Attitude Of Teachers Towards Use Of Mathematics Laboratory In Teaching Learning Process In High Schools

Size: px
Start display at page:

Download "Attitude Of Teachers Towards Use Of Mathematics Laboratory In Teaching Learning Process In High Schools"

Transcription

1 Attitud Of Tachrs Towards Us Of Mathmatics Laboratory In Taching Larning Procss In High Schools Dr. Sangita R. Bihad Assistant Profssor, Shri Shivaji Collg of Education, Amravati Abstract: This papr xamind th ffct of instructional matrials and taching mthodology and high school mathmatics tachrs viw about us mathmatics laboratory achivmnt among high school studnts of Amravati division of Vidarbha. Dscriptiv survy rsarch dsign was adoptd, and th population for th study compriss 120 high school tachrs of Amravati. Simpl random sampling tchniqu was th sampling mthod usd to slct thirty scondary schools in ach ducational district from th four ducational districts in Maharashtra Stat. For th purpos of data collction thr rsarch instrumnts wr usd by th rsarchr, th instrumnts wr titld: Qustionnair on Effct of Instructional Matrials on Mathmatics Achivmnt, Qustionnair on Effct of Taching Mthodology on Mathmatics Achivmnt and rsarchr dvlopd attitud scal for us of Mathmatics laboratory in taching larning procss. Ths instrumnts wr slf-dvlopd qustionnairs. A rliability tst was conductd on th instrumnts using tst-rtst mthod, a rliability cofficint of 0.7 and 0.82 wr obtaind. In ordr to dtrmin th ffct of instructional matrials and taching mthodology on mathmatics achivmnt among high school tachrs. Stat thr rsarch qustions and four hypothss wr formulatd to guid th study. Th hypothss wr tstd at 0.01 lvl of significant using Chi-Squar statistics. Th rsults rvald that taching mthodology has significant ffct on mathmatics achivmnt among high school studnts.it was rcommndd that tachrs should larn how to improvis instructional matrials from th local nvironmnt instad of using forign matrials that studnts ar not familiar with. Tachr should larn how to us divrs mthodology in thir taching rathr than rstrict thmslvs to a particular mthod. Th valu of X 2 is and which is far mor than tachr us of mathmatics laboratory crat intrst among studnts. That s thy hav favorabl attitud towards us of mathmatics laboratory in cration of intrst in th subjct. Mor than 93% of tachrs agr about th us mathmatics laboratory to incras th intrst of studnts in mathmatics subjct. So us of mathmatics laboratory crat intrst in th subjct. Though it is not availabl in thir schools. Kywords: Attitud, Mathmatics Taching, Mathmatics Concpt, Mathmatics Laboratory. I. INTRODUCTION Mathmatics laboratory includs modls of gomtrical shaps or papr cutting, papr folding tchniqus, concrt objcts, charts, graphs, picturs, postrs, blocks gams, circl gam, fraction modl, gomtrical go sticks, masurmnt scals pattrn, sorting, thorm tc. Mathmatics lab is important spcial for studnts of class1 to class 12. Mathmatical gams and puzzls ar important for mntal dvlopmnt of studnts. Th activitis could b don individual by studnts or with tachrs. At this plac studnts do xprimnts with numbrs and gomtrical shaps and try to gnraliz pattrns. Studnts solv ral lif problms with ral data bcaus complx calculations ar no longr a major considration. Studnts mak charts and modls to illustrat mathmatical idas. Th crativity of studnt dvlopmnt is allowd fr play. Studnts find aras and volum of both rgular and irrgular solids. Intrfacs btwn algbra, gomtry, probability, calculus tc ar xprimntd. Studnts njoy larning mathmatics. Mathmatics has always occupid an important plac in school curriculum. Mathmatics Laboratory is a plac whr Pag 181

2 studnts can larn and xplor mathmatical concpts and vrify mathmatical facts and thorms through a varity of activitis using diffrnt matrials. Ths activitis may b carrid out by th tachr or th studnts to xplor, to larn, to stimulat intrst and dvlop favourabl attitud towards mathmatics. That is, a mathmatics laboratory is a plac whr w find a collction of gams, puzzls, taching aids and othr matrials for carrying out activitis. Ths ar mant to b usd both by th studnt by thir own and togthr with thir tachr to xplor th world of mathmatics, to discovr, to larn and to dvlop an intrst in mathmatics. Although mathmatics is not an xprimntal scinc in th way in which physics, chmistry and biology ar, a mathmatics laboratory can contribut grat to th larning of mathmatical concpts and skills. Nd and Purpos of Mathmatics Laboratory Som of th ways in which a Mathmatics Laboratory can contribut to th larning of th subjct ar: It provids an opportunity to studnts to undrstand and intrnaliz th basic mathmatical concpts through concrt objcts and situations. It nabls th studnts to vrify or discovr svral gomtrical proprtis and facts using modls or by papr cutting and folding tchniqus. It hlps th studnts to build intrst and confidnc in larning th subjct. Th laboratory provids opportunity to xhibit th rlatdnss of mathmatical concpts with vryday lif. It givs mor scop for individual participation. It ncourags studnts to bcom autonomous larnrs and allows a studnt to larn at his or hr own spac. It provids scop for gratr involvmnt of both th mind and th hand which facilitats cognition. Th laboratory allows and ncourags th studnts to think, discuss with ach othr and th tachr and assimilat th concpts in a mor ffctiv mannr. It nabls th tachr to dmonstrat, xplain and rinforc abstract mathmatical idas by using concrt objcts, modls, charts, graphs, picturs, postrs, tc. It widns th xprintial bas, and prpars th ground for latr larning of nw aras in mathmatics and of making appropriat connctions. In various puzzls and gams, th studnts larn th us of ruls and constraints and hav an opportunity to chang ths ruls and constraints. In this procss thy bcom awar of th rol that ruls and constraints play in mathmatical problms. Bcaus of th largr tim availabl individual to th studnt and opportunity to rpat an activity svral tims, studnts can rvis and rthink th problm and solution. This hlps to dvlop mta cognitiv abilitis. It builds up intrst and confidnc in th studnts in larning and doing mathmatics. Important, it allows varity in school mathmatics larning. Mathmatics Lab provids a conduciv ambinc for studnts to larn th subjct in a joyful mannr through practical activitis and intraction. Tachrs nd to pay attntion to both th transactional stratgis and valuation stratgis. Simpl xprimnts and projcts will lad to th dvlopmnt of diffrnt skills lik numrical, obsrvation, thinking, anatical and so on. Establishing a Mathmatics Lab dos not involv high cost. Improvisd aids using inxpnsiv matrial can b mad. OBJECTIVES OF THE STUDY Th study aimd at fulfilling th following objctivs: To dvlop th attitud scal towards us of mathmatics laboratory for high school tachrs. To find out th viws tachrs towards us of laboratory as slf larning tool at high school lvl. To find out th viws tachrs towards us of laboratory for cration of intrst of subjct To find out th viws of tachrs towards us of laboratory to all concpts of maths syllabus. To find out th viws tachrs towards us of laboratory lsson plan. HYPOTHESES Th following null hypothss wr formulatd by th abov objctivs: HO: 1 Thr is no significant diffrnc btwn tachr attitud towards us of mathmatics laboratory. HO: 2 Thr is no significant diffrnc btwn viws tachrs towards us of laboratory as slf larning tool at high school lvl. HO: 3 Thr is no significant diffrnc btwn viws tachrs towards us laboratory for cration of intrst of subjct HO: 4 Thr is no significant diffrnc btwn viws tachrs towards us of laboratory to tach all concpts of syllabus. HO: 5 Thr is no significant diffrnc btwn viws tachrs towards us of laboratory lsson plan. II. OPERATIONAL DEFINITIONS ATTITUDE: It is rfrrd as th tndncy to ract favourabl/ positiv or unfavorabl/ngativ towards us of mathmatics laboratory in taching larning procss. HIGH SCHOOL TEACHERS: Tachrs of aidd and grantd schools ar considrd for rsarch purpos taching to 8 th 9 th and 10 th class studnts of stat board school. MATHEMATICS: Mathmatics has th four fundamntal oprations of addition, subtraction, multiplication and division. Mathmatics subjct covrs th topics such as ral numbr systm, algbra, logarithms, gomtry, mnsuration, probability, graphs and statistics at scondary lvl. HIGH SCHOOL: Th high school consists of VIII, IX and X standard studnts Classss in th Educational systm. It was followd by scondary school syllabus. Th prsnt study on slctd grantd school tachrs. MATHEMATICS LABORATORY: Th Mathmatics Laboratory is a room, rich in manipulativ matrial, to which childrn hav rady accss to handl thm, prform Pag 182

