Building a Scanner, Part I
|
|
- Angelina Elliott
- 5 years ago
- Views:
Transcription
1 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 506 at Ric Univrsity hav xplicit prmission to mak copis of ths matrials for thir prsonal us. Faculty from othr ducational institutions may us ths matrials for nonprofit ducational purposs, providd this copyright notic is prsrvd Chaptr 2 in EaC3
2 Th Compilr s Front End sourc cod IR Front End Optimizr Back End IR targt cod stram of charactrs stram of tokns Scannr microsyntax Parsr syntax Smantic Elaboration IR annotations Front End Scannr and Parsr collaborat to chck th syntax of th input program. Scannr maps stram of charactrs into words (words ar th fundamntal unit of syntax) Scannr producs a stram of tokns for th parsr Tokn is <part of spch, word> Part of spch is a unit in th grammar Parsr maps stram of words into a sntnc in a grammatical modl of th input languag COMP 506, Spring
3 Th Compilr s Front End sourc cod IR Front End Optimizr Back End IR targt cod stram of charactrs stram of tokns Scannr microsyntax Parsr syntax Smantic Elaboration IR annotations Front End Scannr looks at vry charactr Convrts stram of charactrs to stram of tokns, or classifid words: <part of spch, word> Efficincy & scalability mattr Scannr is only part of compilr that looks at vry charactr Parsr looks at vry tokn Dtrmins if th stram of tokns forms a sntnc in th sourc languag Fits tokns to som syntactic modl, or grammar, for th sourc languag COMP 506, Spring
4 Th Compilr s Front End sourc cod IR Front End Optimizr Back End IR targt cod stram of charactrs stram of tokns Scannr microsyntax Parsr syntax Smantic Elaboration IR annotations Front End Why sparat scanning & parsing? Primary rational is fficincy Scannr idntifis & classifis words by splling (microsyntax) Parsr constructs drivations in a grammar (syntax) Parsing is hardr than scanning Modrn viw (lss widly hld) Scannr-lss parsrs ar gaining popularity, bcaus thy liminat on mor st of tools Mayb w can afford th ovrhad A littl mor involvd (SGLR parsrs) COMP 506, Spring
5 Implmntation Stratgis sourc cod IR Front End Optimizr Back End IR targt cod stram of charactrs stram of tokns Scannr microsyntax Parsr syntax Smantic Elaboration IR annotations Front End Rlvant Qustion: How can w automat th construction of scannrs & parsrs? Scannr Spcify syntax with rgular xprssions (REs) Construct finit automaton & scannr from th RE Parsr Spcify syntax with contxt-fr grammars (CFGs) Construct push-down automaton & parsr from th CFG COMP 506, Spring
6 Why Can t W Just Us A Rgx Library Rgular xprssions (calld REs, or rgxs, or rgx pattrns) ar ssntially a tiny, highly spcializd programming languag mbddd insid Python and mad availabl through th RE modul. Rgular xprssion pattrns ar compild into a sris of bytcods which ar thn xcutd by a matching ngin writtn in C. For advancd us, it may b ncssary to pay carful attntion to how th ngin will xcut a givn RE, and writ th RE in a crtain way in ordr to produc bytcod that runs fastr. Optimization isn t covrd in this documnt, bcaus it rquirs that you hav a good undrstanding of th matching ngin s intrnals. Th rgular xprssion languag is rlativly small and rstrictd, so not all possibl string procssing tasks can b don using rgular xprssions. Thr ar also tasks that can b don with rgular xprssions, but th xprssions turn out to b vry complicatd. In ths cass, you may b bttr off writing Python cod to do th procssing; whil Python cod will b slowr than an laborat rgular xprssion, it will also probably b mor undrstandabl. From Python documntation, mphasis addd Scannr gnrators us th sam principls as rgx libraris Automaton costs O(1) pr rcognizd charactr, indpndnt of th xprssion Scannr gnrators hav a clan intrfac to gnratd parsrs (.g., flx bison) Rad 2.4 in EaC2 if you want th dtails W will skim th surfac lightly in lctur COMP 506, Spring
