Essay example --

Molecular engine room has become the talk of mainstream science and engineering today. As the melody attention searches for new editions of efficient line, molecular technology would be the only coherent place to start. For this report the various beneficial uses of carbon paper nanotubes, in form of buckminsterfullerene, ordain be both discussed and theorized.The infinitesimal molecular structure of carbon 60 has provided a revolutionary solution for dodging efficiency and furtherance today. Through the use of an experimental material derived from carbon nanotube technology some of flights oldest conjectures will be solved. Manifested in the form of paper similar sheets, these carbon molecules have been transformed into a material better cognize as buckypaper, possessing less than a quarter of steels boilers suit weight and ten times its conductivity(Jade Boyd). Its strong yet jackanapes properties could perhaps pave the way for major breakthroughs within the ever sma ll flight industry today.IntroductionPhenomenal leaps in the flight and melodic line industry today have caused a state of static ignorance end-to-end the minds of innovative aviators. What baffles the mind of contemporary aviation today is not the side by side(p) high speed jet, perhaps soaring some speed fast to that of light. What baffles modern aviation today is the creation of an effective yet possible form manpowered flight. With the creation of the combustion and jet engines the dreams once conceived by many pioneers of early flight have become the lost fables of history. As a result, the aviation industry today has ultimately failed when searching for better forms flight efficiency. In order to modify the wheel it must first be rebuilt using the most practical and effective m... ...et from culture A to destination B with a cheaper and safer means of affordable flight. Though the general human race knows very little about alternative methods of mass production for bu ckypaper, it will probably become the most prolifically used molecule in aviation sector for many years to come. The general public should expect to calculate a technological revolution in the future that will top flight cheaper and more affordable. To retrieve any scientific information pertaining to the contents progress has currently proven to be a toilsome task, partly because there arent many published journals pertaining to the topic of buckminsterfullerene. This, however, does not mean research will remain stagnant and future opportunities lost. Buckypapers diverse fields of application will redefine the status quo of aviation and technology today once research is complete.

Mainstream vs. Alternative Media

Medal In todays day and age, with the advent of vehicles Like the Internet and non-homogeneous social media, the means of communicating break signifi backsidetly developed. This evolution has disposed rise to contemporary media outlets for both the mainstream and alternate media channels. We moldiness now cull how to gain access to our most basic and truly essential tidings and info. Mainstream media tends to afford media outlets maintained by various conglomerates, which atomic number 18 then dispose to their appeals.We come across an assortment of mainstream media outlets broadcasting on the radio, TV, and online or irrigating in newspapers and other such publications. Mainstream media tend to be easily hit upon tie in with vast audiences and are usually preferred by the majority. Mainstream media corporations thrive and lolly by engaging as many people as possible. Their pith centers on Issues and topics that appeal to a considerable number of listeners, readers, and vi ewers.Mainstream media is often seen as more trustworthy and credible. Even though time and again, alternative media breaks a news story, the public is disposed to waiting until mainstream media backs that story. Often times, mainstream media corporations also give hold off on a story until it has earned rough attention in the little alternative outlets before they will cross It. Mainstream media encompasses a comprehensive spectrum of subject matters Like economics, politics, sports, science, fashion, travel, Jobs, entertainment, etc.Mainstream media coverage tends to present considerably longer articles and reports, which are comprised of more sources, interviews and expert outlooks to support the stories. On the oppositeness end of the gamut youll father alternative media. This type of media ends to voice alternative Ideals, opinions, determine and viewpoints In comparison to those expressed through mainstream media. Alternative media Is known to be confronting in content , attitude and tone. It is typically smaller-scale, independent and not-for-profit.Characteristically, alternative media endeavors to authenticate stories in such a manner as to open up perspectives that whitethorn not be touched upon in mainstream media. Alternative media outlets, Like mainstream media, can be accessed on the radio, online, and In newspapers and publications, as well as to a degree on video recording. Alternative media outlets tend to receive a smaller amount of reinforcement and sponsorship, and more restrictive budgets than mainstream media outlets.Because of this, it is easier to come across alternative media sources online, due to the affordability of establishing and maintaining a website over a radio or television program. For these reasons, audiences are required to put forth greater effort into get ahead of listeners, readers, and viewers do not trust mainstream media because they deem that certain stories or specifics are being overlooked or omitted. T hat is why alternative media outlets have smaller, yet faithful followings that yearn for a truly authentic viewpoint.These alternative outlets tend to have links to which to donate money and support the funding of the outlet. Alternative media outlets are by no means looking to illuminate a profit, but simply rely on their audiences support to assay active. Such donation links are not found in mainstream outlets because they are embodiedly financed. Alternative media does not cover as wide of a spectrum of subject matter as mainstream media. You will not usually find coverage of sports, entertainment, lassies, and such, which is understandable taking into consideration how many resources, are affiliated to such an array of areas.The topics usually addressed in alternative media, are delivered through short and concise paragraphs, which are not necessarily plunk for with interviews or expert testimony, and are all usually found on a single page. Skeptics tend to propose that mai nstream media promotes the agenda of the government and its corporate allies to procure funding and sponsorship, while alternative media murmurs and mutters of corruption and junto in hopes of lulling internet traffic.Taken as a whole, both mainstream and alternative medias entrust us with our most basic and truly essential news and information. Mainstream media may have a wider audience attributable to all of the supplementary information they offer, but alternative media offers an alternate perspective that may have be overall missed. Our intake of news needs to mirror our intake of food. Everything must be accepted in moderation and must find a balance. People should ultimately access both mainstream and alternative media in evidence to stay knowledgeable and aware.

Spatial Database Systems and Management Multidimensional Discrete Data

Spacial database systems offer the profound database technology for geographic in functionation systems and former(a) applications. Several terms have been used to describe database systems offering much(prenominal) support, including pictorial image, geometric, geographic, and spatial. The terms pictorial database system and image arise from the position that the data to be managed be often initially captured in the form of digital raster images, remote sensing by planets, or compuer tomography in medical applications. Spatial database management involves two main categories of data sender and raster data.The former has received a lot of in-depth investigation the latter tranquillize lacks a sound frmaework. Current DBMSs either regard raster data as pure byte sequence where the DBMS has no knowledge about the underlying semantics, or they do not complement array social organizations with storage mechanisms qualified for huge arrays, or they are designed as specialized syste ms with in advance(p) imaging functionality, but no general database capabilities. We will discuss more or less of the aspects of spatial data, spatial databae and its management.In various fields, there is a motif to manage geometric, geographic, or spatial data. The space of interest can be, for example, the 2-D outline of the earths surface, or the images of human body including computed tomography (CT), magnetic resonance (MR), ultrasonography(US), projectional computed skiagraphy (CR) etc. These medical imaging systems have revolutionized the means by which images are acquired, providing views of anatomical cross-sections and physiological state. This revolution in the acquisition of radiological study has not yet brought about a parallel revolution in the intelligent management, visualization, integration, or knowledge extraction from data produced by these digital imaging system.In the discipline of visualization,where the areas of computer graphics, image impact, comp uter vision, computer-aided design, distinguish processing, and user interface studies converge into one unifying framework for the processing of visual information, several representation of a scene are distinguished. Kromker (1991) proposes a visualization reference model that is particularly suitable for database investigations because classification is through with(p) along the data structure on hand. Three of the six layers introduced in this reference model are relevant for DBMSs that deal with visualization structure1. The Symbolic Representation Layer deals with abstract scene descriptions, but without an open description of geometry and properties of the entities modeled.2. The Geometry/Feature Layer covers geometric descriptions, appearance properties, and viewing parameters. sender graphics would be a subset of such data structure.3. On the digital Pixel Layer, a scene is discretized in both space and color, amenable a raster image. A raster image consists of a finit e set of points in the discrete coordinate space Z(d) where distributively point has some value, its color, associated. in that location is no algorithm that performs reasonably well on any salmagundi of image and under all corcumstances above all, images frequently contain information that cannot be cast into points, lines, and regions bounded by lines, because the boundary cannot be know without doubt (e.g., tumors in medical imagery), or because there is no buy the farm boundary (e.g., density distributions such as clouds in weather satellite images). In summary, both vector and raster representation are important for spatial data management, because each of them has pacific strengths and weaknesses moreover, both representations are independent from each other in the sense that there is no lossless faulting between them.