3 mathmatical xprimnts, play mathmatical gams, solv mathmatical puzzls and bcom involvd in othr activitis through propr guidanc of tachr. III. RESEARCH DESIGN This rsarch was basical a survy approach with som orintation to xplorations of opinion finding thir roots and also to implmnt thm to actions. Thrfor, th prsnt rsarchr usd a mixd approach in ducational rsarch. SAMPLE This was Random sampl consisting of 120 High school mathmatics tachrs from diffrnt schools of Amravati division of Vidarbha. INSTRUMENT Th instrumnt was a Mathmatics Laboratory Qustionnair, which includd thirty fiv statmnts. Th Mathmatics Laboratory Qustionnair consistd of two parts: Part A sought information on school data: nam of school and stat, typ and location of school. Part B sought information on th xistnc and opration of mathmatics laboratoris in schools and viws of tachr rgarding us of mathmatics laboratory in taching larning procss. STATISTICAL USED: Th man, standard dviation and chi squar - tst wr usd for anazing th data. SOME OF STATEMENTS OF SCALE ARE AS Sr. STATEMENT S.A A U D S.D. No 1. Maths laboratory crat mor intrst in subjct. 2. Maths laboratory play vital rol in maths larning. 3. Maths laboratory is ssntial for slf larning of studnts. 4. Maths laboratory hlps in th compltion of maths syllabus. 5. Th quipmnts which ar usd in maths laboratory ar cost.. Maths laboratory is not usful for ach and vry concpt. 7. Dvlopmnt of Maths laboratory is xpnsiv. 0 (50%) 3 (30%) 12 (10%) (%) (%) 52 (43%) 52 (43%) (20%) 0 (50%) 1 (13%) 20 (17%) 12 (10%) 1 (13%) (%) Tabl 1 CREATE INTEREST Crat intrst SA A U D SD Obsrvd (f o f ) (f o f ) Tabl 2 1. X 2 = ( = = Df = 4 Th valu of X 2 is and which is far mor than tachrs us of mathmatics laboratory crat intrst among studnts. That s thy hav favorabl attitud towards us of mathmatics laboratory in cration of intrst in th subjct. Mor than 93% of tachrs agr about th us mathmatics laboratory to incras th intrst of studnts in mathmatics subjct. So us of mathmatics laboratory crat intrst in th subjct. Though it is not availabl in thir schools. SELF LEARNING Slf Larning: SA A U D SD Obsrvd Frquncy (f o) (f o f ) (f o f ) Tabl 3 X 2 = ( ) = = 5.32 Df = 4 Th valu of X 2 is 5.32 and which is far mor than (tabl valu). So According to viws of high school tachrs us of mathmatics laboratory is ssntial for slf larning of high school studnts. That s thy hav favorabl attitud towards us of mathmatics laboratory in cration of intrst in th subjct. Mor than 0% of tachrs agr about th us mathmatics laboratory as slf larning tool. LABORATORY IS NOT USEFUL FOR EACH CONCEPT Agr Agr Undcid d Obsrvd (f o f ) (f o f ) Tabl 4 X 2 = ( ) = = Th valu of X 2 is and which is far mor than tachrs us of mathmatics laboratory is not usful for ach and vry concpt of mathmatics curriculum. That s thy hav unfavorabl attitud towards us of mathmatics laboratory for ach and vry concpt of mathmatics. LESSON PREPARATION IS TIME CONSUMING Agr Agr Undci dd Obsrvd Pag 183

4 (f o f ) (f o f ) Tabl 5 X 2 = ( = Th valu of X 2 is and which is far mor than tachrs prparation of mathmatics lsson plans ar tim consuming. Thus prparation of mathmatical lsson plans nds mor practic than traditional lsson plans. Thus lab activity planning nds mor tim than th traditional way of taching. I HAVE PREPARED TEACHING AIDS TO PROVE THEOREM Agr Agr Undcid d Obsrvd (f o f ) (f o f ) X 2 = ( = 94.4 Tabl Th valu of X 2 is 94.4 and which is far mor than (tabl valu). So According to viws of high school tachrs prpard taching aids on for th proving th thorm. Thus tachr viw rgarding prparation of mathmatical tachings aids is much mor favorabl. CHALK AND BLACKBOARD ARE MORE USEFUL IN MATHEMATICS LABORATORY Agr Agr Undcid d Obsrvd (f o f ) (f o f ) X 2 = ( = Tabl 7 Th valu of X 2 is and which is far mor than (tabl valu). So According to viws of high school tachrs Chalk and Blackboard ar most usful in mathmatics taching larning. Thus Chalk and Blackboards ar usd by 92% of th high school tachr. DIFFERENT METHODS ARE USED TO TEACH MATHEMATICS Agr Undc Disa l Agr idd gr y Obsrvd (f o f ) (f o f ) Tabl 8 X 2 = ( ) = Th valu of X 2 is and which is far mor than tachrs Diffrnt mthods of taching ar most usful in mathmatics taching. Thus Mathmatics taching nds various skills and stratgis. Thus 98% tachrs ar favorabl in application of diffrnt taching mthods of mathmatics. IV. CONCLUSION Th rsults of this study lad us to an important conclusion that Mor than 93% of tachrs agr about th us mathmatics laboratory to incras th intrst of studnts in mathmatics subjct. So us of mathmatics laboratory crat intrst in th subjct. So According to viws of high school tachrs prpard taching aids on for th proving th thorm. Thus tachr viw rgarding prparation of mathmatical tachings aids is much mor favorabl. So tachrs attitud towards us of mathmatics laboratory is most favorabl. Policy makr should focus on issu of dvlopmnt of mathmatics laboratory at high school lvl in Maharashtra stat. REFERENCES [1] Bhattachrj, A. (2012). Social scinc rsarch: Principls, mthods, and practics. Opn Accss Txtbooks. Book 3. Rtrivd on from [2] Bandura, A. (1977). Social Larning Thory. Prntic- Hall, Englwood Cliffs, N.J. USA. [3] Bharat Singh (2010). Scinc Taching in Schools. Saurabh Publishing Hous, Nw Dlhi. [4] C.R. Kothari (). Rsarch Mthodology: Mthods and Tchniqus. Nw ag intrnational Publishrs, Nw Dlhi. [5] John W. Bst & Jams V.Kahn (23). Rsarch in Education. Parson Education, Inc., Nw Dlhi. [] Lipnvich, A. A., Maccann, C., Krumm, S., Burrus, J., & Robrts, R. D. (2011). Mathmatics attituds and mathmatics outcoms of U.S. and Blarusian middl school studnts. Journal of Educational Psychology, 103(1), doi: /a21949 Pag 184