7 Why Can t W Just Us A Rgx Library Rgular xprssions (calld REs, or rgxs, or rgx pattrns) ar ssntially a tiny, highly spcializd programming languag mbddd insid Python and mad availabl through th r modul. Rgular xprssion pattrns ar compild into a sris of bytcods which ar thn xcutd by a matching ngin writtn in C. For advancd us, it may b ncssary to pay carful attntion to how th ngin will xcut a givn RE, and writ th RE in a crtain way in ordr to produc bytcod that runs fastr. Optimization isn t covrd in this documnt, bcaus it rquirs that you hav a good undrstanding of th matching ngin s intrnals. Th rgular xprssion languag is rlativly small and rstrictd, so not all possibl string procssing tasks can b don using rgular xprssions. Thr ar also tasks that can b don with rgular xprssions, but th xprssions turn out to b vry complicatd. In ths cass, you may b bttr off writing Python cod to do th procssing; whil Python cod will b slowr than an laborat rgular xprssion, it will also probably b mor undrstandabl. From Python documntation, mphasis addd W will com back to this point nxt lctur Scannr gnrators us th sam principls as rgx libraris Automaton costs O(1) pr rcognizd charactr, indpndnt of th xprssion Scannr gnrators hav a clan intrfac to gnratd parsrs (.g., flx bison) Rad 2.4 in EaC2 if you want th dtails W will skim th surfac lightly in lctur COMP 506, Spring
8 Exampl Suppos that w nd to rcogniz th kyword not : How would w look for it? All of your training, to dat, tlls you to call som fancy routin, such as fscanf() in C, th Scannr class in Java, or a rgx library (Python, Java, C++) COMP 506 is a class about implmnting languags, not just about using thm, so w will look a littl dpr In a compilr, w would build a rcognizr How about som cod? Th splling is n followd by o followd by t Th cod, shown to th right, is as simpl as th dscription Cost is O(1) pr charactr Structur allows for prcis rror mssags c nxt charactr If c = n thn { c nxt charactr if c = o thn { c nxt charactr if c = t thn rturn <NOT, not > ls rport rror } ls rport rror } ls rport rror COMP 506, Spring
9 Automata W can rprsnt this cod as an automaton c nxt charactr If c = n thn { c nxt charactr if c = o thn { c nxt charactr if c = t thn rturn <NOT, not > ls rport rror } ls rport rror } ls rport rror s 0 s n Any charactr s 1 Transition Diagram for not o O(1) pr input charactr, if branchs ar O(1) s 2 t s 3 Excution bgins in th start stat, s 0 On an input of n, it follows th dg labld n, and so on, Transition to an rror stat, s, on any unxpctd charactr Halts whn out of input Stats drawn with doubl lins indicat succss (a final stat) Cost is O(1) pr charactr COMP 506, Spring 2018 S 2.2 in EaC2 9
10 Automata To rcogniz a largr st of kywords is (rlativly) asy Say, for xampl, th st of words { if, int, not, thn, to, writ } s 2 if Any charactr s 0 i n s 1 s 5 f n o s 3 s 6 t t s 4 s 7 int not s ERROR Transitions to s ar implicit from vry stat t w s 8 h o s 9 s 10 s 11 s 12 to n thn on-to-on map from final stats to words (mayb parts of spch) s 13 r s 14 i s 15 t s 16 s 17 writ Cost COMP is 506, still Spring O(1) pr 2018charactr 10
11 Spcifying An Automaton Spcifying a collction of words is simpl W list th words Each word s splling is uniqu and concis W can sparat thm with th symbol, rad as or Th spcification if int not thn to writ is a simpl rgular xprssion for th languag accptd by our automaton Evry rgular xprssion corrsponds to an automaton W can construct, automatically, an automaton from th RE W can construct, automatically, an RE from any automaton W can convrt an automaton asily and dirctly into cod Th automaton and th cod both hav O(1) cost pr input charactr So, an RE spcification lads dirctly to an fficint scannr Thr is som subtlty hr. W introducd notation for concatnation and choic (or altrnation). Concatnation: ab is a followd by b Altrnation: a b is ithr a or b COMP 506, Spring
