Assignment Essay Example | Topics and Well Written Essays - 250 words - 14

Assignment - Essay Example This allows each department to focus on one task, allowing the company to work faster. Some of the key departments in a company may be the finance department or the sales department. 3) Chain of command is how management delegates authority to individuals throughout a workplace (Chain of Command). Instead of one manager delegating tasks, department managers can give instructions as they see fit. A normal chain of command would have the president at the top, followed by the vice-president, and so on. 4) Span of control is the number of employees that a manager has under his or her control at one time. Department managers would have a span of control over the employees who they are responsible for. 5) Centralization is the process where key decisions are taken only by top management. On the other hand, decentralization places trust in employees lower down the food chain and allows them to make decisions for

The Simpsons Essay Example for Free

The Simpsons Essay He bought the rights to Winnie the Pooh and made millions from selling merchandise, which has completely changed the way in which people think of Winnie the Pooh who originally didnt wear any clothing, let alone a red top. In reality Walt Disney was not the man that the majority think he was; he took advantage of others ideas and made millions from them. Even now when Walt Disney is mentioned people think of the theme parks and merchandise rather than the films, which he has made. The creators of The Simpsons disliked this and there is clearly a satirical theme when Bart and Lisa visit the Itchy and Scratchy Theme Park, which turns out to be a complete letdown just like most people find the Walt Disney Theme Parks to be when they get the chance to visit. There has been a huge progression from the first Mickey Mouse cartoons which were quite obviously hand-drawn, with jumpy animation to the now free flowing characters which can be seen today. Recent Disney motion pictures dont look like drawings, changing the reality, which was created at the beginning of Walts career. The Simpsons is the complete opposite of this, going right back to the basics of animation, creating non humanistic forms and deliberately showing them as cartoons, not as what could be perceived as a photograph. There is obviously no attempt to make the characters 3 Dimensional and also not much attempt to humanise them as they have yellow bodies and Marges blue hair which is clearly not realistic as its almost the same size as her body and stays up above her head. Also the colours used in The Simpsons are very lurid whereas those in Disney films are a complete opposite. Matt Groening and the other creators have taken Walt Disneys idea to a new level, widening the target audience from children to people of any age or culture. As the cartoon has grown in popularity different characters have been introduced, creating diversity and thus increasing the number of viewers to millions. I think that one of the reasons the programme has become so popular is due to the fact that The Simpsons still has morals within it, the good always triumph over the evil and the characters always doing the right thing, no matter what the consequences. The content of The Simpsons reflects the breadth of this audience by showing characters of diverse nationalities, differing religions and very different overall characters. From Apu to Flanders, Homer to Mr Burns, every person in society is represented in some way by a character in The Simpsons. Although The Simpsons is a grotesque exaggeration of humanity, with all the characters being yellow, the characters personalities being exaggerated so much and the differences so obvious, it is still very affective. Lisa is the perfect child while her brother Bart is portrayed almost as a devil. Homer goes out during most of the cartoons, choosing to spend his time getting drunk rather than spending time with his children while his wife, Marge spends all her time trying to keep the family together and stop everything falling apart around her. The creators of The Simpsons in my opinion had one main aim, to show Americans everything that they are without directly saying thats what they were doing. In this way every American laughs at all the different characters without realising that in fact they are somewhat like the character that is being ridiculed. Every different character represents a different aspect of American society. Homer represents couch potato culture, he believes everything that the media tells him and is completely gullible. He represents every American who spends their lives rooted to the TV. Mr Burns represents the business community and corporate America, he represents every businessperson in America who lives solely to make money and fulfil their greed. Barney represents unemployment and the huge percentage of the population in America who are unemployed or part of blue collar America. He represents all the people who work hard for their living, manual labourers etc who have no hope of finding a new job but have lots of unfulfilled potential. They are capable of so much more than anyone gives them credit for but they cant do anything to prove themselves. Flanders represents evangelical America, the Religious Rights movement and people who close their eyes to the real world only to leave them with what they want to believe. Their lives and beliefs are restricted. The Simpsons has a very diverse appeal, as anyone who watches it can relate to at least one of the characters. Originally The Simpsons was created to be viewed by an American audience but due to its popularity it has grown to become a global product. The aspects of Walt Disneys marketing, which the creators originally ridiculed such as the Disney memorabilia that have come from all the animations, have now become products of The Simpsons. In the A Star is Burns episode alone there are over 20 references to films and American TV programmes, they include Seinfeld, The Guns of Navarone, The Rocky Horror Picture Show, Ben Hur and many more. To me this shows that the makers of The Simpsons try to incorporate the real world into their cartoon. Even darker sides to American culture such as the mob mentality are represented. Also literary culture is represented, in this specific episode by the title A Star is Burns, a pun of the Hollywood musical A Star is Born. As well as all of this The Simpsons makes many references to itself, the sort of in jokes that people will only understand if they have seen the programme before, an example of this is Barts money making schemes and Kent Brockman and his films. There are many different types of humour represented in The Simpsons. The most popular is satire; in the episode A Star is Burns there are many examples of this. The film awards system is portrayed as corrupted when Mr Burns is shown bribing most of the judges of the film contest, the Im only on the board because Im sleeping with the secretary, comment to me represents how corrupted the industry actually is. Another type of humour is bathos that is shown in the scene when Homer says to Marge that yes, their visitor is intellectual but does he know the Oscar Meier Wiener song? The build up to the end of the sentence creates a very effective anticlimax. Incongruity is used in The Simpsons when there is a comment made about Eudora Welty, who was a Mississippian author known for her