5 [7] Paolo Di Sia, Th laboratory of mathmatics in primary school: a practical approach for undrstanding and larning rtrivd from licnss/by/4.0/ [8] Raymond Summit And Tony Rickards A constructivist approach to mathmatics laboratory classs Th 9th Dlta Confrnc on taching and larning of undrgraduat mathmatics and statistics, -29 Novmbr 2013, Kiama, Australia Sharp, V. (22). [9] S.K. & Uma Mangal (29). Essntials of Educational Tchnology. PHI larning Privat Limitd, Nw Dlhi [10] Sybill LK. (21). Evaluation of Taching and Larning stratgis. Md Education, Onlin Rtrivd May 30, 29 from August 1, 29 [11] Dr. Štfan Bržný, PhD. What Softwar To Us In Th Taching Of Mathmatical Subjcts? Rtrivd wb: [12] Wilkinson, Jack Dal, "A laboratory mthod to tach gomtry in slctd sixth grad mathmatics classs" (1970). Rtrospctiv Thssand Dissrtations. 09 rtrivd from Pag 185

Lesson Focus: Finding Equivalent Fractions

Lesson Focus: Finding Equivalent Fractions Lsson Plans: Wk of 1-26-15 M o n Bindrs: /Math;; complt on own, thn chck togthr Basic Fact Practic Topic #10 Lsson #5 Lsson Focus: Finding Equivalnt Fractions *Intractiv Larning/Guidd Practic-togthr in

More information

A Brief Summary of Draw Tools in MS Word with Examples! ( Page 1 )

A Brief Summary of Draw Tools in MS Word with Examples! ( Page 1 ) A Brif Summary of Draw Tools in MS Word with Exampls! ( Pag 1 ) Click Viw command at top of pag thn Click Toolbars thn Click Drawing! A chckmark appars in front of Drawing! A toolbar appars at bottom of

More information

Systems in Three Variables. No solution No point lies in all three planes. One solution The planes intersect at one point.

Systems in Three Variables. No solution No point lies in all three planes. One solution The planes intersect at one point. 3-5 Systms in Thr Variabls TEKS FOCUS VOCABULARY TEKS (3)(B) Solv systms of thr linar quations in thr variabls by using Gaussian limination, tchnology with matrics, and substitution. Rprsntation a way

More information

Principles of Programming Languages Topic: Formal Languages II

Principles of Programming Languages Topic: Formal Languages II Principls of Programming Languags Topic: Formal Languags II CS 34,LS, LTM, BR: Formal Languags II Rviw A grammar can b ambiguous i.. mor than on pars tr for sam string of trminals in a PL w want to bas

More information

8.3 INTEGRATION BY PARTS

8.3 INTEGRATION BY PARTS 8.3 Intgration By Parts Contmporary Calculus 8.3 INTEGRATION BY PARTS Intgration by parts is an intgration mthod which nabls us to find antidrivativs of som nw functions such as ln(x) and arctan(x) as

More information

Type & Media Page 1. January 2014 Libby Clarke

Type & Media Page 1. January 2014 Libby Clarke Nam: 1 In ordr to hlp you s your progrss at th nd of this ntir xrcis, you nd to provid som vidnc of your starting point. To start, draw th a on th lft into th box to th right, dpicting th sam siz and placmnt.

More information

About Notes And Symbols

About Notes And Symbols About Nots And Symbols by Batric Wildr Contnts Sht 1 Sht 2 Sht 3 Sht 4 Sht 5 Sht 6 Sht 7 Sht 8 Sht 9 Sht 10 Sht 11 Sht 12 Sht 13 Sht 14 Sht 15 Sht 16 Sht 17 Sht 18 Sht 19 Sht 20 Sht 21 Sht 22 Sht 23 Sht

More information

Linked Data meet Sensor Networks

Linked Data meet Sensor Networks Digital Entrpris Rsarch Institut www.dri.i Linkd Data mt Snsor Ntworks Myriam Lggiri DERI NUI Galway, Irland Copyright 2008 Digital Entrpris Rsarch Institut. All rights rsrvd. Linkd Data mt Snsor Ntworks

More information

HEAD DETECTION AND TRACKING SYSTEM

HEAD DETECTION AND TRACKING SYSTEM HEAD DETECTION AND TRACKING SYSTEM Akshay Prabhu 1, Nagacharan G Tamhankar 2,Ashutosh Tiwari 3, Rajsh N(Assistant Profssor) 4 1,2,3,4 Dpartmnt of Information Scinc and Enginring,Th National Institut of

More information

FSP Synthesis of an off-set five bar-slider mechanism with variable topology

FSP Synthesis of an off-set five bar-slider mechanism with variable topology FSP Synthsis of an off-st fiv bar-slidr mchanism with variabl topology Umsh. M. Daivagna 1*, Shrinivas. S. Balli 2 1 Dpartmnt of Mchanical Enginring, S.T.J.Institut of Tchnology, Ranbnnur, India 2 Dpt.

More information

Group 2 Mega Crystals Usability Test Report

Group 2 Mega Crystals Usability Test Report Group 2 Mga Crystals Usability Tst Rport Rport Writtn By Katrina Ellis Tam Mmbrs Usr Exprinc Consultants Katrina Ellis Zhitao Qiu HU4628 Julia Wiss Sarah Ingold Jams Staplton CS4760 Kris Gauthir (Android)

More information

Extending z/tpf using IBM API Management (APIM)

Extending z/tpf using IBM API Management (APIM) Extnding using API Managmnt (APIM) Mark Gambino, TPF Dvlopmnt Lab March 23, 2015 TPFUG Dallas, TX Th Big Pictur Goal Mobil Applications Cloud APIs Cloud-basd Srvics On-Prmis Entrpris APIs E n t r p r I

More information

2018 How to Apply. Application Guide. BrandAdvantage

2018 How to Apply. Application Guide. BrandAdvantage 2018 How to Apply Application Guid BrandAdvantag Contnts Accssing th Grant Sit... 3 Wlcom pag... 3 Logging in To Pub Charity... 4 Rgistration for Nw Applicants ( rgistr now )... 5 Organisation Rgistration...