12 Unsignd Intgrs Notic th cyclic transition dgs in th automata In th xampl, ach word had on splling. What about a singl part of spch with many spllings? Considr spcifying an unsignd intgr An unsignd intgr is any string of digits from th st [0...9] W might want to spcify that no lading zros ar allowd Is 0001 allowd? Or 00? Th Automata 0 9 s 0 s 2 With Lading Zros 0 9 unsignd intgr 0 s 0 s s 2 Without Lading Zros unsignd intgr unsignd intgr How do w writ th corrsponding RE? Any charactr s ERROR COMP 0 9 mans 506, Spring any charactr 2018 from 0 to 9, inclusiv; somtims w will writ it as
13 Unsignd Intgrs Th Automata 0 9 s 0 s 2 With Lading Zros 0 9 unsignd intgr 0 s 0 s 1 Th Rgular Exprssions W nd a notation to rprsnt that cyclic dg in th automaton x Th Kln Closur x * rprsnts zro or mor instancs of x Th Positiv Closur x + rprsnts on or mor instancs of x [0-9] + for lading zros; 0 [1-9] [0-9] * for without lading zros Any charactr ERROR COMP 506, Spring s 2 Without Lading Zros unsignd intgr unsignd intgr s 2 x s 0 s 2 x s
14 Unsignd Intgrs Again, th RE corrsponds dirctly to an automaton and an implmntation 0 s 0 s 1 s 2 unsignd intgr unsignd intgr c nxt charactr n 0 if c = 0 thn rturn <CONSTANT,n> ls if ( 1 c 9 ) thn { n atoi(c) c nxt charactr whil ( 0 c 9 ) { t atoi(c) n n * 10 + t } rturn <CONSTANT,n> } ls rport rror Th automaton and th cod can b gnratd automatically Th dtails of th constructions ar givn in 2.4 of EaC2 or th 412 nots COMP Cost is 506, still Spring O(1) 2018 pr charactr 14
15 Rgular Exprssions W nd a mor formal dfinition for a rgular xprssion Rgular Exprssions ovr an Alphabt Σ If x Σ, thn x is an RE dnoting th st { x } or th languag L = { x } If x and y ar REs thn xy is an RE dnoting L(x)L(y) = { pq p L(x) and q L(y) } x y is an RE dnoting L(x) L(y) x * is an RE dnoting L(x) * = 0 k < L(x) k St of all strings that ar zro or mor concatnations of x x + is an RE dnoting L(x) + = 1 k < L(x) k St of all strings that ar on or mor concatnations of x ε is an RE dnoting th mpty st (Kln Closur) (Positiv Closur) Many RE-basd systms support mor complx oprators. Thos oprators ar built on top of altrnation, concatnation, and closur plus, prhaps logical complmnt or ngation. Complmnt is asy and fficint; rvrs th final and non-final stats. COMP 506, Spring
16 Rgular Exprssions How do ths oprators hlp? Rgular Exprssions ovr an Alphabt Σ If x is in Σ, thn x is an RE dnoting th st { x } or th languag L = { x } Th splling of any lttr in th alphabt is an RE If x and y ar REs thn xy is an RE dnoting L(x)L(y) = { pq p L(x) and q L(y) } If w concatnat lttrs, th rsult is an RE (splling of words) x y is an RE dnoting L(x) L(y) Any finit list of words can b writtn as an RE, ( w 0 w 1 w 2 w n ) x * is an RE dnoting L(x) * = 0 k < L(x) k x + is an RE dnoting L(x) + = 1 k < L(x) k W can us closur to writ finit dscriptions of infinit, but countabl, sts ε is an RE dnoting th mpty st In practic, th mpty string is oftn usful COMP 506, Spring
17 Rgular Exprssions Lt th notation [0-9] b shorthand for ( ) Exampls Positiv intgr [0-9] [0-9] * or [0-9] + No lading zros 0 [1-9] [0-9] * Algol-styl Idntifir ([a-z] [A-Z]) ([a-z] [A-Z] [0-9]) * Dcimal numbr 0 [1-9] [0-9] *. [0-9] * Ral numbr ((0 [1-9] [0-9] * ) (0 [1-9] [0-9] *. [0-9] * ) E [0-9] [0-9] * Each of ths REs corrsponds to an automaton and an implmntation. COMP 506, Spring
18 From RE to Scannr W can us rsults from automata thory to construct scannrs dirctly from REs Thr ar svral ways to prform this construction Classic approach is a two-stp mthod. 1. Build automata for ach pic of th RE using a simpl tmplat-drivn mthod Build a spcific variation on an automaton that has transitions on ε and nondtrministic choic (multipl transitions from a stat on th sam symbol) This construction is calld Thompson s construction 2. Convrt th nwly built automaton into a dtrministic automaton Dtrministic automaton has no ε-transitions and all choics ar singl-valud This construction is calld th subst construction Givn th dtrministic automaton, w can run a minimization algorithm on it to rduc th numbr of stats An NFA A DFA Minimization is a spac optimization. Both th original automaton and th minimal on tak O(1) tim pr charactr COMP 506, Spring ?