angelic ways. In this episode she is portrayed as a crude belching woman. Also when Barney, the alcoholic is shown at a girls guide meeting. Irony is used regularly in The Simpsons, an example of this is when Bart is watching TV and a program combining The Flintstones and The Jetsons comes on. He comments on how pointless the program would be when in fact that is what The Simpsons is based on. Hyperbole is shown when a tumbleweed passes through the house after Homers Scooby Doo joke. Trivialisation is represented when Marge writes what she is saying to Homer in the letter that she is writing and when Homer asks if his family are Jewish, then stuffs his face with pork. There is lots of vulgarity in The Simpsons, the constant belching of Homer and Barney and Bart showing a picture of his bottom to a room filled with people are good examples of this. Slapstick is also a very key part of The Simpsons, as shown in the film Man with Football. Black humour is represented with the rapping rabbis and when McBain says : Now, my Woody Allen impression: Im a neurotic nerd who likes to sleep with little girls. The biggest pun in this episode is the title A Star is Burns and the title of Barneys film Pukahontas. Overall The Simpsons has overcome all its expectations, unlike most other TV programmes it incorporates all aspects of life, including every different nationality and culture. Although it sometimes pokes fun at different people and their beliefs or what they do, it is always done in enough of a light hearted way for the creators to not only get away with it but to highlight the aspects which are problematic and sometimes cause there to be changes. I think that The Simpsons will continue to be one of the most popular cartoons of at least the next decade. Its popularity is well deserved, it has taken a lot of hard work and dedication to make and everyone involved should be proud of what they have done. It has highlighted all the problems in American society and made them realise exactly what is wrong within it, every episode of The Simpsons has a hidden meaning, even if only a few people understand what they are saying, at least the creators feel confident enough to try and tell the world exactly what they think about it.

Heroines and Subservience in Ancient Athens :: Free Essays Online

Heroines and Subservience in Ancient Athens Women throughout history have played a subordinate role to men; this holds true in even ancient Athens. Though obvious through the writing of ancient poets, playwrights, and historians this subordinate role dominates religion and its practices. Through an examination of modern and ancient sources it will become apparent that women, even goddesses, played certain roles and they did not have the freedom to step outside these roles. Despite this subordinate stature, women could still receive recognition as heroic figures by achieve the status of heroine. Shrines dedicated to women who exemplify a certain trait, usually self-sacrifice, still exist, even on the Acropolis. The dual role of honored and subservient female is a complicated issue, and deserves further examination. Greek religion permeated every pore of society. Each level of society, from the Boule to the family had its own "separate center for its religious activity" (Mikalson, 83). Tribes worshipped together at the sanctuaries of the heroes for which they were named, each deme had a patron god, and families often performed the rites of the dead at their loved ones' tombs (83). With religion and supplication of the gods as a part of daily life, it not unusual that the gods led lives similar to their human worshippers. Gods married, gave birth, had fights and fits of temper, and human desires for love and sex. For these reasons Greeks were able to turn to religion so often; Gods had experiences similar to those encountered in everyday life, and were therefore capable of offering guidance. By turning to the gods the Greeks justified many of their actions, including wars, colonization, and the subjugating of women. Athenian women tended the oikos, or household. An aristocratic woman made clothes, kept the household accounts, oversaw the slaves, and made sure everything ran smoothly so her husband could concentrate on the running of the polis. "The world of the classical polis was a man's world. Only men could attend the Assembly, vote, hold office, serve on juries, appear in court in their own right, or even own property" (Demand 1996: 228). Women were allowed in public only to fetch water from the fountain houses, and during religious processions. Women functioned only as "prostitutes for the sake of pleasure, concubines for daily care of the body, and wives for the begetting of legitimate children and [as] a reliable guardian of the contents of the house" ([Dem.

Attendance System

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. 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? cateThis 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 Techno logy, 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 lar ge 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 Sys tem Flowchart . . . . . . . . . . . . . . 4 Fingerprint 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 matching . . . . . . . . . . . . . . . . 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 Spuriou s and Boundary Minutiae . . . . . . . 8. 3. 2 Reference 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 Figures 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)Thinne d 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, (d 1)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 Compo site 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? catio n 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 12Chapter 1 Introduction 1. 1 Problem Statement Designing a student attendance management system based on ? ngerprint recognition and faster one to many ident i? 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 Bi ometric 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 recognition 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 en ding 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 o ther 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 t he 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 wh ich 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 softwar e.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 ApproachThis 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 ex cept ? 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 tabl e, 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 in ternet 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. ATT ENDANCE 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 systemsThere 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 of 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 pi xel (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 compute 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. Fi rstly , 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 spati al 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 i nformation, 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 examining 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 Refer ence 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 resolution). 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. 