Attendance System

Student attention remains ground On Fingerprint Recognition and iodin-to-M some(prenominal) coordinated A dissertation submitted in partial ful? llment of the requirements for the form of Bachelor of computing machine Application in Computer wisdom by Sachin ( debate no. 107cs016) and Arun Sharma (Roll no. 107cs015) Under the guidance of Prof. R. C. Tripathi department of Computer Science and applied science National impart of Technology Rourkela Rourkela-769 008, Orissa, India 2 . Dedicated to Our P bents and Indian Scienti? c Community . 3 National Institute of Technology Rourkela Certi? cateThis is to certify that the project entitled, Student attention System establish On Fingerprint Recognition and One-to- m distributively an otherwise(prenominal) Matching submitted by Rishabh Mishra and Prashant Trivedi is an au and sotic cast carried out by them under my supervision and guidance for the partial ful? llment of the requirements for the pillage of Bachelor of Tec hnology Degree in Computer Science and plan at National Institute of Technology, Rourkela. To the best of my knowledge, the matter embodied in the project has non been submitted to any other(a) 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 attending dodge which could e? ectively manage attention of students at workings like NIT Rourkela. Attendance is marked after student identi? cation. For student identi? cation, a ? ngerprint recognition base identi? cation dodge is usaged. Fingerprints be considered to be the best and fastest method for biometric identi? cation. They argon secure to function, unique for e rattling person and does not change in ones life while. Fingerprint recognition is a mature ? ld straightaway, just now s gutter identifying individual from a particularise of enrolled ? ngerprints is a quantif y pickings dish. It was our responsibility to mitigate the ? ngerprint identi? cation system for implementation on heroic databases e. g. of an institute or a country etc. In this project, legion(predicate) advanced algorithms confuse been utilise e. g. gender regard, primaeval establish one to many inter affiliated, removing bourn minutiae. apply these new algorithms, we have real an identi? cation system which is smart in implementation than any other functional today in the market. Although we argon con labor unionption this ? ngerprint identi? cation system for student identi? ation purpose in our project, the co-ordinated results atomic morsel 18 so good that it could perform very well on greathearted databases like that of a country like India (MNIC jut out). This system was implemented in Matlab10, Intel Core2Duo make foror and comparison of our one to many identi? cation was do with animated identi? cation proficiency i. e. one to one identi? cation on uniform platform. Our check outing technique runs in O(n+N) season as comp ard to the existing O(Nn2 ). The ? ngerprint identi? cation system was tested on FVC2004 and Veri? nger databases. 5 Acknowledgments We present our profound gratitude and indebtedness to Prof. B.Majhi, Department of Computer Science and Engineering, NIT, Rourkela for introducing the evince topic and for their inspiring intellectual guidance, constructive criticism and priceless suggestion throughout the project work. We ar also thankful to Prof. Pankaj Kumar Sa , Ms. Hunny Mehrotra and other sta? s in Department of Computer Science and Engineering for need us in up the algorithms. Fin tot every last(predicate)yy we would like to thank our p bents for their substantiate 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 . . . . . . . . . . . . . . . . . . . . . . . . victimisation Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What is ? ngerprint? . . . . . . . . . . . . . . . . . . . . . . . . . . . why use ? ngerprints? . . . . . . . . . . . . . . . . . . . . . . . . . . . exploitation ? ngerprint recognition system for attention counsel . . . 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 picturer ironw atomic bod 18 softwargon program take aim Design . . . . . . . . . . . . . . . . . . . . Attendance Management Approach . . . . . . . . . . . . . . . . . . . on-line(a) Attendance topic Generation . . . . . . . . . . . . . . . . . Ne some(prenominal)rk and selective selective studybase Management . . . . . . . . . . . . . . . . . . utilise radio interlocking instead of local atomic number 18a nedeucerk and bringing portability . . . 2. 5. 1 2. 6 U evilg Por get across ruse . . . . . . . . . . . . . . . . . . . . . . Comparison with other student attending systems . . . . . . . . . . 3 Fingerprint Identi? cation System 3. 1 3. 2 How Fingerprint Recognition whole kit and boodle? . . . . . . . . . . . . . . . . . Fingerprint Identi? cation System Flowchart . . . . . . . . . . . . . . 4 Fingerprint Enhancement 4. 1 4. 2 4. 3 naval division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . predilection estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 6 CONTENTS 4. 4 4. 5 4. 6 4. 7 c everyplaceline relative absolute 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 maneuver characteristic rootage 5. 1 5. 2 decision the Reference beat period . . . . . . . . . . . . . . . . . . . . . . . Minutiae fall and Post-Processing . . . . . . . . . . . . . . . . 5. 2. 1 5. 2. 2 5. 2. 3 5. 3 Minutiae lineage . . . . . . . . . . . . . . . . . . . . . . . Post-Processing . . . . . . . . . . . . . . . . . . . . . . . . . Removing Boundary Minutiae . . . . . . . . . . . . . . . . . . Extraction of the report . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 1 What is paint? . . . . . . . . . . . . . . . . . . . . . . . . . . Simple differentiate . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complex find . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 variance of selective informationbase 6. 1 6. 2 6. 3 sex adherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . consorti? cation of Fingerprint . . . . . . . . . . . . . . . . . . . . . . . naval division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Matching 7. 1 7. 2 7. 3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . alive Matching Techniques . . . . . . . . . . . . . . . . . . . . . One to umteen twin(a) . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 3. 1 7. 4 7. 5 order of One to Many Matching . . . . . . . . . . . . . . . do distinguish match and broad interconnected . . . . . . . . . . . . . . . . Time Complexity of this co-ordinated technique . . . . . . . . . . . . . . 8 Experimental depth psychology 8. 1 8. 2 murder 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 rooftree relative 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 . . . . . . . . . . . . . . . . . . . . . . . later on Removing inau whencetic and Boundary Minutiae . . . . . . . 8. 3. 2 Reference Point Detection . . . . . . . . . . . . . . . . . . . . 8. 4 grammatical gender Estimation and Classi? ation . . . . . . . . . . . . . . . . . . 8. 4. 1 8. 4. 2 sex activity Estimation . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 5 8. 6 Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 6. 1 8. 6. 2 Fingerprint Veri? cation extends . . . . . . . . . . . . . . . . . Identi? cation Results and Comparison with Other Matching techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 70 73 74 75 75 79 8. 7 work Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion 9. 1 Outcomes of this Project . . . . . . . . . . . . . . . . . . . . . . . . . 10 Future calculate and Expectations 10. 1 Approach for Future Work A Matlab functions . . . . . . . . . . . . . . . . . . . . . . . List of human bodys 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 continue ending and a bifurcation . . . . . . . . . . . . . . Hardwargon present in elucidaterooms . . . . . . . . . . . . . . . . . . . . . Classroom Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . profit Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 0 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 1 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 2 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Por turn off tress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Identi? cation System Flowchart . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . (a)Original two-baser, (b) heighten theatrical role, (c)Binarised run into, (d)Thinned Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Row 1 ? lter retort c1k , k = 3, 2, and 1. Row 2 ? lter rejoinder c2k , k = 3, 2, and 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2 5. 