More information

Objectives. Two Ways to Implement Lists. Lists. Chapter 24 Implementing Lists, Stacks, Queues, and Priority Queues

Objectives. Two Ways to Implement Lists. Lists. Chapter 24 Implementing Lists, Stacks, Queues, and Priority Queues Chaptr 24 Implmnting Lists, Stacks, Quus, and Priority Quus CS2: Data Structurs and Algorithms Colorado Stat Univrsity Original slids by Danil Liang Modifid slids by Chris Wilcox Objctivs q To dsign common

More information

CSE 272 Assignment 1

CSE 272 Assignment 1 CSE 7 Assignmnt 1 Kui-Chun Hsu Task 1: Comput th irradianc at A analytically (point light) For point light, first th nrgy rachd A was calculatd, thn th nrgy was rducd by a factor according to th angl btwn

More information

Formal Foundation, Approach, and Smart Tool for Software Models Comparison

Formal Foundation, Approach, and Smart Tool for Software Models Comparison Formal Foundation, Approach, and Smart Tool for Softwar Modls Comparison Olna V. Chbanyuk, Abdl-Badh M. Salm Softwar Enginring Dpartmnt, National Aviation Univrsity, Kyiv, Ukrain Computr Scinc, Faculty

More information

Pacing Guide for Third Grade Version 2011

Pacing Guide for Third Grade Version 2011 GLE 0306.. Undrstand th plac valu of whol numbrs to tn-thousands plac including xpandd notation for all arithmtic oprations. 0306.. Rprsnt whol numbrs up to 0,000 using various modls (such as bastn blocs,

More information

Maxwell s unification: From Last Time. Energy of light. Modern Physics. Unusual experimental results. The photoelectric effect

Maxwell s unification: From Last Time. Energy of light. Modern Physics. Unusual experimental results. The photoelectric effect From Last Tim Enrgy and powr in an EM wav Maxwll s unification: 1873 Intimat connction btwn lctricity and magntism Exprimntally vrifid by Hlmholtz and othrs, 1888 Polarization of an EM wav: oscillation

More information

Tillförlitlig dimensionering mot utmattning UTMIS Vårmöte 2018 på Högskolan i Skövde

Tillförlitlig dimensionering mot utmattning UTMIS Vårmöte 2018 på Högskolan i Skövde Tillförlitlig dimnsionring mot utmattning UTMIS Vårmöt 2018 på Högskolan i Skövd Rami Mansour & Mårtn Olsson KTH Hållfasthtslära mart@kth.s ramimans@kth.s Introduction Ovrviw of rliabl dsign Traditional

More information

The semantic WEB Roles of XML & RDF

The semantic WEB Roles of XML & RDF Th smantic WEB Rols of XML & RDF STEFAN DECKER AND SERGEY MELNIK FRANK VAN HARMELEN, DIETER FENSEL, AND MICHEL KLEIN JEEN BROEKSTRA MICHAEL ERDMANN IAN HORROCKS Prsntd by: Iniyai Thiruvalluvan CSCI586

More information

Overview of the Gifted Services Portfolio Process

Overview of the Gifted Services Portfolio Process Saint Paul Public Schools Ovrviw of th Giftd Srvics Portfolio Procss Talnt Dvlopmnt and Acclration Srvics What is th Portfolio Rviw? Th portfolio rviw offrs all studnts th opportunity to b assssd for giftd

More information

Terrain Mapping and Analysis

Terrain Mapping and Analysis Trrain Mapping and Analysis Data for Trrain Mapping and Analysis Digital Trrain Modl (DEM) DEM rprsnts an array of lvation points. Th quality of DEM influncs th accuracy of trrain masurs such as slop and

More information

Usage of Ontology-Based Semantic Analysis of Complex Information Objects in Virtual Research Environments

Usage of Ontology-Based Semantic Analysis of Complex Information Objects in Virtual Research Environments Usag of Ontology-Basd Smantic Analysis of Complx Information Objcts in Virtual Rsarch Environmnts Julia Rogushina 1, Anatoly Gladun 2, Abdl-Badh M. Salm 3 1 Institut of Softwar Systms of National Acadmy

More information

Utilization a Courseware WEB Portal for Virtual University Requirements

Utilization a Courseware WEB Portal for Virtual University Requirements Utilization a Courswar WEB Portal for Virtual Univrsity Rquirmnts SMUTNÝ P., SMUTNÝ L., FARANA R. & SMUTNÁ J. Dpartmnt of Control Systms & Instrumntation VŠB Tchnical Univrsity Ostrava Av. 17. listopadu

More information

Spectral sensitivity and color formats

Spectral sensitivity and color formats FirWir camras Spctral snsitivity and color formats At th "input" of a camra, w hav a CCD chip. It transforms photons into lctrons. Th spctral snsitivity of this transformation is an important charactristic

More information

I - Pre Board Examination

I - Pre Board Examination Cod No: S-080 () Total Pags: 06 KENDRIYA VIDYALAYA SANGATHAN,GUWHATI REGION I - Pr Board Examination - 04-5 Subjct Informatics Practics (Thory) Class - XII Tim: 3 hours Maximum Marks : 70 Instruction :

More information

Dynamic modelling of multi-physical domain system by bond graph approach and its control using flatness based controller with MATLAB Simulink

Dynamic modelling of multi-physical domain system by bond graph approach and its control using flatness based controller with MATLAB Simulink Dnamic modlling of multi-phsical domain sstm b bond graph approach and its control using flatnss basd controllr with MATLAB Simulink Sauma Ranjan Sahoo Rsarch Scholar Robotics Lab Dr. Shital S. Chiddarwar

More information

To Do. Mesh Data Structures. Mesh Data Structures. Motivation. Outline. Advanced Computer Graphics (Fall 2010) Desirable Characteristics 1

To Do. Mesh Data Structures. Mesh Data Structures. Motivation. Outline. Advanced Computer Graphics (Fall 2010) Desirable Characteristics 1 Advancd Computr Graphics (Fall 200) CS 283, Lctur 5: Msh Data Structurs Ravi Ramamoorthi http://inst.cs.brkly.du/~cs283/fa0 To Do Assignmnt, Du Oct 7. Start rading and working on it now. Som parts you

More information

To Do. Advanced Computer Graphics. Motivation. Mesh Data Structures. Outline. Mesh Data Structures. Desirable Characteristics 1

To Do. Advanced Computer Graphics. Motivation. Mesh Data Structures. Outline. Mesh Data Structures. Desirable Characteristics 1 Advancd Computr Graphics CSE 63 [Spring 207], Lctur 7 Ravi Ramamoorthi http://www.cs.ucsd.du/~ravir To Do Assignmnt, Du Apr 28 Any last minut issus or difficultis? Starting Gomtry Procssing Assignmnt 2

More information

A New Algorithm for Solving Shortest Path Problem on a Network with Imprecise Edge Weight

A New Algorithm for Solving Shortest Path Problem on a Network with Imprecise Edge Weight Availabl at http://pvamudu/aam Appl Appl Math ISSN: 193-9466 Vol 6, Issu (Dcmbr 011), pp 60 619 Applications and Applid Mathmatics: An Intrnational Journal (AAM) A Nw Algorithm for Solving Shortst Path

More information

te Finance (4th Edition), July 2017.

te Finance (4th Edition), July 2017. Epilogu Aftrthoughts and Opinions You hav travld a long distanc with m through this book. W hav now rachd th pilogu, whr, by tradition, I am allowd to voic my own prsonal opinions in ffct, to pontificat.