1Minutiae 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 Continu ing 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 minutiaeFigure 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 ? ngerprin ts 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 minutiae 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: Estima ted 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) and (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 Lo op, (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 most 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 di stance from their centroid or core. 7. 2 Existing Matching TechniquesMost 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 ar e 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 = success 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 minu tiae 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 worst 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 NormalizationSegmentation 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. 4Gabor 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 thinni ng 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 ANALYSISFigure 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, reference 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 reduce d 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 EnrollingAt 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, ru nning sql queries for MS Access DB. 8. 6 Matching Fingerprint matching is required by both veri? cation 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 Tim e(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: G raph: Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) reducing 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

Kant the Sublime

The Sublime In Lyotard’s reading Lessons on the Analytic of the Sublime, he explains how critical thought exists within an infinite amount of creativity with no principles but in search of them. Lyotard understands the Kantian sublime as a way to comply with the standards that critically analyze postmodernism using deconstruction. Kant differentiated the sublime between the vastness and greatness and the dynamic sublime. The vastness sublime is so great we can’t just use our senses like we normally do; it requires us to heighten our senses beyond comprehension. The dynamic sublime is the way in which rationalizes things and his perceptions.Lyotard describes the boundlessness of the imagination and reason as a ‘differend’ and this is ‘to be found at the heart of sublime feeling: at the encounter of two absolutes equally present to thought, the absolute whole when it conceives, the absolutely measured when it presents. ’ (Lyotard) Our imagination understands forms and measures while reason understands something without form of an infinite nature of something. There is a separation of imagination and reason and when we use the ‘enigmatic’ power of critical thought we can reflectively judge something.Kant's presentation of the sublime has been taken up by Lyotard and he explores different ways of finding a philosophical understanding of different artworks. Through Longinus and Burke we can explore the pre-modern and modern conceptions of the sublime and through all these critiques we can draw different manifestations of the sublime in art. Kant questions how can someone judge an object before knowing how to properly judge that object and how do they know what proper judging is? Longinus in part of his critique implies that man can go beyond his limitations as a human being by experiencing emotions and language.The art or technical talents was described, as the human while the sublime was something that escaped ou r experience of art. ‘Sublimity consists in a certain excellence and distinction in expression’ (Longinus, pg 100). To understand and have knowledge of the sublime, there needs to be a vague understanding of something that is beyond our experience or senses. He explains that there is more to the human ordinary life because we feel this through the senses, but these senses are an incorrect interpretation caused by a physical perception as opposed to a psychological one.If you think about the sublime, it cannot be pictured or imagined but we have translated and suggested through the arts and poetry. Longinus tells us that nature is the creative and the first principle of the sublime and what follows is a matter luck and good mentors. In Goodbye Lenin, a German film directed by Wolfgang Becker, Lenin tries to hide the unification of Germany during the 1990’s from his mother who was in a coma throughout those months and has to stay in bed. He successfully conned her into thinking nothing had changed by using tricks like old product bottles and filming his own news broadcasts.This film successfully executes the idea of the sublime in what was going on between his mother and the rest of the world. ‘Our faults spring from the same place as our virtues. ’(Longinus)She believed everything around her was real and time had not changed but the people around her were well aware that this was a deception of reality. The way in which Lenin created the same world and reality his mother had always known is overwhelming to the senses and questions what is really for real? Burke believes that the ideas of pain and pleasure cannot be defined, but pleasure of every kind satisfies quickly.He goes on to say that there are two kinds of pleasure: the first that simply is and has no relation and the second that cannot exist without relation. The film has preserved and stopped time that defies nature and in reality cannot be done. The son is so scared an d terrified that his mother will die that he tries to please her by keeping the world exactly the same. This terror is the source of sublime because it creates the most emotion and he imagines the worst. The fear that his mother will die has caused him to be terrified. It explores different areas of the mind by letting her believe this lie.His mother would not survive the fact that everything she has believed in had crumbled while she was asleep and that the world she once knew was suddenly a capitalistic society that now included Burger king, Pepsi and Coca-Cola. Lenin did not want to devastate his mother and cause another heart attack so he had to make everything appear, as it always was wile the outside world was growing and expanding at such an accelerated rate. The kitsch setting of the typical German 1989 apartment filled with yellow flowered wallpaper and dark wood furniture brings calmness to his communist mother.The film captures the nature of humans and their strong social beliefs and how it can affect the human psyche. Burke believes that terror is the source of the sublime and that pain is the more powerful than pleasure. Lyotard believes that pain is not the end point, there is the ‘movement’ of pain to ‘pleasure’ In Joseph Turners Impressionistic painting titled Slave Ship, he has created a seen of vastness that relates to the Kantian sublime but moreover he has demonstrated the use of color and dynamic composition to create a sense of horror.The brushstrokes give a natural look to the painting and the seemingly unintentional brushstrokes create an organized composition. We are aesthetically pleased when we look at the painting because an overwhelming response is experienced when looking at the picture. Burke believed that the Beautiful was very different from the sublime. â€Å"All privation is great because they are all terrible: Vacuity, darkness, solitude, and silence. Low and intermittent sounds and shadows