3 Examples of (a) continue-ending (CN=1) and (b)bifurcation pel (CN=3) 42 43 40 18 22 23 25 26 27 27 28 29 34 37 Examples of typical morose 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 windowpane centered at margin minutiae . . . . . . . . . . hyaloplasm Representation of boundary minutiae . . . . . . . . . . . . . cay Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 10 6. 2 6. 3 LIST OF FIGURES 135o occlusions of a ? ngerprint . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d1) bend, (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 divide Database . . . . . . . . . . . . . . . . . . . . . . . . . . One to Many Matching . . . . . . . . . . . . . . . . . . . . . . . . . . Normalized Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ridge Frequency Image . . . . . . . . . . . . . . . . . . . . . . . . . . Left-Original Image, Right- raise Image . . . . . . . . . . . . . . Binarised Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinned Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . entirely Extracted Minutiae . . . . . . . . . . . . . . . . . . . . . . . . . . manifold Image with spurious and boundary minutiae . . . . . . . . Minutiae Image after post-processing . . . . . . . . . . . . . . . . . 51 52 57 59 60 60 61 61 62 62 63 63 64 65 50 8. 10 Composite Image after post-processing . . . . . . . . . . . . . . . . . 8. 11 Plotted Minutiae with Reference Point(Black Spot) . . . . . . . . . . 8. 12 represent Time interpreted for Identi? cation vs surface of Database(key based one to many identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . 8. 13 interpret Time taken for Identi? cation vs Size of Database (n2 ide nti? cation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 14 Expected represent 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 calculate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Properties of Crossing physical body . . . . . . . . . . . . . . . . . . . . . 22 43 64 65 66 66 67 67 68 fair(a) tot of Minutiae before and after post-processing . . . . Ridge assiduity Calculation Results . . . . . . . . . . . . . . . . . . . . Classi? cation Results on Original Image . . . . . . . . . . . . . . . . Classi? cation Results on Enhanced Image . . . . . . . . . . . . . . . Time taken for Classi? cation . . . . . . . . . . . . . . . . . . . . . . .Time taken for Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . Error Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance of ours and n2 matching based identi? cation techniques on a database of size 150 . . . . . . . . . . . . . . . . . . . . . . . . . 70 11 List of Algorithms 1 2 3 4 Key Extraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation Algorithm . . . . . . . . . . . . . . . . . . . . . . . Key ground One to Many Matching Algorithm . . . . . . . . . . . . . . Matching Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 49 55 56 12Chapter 1 Introduction 1. 1 Problem Statement Designing a student attending management system based on ? ngerprint recognition and faster one to many identi? cation that manages records for attending 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 becoming record of attention of students or employees for e? ective functioning of organization. Designing a better attend ing management system for students so that records be retained with ease and accuracy was an important key behind motivating this project.This would improve accuracy of attention records because it go away despatch all the hassles of roll life history and depart save valuable time of the students as well as t all(prenominal)ers. Image processing and ? ngerprint recognition be 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 apply key based one to many matching. 13 14 CHAPTER 1. INTRODUCTION 1. 3 exploitation Biometrics Biometric Identi? cation Systems ar astray apply for unique identi? cation of valet de chambre mainly for veri? cation and identi? ation. Biometrics is apply as a form of identity operator access management and access control. So use of biometrics in student attendance management system is a secur e approach. thither be many typewrites of biometric systems like ? ngerprint recognition, face recognition, voice recognition, iris recognition, handle recognition etc. In this project, we apply ? ngerprint recognition system. 1. 4 What is ? ngerprint? A ? ngerprint is the pattern of ridges and valleys on the surface of a ? ngertip. The end sharpens and crossing points of ridges are called minutiae. It is a widely take awayed assumption that the minutiae pattern of each ? ger is unique and does not change during ones life. Ridge endings are the points where the ridge tailor terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. project 1 illustrates an example of a ridge ending and a bifurcation. In this example, the black pels stop to the ridges, and the clear pixels correspond to the valleys. embodiment 1. 1 Example of a ridge ending and a bifurcation When human ? ngerprint experts determine if two ? ngerprints are from th e same ? nger, the matching degree in the midst of two minutiae pattern is one of the close to important factors.Thanks to the affinity 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 fingermarkS? 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 either person and does not change in ones lifetime. Besides these, implementation of ? ngerprint recognition system is cheap, prosperous and accurate up to satis? ability. Fingerprint recognition has been widely used in twain forensic and civilian applications.Compared with other biometrics gives , ? ngerprint-based biometrics is the most proven technique and has the largest market shares . Not only it is faster than other techniques alone also the energy consumption by such systems is every(prenominal)placely l ess. 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 tie in work automatic and on-line, we have designed an attendance management system which could be implemented in NIT Rourkela.It uses a ? ngerprint identi? cation system developed in this project. This ? ngerprint identi? cation system uses existing as well as new techniques in ? ngerprint recognition and matching. A new one to many matching algorithm for large databases has been introduced in this identi? cation system. 1. 7 Organization of the thesis This thesis has been organized into ten chapters. Chapter 1 introduces with our project. Chapter 2 explains the proposed design of attendance management system. Chapter 3 explains the ? ngerprint identi? cation system used in this project.Chapter 4 explains sweetener techniques, Chapter 5 explains feature filiation methods, Ch apter 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 excess over answers of students, waste of paper etc. are the disadvantages of manual attendance system. Moreover, the attendance report is also not generated on time. Attendance report which is circulated over NITR webmail is two months old. To overcome these non-optimal situations, it is essential that we should use an automatic on-line attendance management system. So we present an implemen sidestep a ttendance management framework. Student attendance system framework is divided into tether parts Hardware/Software Design, Attendance Management Approach and on-line(a) Report Generation. distributively of these is explained below. 2. 1 Hardware Software Level Design take hardware used should be easy to maintain, implement and easily available. Proposed hardware consists succeeding(a) parts (1)Fingerprint S brush asidener, (2)LCD/Display Module (optional), (3)Computer 16 2. 2. attendance anxiety APPROACH Table 2. 1 Estimated Budget Device Cost of Number of total Name One Unit Units Required Unit Budget S targetner 500 100 50000 PC 21000 100 2100000 Total 21,50,000 (4)local area network data link 17 Fingerprint s rear endner leave be used to excitant ? ngerprint of instructors/students into the computer software.LCD present lead be displaying rolls of those whose attendance is marked. Computer Software go away be interfacing ? ngerprint s bunsner and LCD and depart be connected to the network. It lead arousal ? ngerprint, will process it and extract features for matching. after(prenominal) matching, it will update database attendance records of the students. project 2. 1 Hardware present in varietyrooms 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 more or less 100. So number of units necessary will be 100. 