19 Kn Thompson, CACM, 1968 Thompson s Construction (in 2 slids) (s 2.4.2) Th Ky Ida For ach RE symbols and oprator, w hav a small tmplat Build thm, in prcdnc ordr, and join thm with ε-transitions a S 0 S 1 a S 0 S 1 b S 3 S 4 NFA for a NFA for ab S 0 a S 1 S 2 b S 3 S 4 S 5 S 0 S 1 a S 3 S 4 NFA for a b NFA for a * COMP Prcdnc 506, Spring REs: 2018Parnthss, thn closur, thn concatnation, thn altrnation 19
20 Thompson s Construction (in 2 slids) Lt s build an NFA for a ( b c ) * 1. a, b, & c 2. b c a S 0 S 1 c S 0 S 1 b S 0 S 1 b S 1 S 2 S 0 S 5 c S 3 S 4 3. ( b c ) * b S 2 S 3 S 0 S 1 S 6 S 7 c S 4 S 5 4. a ( b c ) * a S 0 S 1 b S 4 S 5 S 2 S 3 S 8 S 9 S 6 c S 7 COMP Prcdnc 506, Spring REs: 2018Parnthss, thn closur, thn concatnation, thn altrnation 20
21 Subst Construction (s 2.4.3) Th Concpt Build a simplr automaton (no ε-transitions, no multi-valud transitions) that simulats th bhavior of th mor complx automaton Each stat in th nw automaton rprsnts a st of stats in th original NFA a n 0 n 1 a ( b c )* b n 4 n 5 n n 3 n 2 8 n 9 c n 6 n 7 b DFA NFA DFA b a d 0 d 1 c b d 2 c d 0 n 0 d 1 n 1 n 2 n 3 n 4 n 6 n 9 d 2 n 5 n 8 n 9 n 3 n 4 n 6 d 3 d 3 n 7 n 8 n 9 n 3 n 4 n 6 c COMP 506, Spring 2018 Mapping btwn NFA and DFA stats 21
22 Minimization (s & 2.6.3) DFA Minimization algorithms work by discovring stats that ar quivalnt in thir contxts and rplacing multipl quivalnt stats with a singl on Minimization rducs th numbr of stats, but dos not chang th costs b b a d 0 d 1 c b d 2 c minimization a s 0 s 1 b c d 3 c Minimal DFA Stat s 0 s 1 Original DFA Stats d 0 d 1, d 2, d 3 COMP 506, Spring
23 Implmnting an Automaton (s 2.5) A common stratgy is to simulat th DFA s xcution Sklton parsr + a tabl that ncods th automaton stat s 0 char NxtChar( ) whil (char ¹ EOF) { stat d[stat,char] char NxtChar( ) } if (stat is a final stat) thn rport succss ls rport an rror δ a b c s 0 s 1 s s s 1 s s 1 s 1 Transition tabl for our minimal DFA Simpl Sklton Scannr Th scannr gnrator constructs th tabl Th sklton parsr dos not chang (much) COMP 506, Spring
24 Implmnting an Automaton (s 2.5) A common stratgy is to simulat th DFA s xcution Sklton parsr + a tabl that ncods th automaton stat s 0 char NxtChar( ) whil (char ¹ EOF) { stat d[stat,char] char NxtChar( ) } if (stat is a final stat) thn rport succss ls rport an rror Simpl Sklton Scannr Th scannr gnrator constructs th tabl Th sklton scannr dos not chang (much) Notic that th sklton scannr uss a whil loop and an array accss to rplac th if-thn-ls from our arlir xampls. This structur δ translats a b into an c nd-ofloop jump s 0 and sa 1 branch s for th s tst an asily prdictabl branch. (1 cycl vs. 4 or 5?) s 1 s s 1 s 1 Th branchs in th if-thn-ls ar hard to prdict, making that cod much slowr than th Transition sklton tabl scannr. for our minimal DFA Studnts ar oftn surprisd to discovr how much tim scanning and parsing taks. COMP 506, Spring
25 Automatic Scannr Construction sourc cod Sklton Scannr Tabls <part of spch, word> pairs Knowldg ncodd in tabls to driv sklton spcifications (as REs) Scannr Gnrator Scannr Gnrator Taks in a spcification writtn as a collction of rgular xprssions Combins thm into on RE using altrnation ( ) Builds th minimal automaton ( 2.4, 2.6.2) Emits th tabls to driv a sklton scannr ( 2.5) COMP 506, Spring
26 Automatic Scannr Construction sourc cod Scannr <part of spch, word> pairs Knowldg mbddd in gnratd program txt spcifications (as REs) Scannr Gnrator Scannr Gnrator As altrnativ, th gnrator can produc cod rathr than tabls Dirct-codd scannrs ar ugly, but oftn fastr than tabl-drivn scannrs Othr than spd, th two ar quivalnt Us th scannr gnrator availabl to you COMP 506, Spring
27 Automatic Gnration of Scannrs and Parsrs Thr tim frams At dsign tim, th compilr writr writs spcifications for th microsyntax (splling) and th syntax (grammar) At build tim, th tools convrt spcifications to cod and compil that cod to produc th actual compilr At compil tim, th usr invoks th compilr to translat an application into an xcutabl form spcifications as CFGs spcifications as REs stram of charactrs Parsr Gnrator Scannr Gnrator Scannr Parsr Smantic Elaboration Compilr-build tim Compil tim IR annotations Front End In COMP lab 1, 506, you Spring will us 2018 a scannr gnrator & a parsr gnrator 27
28 Automatic Scannr Construction sourc cod 2. Whn th compilr runs, it uss th gnratd scannr to convrt sourc cod into a stram of tokns. 1. W writ REs and gnrat a scannr whn w build th compilr spcifications writtn as rgular xprssions Scannr Scannr Gnrator stram of <part of spch,word> pairs compil tim scannr gnration tim Scannr and parsr gnrators oprat at compilr-build tim Th compilr writr crats a input fil that dscribs th microsyntax (splling) of th words in th sourc languag * Whn th compilr is built, th scannr gnrator crats th scannr Builds an automaton, convrts it to a tabl or cod * Whn th compilr runs, it invoks th gnratd scannr to tokniz input Scannr rturns a stram of <part of spch,word> pairs to th parsr COMP 506, Spring
