). In Australia in 2008, 97 percent of ventures were little organizations, 87 percent of the endeavors utilized five or fewer individuals and the SMEs (organizations utilizing under 99 individuals) represented 37.3% of the economy's aggregate yield. In Australia, SME division represents 99% of all organizations, and inside the United States and the European Union, they represent more than 97% of all organizations. Given the imperative pretended by little organizations in national economies and given a vast extent of these little organizations use IT for records, we trust, this examination on actualizing SBA programming in little organizations wi ll be helpful for little organizations and also scientists. These studies propose that IT can be utilized to achieve expanded operational and regulatory efficiencies, cost investment funds, upgraded inward correspondence, extend client base, build deals, better client administration, execute business methodology and all in all expanded intensity (Alford, 2002). In any case, as indicated by Burgess, "little organizations are regularly put in the circumstance of realizing that IT can bolster their organizations somehow. However, they need the mastery and assets to know how it can be successfully connected." Typically, the presentation of IT in SMEs is piecemeal and divided without any system and is not very much oversaw. The accompanying is an outline of boundaries and difficulties confronted by little organizations as reported in the IT writing on little organizations. Moderateness: The expenses connected with actualizing an IT anticipate incorporate the loss of equipment and program ming, the expense of employing advisors, the expense of representative preparing and the expense of on-going upkeep (Tracy, 2014). That is an outstanding issue with little organizations as they work on extremely limited spending plans and don't have adequate money to contribute towards best in class innovations. IT ability: Small organizations don't have any specialized learning then again abilities and are careless in regards to the regale that IT can bring. They need data on open innovations and finding valuable unbiased exhortation is troublesome. The obliviousness of the force of IT is a unique hindering component for little organizations. Development of discussion Since this study endeavors to get a more profound comprehension of the issues and challenges confronted by little organizations in Australia in actualizing bookkeeping bundles, we have utilized an interpretive methodology based upon a subjective research philosophy (Kumar, 2005). This strategy is helpful intending to common sense issues where the experience of the individuals who are acquainted with and included in the particular situation is imperative and the connection of their activities is essential. As per Shardy et al., the interpretive examination is helpful " in concentrate actual world practices, choices, and settings, with the goal of examining, deciphering Furthermore, and understanding them: in this way recognizing answers for down to business issues (Pring, 2004). Its center is the ordinary life of associations as they exist "on the ground"; rather than exploring theoretical problems and giving fake arrangements, "sitting at a separation" Furthermore, utilizing some rem ote focal point held by an "isolated" scientist. The essential point of understanding is to investigate individual and aggregate encounters so as to create a comprehensive comprehension of individuals' activities and communications in the field." We chose a suitable specimen of eight little organizations (proprietor oversaw autonomous organizations with under 20 representatives and utilizing some PC-based bookkeeping programming) and eight IT specialists who give administrations to SMEs as our exploration members. What's more, point by point exchanges were moreover held with the Managing Director of a primary SBA programming organization (Rosner, Halcrow, Levins, 2001). We chose the example of little organizations and experts from a nearby business index. To give us most final chance to catch all issues confronted by these organizations, we attempted to incorporate the little