2. 2 Attendance Management ApproachThis part explains how students and teachers will use this attendance management system. Following points will make legitimate that attendance is marked correctly, without any problem (1)All the hardware will be interior classroom. So outside interference will be absent. (2)To annihilate unauthorized access and unwanted attempt to corrupt the hardware by students, all the hardware except ? ngerprint s usher outner could be put inside a small 18 CHAPTER 2. ATTENDANCE MANAGEMENT material cab in. As an alternate solution, we give the gate install CCTV cameras to hold back unprivileged activities. (3)When teacher enters the classroom, the attendance scrape will start.Computer software will start the process after arousalting ? ngerprint of teacher. It will ? nd the Subject ID, and Current Semester exploitation the ID of the teacher or could be right manually on the software. If teacher doesnt enter classroom, attendance marking will not start. (4)After some time, say 20 proceedings of this process, no attendance will be exitn because of late entrance. This time period can be increased or decreased as per requirements. get in 2. 2 Classroom Scenario 2. 3 On-Line Attendance Report Generation Database for attendance would be a table having pursual ? elds as a combination for primary ? ld (1)Day,(2)Roll,(3)Subject and following non-primary ? elds (1)Attendance,(2)Semester. Using this table, all the attendance can be managed for a student. For on-line report generati on, a unprejudiced website can be hosted on NIT Rourkela servers, 2. 4. communicate AND DATABASE MANAGEMENT 19 which will access this table for showing attendance of students. The sql queries will be used for report generation. Following inquiry will give total amount 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 call into question 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 net income. Network diagram is shown in ? g. 2. 3. Using this network, attendance reports will be made available over internet and e-mail. A monthly report will be sent t o 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 receiving rigid network instead of LAN and bringing portability We are using LAN for communication among servers and hardwares in the classrooms. We can instead use radiocommunication LAN with portable inventions.Portable braid will have an embedded ? ngerprint scanner, wireless connection, a microprocessor loaded with a software, recollection and a display terminal, see ? gure 2. 5. Size of widget could be small like a erratic speech sound depending upon how well the gimmick is manufactured. 20 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK Figure 2. 3 Network Diagram 2. 5. USING WIRELESS NETWORK quite 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 transport data using wirele ss 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 firing 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 softw are 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 teachers 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 teachers mood but our suggested esteem is 25 minutes. This is done to prevent late entrance of students. This mistreat will hardly take a couple of(prenominal) seconds. and 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 direct 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 fix of class for attendance marking which is not dynamic and if schedule or location of the class changes, wrong attenda nce might be marked.Problem with RFID based systems is that students have to reach out RFID cards and also the RFID detectors are needed to be installed. Nonetheless, students may give proxies easily using friends 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 faraway 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 precise information is not available. It may involve matching available features of prognosis like ? ngerprints with those already enrolled in database. 3. 1 How Fingerprint Recognition works? Fingerprint simulacrums that are found or scanned are not of optimal quality. So we remove echos and enhance their quality. We extract features like minuti ae 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 go which may include showing details of identi? ed candidate, marking attendance etc.A drawing ? 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 acknowledgement SYSTEM Figure 3. 1 Fingerprint Identi? cation System Flowchart Chapter 4 Fingerprint Enhancement The mental word-painting acquired from scanner is sometimes not of finished quality . It gets corrupted collectible 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 agai nst valleys, various techniques are applied as following. 4. 1 Segmentation Image segmentation 1 disperses the foreground regions and the home regions in the image. The foreground regions refers to the take ? ngerprint area which ends 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 unreasonable 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 usher a very low grey scale variance jimmy , whereas the foreground regions have a very high variance . for the first time , the image is divided into seal offs and the grey-scale variance is calculated for each pig out in the image .If the variance is less than the global threshold , then the scarf out is assigned to be part of background region or else 29 30 CHAPTER 4. FINGERPRINT sweetening it is part of foreground . The grey direct variance for a third power 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 take aim variance for the block k , G(i,j) is the grey level value at pixel (i,j) , and M(k) denotes the think of grey level value for the corresponding block k . 4. 2 Normalization Image normalization is the next step in ? ngerprint sweetener process.Normalization 1 is a process of standardizing the intensity value in an image so that these intensity values lies indoors a certain desired range. It can be done by adjusting the range of grey-level values in the image. permit G(i, j) denotes the grey-level value at pixel (i, j), and N(i, j) represent the normalized grey-level value at pixel (i, j). Then the normalized image can de? ned as ? ? M + 0 N (i, j) = ? M ? 0 V0 (G(i,j)? M )2 V V0 (G(i,j)? M )2 V , if I(i, j) > M , otherwise where M0 and V0 are the estimated mean and variance of I(i, j), respectively . 4. 3 Orientation estimation The orientation course ? 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 Siamese 1 is used to compute the orientation image. However, instead of estimating the orientation block-wise, we have elect to extend their method into a pixel-wise scheme, which set ups a ? ner and more accurate estimation of the orientation ? eld. The steps for calculating the orientation at pixel i, j) are as follows 4. 3. ORIENTATION ESTIMATION 31 1. Firstly , a block of size W x W is centered at pixel (i, j) in the normalized ? ngerprint image. 2. For each pixel in the block, compute the gradients dx (i, j) and dy (i, j), which are the gradient magnitudes in the x and y directions, respectively. The horizontal Sobel operator6 is used to compute dx(i, j) 1 0 -1 2 0 -21 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 other 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 ea ch pixels located inside each block on a direction perpendicular to the local ridge orientation. This projection results in an virtually 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 cut down the e? ect of noise in the projection. The ridge space 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 extend 33 4. 5 Gabor ? lter Gabor ? lters 1 are used because they have orientation-selective and frequencyselective properties. Gabor ? lters are called the induce 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 bring down nois e. An even-symmetric Gabor ? lter in the spatial domain is de? ned as 1 x2 y2 G(x, y, ? , f ) = exp? ? + ? cos 2? f x? , 2 2 2 ? x ? y (4. 11) x? = x cos ? + y sin ? , (4. 12) y? ? x sin ? + y cos ? , (4. 13) where ? is the orientation of the Gabor ? lter, f is the frequency of the cosine wave, ? x and ? y are the standard deviations of the Gaussian gasbag 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 on that point 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 deepen 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 burden of binarisation is a binary image which contains two levels of information, the background valleys and the foreground ridges. . 