29 Automatic Scannr Gnration Th Point (for Scannrs) Th tchnology lts us writ a st of REs and gnrat a good scannr Scannr gnrator builds th NFA, th DFA, th minimal DFA, and thn writs out a tabl-drivn or dirct codd scannr Th tools rliably produc fast, robust scannrs For most modrn languag faturs, this works and works wll You should think twic bfor introducing a languag fatur that dfats a DFA-basd scannr W hav sn som ovr tim; thy hav not bn particularly usful Insignificant blanks in FORTRAN, non-rsrvd kywords in PL/I Of cours, not vrything fits into th rgular languag framwork which is why w nd parsrs COMP 506, Spring
30 Altrnativ (arly) nding COMP 506, Spring
31 Summary Automating Scannr Construction 1. Writ down th REs for th input languag and connct thm with 2. Build a big, simpl NFA 3. Build th DFA that simulats th NFA 4. Minimiz th numbr of stats in th DFA 5. Gnrat an implmntation from th minimal DFA Scannr Gnrators lx, flx, jflx, and such all work along ths lins Algorithms ar wll-known and wll-undrstood Ky issus ar: finding longst match rathr than first match, and nginring th intrfac to th parsr You could build a scannr gnrator in a wknd COMP Find th 506, longst Spring 2018 match is whr O(1) pr charactr can turn into O(n). Mor nxt lctur. 31
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 informationMidterm 2 - Solutions 1
COS 26 Gnral Computr Scinc Spring 999 Midtrm 2 - Solutions. Writ a C function int count(char s[ ]) that taks as input a \ trminatd string and outputs th numbr of charactrs in th string (not including th
More informationSummary: Semantic Analysis
Summary: Smantic Analysis Chck rrors not dtctd by lxical or syntax analysis Intrmdiat Cod Scop rrors: Variabls not dfind Multipl dclarations Typ rrors: Assignmnt of valus of diffrnt typs Invocation of
More informationShift. Reduce. Review: Shift-Reduce Parsing. Bottom-up parsing uses two actions: Bottom-Up Parsing II. ABC xyz ABCx yz. Lecture 8.
Rviw: Shift-Rduc Parsing Bottom-up parsing uss two actions: Bottom-Up Parsing II Lctur 8 Shift ABC xyz ABCx yz Rduc Cbxy ijk CbA ijk Prof. Aikn CS 13 Lctur 8 1 Prof. Aikn CS 13 Lctur 8 2 Rcall: h Stack
More informationSystems 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 informationObjectives. 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 informationTo 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 informationTo 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 information8.3 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 informationMotivation. 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 informationProblem 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 informationTo 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 informationThe 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 informationRecorder 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 informationLesson 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 informationSpectral 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" 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 information2018 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 informationType & 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 informationXML 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 informationPresentation 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 informationWorkbook 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 informationDesign 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 informationThe 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 informationThe 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 informationCPSC 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 informationSPECIFIC CRITERIA FOR THE GENERAL MOTORS GLOBAL TRADING PARTNER LABEL TEMPLATE:
SPCIFIC CRITRIA FOR TH GNRAL MOTORS GLOBAL TRADING PARTNR LABL TMPLAT: TH TMPLAT IDNTIFIS HOW AND WHR DATA IS TO B PLACD ON TH LABL WHN IT IS RQUIRD AS PART OF A GM BUSINSS RQUIRMNT FONT SIZS AR SPCIFID
More information1. 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 informationA 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 informationRegister 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 informationReimbursement Requests in WORKS
Rimbursmnt Rqusts in WORKS Important points about Rimbursmnts in Works Rimbursmnt Rqust is th procss by which UD mploys will b rimbursd for businss xpnss paid using prsonal funds. Rimbursmnt Rqust can
More informationTCP 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 informationAbout 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 informationLecture 3: CUDA Programming & Compiler Front End
CS 515 Programming Language and Compilers I Lecture 3: CUDA Programming & Compiler Front End Zheng (Eddy) Zhang Rutgers University Fall 2017, 9/19/2017 1 Lecture 3A: GPU Programming 2 3 Review: Performance
More informationTerrain 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 informationSPECIFIC CRITERIA FOR THE GENERAL MOTORS GLOBAL TRADING PARTNER LABEL TEMPLATE:
SPCIFIC CRITRIA FOR TH GNRAL MOTORS GLOBAL TRADING PARTNR LABL TMPLAT: TH TMPLAT IDNTIFIS HOW AND WHR DATA IS TO B PLACD ON TH LABL WHN IT IS RQUIRD AS PART OF A GM BUSINSS RQUIRMNT FONT SIZS AR SPCIFID
More informationGreedy Algorithms. Interval Scheduling. Greedy Algorithm. Optimality. Greedy Algorithm (cntd) Greed is good. Greed is right. Greed works.