bring a bout feelings of the sublime. Above all, the actions of the mind are affected by the sublime. â€Å"The way in which Turner has used rough brush strokes and lots of contrast between moods and contrasting colors creates the Sublime as opposed to the Beautiful. The use of purple and blue shadows that cast over the foggy night, the bloodline skyline, the flaming enraged sea and the insignificant ship create a sense of horror. The Sublime is experienced in this painting because it is detached from the actual danger of being in an actual situation and rather experienced through the visual arts and poetry in its overwhelming vastness. There is a sense of elation and intimidation when traveling through the painting.Thomas Cole’s Landscape with Tree Trunks create a struggle to be able to understand what is being implied. It becomes uninviting to the viewer as the scary tree trunks in the foreground block our path into the painting and we get further into only for our view to be bloc ked again because of the mountains that come right into the middle part. The painting does not accommodate to human feelings. These are sublime components such as the scary trees, the contrasting light and dynamic sky create frustration in entering the painting make us feel that we are not welcomed and that we are in a wilderness that we cannot control.The overwhelming tree and sky not only creates and unexplained phenomena but also implies that time and power of nature is vast and infinite and these unpredictable forces can in themselves become sublime. The painting composition seems to have been distorted by Cole yet it feels genuine and believable. In the Pianist directed by Roman Polanski we are safe to knowing the reality of the actual circumstance of Jewish victims trapped in a concentration camp but we still enjoy the horrific actions taken against them caused by the overwhelming power of human nature.We are awed, disgusted and in disbelief by the question: â€Å"How can hum an beings do such things? † ‘There is no explanation for the communication of passion, but it is concluded through the experience or premonitions of others. ’(Burke) there is no way to rationalize how the Jews were treated during this time of suffering and we can’t even fathom such events to reoccur, but it still exists today. The pain and suffering that we experience as a witness is subliminal and in cannot be justified. Within the film Szpilman is able to detach from the war and all the pain and suffering he has experienced by playing the piano. When different men have a common faith in the object of admiration we come to the Sublime’ (Longinus). Szpilman creates his own sublime world through the piano. He creates a stimulus of powerful and inspired emotion and he seems to be dignified and elevated when playing the piano, not guilty or ashamed for being a Jew. His talent brings even his worst enemies to respect him and be in completely awestruck by his music breaking all boundaries of human laws and standards and elevating itself to a much higher place that is universal. The unknown and unbound is created in the film when everyone is awestruck and in total admiration of the music.In the end, art has the vastness that allows the mind to be free of obstacles of distraction. Basically, I understand Kant in his way of thinking of art as a way of means to letting the senses experience a feeling far greater than the everyday ordinary life and sublimity only exists in our minds and not in nature, and if we are conscious of this we can become superior to nature within and to nature which does exists without us. Lyotard explains that art should work with no rules and that it therefore cannot be judged and instead be combined and pluralized.Longinus explains that the human is the technical aspect of art while the sublime is the existential part of the human psyche that goes beyond our experience of art. Burke’s modern view of su blimity in art can only be experienced through actual knowledge an actual experience where things are only recreated and reordered. He thinks that death and fear are feelings of the sublime and that what one might expect is very different to what actual really happens and that expectation creates fear which in turn makes us unreasonable and therefore brings about the sublime.

Movie Trailers Essay Example

Movie Trailers Essay Example Movie Trailers Essay Movie Trailers Essay We studied a number of film trailers and came to the following conclusions about their conventions. Each one of them has the common features that I have discovered. At the start of every movie trailer, there is a production company logo and name. This is very important in every trailer because it notifies the audience that the movie was made by this particular company. Each company has its own logo brand and copyright name/logo, as it has been used for many years, on different types of movies. I have also noticed that a company can make and supports a certain type of genre film. There are many different production companies that have been around for many years; 20th Century Fox, Pathà ¯Ã‚ ¿Ã‚ ½, DreamWorks, Universal, Miramax Films, Walts Disney, Paramount, Warner Brothers etc. These companies have made so many different films which people would now recognize in a flash, even without looking at the name of the company. This is because the company is so familiar and we see it in everyday life, all around us. After a trailer has shown the production company (which only takes about two to three seconds), the film shows a very brief number of clips/images taken from the movie but only the most exciting moments. These clips and images inform the audience about the genre of the film, it also gives us the some idea of the film, whether its genre is action, comedy, sci-fi, horror, romance etc. The clips show a number of different exciting or interesting moments to try and persuade the audience that this film is a winner. They do this by showing images from all over the movie in swift cuts so that we dont know what we are seeing in a one second clip, but when the whole trailer has ended, the exciting moments remain in our heads. There are swift cuts to help keep the audience entertained and interested in the trailer, so that once the trailer has ended it makes you want to go and see the movie. Some trailers start off slow and build up the impact and some shots lasts longer than others this is t o add more affect to the trailer it all depends on the genre of the movie. Film trailers inform the audience about the genre of the film and also give the audience the idea of the film, plus the opportunity to see what type of film it is. It gives them the idea of the film in the way the trailer is presented. If the movie is an action film, the trailer would be a fast paced one with hard hitting music, which can get your attention in seconds. If the movie is a romantic film, the trailer would have slow and calm type music with slow cuts, but with a longer shot than an action trailer would. These trailers are usually set in the same format. If you want your movie to be a big and a winning movie, you have to have a known actor, actress or director, producer to be part of the film. If a new film was just released with the actors, actress or director, producer that is unknown around the world, the film wouldnt do as well as a film with a known character or cast. A production company knows that a big star in a film they support will make them profit. All stars are