More information

An Agent-Based Architecture for Service Discovery and Negotiation in Wireless Networks

An Agent-Based Architecture for Service Discovery and Negotiation in Wireless Networks An Agnt-Basd Architctur for Srvic Discovry and Ngotiation in Wirlss Ntworks Abstract Erich Birchr and Torstn Braun Univrsity of Brn, Nubrückstrass 10, 3012 Brn, Switzrland Email: braun@iam.unib.ch This

More information

Fequent Pattern Recognization From Stream Data Using Compact Data Structure

Fequent Pattern Recognization From Stream Data Using Compact Data Structure Fqunt Pattrn Rcognization From Stram Data Using Compact Data Structur Fabin M Christian 1, Narndra C.Chauhan 2, Nilsh B. Prajapati 3 1 PG Scholar, CE Dpartmnt, BVM Engg. Collg, V.V.Nagar, fabin.christian@gmail.com

More information

Recorder Variables. Defining Variables

Recorder Variables. Defining Variables Rcordr Variabls Dfining Variabls Simpl Typs Complx Typs List of Rsrvd Words Using Variabls Stting Action Paramtrs Parsing Lists and Tabls Gtting Valu from Lists and Tabls Using Indxs with Lists Using Indxs

More information

XML Publisher with connected query: A Primer. Session #30459 March 19, 2012

XML Publisher with connected query: A Primer. Session #30459 March 19, 2012 XML Publishr with connctd qury: A Primr Sssion #30459 March 19, 2012 Agnda/ Contnts Introduction Ovrviw of XMLP Gtting Startd Bst practics for building a basic XMLP rport Connctd Qury Basics Building a

More information

LAB1: DMVPN Theory. DMVPN Theory. Disclaimer. Pag e

LAB1: DMVPN Theory. DMVPN Theory. Disclaimer. Pag e LAB1: DMVPN Thory Disclaimr This Configuration Guid is dsignd to assist mmbrs to nhanc thir skills in rspctiv tchnology ara. Whil vry ffort has bn mad to nsur that all matrial is as complt and accurat

More information

Forward and Inverse Kinematic Analysis of Robotic Manipulators

Forward and Inverse Kinematic Analysis of Robotic Manipulators Forward and Invrs Kinmatic Analysis of Robotic Manipulators Tarun Pratap Singh 1, Dr. P. Sursh 2, Dr. Swt Chandan 3 1 M.TECH Scholar, School Of Mchanical Enginring, GALGOTIAS UNIVERSITY, GREATER NOIDA,

More information

FALSE DYNAMIC EIV MODEL IDENTIFICATION IN THE PRESENCE OF NON-PARAMETRIC DYNAMIC UNCERTAINTY

FALSE DYNAMIC EIV MODEL IDENTIFICATION IN THE PRESENCE OF NON-PARAMETRIC DYNAMIC UNCERTAINTY Intrnational Journal of Application or Innovation in Enginring & Managmnt (IJAIEM) Wb Sit: www.ijaim.org Email: ditor@ijaim.org Volum 3, Issu, May 4 ISSN 39-4847 FALSE DYNAMIC EIV MODEL IDENTIFICATION

More information

Installation Saving. Enhanced Physical Durability Enhanced Performance Warranty The IRR Comparison

Installation Saving. Enhanced Physical Durability Enhanced Performance Warranty The IRR Comparison Contnts Tchnology Nwly Dvlopd Cllo Tchnology Cllo Tchnology : Improvd Absorption of Light Doubl-sidd Cll Structur Cllo Tchnology : Lss Powr Gnration Loss Extrmly Low LID Clls 3 3 4 4 4 Advantag Installation

More information

Presentation for use with the textbook, Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, Directed Graphs BOS SFO

Presentation for use with the textbook, Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, Directed Graphs BOS SFO Prsntation for us with th txtbook, Algorithm Dsign and Applications, by M. T. Goodrich and R. Tamassia, Wily, 2015 Dirctd Graphs BOS ORD JFK SFO LAX DFW MIA 2015 Goodrich and Tamassia Dirctd Graphs 1 Digraphs

More information

Understanding Patterns of TCP Connection Usage with Statistical Clustering

Understanding Patterns of TCP Connection Usage with Statistical Clustering Th UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Undrstanding Pattrns of TCP Connction Usag with Statistical Clustring Félix Hrnándz-Campos Kvin Jffay Don Smith Dpartmnt of Computr Scinc Andrw Nobl Dpartmnt

More information

Student, MCA, P.E.S s Modern College of Engineering Pune, Maharashtra, India

Student, MCA, P.E.S s Modern College of Engineering Pune, Maharashtra, India 2018 IJSRSET Volum 4 Issu 8 Print ISSN: 2395-1990 Onlin ISSN : 2394-4099 Thmd Sction : Enginring and Tchnology Rviw on Vrsion Control with Git Pranjal Govkar 1, Dr. Shivani Budhkar 2 1 Studnt, MCA, P.E.S

More information

XML security in certificate management

XML security in certificate management XML scurity in crtificat managmnt Joan Lu, Nathan Cripps and Chn Hua* School of Computing and Enginring, Univrsity of Huddrsfild, UK J.lu@hud.ac.uk *Institut of Tchnology, Xi'an, Shaanxi, P. R. China Abstract

More information

KENDRIYA VIDYALAYA SANGATHAN, CHENNAI REGION CLASS XII COMMON PRE-BOARD EXAMINATION

KENDRIYA VIDYALAYA SANGATHAN, CHENNAI REGION CLASS XII COMMON PRE-BOARD EXAMINATION KENDRIYA VIDYALAYA SANGATHAN, CHENNAI REGION CLASS XII COMMON PRE-BOARD EXAMINATION 03-4 Sub : Informatics Practics (065) Tim allowd : 3 hours Maximum Marks : 70 Instruction : (i) All qustions ar compulsory

More information

Whitepaper: IEEE P1687 Internal JTAG (IJTAG) taps into embedded instrumentation

Whitepaper: IEEE P1687 Internal JTAG (IJTAG) taps into embedded instrumentation Whitpapr: IEEE P1687 Intrnal JAG (IJAG) taps into mbddd instrumntation By Al Crouch Chif chnologist, Cor Instrumntation ASSE Intrch Co-Chairman, IEEE P1687 IJAG Standard Working Group ASSE Intrch, Inc.

More information

Review and analysis of Cloud Computing Quality of Experience

Review and analysis of Cloud Computing Quality of Experience Rviw and analysis of Cloud Computing Quality of Exprinc Fash Safdari 1, Victor Chang 1 1 School of Computing, Crativ Tchnologis and Enginring, Lds Mtropolitan Univrsity, Hadinly, Lds LS6 3QR {F.Safdari;

More information

University of Pune. Three Year B. Sc. Degree Course in Electronic Science. Subject: Electronic Equipment Maintenance (Vocational)

University of Pune. Three Year B. Sc. Degree Course in Electronic Science. Subject: Electronic Equipment Maintenance (Vocational) Univrsity of Pun Thr Yar B. Sc. Dgr Cours in Elctronic Scinc Subjct: Elctronic Equipmnt Maintnanc (Vocational) 1 1) Titl of th Cours: S. Y. B. Sc. Elctronic Equipmnt Maintnanc (Vocational) (To b implmntd

More information

Non Fourier Encoding For Accelerated MRI. Arjun Arunachalam Assistant Professor Electrical engineering dept IIT-Bombay

Non Fourier Encoding For Accelerated MRI. Arjun Arunachalam Assistant Professor Electrical engineering dept IIT-Bombay Non Fourir Encoding For Acclratd MRI Arjun Arunachalam Assistant Profssor Elctrical nginring dpt IIT-Bombay Outlin of th Prsntation An introduction to Magntic Rsonanc Imaging (MRI Th nd for spd in MRI

More information

Descriptors story. talented developers flexible teams agile experts. Adrian Dziubek - EuroPython

Descriptors story. talented developers flexible teams agile experts. Adrian Dziubek - EuroPython Dscriptors story talntd dvloprs flxibl tams agil xprts Adrian Dziubk - EuroPython - 2016-07-18 m t u o b A Adrian Dziubk Snior Python dvlopr at STX Nxt in Wrocław, Crating wb applications using Python

More information

EXTENSION OF RCC TOPOLOGICAL RELATIONS FOR 3D COMPLEX OBJECTS COMPONENTS EXTRACTED FROM 3D LIDAR POINT CLOUDS

EXTENSION OF RCC TOPOLOGICAL RELATIONS FOR 3D COMPLEX OBJECTS COMPONENTS EXTRACTED FROM 3D LIDAR POINT CLOUDS Th Intrnational rchivs of th Photogrammtry, mot Snsing and Spatial Information Scincs, Volum XLI-, 016 XXIII ISPS Congrss, 1 19 July 016, Pragu, Czch public EXTENSION OF CC TOPOLOGICL ELTIONS FO D COMPLEX