Algorithm Grdy Algorithm 5- Grdy Algorithm Grd i good. Grd i right. Grd work. Wall Strt Data Structur and Algorithm Andri Bulatov Algorithm Grdy Algorithm 5- Algorithm Grdy Algorithm 5- Intrval Schduling
More informationRFC 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 informationPolygonal Models. Overview. Simple Data Structures. David Carr Fundamentals of Computer Graphics Spring 2004 Based on Slides by E.
INSTITUTIONEN FÖR SYSTEMTEKNIK LULEÅ TEKNISKA UNIVERSITET Polygonal Modls David Carr Fundamntals of Computr Graphics Spring 200 Basd on Slids by E. Angl Fb-3-0 SMD159, Polygonal Modls 1 L Ovrviw Simpl
More informationI - 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 informationDO 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 informationInterfacing the DP8420A 21A 22A to the AN-538
Intrfacing th DP8420A 21A 22A to th 68000 008 010 INTRODUCTION This application not xplains intrfacing th DP8420A 21A 22A DRAM controllr to th 68000 Thr diffrnt dsigns ar shown and xplaind It is assumd
More informationNon 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 informationAN 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 informationCSE 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: Mesh Processing. Chapter 6
600.657: Msh Procssing Chaptr 6 Quad-Dominant Rmshing Goal: Gnrat a rmshing of th surfac that consists mostly of quads whos dgs align with th principal curvatur dirctions. [Marinov t al. 04] [Alliz t al.
More informationHEAD 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 informationi 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 informationCS364B: Frontiers in Mechanism Design Lecture #10: Coverage Valuations and Convex Rounding
CS364B: Frontirs in Mchanism Dsign Lctur #10: Covrag Valuations and Convx Rounding Tim Roughgardn Fbruary 5, 2014 1 Covrag Valuations Rcall th stting of submodular biddr valuations, introducd in Lctur
More informationOn Some Maximum Area Problems I
On Som Maximum Ara Problms I 1. Introdution Whn th lngths of th thr sids of a triangl ar givn as I 1, I and I 3, thn its ara A is uniquly dtrmind, and A=s(s-I 1 )(s-i )(s-i 3 ), whr sis th smi-primtr t{i
More informationPacing 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 informationExtending 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 informationVignette 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 informationReliability Coordinator Base Schedule Aggregation Portal (RC BSAP) Interface Specification for RC BSAP Services
Rliability Coordinator Bas Schdul Aggrgation Portal (RC BSAP) Intrfac Spcification for RC BSAP Srvics (Businss Ruls v 10.x(Spring 2019) or latr) Vrsion: 1.1 vmbr 6, 2018 Rvision History Dat Vrsion By Dscription
More informationFLASHING CHRISTMAS TREE KIT
R4 FLASHING CHRISTMAS TREE KIT 9 10 8 7 11 6 R3 12 T4 C4 5 T3 R5 R7 13 C3 C2 4 14 R1 T2 R6 3 OWNER S MANUAL T1 R8 15 2 C1 R2 1 16 Cat. No. 277-8001 CUSTOM MANUFACTURED FOR TANDY CORPORATION LTD ASSEMBLY
More informationOntology and Context. Isabel Cafezeiro Departamento de Ciência da Computação Universidade Federal Fluminense Niterói - RJ, Brazil
Ontology and Contxt Isabl Cafziro Dpartamnto d Ciência da Computação Univrsidad Fdral Fluminns Nitrói - RJ, Brazil isabl@dcc.ic.uff.br dward Hrmann Hauslr, Alxandr Radmakr Dpartamnto d Informática Pontifícia
More informationLAB1: 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 informationMaxwell 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 informationCS415 Compilers. Lexical Analysis
CS415 Compilers Lexical Analysis These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University Lecture 7 1 Announcements First project and second homework
More informationBetween Testing and Formal Verification
Btwn Tsting and Formal Vrification Jan Tobias Mühlbrg jantobias.muhlbrg@cs.kuluvn.b imc-distrint, KU Luvn, Clstijnnlaan 200A, B-3001 Blgium ScAppDv, Luvn, March 2017 1 /19 Jan Tobias Mühlbrg Btwn Tsting
More informationModelling CoCoME with DisCComp
Modlling CoCoME with DiCComp Dagtuhl Workhop, Sbatian Hrold Clauthal Univrity of Tchnology Dpartmnt of Informatic Softwar Sytm Enginring Chair of Prof. Dr. Andra Rauch Ovrviw Introduction Introduction
More informationThis module calculates the motor speed based on a rotor position measurement when the direction information is available.