organizations from various industry segments. The foundations of the contextual analysis organizations inc lude radio transmitter assembling and dissemination, article of clothing deals and dispersion, giftware wholesale, vegetable deals and conveyance, and printing and outline (Choi, 2003). The quantity of years they were in the business at the season of this study ran from 2 to 20 years with representative sizes extending from 6 to 15. At the time the information was gathered, five of the study organizations were utilizing MYOB, and the staying three organizations were utilizing QuickBooks. Of the eight organizations, two organizations changed from DOS based bookkeeping frameworks to Windows based off-the-rack frameworks. Three of the eight organizations were utilizing just a structural adaptation of the bookkeeping programming essentially for accounting (Siegel Shim, 2006). The rest of the organizations had propelled renditions of the accounting programming. Conclusion The motivation behind this exploration is to build our comprehension of issues and challenges confronted by little organizations in Australia in the execution of bookkeeping bundles. Given that little organizations represent 97 percent of Australia organizations and a substantial extent of these little organizations use IT for accounts, this examination has noteworthy pertinence to Australia economy. Not one or the other neither the proprietors nor the external advisors reported any significant disappointments with the product usage. In any case, there were various repeating issues, for example, owner/ director's absence of certainty, the lack of attitudes in IT and bookkeeping and problems connected with IT frameworks coordination. An essential ramification of this examination for merchants is that there is parcel more degree for further enhancing the SBA bundles keeping the 'idiotic little entrepreneur/Chief' as the top priority. Outside advisors assume a fundamental part in the us age of bookkeeping programming in small organizations. Likewise, IT consultancy organizations are becoming quickly. In a perspective of the basic part played by outside advisors, further research is expected to comprehend their role what's more, adequacy in actualizing SBA programming. Right now, distributed exploration in this region and, all in all, on the execution of SBA programming is constrained. As a direct aftereffect of this exploration, the creator is as of now undertaking an examination venture that investigates the part and adequacy of specialists in actualizing SBA programming. This study has a few constraints basically because of its exploratory nature utilizing a little test size. The study test, while speaks to both little organizations from diverse businesses and experts, was an advantageous specimen which makes it troublesome, to sum up, the outcomes acquired. A more thorough hypothesis building and empirical investigation are required for further research. References Alford, J. (2002). Defining the Client in the Public Sector: A Social-Exchange Perspective.Public Administration Review,62(3), 337-346. https://dx.doi.org/10.1111/1540-6210.00183 Choi, F. (2003).International finance and accounting handbook. Hoboken, N.J.: J. Wiley. Fuller, D. Cummings, E. (2003). Indigenous Economic and Human Development in Northern Australia.Development,46(1), 95-101. https://dx.doi.org/10.1057/palgrave.development.1110427 Grefenstette, G. Wilber, L. (2010).Search-Based Applications. San Rafael: Morgan Claypool Publishers. Kumar, R. (2005).Research methodology. London: SAGE. Laughlin, S. (2014).Frameworks. London: The Velvet Cell. Leonard, J. Higson, H. (2014). A strategic activity model of Enterprise System implementation and use: Scaffolding fluidity.The Journal Of Strategic Information Systems,23(1), 62-86. https://dx.doi.org/10.1016/j.jsis.2013.11.003 Osman, J. (2010).The network. Albany, NY: Fence Books. Pring, R. (2004).Philosophy of educational research. London: Continuum. Rosner, B., Halcrow, A., Levins, A. (2001).Communication. New York: McGraw-Hill. Siegel, J. Shim, J. (2006).Accounting handbook. Hauppauge, NY: Barron's. Tracy, B. (2014).Leadership. New York: American Management Association.