More information

Vignette to package samplingdatacrt

Vignette to package samplingdatacrt Vigntt to packag samplingdatacrt Diana Trutschl Contnts 1 Introduction 1 11 Objctiv 1 1 Diffrnt study typs 1 Multivariat normal distributd data for multilvl data 1 Fixd ffcts part Random part 9 3 Manual

More information

The Size of the 3D Visibility Skeleton: Analysis and Application

The Size of the 3D Visibility Skeleton: Analysis and Application Th Siz of th 3D Visibility Sklton: Analysis and Application Ph.D. thsis proposal Linqiao Zhang lzhang15@cs.mcgill.ca School of Computr Scinc, McGill Univrsity March 20, 2008 thsis proposal: Th Siz of th

More information

est with berkeley / uc berkeley With BERkELEY exten xtension / be your best with berkele

est with berkeley / uc berkeley With BERkELEY exten xtension / be your best with berkele c rkley xt st w rkly c rkley xt st with rkly c rkley xtn Crtificat Program st in with rkly c rk xt st with rkl c rkley xt st w rkly Ladrship c rkley and xt st with rkly c rkley xtn Managmnt st with rkly

More information

Blue-Bot. Marketing Guide BLUETOOTH FLOOR ROBOT. Computing and ICT

Blue-Bot. Marketing Guide BLUETOOTH FLOOR ROBOT. Computing and ICT Blu-Bot BLUETOOTH FLOOR ROBOT Computing and ICT Markting Guid Blu-Bot_Usr Guid.indd 1 About this guid Introduction This markting guid has bn cratd to nsur that you ar informd of all of th ky faturs and

More information

2 Mega Pixel. HD-SDI Bullet Camera. User Manual

2 Mega Pixel. HD-SDI Bullet Camera. User Manual 2 Mga Pixl HD-SDI Bullt Camra Usr Manual Thank you for purchasing our product. This manual is only applicabl to SDI bullt camras. Thr may b svral tchnically incorrct placs or printing rrors in this manual.

More information

" dx v(x) $ % You may also have seen this written in shorthand form as. & ' v(x) + u(x) '# % ! d

 dx v(x) $ % You may also have seen this written in shorthand form as. & ' v(x) + u(x) '# % ! d Calculus II MAT 146 Mthods of Intgration: Intgration by Parts Just as th mthod of substitution is an intgration tchniqu that rvrss th drivativ procss calld th chain rul, Intgration by parts is a mthod

More information

Graphing Calculator Activities

Graphing Calculator Activities Graphing Calculator Activitis Graphing Calculator Activitis Copyright 009, IPG Publishing IPG Publishing 86 Erin Bay Edn Prairi, innsota 47 phon: (6) 80-9090 www.iplaymathgams.com ISBN 978--948--6 IPG

More information

SPECKLE NOISE REDUCTION IN SAR IMAGING USING 2-D LATTICE FILTERS BASED SUBBAND DECOMPOSITION

SPECKLE NOISE REDUCTION IN SAR IMAGING USING 2-D LATTICE FILTERS BASED SUBBAND DECOMPOSITION 7th Europan Signal Procssing Confrnc EUSIPCO 9 Glasgow Scotland August 4-8 9 SPECKLE REDUCTION IN SAR IMAGING USING -D LATTICE FILTERS ASED SUAND DECOMPOSITION Göhan Karasaal N.. Kaplan I. Err Informatics

More information

From Last Time. Origin of Malus law. Circular and elliptical polarization. Energy of light. The photoelectric effect. Exam 3 is Tuesday Nov.

From Last Time. Origin of Malus law. Circular and elliptical polarization. Energy of light. The photoelectric effect. Exam 3 is Tuesday Nov. From Last Tim Enrgy and powr in an EM wav Exam 3 is Tusday Nov. 25 5:30-7 pm, 2103 Ch (hr) Studnts w / schduld acadmic conflict plas stay aftr class Tus. Nov. 18 to arrang altrnat tim. Covrs: all matrial

More information

To Do. Advanced Computer Graphics. Motivation. Mesh Data Structures. Outline. Mesh Data Structures. Desirable Characteristics 1

To Do. Advanced Computer Graphics. Motivation. Mesh Data Structures. Outline. Mesh Data Structures. Desirable Characteristics 1 Advancd Computr Graphics CSE 63 [Spring 208], Lctur 7 Ravi Ramamoorthi http://www.cs.ucsd.du/~ravir To Do Assignmnt, Du Apr 27 Any last minut issus or difficultis? Starting Gomtry Procssing Assignmnt 2

More information

Parser Self-Training for Syntax-Based Machine Translation

Parser Self-Training for Syntax-Based Machine Translation arsr Slf-Training for Syntax-Basd Machin Translation Makoto Morishita, Koichi Akab, Yuto Hatakoshi Graham ubig, Koichiro Yoshino, Satoshi akamrua Graduat School of Information Scinc ara Institut of Scinc

More information

Examination of Player Enjoyment and Learning with Explicit Versus Implicit Tutorials

Examination of Player Enjoyment and Learning with Explicit Versus Implicit Tutorials 1 Examination of Playr Enjoymnt and Larning with Explicit Vrsus Implicit Tutorials Campbll Crapsy, Myqu Oulltt Abstract Th study dmonstrats th important diffrncs btwn Explicit and Implicit tutorials and

More information

Managing Trust Relationships in Peer 2 Peer Systems

Managing Trust Relationships in Peer 2 Peer Systems Managing Trust Rlationships in Pr 2 Pr Systms R.S.SINJU PG STUDENT, DEPARTMENT OF COMPUTER SCIENCE, PONJESLY COLLEGE OF ENGINEERING NAGERCOIL, TAMILNADU, INDIA C.FELSY ASST.PROF, DEPARTMENT OF COMPUTER

More information

Register Allocation. Register Allocation

Register Allocation. Register Allocation Rgistr Allocation Jingk Li Portlan Stat Univrsity Jingk Li (Portlan Stat Univrsity) CS322 Rgistr Allocation 1 / 28 Rgistr Allocation Assign an unboun numbr of tmporaris to a fix numbr of rgistrs. Exampl:

More information

Towards Fractal Approach in Healthcare Information Systems: A Review

Towards Fractal Approach in Healthcare Information Systems: A Review t 2 L E P U T PER J O R TI D I OCI IC 2011 O E I UIV UDET E T R I TI I K E E B DO G I M L Y I l a n o i t a rn In c i n tif i c C o n IC 2011 f r n c 0 1 Procding of th Intrnational Confrnc on dvancd cinc,

More information

Efficient Obstacle-Avoiding Rectilinear Steiner Tree Construction

Efficient Obstacle-Avoiding Rectilinear Steiner Tree Construction Efficint Obstacl-Avoiding Rctilinar Stinr Tr Construction Chung-Wi Lin, Szu-Yu Chn, Chi-Fng Li, Yao-Wn Chang, and Chia-Lin Yang Graduat Institut of Elctronics Enginring Dpartmnt of Elctrical Enginring