7 Thinning Thinning is a morphological carrying out 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 point set of pixel-deletion criteria, it decides whether the pixel can be withdraw 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 change state images. We extract address point, minutiae and key(used for one to many matching). 5. 1 revealing the Reference Point Reference point is very important feature in advanced matching algorithms because it provides the location of origin for marking minutiae. We ? nd the interview 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 meaning 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 clotures. 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 herelevel 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(predicate) 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 fall 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 anticipate is done in a window computed in the preliminary higher(prenominal) 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 utilise 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 scan 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 one-third corresponds to a bifurcation. 5. 2. MINUTIAE EXTRACTION AND POST-PROCESSING Table 5. 1 Properties of Crossing Number C N seat 0 Isolated Point 1 Ridge Ending Point 2 Continuing Ridge Point 3 Bifurcation Point 4 Crossing Point 37 Figure 5. 2 Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 5. 2. 2 Post-Processing rancid 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 Min utiae For removing boundary minutiae, we used pixel- immersion approach. Any point on the boundary will have less white pixel parsimony in a window centered at it, as compared to inner minutiae. We calculated the limit, which indicated that pixel tautness 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 intercellular substance 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 a few(prenominal) minutiae close st to reference point. We match minutiae sets, if the keys of savour and interview ? ngerprints matches. Keys are stored along with minutiae sets in the database.Advantage of using key is that, we do not perform honest matching every time for non-matching minutiae sets, as it would be time consuming. For large databases, if we go on matching mount 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 infinite 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 staminates and 100 fe masculines revealed that signi? slope sex d i? 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. establish on this estimation, distinct 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 womanly ridge densities will be se twisted. If ridge density of query ? ngerprint is less than the lowest ridge density value of females, the query ? ngerprint is seemingly 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 Esti mation 6. 1. GENDER ESTIMATION Data Size of Database = N Ridge compactness of query ? ngerprint = s Result Estimated Gender i. e. male or female maleupperlimit=0 femalelowerlimit=20 mean=0 for image < femalelowerlimit then femalelowerlimit 43 if s < maleupperlimit then estimatedgender 44 CHAPTER 6.PARTITIONING OF DATABASE 6. 2 Classi? cation of Fingerprint We divide ? ngerprint into ? ve classes arch or tented arch, left loop, right loop, peal 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 structuresnonrecurring 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 categorize the input using the core and delta mind algorithm4. 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 algorithm4. 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 algorithm4. 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 condition s is satis? ed, then egest the ? ngerprint. 45 Figure 6. 3 Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d1)Arch, (d2)Tented Arch . 3 Partitioning After we estimate gender and ? nd the class of ? ngerprint, we know which ? ngerprints to be searched in the database. We nearly 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 like ? 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 lucky 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 organizeed(registered) with each other. For alignment problems, we used hough transform based registration technique connatural to one used by Ratha et al5. 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 chemise and displacement 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. co-ordinated parameters of ith minutia of query minutiae set and j th minutia of database minutiae set. If condition is satis? ed, A(i,j,k) is ? agged as 1 else 0. For all these ? agged values, (? x, ? y) is calculated using following formula ? (? x , ? y ) = qj ? ? cos? sin? ? ? ? pi , (7. 1) ?sin? cos? where qj and pi are the coordinates of j th minutiae of database minutiae set and ith minutiae of query minutiae set respectively. Using these < ? x, ?y, ? k > values, whole query minutiae set is aligned.This aligned minutiae set is used to compute pairing score. Two minutiae are said to be paired only when they lie in same bounding box and have same orientation. Pairing score is (number of paired minutiae)/(total number of minutiae). The i,j,k values which have highest pairing score are ? nally used to align minutiae set. Co-ordinates of aligned minutiae are found using the formula ? qj = ? cos? sin? ? ? ? pi + (? x , ? y ), (7. 2) ?sin? cos? After alignment, minutiae are stored in sorted order of their distance from their centroid or core. 7. 2 Existing Matching TechniquesMost popular matching technique of today is the simple disposed(p) 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 broad(a) minutiae matching. Key matching and rise matching are performed using k*n matching algorithm discussed in later section. Following section gives method for one to many matching. Data Query Fingerprint Result Matching Results Acquire Fingerprint, Perform Enhancement, Find Fingerprint Class, Extract Minutiae, Remove Spurious and Boundary Minutiae, Extract Key,Estimate Gender M . 3. 1 Method of One to Many Matching The matching algorithm will be involving matching the key of the query ? ngerprint with the many(M) keys of the database. Those which matches ,their full matching will be processed, else the query key will be matched with next M keys and so on. 50 Data Gender, Class, i Result Matching Results egender CHAPTER 7. MATCHING if keymatchstatus = 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 immutable( recommended value is 15) chosen by us.In this method, we match ith minutiae of query set with k leftover minutiae of sample set. Both the query sets and sample sets must be in sorted order of distance from reference point or centroid. ith minutia of query minutiae list is matched with top k unmatched minutiae of database minutiae set. This type of matching reduces matching time of n2 to k*n. If minutiae are 80 in number and we chose k to be 15, the total number of comparisons will reduce from 80*80=6400 to 80*15=1200. And this means our matching will be k/n times faster than n2 matching. 7. 5. conviction 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 frustrated 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 minify 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(prenominal) databases like FVC2004, FVC2000 and Veri? nger databases. We used a computer with 2GB RAM and 1. 83 gigahertz Intel Core2Duo processor and softwares like Matlab10 and MS access10. 8. 2 8. 2. 1 Fingerprint Enhancement Segmentation and NormalizationSegmentation was performed and it generated a mask matrix which has valu es 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 diligent 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. observational outline Figure 8. 4 Left-Original Image, Right-Enhanced Image 8. 2. 5 Binarisation and Thinning After the ? ngerprint image is enhanced, it is then converted to binary form, and submitted to the thinning algorithm which reduces the ridge thickness to one pixel wide.Results of binarisation are shown in ? gure 8. 5 and of thinning are shown in ? gure 8. 6. Figure 8. 5 Binarised Image 8. 3. FEATURE EXTRACTION 55 Figure 8. 6 Thinned Image 8. 3 8. 3. 1 Feature Extraction Minutiae Extraction and Post Processing Minutiae Extraction Using the crossing number method, we extracted minutiae. For this we used skeleton image or the thinned image. Due to low quality of ? ngerprint, a lot of false and boundary minutiae were found. So we moved forward for post-processing step. Results are shown in ? gure 8. 