unknown at first. They get to the top by either working with a bigger star or by starting in a winning movie. For example, the film Gladiator was an Oscar winning movie all around the world, but the actor who played a main role, Russell Crowe, was unknown before the movie. But because the film was an epic, action-drama, animation movie he is now known. Also the production company of this film was a joint production film also helping to make it a big movie. The main thing that tells you about a movie is the title. The title can informs us what type of genre the movie is. For example, if the title was Galaxy, you would know the movie would have something to do with space and this goes for sci-fi as well. Many people think that, if the title is eye catching the movie will have an impact on them, so all movie titles have to be eye catching and simple. The title would be presented in the way of the genre of the movie. For example, if the movies genre is to do with History, the title could be in a shape of a rock. Or the title could be in a simple text, but the background picture would have something to do with History. Either way the poster of the movie will have something to do with the genre of the movie, weather its the title or the background. In a film trailer, they use music also for impact and to get your attention. The music played would go along with the genre of the film. For example, if the genre was action, a fast paced music will be played. Without music, all trailers wouldnt look interesting and wouldnt have an impact at the audience. Some movies use their own soundtrack to the movie. These soundtracks are songs from the movies (music played in the film), and this is another way to promote the film. For example, Celion Dion sang a song for the film titanic, and in the video to this song, it had motion pictures taken from the film. Some trailers have music in the background, whilst a voiceover in front. Another word for this is called Portentous Voice, which sounds like a warning type voice, which is deep and which can get your attention. The voice informs the audience who made the film, who acted in the film, if it had won any awards, what is the releasing date and the production company that helped make the film. I think this is a better way to present a trailer instead of reading the text yourself, because the voiceover can give more information and is clear to understand. For example, you can watch the screen and listen at the same time, which gives you more to take in. As part of learning about trailers, we analysed twelve different trailers and picked out four which are all different genres. The first trailer I have chosen to write about is Paramounts Mission Impossible 2.Mission Impossible 2 is a sequel to Mission Impossible 1. The title relates to the genre of the movie, action. Like most action trailers, it starts off at a medium pace, and then builds up to a fast pace, with loud music pace music and a voice-over. It has swift cuts in between each frame as the genre is action, and also the movie uses the same soundtrack from the previous movie (Mission Impossible 1), but with a bit of adjustment to it. This time the soundtrack sounds more like rock, while for Mission Impossible 1 it sounded more acoustic. The second trailer I have chosen is X-Men. The production company for this film is 20th Century Fox. The genre of this movie is a mixture of action and sci-fi, and we get an early clue of this in the first few seconds of the trailer when the trailer shows a sci-fi fighting clip. The soundtrack to the film is the same soundtrack used in the cartoon X-Men, but there was no voice-over. At the end of the trailers it showed the release date with the website below it. Also the trailer showed us a slogan that relates to the genre of the movie, We are not we seem? a sci-fi meaning. The third trailer I have chosen is an award winning movie, Gladiator. This film has a joint production company, Universal and DreamWorks, making this film a big movie. The genre of this film is a mixture of action and history. The music in the background is a mixture of people shouting and heroes welcome music. Like most action trailers this too starts off at in a medium pace and then buildings up to fast pace trailer, showing clips in swift cuts. At the end of the trailer, it shows us the website of the movie and its slogan. The trailer also had a voice over explaining that the movie had won an award and was recommended by newspapers. The fourth and final trailers I have chosen is Pokà ¯Ã‚ ¿Ã‚ ½mon, and the production company is Warner Brothers. The genre of the movie is a mixture of action and animation cartoon. It uses its cartoon theme tune for the soundtrack, and as the soundtrack is playing in the background there is a voice-over. We hear the main characters say a few lines, and we also see the release date. The release date is shown at the end of the trailer saying, Coming This Summer, because the cartoon is aimed at children and summer is the time when children have their holiday and so is part of the marketing strategy. The trailers also showed the films website to promote the film. Summary The common feature for all these movie trailers depends on the genre of the movie. If the genre is action, then most action movie trailers will be in the same format. For example, the trailer starts of in a medium pace then ends up being fast, with hard hitting music. If the genre is romance, the format would be different compare to action trailers, but the format would be similar with all romance trailers. For example, the genre of the romance trailer would be slow, calm music in background, love story etc. But there are a few things that all movie trailers share. They all have one of the following; Voice-over, website, release date, music and Production Company. Most of the trailers use music from the movies soundtrack and most use the same soundtrack in the movies and its sequence but edit it like we see in Mission Impossible. This is done to give the audience a new taste to the new movie showing that it isnt the same as the previous film, but better. Overall all trailers try to k eep their message understandable, entertaining and short. Treatment The basic idea of the film is about a fourteen-year-old boy whose rebellious behaviour has him expelled by the headmaster of Shady Glen School. After taking his sister to class one day, the boy sneaks into the basement to set up one last prank before heading back to his dad who is waiting in the car. But there, the boy discovers that the school security consultant has taken a successful Entrepreneur, the headmasters daughter hostage. She is also a Shady Glen student. The boy decides to go back and tell his father, who is a police officer about what is happening, but he too is taken hostage. At this point the mastermind, who is behind the hostage-taking (the leader/boss), orders his men to capture the whole school and to hold them hostage. The father who has been waiting in the car for almost twenty minutes decides to find his son. As the front entrance is closed, he heads through a back entrance. He is then just about to enter the main hall, when he peeks through a small window on the door. To his shock, he sees the whole school, including his children and teachers on their knees with rifles pointing towards them. The father then becomes a hero as he fights the men using his gun as well as everything and anything to fight the men and saves the whole school.