SPEED_FRQ Spd Calulator Basd on Rotor Angl With Dirtion Information Dsription This modul alulats th motor spd basd on a rotor position masurmnt whn th dirtion information is availabl. thta_l dir_qep SPEED_FRQ
More informationGernot 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 informationInstallation 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 informationWhitepaper: 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 informationProbabilistic inference
robabilistic infrnc Suppos th agnt has to mak a dcision about th valu of an unobsrvd qury variabl X givn som obsrvd vidnc E = artially obsrvabl, stochastic, pisodic nvironmnt Eampls: X = {spam, not spam},
More informationLexical Analysis. Note by Baris Aktemur: Our slides are adapted from Cooper and Torczon s slides that they prepared for COMP 412 at Rice.
Lexical Analysis Note by Baris Aktemur: Our slides are adapted from Cooper and Torczon s slides that they prepared for COMP 412 at Rice. Copyright 2010, Keith D. Cooper & Linda Torczon, all rights reserved.
More informationEE 231 Fall EE 231 Homework 10 Due November 5, 2010
EE 23 Fall 2 EE 23 Homwork Du Novmbr 5, 2. Dsign a synhronous squntial iruit whih gnrats th following squn. (Th squn should rpat itslf.) (a) Draw a stat transition diagram for th iruit. This is a systm
More informationte 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 informationAn Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU
An Auto-tund Mthod for Solving Larg Tridiagonal Systms on th GPU Andrw Davidson Univrsity of California, Davis aaldavidson@ucdavis.du Yao Zhang Univrsity of California, Davis yaozhang@ucdavis.du John D.
More informationKENDRIYA 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 informationIntersection-free Dual Contouring on Uniform Grids: An Approach Based on Convex/Concave Analysis
Intrsction-fr Dual Contouring on Uniform Grids: An Approach Basd on Convx/Concav Analysis Charli C. L. Wang Dpartmnt of Mchanical and Automation Enginring, Th Chins Univrsity of Hong Kong E-mail: cwang@ma.cuhk.du.hk
More informationComment (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 informationWhat is state? Unit 5. State Machine Block Diagram. State Diagrams. State Machines. S0 Out=False. S2 out=true. S1 Out=False
5.1 What i tat? 5.2 Unit 5 Stat Machin You a DPS officr approaching you. Ar you happy? It' lat at night and. It' lat at night and you'v bn. Your intrprtation i bad on mor than jut what your n ar tlling
More informationFrom 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 informationUtilization 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 informationCISC 360 Fa2009. Overview. Computational Example. Real-World Pipelines: Car Washes. Michela Taufer. Pipelined
CISC 360 a2009 Ovrviw ichla Taufr Powrpoint Lctur Nots for Computr Systms: Prorammr's Prspctiv,. ryant and. O'Hallaron, Prntic Hall, CS:PP 2003 2 CISC 360 aʼ08 al-orld Piplins: Car ashs Computational xampl
More informationGSM on the Net. Background. System concept. Olle Granberg
GSM on th Nt Oll Granbrg GSM on th Nt introducs an ntirly nw concpt for businss communications, offring voic, data and multimdia srvics ovr corporat intrants. Th voic srvic can b ithr fixd or mobil (in
More informationAn 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 informationUsage 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 informationMultihop MIMO Relay Networks with ARQ
Multihop MIMO Rlay Ntworks with ARQ Yao Xi Dniz Gündüz Andra Goldsmith Dpartmnt of Elctrical Enginring Stanford Univrsity Stanford CA Dpartmnt of Elctrical Enginring Princton Univrsity Princton NJ Email:
More informationNimsoft Monitor. ldap_response Guide. v1.3 series
Nimsoft Monitor ldap_rspons Guid v1.3 sris Lgal Notics Copyright 2012, Nimsoft Corporation Warranty Th matrial containd in this documnt is providd "as is," and is subjct to bing changd, without notic,