More information

Gernot Hoffmann Sphere Tessellation by Icosahedron Subdivision. Contents

Gernot Hoffmann Sphere Tessellation by Icosahedron Subdivision. Contents Grnot Hoffmann Sphr Tssllation by Icosahdron Subdivision Contnts 1. Vrtx Coordinats. Edg Subdivision 3 3. Triangl Subdivision 4 4. Edg lngths 5 5. Normal Vctors 6 6. Subdividd Icosahdrons 7 7. Txtur Mapping

More information

AN EVALUATION MODEL FOR THE CHAINS OF DISTRIBUTED MULTIMEDIA INDEXING TOOLS RESPECTING USER PREFERENCES

AN EVALUATION MODEL FOR THE CHAINS OF DISTRIBUTED MULTIMEDIA INDEXING TOOLS RESPECTING USER PREFERENCES AN EVALUATION MODEL FOR THE CHAINS OF DISTRIBUTED MULTIMEDIA INDEXING TOOLS RESPECTING USER PREFERENCES 1 Bassm HAIDAR, 2 Bilal CHEBARO, 3 Hassan WEHBI 1 Asstt Prof., Dpartmnt of Computr Scincs, Faculty

More information

Evolutionary Clustering and Analysis of Bibliographic Networks

Evolutionary Clustering and Analysis of Bibliographic Networks Evolutionary Clustring and Analysis of Bibliographic Ntworks Manish Gupta Univrsity of Illinois at Urbana-Champaign gupta58@illinois.du Charu C. Aggarwal IBM T. J. Watson Rsarch Cntr charu@us.ibm.com Jiawi

More information

TCP Congestion Control. Congestion Avoidance

TCP Congestion Control. Congestion Avoidance TCP Congstion Control TCP sourcs chang th snding rat by modifying th window siz: Window = min {Advrtisd window, Congstion Window} Rcivr Transmittr ( cwnd ) In othr words, snd at th rat of th slowst componnt:

More information

Intersection-free Contouring on An Octree Grid

Intersection-free Contouring on An Octree Grid Intrsction-fr Contouring on An Octr Grid Tao Ju Washington Univrsity in St. Louis On Brookings Driv St. Louis, MO 0, USA taoju@cs.wustl.du Tushar Udshi Zyvx Corporation North Plano Road Richardson, Txas

More information

CPSC 826 Internetworking. The Network Layer: Routing & Addressing Outline. The Network Layer: Routing Algorithms. Routing Algorithms Taxonomy

CPSC 826 Internetworking. The Network Layer: Routing & Addressing Outline. The Network Layer: Routing Algorithms. Routing Algorithms Taxonomy PS Intrntworking Th Ntwork Layr: Routing & ddrssing Outlin Th Ntwork Layr: Routing lgorithms Michl Wigl partmnt of omputr Scinc lmson Univrsity mwigl@cs.clmson.du Novmbr, Ntwork layr functions Routr architctur

More information

Decision Support Systems as the Bridge between Marketing Models and Marketing Practice

Decision Support Systems as the Bridge between Marketing Models and Marketing Practice Dcision Support Systms as th Bridg btwn Markting Modls and Markting Practic by Brnd Wirnga,. 1. Introduction Th fild of markting dcision modls mrgd about fifty yars ago. In th bginning, optimization tchniqus

More information

SELECTE OTIC EVALUATION OF THE ORDNANCE DETECTION EXPERT SUPPORT APPLICATION (ODESA) OCTOBER U.S. Army Environmental Center

SELECTE OTIC EVALUATION OF THE ORDNANCE DETECTION EXPERT SUPPORT APPLICATION (ODESA) OCTOBER U.S. Army Environmental Center Rport No. SFIM-AEC-ET-CR-95084 OTIC SELECTE 0. JAN c 2 4 1995.~';,:; U.S. Army Environmntal Cntr EVALUATION OF THE ORDNANCE DETECTION EXPERT SUPPORT APPLICATION (ODESA) OCTOBER 1995, Prpard by th Naval

More information

Motivation. Synthetic OOD concepts and reuse Lecture 4: Separation of concerns. Problem. Solution. Deleting composites that share parts. Or is it?

Motivation. Synthetic OOD concepts and reuse Lecture 4: Separation of concerns. Problem. Solution. Deleting composites that share parts. Or is it? Synthtic OOD concpts and rus Lctur 4: Sparation of concrns Topics: Complx concrn: Mmory managmnt Exampl: Complx oprations on composit structurs Problm: Mmory laks Solution: Rfrnc counting Motivation Suppos

More information

JMFMoD: A New System for Media on Demand Presentations on Education

JMFMoD: A New System for Media on Demand Presentations on Education JMFMoD: A Nw Systm for Mdia on Dmand Prsntations on Education Authors: Ángla Blda, Univrsidad Politécnica d Valncia, Camino d Vra s/n, Valncia, Spain, anblor@doctor.upv.s Juan José Crmño, Univrsidad Politécnica

More information

running at 133 MHz bus. A Pentium III 1.26GHz with 512K cache running at 133 MHz bus is an available option. Fits Your Needs

running at 133 MHz bus. A Pentium III 1.26GHz with 512K cache running at 133 MHz bus is an available option. Fits Your Needs 3715 Industrial PCs 15.0" LCD Flat Panl Display DS-371500(E) Xycom Automation's nwst gnration of Industrial PCs is dsignd and tstd for th tough nvironmnts rquird for plant floor us. Our standard PC configurations

More information

i e ai E ig e v / gh E la ES h E A X h ES va / A SX il E A X a S

i e ai E ig e v / gh E la ES h E A X h ES va / A SX il E A X a S isto C o C or Co r op ra p a py ag yr g ri g g gh ht S S S V V K r V K r M K v M r v M rn v MW n W S r W Sa r W K af r: W K f : a H a M r T H r M rn w T H r Mo ns w T i o S ww c ig on a w c g nd af ww

More information

1. Trace the array for Bubble sort 34, 8, 64, 51, 32, 21. And fill in the following table

1. Trace the array for Bubble sort 34, 8, 64, 51, 32, 21. And fill in the following table 1. Trac th array for Bubbl sort 34, 8, 64, 51, 3, 1. And fill in th following tabl bubbl(intgr Array x, Intgr n) Stp 1: Intgr hold, j, pass; Stp : Boolan switchd = TRUE; Stp 3: for pass = 0 to (n - 1 &&

More information

DO NOW Geometry Regents Lomac Date. due. Similar by Transformation 6.1 J'' J''' J'''

DO NOW Geometry Regents Lomac Date. due. Similar by Transformation 6.1 J'' J''' J''' DO NOW Gomtry Rgnts Lomac 2014-2015 Dat. du. Similar by Transformation 6.1 (DN) Nam th thr rigid transformations and sktch an xampl that illustrats ach on. Nam Pr LO: I can dscrib a similarity transformation,

More information

Problem Set 1 (Due: Friday, Sept. 29, 2017)

Problem Set 1 (Due: Friday, Sept. 29, 2017) Elctrical and Computr Enginring Mmorial Univrsity of Nwfoundland ENGI 9876 - Advancd Data Ntworks Fall 2017 Problm St 1 (Du: Friday, Spt. 29, 2017) Qustion 1 Considr a communications path through a packt

More information

Fuzzy Intersection and Difference Model for Topological Relations

Fuzzy Intersection and Difference Model for Topological Relations IFS-EUSFLT 009 Fuzzy Intrsction and Diffrnc Modl for Topological Rlations hd LOODY Flornc SEDES Jordi INGLD 3 Univrsité Paul Sabatir (UPS) Toulous, 8 Rout d Narbonn, F-306-CEDEX 9, Franc Institut d Rchrchn

More information

Comment (justification for change) by the MB

Comment (justification for change) by the MB Editor's disposition s CD2 19763-12 as at 2013-11-03 Srial Annx (.g. 3.1) Figur/ Tabl/t (.g. Tabl 1) 001 CA 00 All All - G Canada disapprovs th draft for th rasons blow. 002 GB 01 Gnral d numbring has

More information

Clustering Algorithms

Clustering Algorithms Clustring Algoritms Hirarcical Clustring k -Mans Algoritms CURE Algoritm 1 Mtods of Clustring Hirarcical (Agglomrativ): Initially, ac point in clustr by itslf. Rpatdly combin t two narst clustrs into on.