7 and 8. 8. Figure 8. 7 All Extracted Minutiae 56 CHAPTER 8. EXPERIMENTAL 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 sect ion. 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 reduce 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 mini mum and maximum ridge densities shown in table 8. . Mean ridge density was used to divide the database into two parts. This trim back 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 absorption 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 annoy 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 pr ocess of enrolling was faster as compared to storing the values in MS Access DB. It was due to the overhead of connections, running sql queries for MS Access DB. 8. 6 Matching Fingerprint matching is required by both veri? 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 doesnt, 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 ( faraway) and False lour Rate (FRR) are the error rates which are used to express matching trustability. FAR is de? ned by the formula 60 CHAPTER 8. EXPERIMENTAL ANALYSIS Table 8. 5 Time taken for Classi? cation Image Average Total Taken Time(sec) Time(sec) Original 0. 5233 69. 07 Enhanced 0. 8891 117. 36 Table 8. Time taken for Enrolling No. of Storage Average Total Images Type Time(sec) Time(hrs) 294 MS Access DB 24. 55 2. 046 60 MS Access DB 29. 37 0. 49 150 TXT ? les 15. 06 1. 255 F AR = FA ? 100, N (8. 1) FA = Number of False Accepts, N = Total number of veri? cations FRR is de? ned by the formula FR ? 100, N F RR = (8. 2) FR = Number of False Rejects. FAR and FRR calculated over six templates of Veri? nger DB are shown in table 8. 8. This process took nearly 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 pokey 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 explaine d in sec 5. 3 helps in 8. 6. MATCHING Table 8. 7 Error Rates FAR FRR 4. 56 12. 5 14. 72 4. 02 61 Figure 8. 12 Graph Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) reducing matching time over non-matching ? ngerprints. For non-matching enrolled ? gerprints, we dont 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

Human Resource Information System Essay

The function of clement resources (HR) departments is generally administrative and common to all organizations. Organizations whitethorn have formalized selection, evaluation, and payroll processes. Efficient and effective management of gay Capital progressed to an increasingly imperative and complex process. The HR function consists of tracking existing employee data which traditionally includes personal histories, skills, capabilities, accomplishments and salary. To reduce the manual workload of these administrative activities, organizations began to electronically automate many of these processes by introducing specialized human resource management systems. HR executives rely on internal or external IT professionals to develop and master(prenominal)(prenominal)tain an integrated HRMS.Before the client legion architecture evolved in the late 1980s, many HR automation processes were relegated to central processor computers that could handle large amounts of data trans natural processs. In consequence of the high jacket investment necessary to buy or program proprietary softw ar, these internally developed HRMS were limited to organizations that possessed a large amount of capital. Raija and Hlonen (2009) expound the role of entropy systems in the process of combining district organizations which habituate information system in financial administration, HRM and social welfare.They explored the role of IS in decision-making in public sector. The lack of inter-opera ability between legacy systems and novel information systems was perceived as a huge problem. In the innovate situation of our economy, most of the companies used manual system in their company, to a fault some are using computerized system which lessen the expenses and saves time. An effective and well-designed mankind alternative Information System shall make it easier to manage record, update files and recall records in Employee records. Confidentially, accuracy and integrity are mai ntained. The use of computer enables exploiter to minimize efforts to do a certain job or action nowadays.When the Information Technology emerged, it gave a great impact when it comes to business problems particularly when it comes to data entry accuracy, managing datas and retrievals. Dr. Karishna & vitamin A Meena (2010) set the assorted functional areas to which ICT is deployed for information administration in Higher Education institutions. sure level of usage indicates a clear integration of ICT for managerial or information based administration in higher(prenominal) education institutes. Matthew &Douglas (2009) analyzed that nature of developing IS in any organization is characterized by multi dimensional and often messy problems, involving technical organizations and personal dimensions.David et al(2010) analyzed the main traits of efficient firms and the main sources of firms efficiency through samples of Catalan firms. Firms efficiency shows a significant improvemen t when advanced ICT uses are have with human resource practices, Dileep (2010) indicate that HRIS is an integration of HRM and information systems. HRIs helps HR managers run HR functions in a more effective and systematic expressive style using technology. HRIs system usually a part of the organizations larger management information system which would include accounting, production and trade functions. Ikhlas & Zaid (2010) indicate that the quick response and access to information were the main benefits of HRIS implementation. They in any case identified the cultural and financial barriers to the implementation of HRIS.Kristine & David (2010) identified the implementations or upgradation of HRIS has been undertaken with the aim of utilizing HRM functions.. Barriers also associated with the acceptance of new or upgraded HRIS.HRIS work out an important role in shaping user perception and behavior.The asset of information technology to the human resource industry has revo lutionized the contemporary workplace. HR professionals now have an increased capacity not only to put on information, however also to warehousing and retrieve it in a apropos and effective expressive style. This has not only increased the efficiency of the organization but also the effectiveness of management functions. New technology has also created opportunities for higher levels of stress for younger and older workers alike (Mujtaba, Afza, and Habib, N. (2011), unethical temptations and behaviors (Mujtaba, 2011), and opportunities for better leadership practices (Mujtaba and Afza, 2011).After delivering our suggestions for the Customer Appreciation Program, Kudler has asked us to evaluate the Human Resource Department, to see what alternates need to made, to make the process better and smoother. Before making suggestions, we have to analyze their current setup. The Human Resources Department is a important department, so we want to assure that Kudlers is running smoothl y and efficiently.Kudlers current HR Department is setup in the following manner they use Quick Books and outsourced their payroll to Intuit. Intuit tracks all the following information for each employee. They track the employees personal information, pay rate, tax exemptions, hold date, seniority date, and organizational information. Changes to any of this information, can only be change by submitting a special form in writing by the employees supervisor and entered by the accounting clerk. The accounting clerk also keeps a file with the appropriate tax forms for each employee and all changes to employee data.The employees annul in manual time sheets weekly, which are approved by the computer memory manager. The time sheets are then faxed to accounting, where they are entered for payroll. All changes have to be approved by the direct supervisor and store manager. Each store manager is responsible for keeping the files of their employees. They are kept in a locked file and includ e the following information job application or resume, performance reviews, I-9 forms, and any disciplinary memos or performance management issues. The managers are also responsible for tracking any time off, which includes vacations.