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Zinc Facts - Periodic Table of the Elements

Zinc Facts - Periodic Table of the Elements Atomic Number: 30 Symbol: Zn Atomic Weight: 65.39 Discovery: known since prehistoric time Electron Configuration: [Ar] 4s2 3d10 Word Origin: German zinke: of obscure origin, probably German for tine. Zinc metal crystals are sharp and pointed. It could also be attributed to the German word zin meaning tin. Isotopes: There are 30 known isotopes of zinc ranging from Zn-54 to Zn-83 . Zinc has five stable isotopes: Zn-64 (48.63%), Zn-66 (27.90%), Zn-67 (4.10%), Zn-68 (18.75%) and Zn-70 (0.6%). Properties Zinc has a melting point of 419.58Â °C, a boiling point of 907Â °C, a specific gravity of 7.133 (25Â °C), with a valence of 2. Zinc is a lustrous blue-white metal. It is brittle at low temperatures but becomes malleable at 100-150Â °C. It is a fair electrical conductor. Zinc burns in air at high red heat, evolving white clouds of zinc oxide. Uses: Zinc is used to form numerous alloys, including brass, bronze, nickel silver, soft solder, Geman silver, spring brass, and aluminum solder. Zinc is used to make die castings for use in the electrical, automotive, and hardware industries. The alloy Prestal, consisting of 78% zinc and 22% aluminum, is nearly as strong as steel yet exhibits superplasticity. Zinc is used to galvanize other metals to prevent corrosion. Zinc oxide is used in paints, rubbers, cosmetics, plastics, inks, soap, batteries, pharmaceuticals, and many other products. Other zinc compounds are also widely used, such as zinc sulfide (luminous dials and fluorescent lights) and ZrZn2 (ferromagnetic materials). Zinc is an essential element for humans and other animal nutrition. Zinc-deficient animals require 50% more food to gain the same weight as animals with sufficient zinc. Zinc metal is not considered toxic, but if fresh zinc oxide is inhaled it can cause a disorder referred to as zinc chills or oxide shakes. Sources: The primary ores of zinc are sphalerite or blende (zinc sulfide), smithsonite (zinc carbonate), calamine (zinc silicate), and franklinite (zinc, iron, and manganese oxides). An old method of producing zinc was by reducing calamine with charcoal. More recently, it has been obtained by roasting the ores to form zinc oxide and then reducing the oxide with carbon or coal, followed by distillation of the metal. Zinc Physical Data Element Classification: Transition Metal Density (g/cc): 7.133 Melting Point (K): 692.73 Boiling Point (K): 1180 Appearance: Bluish-silver, ductile metal Atomic Radius (pm): 138 Atomic Volume (cc/mol): 9.2 Covalent Radius (pm): 125 Ionic Radius: 74 (2e) Specific Heat (20Â °C J/g mol): 0.388 Fusion Heat (kJ/mol): 7.28 Evaporation Heat (kJ/mol): 114.8 Debye Temperature (K): 234.00 Pauling Negativity Number: 1.65 First Ionizing Energy (kJ/mol): 905.8 Oxidation States: 1 and 2. 2 is the most common. Lattice Structure: Hexagonal Lattice Constant (Ã…): 2.660 CAS Registry Number:7440-66-6 Zinc Trivia: Zinc is the 24th most abundant element in the Earths crust.Zinc is the fourth most common metal used today (after iron, aluminum, and copper).Zinc exposed to air will form a layer of zinc carbonate by reacting with carbon dioxide. This layer protects the metal from further reactions with air or water.Zinc burns white-green in a flame test.Zinc is the last period four transition metal.Zinc oxide (ZnO) was once called philosophers wool by alchemists because it looked like wool when collected on a condenser after burning zinc metal.Half of the zinc produced today is used to galvanize steel to prevent corrosion.The U.S. penny is 97.6% zinc. The other 2.4% is copper. Sources Los Alamos National Laboratory (2001), Crescent Chemical Company (2001), Langes Handbook of Chemistry (1952), CRC Handbook of Chemistry Physics (18th Ed.) International Atomic Energy Agency ENSDF database (Oct 2010)

Water Resource Plan Essay Example | Topics and Well Written Essays - 750 words

Water Resource Plan - Essay Example The equipment required for fishing including the boats and nets are expensive to maintain. So, the last thing in their mind is the conservation of the marine environment. Also, the nature of marine life is such that it is difficult to estimate the existing population levels at various marine habitats. This means that the problem of over-fishing comes to light when it’s too late to reverse the trend (Alive, 2007). At this juncture, a systematic, scientific and feasible plan is required to manage marine resources and ensure sustainability. Many experts within the fishing industry are working towards healthy, sustainable marine ecosystems, so that the future for its inhabitants is made secure. What is called for is a legitimate, proactive plan of action, with long term objectives in order that fisheries across the globe will be healthy and ecologically-balanced. Such a state of affairs will make sure that fishing does not have a negatively effect on marine ecosystems. (Neori, et. al., 2007) To start with, fisheries management requires taking careful account of the more vulnerable marine ecosystems whose conditions may have a huge impact on fish stocks and their productivity. On identifying these, no-take zones or no-travel zones could be imposed on commercial fishing expeditions to prevent disruption of â€Å"fish spawning, breeding, and annual marine migrations†. Protection of these sensitive habitats at crucial junctures in time helps depleted fish populations to replenish and makes sure that the process of long-term sustainability and productivity of a fishery is underway (Alive, 2007). Other measures are also required as part of the sustainability management plan. For example, in order for a marine ecosystem to maintain its health, instances fish catching expeditions will have to be curtailed to allow the target species (the ones identified to be on the verge of extinction but whose role in the marine