Avian Influenza Essay Example

Avian Influenza Essay Example Avian Influenza Paper Avian Influenza Paper Essay Topic: The Wild Duck Avian Influenza Introduction Bird flu in most cases begins with discomfort of lower respiratory ways and in unusual casesfrom upper respiratory air-ways. Elevated viral titer is isolated from pharynx but not from nose. Initial symptoms of the H5N1 influenza are: high grade fever, mild cold, cough and shortness of breath. Practically all patients develop viral pneumonia complicating to secondary bacterial infection, mild to severe respiratory distress, diarrhea, vomiting and abdominal pain. Conjunctivitis is entity. Sometimes gastrointestinal disorder develops earlier than respiratory symptoms. Avian influenza viruses are shed in respiratory secretions and feces of birds. Infected ducks, for example, shed virus for at least 30 days. Influenza virus from the feces of waterfowl can be recovered from surface water. Avian species develop infection that ranges from asymptomatic to lethal. Avian influenza has caused major outbreaks in poultry farms.   Influenza virus can undergo genetic mutations in hemagglutinin or neuraminidase (antigens on the surface of the virus) that can lead to epidemics. Much less commonly, a completely new hemagglutinin or neuraminidase emerges- with the new genetic material coming from animals. This genetic shift typically leads to pandemics. Early chronology: 1929 Last evidence (serologic) of circulation in humans of a swine-like influenza virus 1930 Isolation of an influenza virus from swine 1933 First isolation of an influenza virus from humans Until 1995, only three of the 15 influenza hemagglutinins that had been identified were known to cause infections in humans. Birds have all 15 identified hemagglutinins and nine neuraminidases. New influenza viruses often emerge from southern China, a region characterized by a large, densely settled human population and abundant pigs and ducks living in close proximity to humans. Until events in Hong Kong in 1997, scientists thought that avian influenza posed no direct threat to humans. In 1997, after causing influenza outbreaks on chicken farms, avian influenza (H5N1) spread to humans (Claas et al. 1998). Eighteen human cases were confirmed, six of them fatal. Infection was concentrated in children and young adults, unlike the pattern in most outbreaks where morbidity and death are most common in older adults. The virus recovered from humans was identical to that found in birds (Subbarao et al. 1998). Epidemiological studies suggested that there had been multiple independent introductions of the influenza virus into the human population from birds, but that very limited person-to-person spread occurred. At the time of the human cases, there were estimated to be 300–600 live bird markets in Hong Kong, where mixing of different avian species (ducks, chickens, pheasants, pigeons, wild birds) was possible. When the Hong Kong live bird markets were studied , 10% or more of birds were found to be shedding H5N1, in multiple avian species (geese, chickens, ducks). The birds (more than one million) were killed, and no additional human cases of H5N1 have been documented. In 1999, human infection with H9N2, another avian influenza strain widespread in Asia, was also documented for the first time in humans, at a time of enhanced surveillance (Peiris et al. 1999). The events in Hong Kong have led to heightened global surveillance for influenza in humans and animals. There was reason to be concerned about the events in Hong Kong, a densely populated city with extensive links to the rest of the world. In 1993, there were an estimated 41.4 million passenger movements (boat, train, car, airplane) and from Hong Kong. The influenza viruses that afflict humans are divided into three types: A, B, and C. Influenza A is responsible for the epidemics and infects not only man but also pigs, horses, seals, and a large variety of birds. Indeed, influenza A has been isolated worldwide from both domestic and wild birds, primarily waterbirds including ducks, geese, terns, and gulls and domesticated birds such as turkeys, chickens, quail, pheasants, geese, and ducks. Studies of wild ducks in Canada from 1975 to 1994 indicated that up to 20 percent of the juveniles were infected, and fecal samples from their lakeshore habitats contained the virus. These birds usually shed the virus from five to seven days (with a maximum of thirty days) after becoming infected even though they show no sign of the disease. Obviously, this virus and its hosts have adapted mutually over many centuries and created a reservoir that ensures perpetuation of the virus. Duck virus has been implicated in outbreaks of influenza in animal s such as seals, whales, pigs, horses, and turkeys. Extensive analysis of the viruss genetic structure, or nucleic acid sequences, supports the hypotheses that mammalian influenza viruses, including those infecting man, may well originate in aquatic birds. (Suarez DL, Spackman E, Senne DA, 2003) Subtypes of influenza A, the various strains of these avian viruses can be classified as either highly pathogenic or as of low pathogenicity, based on their genetic features and the severity of illness they cause in birds. There are currently 27 potential forms of the three subtypes of avian influenza viruses differentiated by variations in the neuraminidase surface antigen. Thus, H5, H7, and H9 avian influenza viruses, so named for their hemagglutinin surface antigen, can each be matched with nine possible neuraminidase surface antigens, N1, N2, N3, etc. Thus, there could be H5N1 through H5N9, H7N1 through H7N9, and H9N1 through H9N9 strains. H9 viruses appear to be of low pathogenicity, while H5 and H7 viruses can be highly pathogenic for birds. However, low pathogenic forms of these viruses seem to be the cause of most outbreaks among poultry causing only mild or imperceptible illness and low mortality rates. Nonetheless, both H5 and H7 can develop high levels of pathogenicity in which case mortality rates in poultry flocks can reach 100%. The natural history of avian influenza viruses is characterized by spread through infected nasal, respiratory and fecal material, and a reservoir state in healthy birds. (Pascal James Imperato, 2005) www.springerlink.com/index/H6427776HH34G857.pdf Pathogenesis The pathogenesis of avian influenza A (H5N1) virus in humans has not