Comprehensive Classroom Technology Plan Essay

Section One imagination StatementAs an educator I im segment encourage the educatees to do their best in the schoolroom leading to academic growth. Utilizing new applied science to enhance what is taught from the textbook ordain avail the children r distributively academic plateaus and beyond. search has sh bear that technology is universe utilise for communication in the educational environment. This forwarding has all in allowed for new possibilities in regards to collaboration and sharing of selective randomness and knowledge that lead be expected to expand over time. New forms of technology give birth the power to improve p arnt- get winder relationship by providing easy, efficient, and effective methods of communicating training regarding assimilators (Zieger and Tan 30-54). Section Two Mission StatementI provide provide all of my savants a honest and nurturing environment to allow them to blow up and grow both academically and personally. E really di sciple go out be given the upmost respect which pass on teach them to reciprocate these behaviors amongst themselves. with the creation of an environment conducive for eruditeness the students back end utilize the legion(predicate) resources avail satisfactory so they may achieve their academic goals. These resources take much educational wind vanesites, in increment to some other technical resources, that will help students surpass academic standards. A variant web site is an informatory beam of light that provides access to what is being taught in the schoolroom and tidy sum be presented by dint of as reliables and videos. This agent of communication helps to reinforce, to the student, lessons they s legal instrument been recently taught. The class web site is a solid link, when utilized by pargonnts, amidst stem and tutor. The site often includes the p argonnt handbook, dental plate plump assignments and class activities. This advancement in technol ogy allows the p bents an opportunity to stay connected with the enlightening community (Vitalaki, Anastasiadesm and Tsouvelas 125-135, 2014). trend web sites reach out to the local and global communities as healthful. The school righteousness committee encourages and provides opportunities for citizens to be gnarled in the planning and military rating of the schools memoriseional program and quality improvement procedure (Vitalaki et al). Planning for unexpected emergencies is something every school must consider and fuddle protocol in place for contacting parents. An important element of the protocol is the arrest contact list. This information is necessary to implement student release procedures and should be updated every school course of study. This will delve back timely communication between the school and parents/guardians if the student should get sick or injured during school hours. As an educator, I have Found EPALS to be an educational tool to connect my stud ents to the global community. This resource allows instructors and their students to participate in collaborative projects. by this opportunity the children are connecting with students around the manhood speckle taking part in educational activities, proveions, and games. One of the projects through EPAL is sharing cultures- a collaborative project between China and the United States. This project gives American school children the opportunity to learn most China while sharing their experiences with students from some other country (Vitalaki et al).The cyber world can be a very scarey place, but one that isnt going a path. For this reason, shielding children from negative portions of the earnings has been a growing patronage over the past few age for both the parents and the educators. Teachers today have the duty to inform their students on how the lucre can be drug abused as a resource for purpose information in a safe manner. Misleading and inappropriate informatio n on the web is one of the major problems that children tend to have while navigating the internet (Marcoux 67-68). mesh natural rubber device is an issue I will discuss early in the division with my students. During the discussion I will teach them that some people that are online have bad intentions. These people could be bullies or predators. Cyber bullying is a just problem that takes on m whatever forms such as sending mean messages or threats to a cellphone phone and spreading rumors online. Cyber bullying can be damaging to adolescents and teens.It can lead to anxiety, depression, and even suicide. Young people who have been a victim of cyber bullying are between two to nine times to a greater extent likely to consider suicide than non-victims (Marcoux 67-68). To help alleviate this problem it is the responsibility of educators to discuss this with their students as part of their internet safety discussion. through the efforts of educators crossways the country w e can promote internet security and encourage safe surfing. Communication through technology serves many purposes in the educational setting. The many benefits range from improving relationships with parents victimization versatile means through an emergency plan when the unexpected happens. These advances, plus many more, enhance the educational experience of students today. Section Three consolidation Instructional TechnologyDevelopments in technology have found their way into many aspects of our cursory lives. Integrating instructional technology into the 21st blow classroom is no exclusion and is essential in providing the teacher and student with the resources assumeed to be successful. The importance of this integration has been recognized to the point that, for more than three decades, researchers, policymakers, and industry leadership have promoted calculating machine technology use within and across encyclopaedism environments to enhance teaching and eruditeness (Walery). The integration of technology in the classroom increases academic achievement and encourages creativity. Teachers are expanding their use of technology for instruction because they consider it enhances their ability to make pass with the children and offers stimulating, inter mobile access to the numerous resources offered on the internet.Through the integration of technology students are encouraged to collaborate, provide input, and share ideas. The students are requesting permission to install educational apps on their IPads which generates an increased level of ardour for learning for the student in a digital era. Teachers and students alike are always pure toneing for the next great idea or the up-to-the-minute app, software or computer program to enhance the learning move (Walery). To effectively integrate technology into the classroom instructors want to understand how to fellow appropriate technology to the learning goals and expected student outcomes. As wi th any topic there are pros and cons and integrating instructional technology into the educational setting is no exception. As an educator I feel the haughty side of technology in the classroom is the excitement that it brings to the student. in that respect are many interactive websites obtainable that brings learning to life and they are able to work independently and at their own pace. Students today are no longer sitting at their desk flipping through the scallywags of a textbook. grooming in the 21st century has fix more interactive and engages the student in the learning process.The down side to the integration of technology in the classroom is the vast meter of information available through the internet. It would be very easy for a student to get lost in the sea of information that is available. Doing research, even as an educator, requires determination and patience. To make research easier for my students I would provide a list of recommended online resources and allow them to make the choice on which ones suit their needs the best. Students today have access to technologies at home and school, especially in the form of mobile technology such as smart phones and iPods. Wireless classrooms are evolving to keep pace with mobile technology. A wireless classroom utilizes the use of I pads which is a mobile computer that can access the internet (Walery). . This resource allows students to send e-mails through a secure site to their teacher and to communicate, for educational purposes, to a nonher student. In summation to this many apps are available that can be used as management tools by teachers and students. rough of these resources include a calendar and calculator.A wired classroom, in contrast, has multiple desktop computers. While you have the accessibility of the features in a computer you do not have the license to be mobile with technology. The integration of technology in the classroom is essential to support and improve the teach ing ability of the instructor and the learning that takes place within the student. Without technology integration in the 21st century classroom our students would be unable to compete in the real world upon completion of their high school education. Section Four Software to birth sagacityAssessments, moldable and additive, allow teachers to collect information to improve student learning. These minds are an ongoing and continuing process with the major goals being to stick out out where students are struggling and put more emphasis on those areas. Assessment in the classroom setting helps the teacher examine the expectations he/she has for the students. The assessment process also provides administration, teachers, and support staff to evaluate the curriculum. When a thorough assessment program at the classroom level balances formative and summative pupil learning/achievement data, a clear picture arises of where a student is in relationship to learning goals and standard s (Garrison & antiophthalmic factor Ehringhaus). on that point are two types of assessments used to evaluate a students progress, formative and summative. Both assessments are central portions of the collection of information and allow educators to get a balanced picture of a students strengths and weaknesses (Ehringhaus). According to Heritage, Kim, and Vendlinski (2007), formative assessment is a systematic process to interminably gather evidence about learning and if incorporated into the classroom exert it provides the information needed to adjust teaching and learning while they are happening. The data is then(prenominal) used to fortune upon a students capacity to understand, learn, and adapt lessons to help the student to reach the desired learning goal. In the formative assessment the students are active participants along with their teacher.The student shares their learning goals expressing an sense on how their learning is progressing in addition to how well the mat erial is being mastered. During the assessment process the student receives feedback which serves two functions to identify problem areas and to provide positive reinforcement of successful achievement. One of the tools used in the formative assessment, which contributes to the success of the child is the student multitude During the conference the pupil sits down with the teacher for a couple of minutes to ensure there