More information

The Network Layer: Routing Algorithms. The Network Layer: Routing & Addressing Outline

The Network Layer: Routing Algorithms. The Network Layer: Routing & Addressing Outline PS 6 Ntwork Programming Th Ntwork Layr: Routing lgorithms Michl Wigl partmnt of omputr Scinc lmson Univrsity mwigl@cs.clmson.du http://www.cs.clmson.du/~mwigl/courss/cpsc6 Th Ntwork Layr: Routing & ddrssing

More information

Analysis of Influence AS Path Prepending to the Instability of BGP Routing Protocol.

Analysis of Influence AS Path Prepending to the Instability of BGP Routing Protocol. ISSN : 2355-9365 -Procding of Enginring : Vol.5, No.1 Mart 2018 Pag 1112 Analysis of Influnc AS Path Prpnding to th Instability of BGP Routing Protocol. Hirwandi Agusnam 1, Rndy Munadi 2, Istikmal 3 1,2,3,

More information

Design Methodologies and Tools

Design Methodologies and Tools Dsign Mthodologis and Tools Dsign styls Full-custom dsign Standard-cll dsign Programmabl logic Gat arrays and fild-programmabl gat arrays (FPGAs) Sa of gats Systm-on-a-chip (mbddd cors) Dsign tools 1 Full-Custom

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining Lctur #15: Clustring-2 Soul National Univrsity 1 In Tis Lctur Larn t motivation and advantag of BFR, an xtnsion of K-mans to vry larg data Larn t motivation and advantag of

More information

RFC Java Class Library (BC-FES-AIT)

RFC Java Class Library (BC-FES-AIT) RFC Java Class Library (BC-FES-AIT) HELP.BCFESDEG Rlas 4.6C SAP AG Copyright Copyright 2001 SAP AG. All Rcht vorbhaltn. Witrgab und Vrvilfältigung disr Publikation odr von Tiln daraus sind, zu wlchm Zwck

More information

NASPI Work Group meeting April 24-26, 2018 Albuquerque, NM

NASPI Work Group meeting April 24-26, 2018 Albuquerque, NM NASPI Work Group mting April 24-26, 2018 Albuqurqu, NM Pavl Kovalnko Viktor Litvinov from Data to Action Prmium Information Srvics from Data to Action Dsign, Dvlop and Dploy - digital transformation solutions

More information

Preview. Digital Image Processing Unit 7: Image Restoration. Unit Outline. Preview (cont.)

Preview. Digital Image Processing Unit 7: Image Restoration. Unit Outline. Preview (cont.) --6 Digital Procssing Unit 7: Rstoration Prviw Goal of imag rstoration Improv an imag in som prdfind sns Diffrnc with imag nhancmnt? Faturs rstoration v.s imag nhancmnt Objctiv procss v.s. subjctiv procss

More information

A Vision-based Navigation System of Mobile Tracking Robot

A Vision-based Navigation System of Mobile Tracking Robot A Vision-basd Navigation Systm of Mobil Tracking Robot Ji Wu Vac1av Snasl Dpt. Computr Scinc FCS VSB - Tchnical Univrsity of Ostrava Ostrava Czch Rpublic dfrmat2008 @hotmail.com vaclav. snasl @vsb.cz Ajith

More information

Misbehavior in Nash Bargaining Solution Allocation

Misbehavior in Nash Bargaining Solution Allocation Misbhavior in Nash Bargaining Solution Allocation Ilya Nikolavskiy, Andry Lukyannko, Andri Gurtov Aalto Univrsity, Finland, firstnam.lastnam@aalto.fi Hlsinki Institut for Information Tchnology, Finland,

More information

Lightweight Polymorphic Effects

Lightweight Polymorphic Effects Lightwight Polymorphic Effcts Lukas Rytz, Martin Odrsky, and Philipp Hallr EPFL, Switzrland, {first.last}@pfl.ch Abstract. Typ-and-ffct systms ar a wll-studid approach for rasoning about th computational

More information

Workbook for Designing Distributed Control Applications using Rockwell Automation s HOLOBLOC Prototyping Software John Fischer and Thomas O.

Workbook for Designing Distributed Control Applications using Rockwell Automation s HOLOBLOC Prototyping Software John Fischer and Thomas O. Workbook for Dsigning Distributd Control Applications using Rockwll Automation s HOLOBLOC Prototyping Softwar John Fischr and Thomas O. Bouchr Working Papr No. 05-017 Introduction A nw paradigm for crating

More information

Building a Scanner, Part I

Building a Scanner, Part I COMP 506 Ric Univrsity Spring 2018 Building a Scannr, Part I sourc cod IR Front End Optimizr Back End IR targt cod Copyright 2018, Kith D. Coopr & Linda Torczon, all rights rsrvd. Studnts nrolld in Comp

More information

PROSPECTS IN APPLICATION OF THE TECHNOLOGY FOR OF RECEPTION AND TRANSMISSION OF STREAMING DATA IN WEB BROWSER

PROSPECTS IN APPLICATION OF THE TECHNOLOGY FOR OF RECEPTION AND TRANSMISSION OF STREAMING DATA IN WEB BROWSER PROSPECTS IN APPLICATION OF THE TECHNOLOGY FOR OF RECEPTION AND TRANSMISSION OF STREAMING DATA IN WEB BROWSER ANTON PAVLOVICH TEYKHRIB Company NAUMEN (Nau-), Tatishchva Strt, 49a, 4th Floor, Ekatrinburg,

More information

Dual-mode Operation of the Finger-type Manipulator Based on Distributed Actuation Mechanism

Dual-mode Operation of the Finger-type Manipulator Based on Distributed Actuation Mechanism 11 th World Congrss on Structural and Multidisciplinary Optimisation 07 th -1 th, Jun 015, Sydny Australia Dual-mod Opration of th Fingr-typ Manipulator Basd on Distributd Actuation Mchanism Jong Ho Kim

More information

High-Frequency RFID Tags: An Analytical and Numerical Approach for Determining the Induced Currents and Scattered Fields

High-Frequency RFID Tags: An Analytical and Numerical Approach for Determining the Induced Currents and Scattered Fields High-Frquncy RFID Tags: An Analytical and Numrical Approach for Dtrmining th Inducd Currnts and Scattrd Filds Bnjamin D. Braatn Elctrical and Computr Enginring North Dakota Stat Univrsity Fargo, North

More information

D11.2 Service concepts, models and method: Model Driven Service Engineering M12 issue

D11.2 Service concepts, models and method: Model Driven Service Engineering M12 issue D11.2 Srvic concpts, modls and mthod: Modl Drivn Srvic Enginring M12 issu Documnt Ownr: Y. Ducq (UB1), G. Doumingts (I-VLab), C. Liu (I-VLab), D. Chn (UB1), T. Alix (UB1), G. Zacharwicz (UB1) Contributors:

More information