been clearly explained. Apoptosis might also play a vital part. Apoptosis has been observed in alveolar epithelial cells, which is the major target cell type for the viral replication. Many apoptotic leukocytes were observed in the lungs of patients who died on day 6 of illness. Apoptosis may play a major role in the pathogenesis of influenza (H5N1) virus in humans by destroying alveolar epithelial cells. This pathogenesis causes pneumonia and destroys leukocytes, leading to leucopenia, which is an outstanding clinical feature of influenza (H5N1) virus in humans. Whether observed apoptotic cells were a directly related to viral replication or outcome of an over activation of the immune system needs further studies. (Uiprasertkul M, 2007) www.cdc.gov/EID/content/13/5/708.htm Infected birds were the major source of the H5N1 influenza virus among humans in Asia. Mainly humans became infected by eating infected birds, by poor hygiene procedures when cooking infected birds, or by close contact with infected poultry. (Reina J, 2002). Certain birds, particularly water birds, act as hosts for influenza viruses by carrying the virus in their intestines and shedding it. Infected birds shed virus in saliva, nasal secretions, and feces. Susceptible birds can become infected with avian influenza virus when they have contact with contaminated nasal, respiratory, or fecal material from infected birds. Fecal-to-oral transmission is the most common mode of spread among birds. Most often, the wild birds that are the hosts for the virus do not get sick, but they can spread influenza to other birds. (CDC, 2006) www.cdc.gov/flu/avian/gen-info/spread.htm At present spread of the H5N1 influenza from human to human by air born route has not been registered, but enduring monitoring for identification mutation and adaptation of H5N1 influenza virus to human is needed. Most studies performed in avian viral strains elucidates that virulence is a polygenic phenomenon. However, hemagglutinin and neuraminidase and the genes codifying these substances (genes 4 and 6) play a vital role in viral pathogenesis. (Gu J, Xie Z, Gao Z, Liu J, Korteweg C, Ye J, Lau LT, Lu J, Gao Z, Zhang B, McNutt MA, Lu M, Anderson VM, Gong E, Yu AC, Lipkin WI, 2007). Avian strains can be classified as virulent or avirulent according to the capability of hemagglutinin to be triggered by endoproteases of the respiratory tract merely or by proteases from other tissues. This ability is based on the ever going mutations that lead to the substitution of the normal amino acids at the point of hemagglutinin hydrolysis by the other basic amino acids that determine the amplifi cation of the spectrum of hydrolysis and activation. Neuraminidase contributes in the acquisition of virulence through its ability to attach to plasminogen and by escalating the concentration of activating proteases. Adaptation to the host, by recognition of the cell receptor, is an additional factor determining the virulence and interspecies spread of avian strains. (Reina J, 2002) Transmission to mammals Influenza A viruses from aquatic birds grow poorly in human cells, and vice versa. However, both avian and human influenza viruses can replicate in pigs. We have known that pigs are susceptible to influenza viruses that infect man ever since the veterinarian J. S. Koen first observed pigs with influenza symptoms closely resembling those of humans. Retrospective tests of human blood indicate that the swine virus isolated by Shope in 1928 was similar to the human virus and likely responsible for the human epidemic. Swine influenza still persists year-round and is the cause of most respiratory diseases in pigs. Interestingly, in 1976, swine influenza virus isolated from military recruits at Fort Dix was indistinguishable from virus isolates obtained from a man and a pig on a farm in Wisconsin. The examiners concluded that animals, especially aquatic birds and pigs, can be reservoirs of influenza virus. When such viruses or their components mix with human influenza virus, dramatic geneti c shifts can follow, creating the potential of a new epidemic for humans. The influenza virus continually evolves by antigenic shift and drift. Early studies in this area by Robert Webster and Graeme Laver established the importance of monitoring influenza strains in order to predict future epidemics. Antigenic shifts are major changes in the structure of the influenza virus that determines its effect on immune responses. Of the viral proteins, the hemagglutinin (H), a major glycoprotein of the virus, plays a central role in infection, because breakdown of hemagglutinin into two smaller units is required for virus infectivity. (Suarez DL, Spackman E, Senne DA, 2003). Shifts in the composition of the hemagglutinin (H) or neuraminidase (N), another glycoprotein, of influenza virus were observed in the 1933, 1957, 1968, and 1977 epidemics: 1933: H1N1 1957: H2N2 (Asian flu) 1968: H3N2 (Hong Kong flu) 1977: reappearance of H1N1, called the Russian flu The reappearance in 1977 of the Russian flu, a virus first isolated in 1933, raises the uneasy possibility that a return of the 1918-19 influenza epidemics with its devastation of human life is possible and perhaps likely. In March of 1997, part of influenza virus nucleic acid was isolated from a formalin-fixed lung tissue sample of a twenty-one-year-old Army private that died during the 1918-19 Spanish influenza pandemic. Since the first influenza viruses were not isolated until the 1930s, characterization of the 1918-19 strain relied on molecular definition of the viruss RNA. Chemical evidence indicated a novel H1N1 sequence of a viral strain that differed from all other subsequently characterized influenza strains and that the 1918 HA human sequence correlated best with swine influenza strains. Once the entire sequence is on hand, a virulent marker for the influenza virus associated with killing over 675,000 Americans from 1918 to 1919 may be uncovered and a vaccine planned that might abort the return of this virus form of influenza.   