is a sufficient level of knowledge with the lesson being taught. teacher feedback serves to identify the degree to which the instructor was successful and to identify needed changes in instruction that need to be made to assist the student in mastering the material. Other tools used in the formative assessment include the formation of a graphic organizer and having a student turn in sentences identifying the main point of a lesson that has been taught. These tools allow the teacher to brand the students comprehension of the material that has been presented. A summ ative assessment, in contrast, is used to evaluate student learning at the end of an instructional unit to determine what a student knows and does not know. This type of assessment is more formal and at the district/classroom level is an accountability measure that is part of the grading process (Garrison).The students comprehension through the summative assessment is measured by an exam, project, or paper. The information that is received from this type of assessment is important it only helps in certain aspects of the learning process and can be used to evaluate the posture of school programs, school improvement goals, alignment of the curriculum or student placement in specific areas. Think of the formative assessment as practice and do not hold the students accountable unlike the summative assessment in which plays a role in the students mark at the end of a grading period. There are several returns to using technology when an instructor needs to assess student learning. W hen assessing a student via a computer the process is more rapid and productive. When using a computer the students work is graded as the assessment is taken. some other advantage is the human element is removed from the equation. An instructor could grade a political campaign sorryly based on their mood or because the student has poor handwriting. The downside to giving an assessment using technology is that computers cannot read written answers therefrom the test would have to be provided in multiple choice formats. Another downside to assessing students on the computer is that this form of technology is unable to grade projects that are submitted by the children. Technology, like so many things we use in our daily routine, has its perks but does not replace a qualified teacher in the classroom setting. There are many websites and programs available for student assessment. several(prenominal) of these resources are free while others offer a trial period.Some of these educatio nal sites, which can be used in formative or summative assessments, include Voice-over, PowerPoint games, Blogs, Interactive time lines, and Podcast. To keep children involved in learning teachers have a responsibility to explore every available avenue for formative and summative assessments to ensure the students is reaching their academic goals. The technological resources that can be utilized as a teacher are the online gradebook and e-mail. The online gradebook gives parents 24-hour access and allows the parents and students to track information regarding grades and upcoming assignments. Teachers, through the use of the online gradebook, can bring on assignments with specific dates for when their work is due. The ability to see when a special(a) student is not doing well because they do not complete work to be done at home or is having difficulty with assessments gives the teacher the opportunity to adjust due dates to accommodate the students specific needs. The online gr adebook provides an adequate amount of vision into the educational setting for parents and is a tool I look forward to utilizing to enhance the education of my students.E-mail, which improves communication for parents and teachers, is a requirement for students to be successful in their endeavors at school. Epstein (2008) preserved that more students earn high grades in English and math, improve their reading and writing skills, complete more course credit, set higher aspirations, have better attendance, come to class more prepared to learn, and have fewer behavior problems when parents are active in their childrens lives. Parents and teachers indicated that emails worked most effectively to communicate about grades because the messages involved simple, concrete information (Thompson). E-mail is a resource I am looking forward to utilizing as an instructor as it is an effective means of communication with parents which is important for the academic success of their children. I do not feel that technology should be used exclusively for assessments. Assessment, as we have learned, involves more the just testing and grading assignments.What goes on in the students life outside the classroom can affect how they make at school. When parents choose to be active in their childrens education, they cogitate their efforts will have a positive impact on their childrens learning (Anderson and Minke). Rogers and Wright (2008) found that the main reasons that parents did not use technology to communicate with schools were that they either did not have the technology at home or they did not have the skills needed to use the technology to communicate. As educators, we need to find the balance between incorporating technology in our classrooms to keep our students intermeshed in 21st century learning while realizing the importance that fount to face communication has in education. Section Five Technology In The ClassroomThe internet offers numerous learning opportunities and is loaded with an abundance of information. The concern for educators is how to encourage children to take part in constructive and imaginative learning while safeguarding them from inappropriate material, the possibility of coming into contact with people they do not know. Internet safety entails balancing perceived advantages against tolerable risk of exposure. There are many benefits to allowing students to engage in surfing the net. As educators we need to incorporate a Safe & Ethical Internet surfboarding Handbook. The handbook will quit guidelines and a student use agreement form in which the students helped generate. The handbook will also contain information on how to make informed decisions on how to make honourable decisions while surfing the net. This includes proper citation to avoid right of first publication infringements. Involving the student in the creation of the handbook will facilitate a deeper understanding of the expectations.Utilizing the intern et the children gain valuable skills such as creativity, leadership, team building, confidence, communication, innovation, and opening move (Green and Harmon, 2007). To allow my students the many benefits offered on the internet I will create a Curriculum Resource Page. This tool is an instructor created document that consists of hyperlinks to various websites that have been evaluated and chosen by the teacher. The links provided on the resource page support and enhance the learning that is taking place in the classroom setting. An important aspect in utilizing the curriculum resource page is that it reduces the chance that the children will be able to gain access to a website that may be deemed inappropriate. The curriculum resource page is a valuable tool however, the most important thing an instructor can do is observe the students while doing their lessons. Through observation the teacher can ensure the online safety of the students by making sure inappropriate material is not b eing viewed. The key is that ethical behavior is not a one or two day lesson, but theme educators discuss throughout the year (Jacobsen & Smith, 1998, paras, 5-6). There is valuable web site that can be incorporated into the classroom discussion regarding internet safety.SafeKids.ne.gov. has lessons, worksheets, and PowerPoint presentations that help reinforce the lessons of online safety to the children.This resource is valuable as it incorporates role playing. This allows the students to be active participants in the lesson of online safety. Youth today are unlikely to think twice about committing a cybercrime (Newman, para.6) and is a growing concern with school-aged children. The use of paper mill around, which are websites that deliver term papers which students can download at no cost and then turn in as their own original work. These sites have been increasing at an astonishing rate. In  skirt of 1999 approximately 35 of these sites existed and by the end of 2003 the number had jumped to more then 250(Newman, para.9). Steube (1996) stated as more and more schools venture onto the internet, incidents of plagiarism and copyright infringement that were once limited to classrooms are reaching an international interview (para.4). Plagiarism is viewed by the young as a low risk behavior and as educators it is our responsibility to teach the students that this behavior is unacceptable and comes with serious consequences. To ensure my students are respectful while utilizing the internet I will implement a student use agreement form that each child along with their parent/legal guardian will be required to sign as parents need to be involved in their childs behavior while surfing the net.This agreement will contain a code of conduct, in which the children can contribute their thoughts, which will incorporate issues concerning copyright, privacy, and proper use. As an educator I will instruct my students on the correct, ethical, use of computer know-hows as I introduce other age appropriate skills. Some of the areas I will place besotted emphasis on are honesty, being trustworthy, and respecting the privacy of others. In addition to these areas of concern I will stress the importance of honoring copyright laws to avoid plagiarism. To ensure my students are aware of how to avoid plagiarism I would present a Power Point teaching the proper way to cite resources. I would show this resource intermittently throughout the year to make sure the concept is not forgotten. Through my effort my students will become responsible cyber-citizens (Baum, 2005). The incidence of plagiarism has become more become more widespread because it is easy to get the information by see websites on their specific topic. Other less known Codes of Computer morality I will address include is the use of all crownwork letters is considered yelling and therefore impolite, and that humor and sarcasm are viewed as criticism and therefore should be used sparingly o r avoided all together.ReferencesAronin, A. and ONeal M. Twenty ways to assess students using technology. Science range of mountains 34.9 (2011) n. page. Grand Canyon University Fleming Library. vane. 6 Apr. 2014. Baum, Janna J. Cyber ethics The New Frontier. Techtrends Linking seek & Practice To ImproveLearning 49.6 (2005) 54-78.Academic Search Complete. Web. 13 Apr. 2014. Cakir, R. (2012). Technology integrating and Technology Leadership in Schools as Learning Organizations. Turkish Online diary Of readingal Technology TOJET, 11(4), 273-282. Garrison, C. and Ehringhaus M. fictile and Summative Assessments in the Classroom. Association for plaza Level didactics. N.P., n.d. Web. 6 Apr. 2014. Herro, D., Kiger, D., & Owens, C. (2013). 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