Low risk 1 his civi Essay Example | Topics and Well Written Essays - 250 words

Low risk 1 his civi - Essay Example Limit to immediate as well as ordinary jurisdiction by the pope. In this law, the pope has power accorded to him by virtue of the office that he holds. In this regard, due to the virtue of his office, the pope has some powers to forgive sins and mistakes as per his judgment of the nature of the sins (Spielvogel, 5). Despite having massive powers bestowed upon him, the pope is not above God. God is the supreme creator and controller of the earth who everyone ought to worship (Ratzinger, 12). Therefore, pope is just but a servant who leads his flock or people to the direction that pleases him. In case the faithful and the pope goes astray, he humbles them. This case limits the powers of the pope to the interpretation of the natural law. Therefore, all powers of determining that is lawful in the family and society rests with the pope (Newadvent, 1). The pope thereby, interprets the manner and way by which the faithful are supposed to live on earth since they are living things. The pope has a limit to the legislative power where he has powers to alter, abrogate, and alter the laws he has established or those crafted by the predecessors. The pope is the only person who can free people from canonical rules, thus releasing them and forgiving the sins that they had committed (Ratzinger, 18). The canonical rules are issues of great moment and thus, require the intervention of the pope who provides direction and resolves the technical issues or cases. Moreover, the pope can dispense the faithful from committing to pure canonical or ecclesiastical laws as well as grant exemptions and privileges as per their

Maritime Technology 2 Essay Example | Topics and Well Written Essays - 2000 words

Maritime Technology 2 - Essay Example Development of Container Ships Container ships are usually defined in terms of TEU or twenty foot equivalent units. An 8500 TEU for example can transport 8500; twenty foot equivalent units of containers between two ports. With time being a critical factor for most globalized operations and fuel costs increasing by the day it has become inevitable for the shipping owners to transport maximum number of containers possible in one single voyage. This has lead to a continuous research and study into the development of new designs of increased capacity that would be capable of withstanding the rigours at sea. At the time, it should also be able to navigate easily through different canals and seas offering varying degrees of drafts. (Container Ship Types, 2000) Source: 4250 TEU Container ship, (Container Ship Focus, June 2006) Technical Requirements Purchasing of 18000 TEU ships is a matter of great achievement for any company and it is said that only Maersk which is the leader in Container ship transportation have ventured into buying 10 number of ships from Daewoo. The increased container capacity poses lot of technical queries, which need to be taken care of. 1. To account for the increased number of containers the length and width of the ship would be needed to be increased proportionately. This increase would again pose problems to the ships manovereability. It is known that ships have to navigate through various canals that exist between high seas to cross across continents. The PanaMax of size 4100 TEU’s delivered in 1980 was the largest to be delivered in those times and was named by its ability to pass through the Panama Canal. There was however no major change in the next twelve years and the size hovered around 4500-5500 TEU’s. Ships of length 294.1m, width 32.3m and draft of 12m was the maximum dimension of a ship capable of passing the Panama Canal. An accident leading to the slippage of 4 containers containing lethal arsenic oxide into the sea in 1992 near New Jersey lead to the International Maritime Organization (IMO) adopting the guidelines on safe securing of cargoes and became part of the International Convention for Safety of life at sea, 1974. (SOLAS) This required the ship must have a Cargo securing manual approved by the Flag State.( Container Ship Types, 2000) Most container ships built thereafter were an offshoot of the PanaMax category with increased TEU. The Post-PanaMax built in 1996 had a capacity of 6400TEU. By 1999 this size had increased to 9000 TEU’s. These ships have cell guides which enable better arrangement of container cargo above deck. However five cargo holds were unprotected from rain and rough seas which made it very critical to have an efficient bilge or waste water disposal system. The Suez-Max Large container ships (ULCS) built thereafter were capable of carrying 12000 TEU’s. The Post-Suez-Max ships are classified as those ships which can carry capacities upto 18000 TEU. Th is would require a ship breadth of 60m with a maximum draft of 21m. The Suez Canal is being revamped to accommodate these increased sizes of ships in the current years. Ships of 18000TEU are classified as Malacca Max since the Malacca strait offers a draft of 21m. The harbours of Singapore and Rotterdam are the other ports that offer such drafts. (Container Ship Types, 2000). Therefore it is inevitable that while placing orders for such large container shi