When such antigenic shifts occur, the appearance of disease is predictable. Therefore, surveillance centers have been established all over the world where isolates of influenza are obtained and studied for alterations, primarily in the hemagglutinin. According to the evidence from these centers, isolates identified in late spring are excellent indicators of potential epidemics in the following winter. Both avian and human influenza viruses can replicate in pigs, and genetic reassortants or combinations between them can be demonstrated experimentally. A likely scenario for such an antigenic shift in nature occurs when the prevailing human strain of influenza A virus and an avian influenza virus concurrently infect a pig, which serves as a mixing vessel. Reassortants containing genes derived mainly from the human virus but with a hemagglutinin and polymerase gene from the avian source are able to infect humans and initiate a new pandemic. In rural Southeast Asia, the most densely populated area of the world; hundreds of millions of people live and work in close contact with domesticated pigs and ducks. This is the likely reason for influenza pandemics in China. Epidemics other than the 1918-19 catastrophes have generally killed 50,000 or fewer individuals, although within a year over one million people had been infected with these new strains. Conclusion Three major hypotheses have been put forth to explain antigenic shifts. First, as described above, a new virus can come from a reassortant in which an avian influenza virus gene substitutes for one of the human influenza virus genes. The genome of human influenza group A contains eight RNA segments, and current wisdom is that the circulating influenza hemagglutinin in humans has been replaced with an avian hemagglutinin. A second explanation for antigenic shifts that yield new epidemic viruses is that strains from other mammals or birds become infectious for humans. Some believe that this is the cause of the Spanish influenza virus epidemic in 1918-19, with the transmission of swine influenza virus to humans. A third possibility is that newly emerging viruses have actually remained hidden and unchanged somewhere but suddenly come forth to cause an epidemic, as the Russian H1N1 virus once did. H1N1 first was isolated in 1933, then disappeared when replaced by the Asian H2N2 in 1957. H owever, twenty years later the virus reappeared in a strain isolated in northern China and subsequently spread to the rest of the world. This virus was identical in all its genes to one that caused human influenza epidemics in the 1950s. (Gu J, Xie Z, Gao Z, Liu J, Korteweg C, Ye J, Lau LT, Lu J, Gao Z, Zhang B, McNutt MA, Lu M, Anderson VM, Gong E, Yu AC, Lipkin WI, 2007) Where the virus was for twenty years is not known. Could it have been inactivated in a frozen state, preserved in an animal reservoir, or obscured in some other way? If this is so, will the Spanish influenza virus also return, and what will be the consequences for the human population? In addition to antigenic shift, which signifies major changes in existing viruses, antigenic drift permits slight alterations in viral structure. These follow pinpoint changes (mutations) in amino acids in various antigen domains that relate to immune pressure, leading to selection. For example, the hemagglutinin molecule gradually changes while undergoing antigenic drift. Such mutations allow the virus to escape from attack by antibodies generated during a previous bout of infection. Because these antibodies would ordinarily protect the host by removing the virus, this escape permits the related infection to remain in the population. With these difficulties of antigenic shift and, drift and animal reservoirs, it is not surprising that making an influenza vaccine as effective as those for smallpox, pohovirus, yellow fever, or measles is difficult to achieve. Another complication is that immunity to influenza virus is incomplete; that is, even in the presence of an immune response, influenza can still occur. Nevertheless, the challenge of developing vaccines based on surveillance studies has been met. A chemically treated, formalin-inactivated virus has been formulated in a vaccine that is 30 to 70 percent effective in increasing resistance to influenza virus. The vaccine decreases the frequency of influenza attacks or, at least, the severity of disease in most recipients, although protection is not absolute. In addition, the secondary bacterial infections that may accompany influenza are today treatable with potent antibacterial drugs previously unavailable. Nonetheless, of the plagues that visit humans, influenza is among those that require constant surveillance, because we can be certain that some form of influenza will continue to return. References: CDC. Spread of Avian Influenza Viruses among Birds; Journal of Environmental Health, Vol. 68, 2006.www.cdc.gov/flu/avian/gen-info/spread.htm Claas, E. C. J., A. D. M. E. Osterhaus, R. van Beek, J. C. De Jong, G. F. Rimmelzwaan, D. A. Senne, S. Krauss, K. F. Shortridge, and R. G. Webster. 1998. Human influenza A H5N1 virus related to a highly pathogenic avian influenza virus. Lancet 351:472–477. Gu J, Xie Z, Gao Z, Liu J, Korteweg C, Ye J, Lau LT, Lu J, Gao Z, Zhang B, McNutt MA, Lu M, Anderson VM, Gong E, Yu AC, Lipkin WI. H5N1 infection of the respiratory tract and beyond: a molecular pathology study; Lancet Sep 29; 370(9593):1106-8, 2007 Pascal James Imperato. The Growing Challenge of Avian Influenza; Journal of Community Health, Vol. 30, 2005. www.springerlink.com/index/H6427776HH34G857.pdf Peiris, M., K. Y. Yuen, C. W. Leung, K. H. Chan, P. L. S. Ip, R. W. M. Lai, W. K. Orr, and K. F. Shortridge. 1999. Human infection with influenza H9N2. Lancet 354:916–917. Reina J. Factors affecting the virulence and pathogenicity of avian and human viral strains (influenza virus type A)] Enferm Infecc Microbiol Clin; 20(7):346-53 (ISSN: 0213-005X) Hospital Universitario Son Dureta, Palma de Mallorca, Espaà ±a, 2002 direct.bl.uk/research/48/44/RN119578176.html Suarez DL, Spackman E, Senne DA. Update on molecular epidemiology of H1, H5, and H7 influenza virus infections in poultry in North America; Avian Dis. 2003; 47(3 Suppl): 888-97 ncbi.nlm.nih.gov/sites/entrez Subbarao, K., A. Klimov, J. Katz, H. Renery, W. Lim, H. Hall, M. Perdue, D. Swayne, C. Bender, J. Huang, M. Hemphill, T. Rowe, M. Shaw, X. Xu, K. Fukuda, and N. Cox. 1998. Characterization of an avian influenza A (H5N1) virus isolated from a child with a fatal respiratory illness. Science 279:393–396. Uiprasertkul M. Apoptosis and Pathogenesis of Avian Influenza A (H5N1) Virus in Humans Emerg Infect Dis; 13(5):708-12 (ISSN: 1080-6040) Mahidol University, Bangkok, Thailand.2007 www.cdc.gov/EID/content/13/5/708.htm