More informationFormal 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 information2 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 informationBase Schedule Aggregation Portal (BSAP) Interface Specification for BSAP Services
Bas Schdul Aggrgation Portal (BSAP) Intrfac Spcification for BSAP Srvics (Businss Ruls v 9.x(Fall 2017) or latr) Vrsion: 1.3 Dcmbr 19, 2017 Rvision History Dat Vrsion By Dscription 12/19/2017 1.3 WT Additional
More informationGroup 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 informationA 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 informationOutline. Tasks for Exercise Six. Exercise Six Goals. Task One: Kinetic Energy Table. Nested for Loops. Laboratory VI Program Control Using Loops
Ercis 6 -- Loopig March 9, 6 Laboratory VI Program Cotrol Usig Loops Larry Cartto Computr Scic 6 Computig i Egirig ad Scic Outli Ercis si goals Outli tasks for rcis si Itroduc ida of std loops ad tabl
More informationTillförlitlig 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 informationSection II. PCB Layout Guidelines
Sction II. PCB Layout Guidlins This sction provids information for board layout dsignrs to succssfully layout thir boards for MAX II dvics. It contains th rquird printd circuit board (PCB) layout guidlins,
More informationThe Front End. The purpose of the front end is to deal with the input language. Perform a membership test: code source language?
The Front End Source code Front End IR Back End Machine code Errors The purpose of the front end is to deal with the input language Perform a membership test: code source language? Is the program well-formed
More informationAnnouncements. q This week s schedule. q Next week. q Grading. n Wednesday holiday. n Thursday class from am
Announcmnts This wk s schdul n Wdnsday holiday n Thursday class from 9.00-0.30am Nxt wk n Monday and Tusday rgular class n Wdnsday Last uiz for th cours Grading n Quiz 5, 6 and Lab 6 ar du. Applications
More informationA Loadable Task Execution Recorder for Hierarchical Scheduling in Linux
A Loadabl Task Excution Rcordr for Hirarchical Schduling in Linux Mikal Åsbrg and Thomas Nolt MRTC/Mälardaln Univrsity PO Box 883, SE-721 23, Västrås, Swdn {mikalasbrg,thomasnolt@mdhs Shinpi Kato Carngi
More informationDynamic Light Trail Routing and Protection Issues in WDM Optical Networks
This full txt papr was pr rviwd at th dirction of IEEE Communications Socity subjct mattr xprts for publication in th IEEE GLOBECOM 2005 procdings. Dynamic Light Trail Routing and Protction Issus in WDM
More informationIN the early seventies, the IEEE recommended a Common
PAPER SUBMITTED TO 2009 IEEE PES GENERAL MEETING. 1 Opn Modl For Exchanging Powr Systm Data F. Milano, Mmbr, IEEE, M. Zhou, Mmbr, IEEE, and GuanJi Hou Abstract This papr prsnts an XML-basd opn data modl
More information[MS-OXRTFEX]: Rich Text Format (RTF) Extensions Algorithm. Intellectual Property Rights Notice for Open Specifications Documentation
[MS-OXRTFEX]: Intllctual Proprty Rights Notic for Opn Spcifications Documntation Tchnical Documntation. Microsoft publishs Opn Spcifications documntation ( this documntation ) for protocols, fil formats,
More informationDescriptors 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 informationAndroid Graphic System Acceleration Based on DirectFB
Rsarch Journal of Applid Scincs, Enginring and Tchnology 5(18): 4438-4443, 2013 ISSN: 2040-7459; -ISSN: 2040-7467 Maxwll Scintific Organization, 2013 Submittd: July 27, 2012 Accptd: Sptmbr 17, 2012 Publishd:
More informationChapter 6: Synthesis of Combinatorial and. Sequential Logic. What is synthesis?
Chaptr 6: Synthsis of Combinatorial and What is synthsis? Squntial Logic Synthsis is th mapping of your cod to a mixtur of logic gats and rgistrs. Th availabl logic gats and rgistrs ar dtrmind from th
More informationAV1640 ANAG VISION. 16x4 Character
AV1640 16x4 Character 5x7 dots with cursor 1/16 duty +5V single supply Controller built in (KS0066 or quivalent) B/L driven by pin1 and 2, 15 and 16 or A,K Pin Assignment No. Symbol Function 1 Vss Gnd,
More information