Computer System Structures cz:struktury počítačových systémů

Size: px
Start display at page:

Download "Computer System Structures cz:struktury počítačových systémů"

Transcription

1 4..25 omputer System Structures cz:struktury počítčových systémů Version:. Lecturer: Richrd Šust ČVUT-FEL in Prgue, R suject 35SPS Mini-question: Minimize KM of prime detector F(w,x,y,z) = S(,2,3,5,7,,3) Krok - ssigning numers of input sttes Krok 2 cover KM '+.'.+..'+.'.' =.'+.( xor )+.'.' SPS 2

2 4..25 Mini-question: re functions equivlent? x<=( nd not ) or ( nd not ); x2<=( nd not ) xor ( nd ); x3<=( or ) nd (not or not ); x4<=( xor ) or ( nd not ); SPS 3 Mini-question: re functions equivlent? x<=( nd not ) or ( nd not ); x2<=( nd not ) xor ( nd ); x3<=( or ) nd (not or not ); x4<=( xor ) or ( nd not ); x, x3, x4 We solve y KM x2 xor of mps SPS 4 2

3 4..25 K-mps - Implicnts - w w y wx yz x z + xz + wx + yz z More solutions (coverges) cn exist F(w,x,y,z) = S(,2,3,5,7,8,9,,,3,5) x z + xz + wz + yz y wx yz z x x w y wx yz z wx yz m m m 3 m 2 m 4 m 5 m 7 m 6 m 2 m 3 m 5 m 4 m 8 m 9 m m SPS 6 x z + xz + wz + x y x w w z y wx yz x z + xz + wx + yz y z x x 3

4 4..25 Implicnt it covers ""s Prime implicnt lrger s possile Essentil prime implicnt it lone covers "" Two-level K-mp simplifiction Method: Grow implicnts into prime implicnts over ll "" (minimize numer of product terms) SPS 7 Exmple of Implicnts X 6 prime implicnts: '', ',, '',, ' essentil minimum cover: + ' + '' 5 prime implicnts:, ',, ', '' essentil minimum cover: 4 essentil implicnts SPS 8 4

5 4..25 lgorithm for two-level simplifiction K-mp lgorithm Step : choose "" Step 2: mximize covering implicnts to prime implicnts (numer of elements must e power of 2) Repet Steps nd 2 until finding ll prime implicnts Step 3: select essentil implicnts Step 4: cover remining "" y the smllest numer of prime implicnts SPS 9 X f(,,,) = m(4,5,6,8,9,,3) + d(,7,5) X X X X X X 2 primes round ''' X X 2 primes round ' X X X X X 3 primes round ''' X X 2 essentil primes X X minimum cover (3 primes) [Seungryoul M.: omintion Logic, KIST 22] SPS 5

6 4..25 Quine-Mcluskey Method Tulr method to systemticlly find ll prime implicnts f(,,,) = Sm(4,5,6,8,9,,3) + Sd(,7,5) Stge : Find ll prime implicnts Step : Fill olumn with ON-set nd -set minterm indices. Group y numer of 's. Step 2: pply Uniting Theorem E.g., vs. yields - vs. yields - When used in comintion, mrk with check. If cnnot e comined, mrk with str. These re the prime implicnts. Impliction Tle olumn I olumn II olumn III _* _* * _ _ * _* _* _ _ _ _* _ _ Repet until no further comintions cn e mde. Stge2: over ON-set [Seungryoul M.: omintion Logic, KIST 22] SPS Morse econ EE y Mcluskey : 5: 7: 8: 9: 2: 3: 4: 5: *.5:- *.9:- *5.7:- *5.3:- *7.5:- *8.9:- *8.2:- *9.3:- *2.3:- *2.4:- *3.5:- *4.5:- (.5).(9.3):-- (.9).(5.3):-- (5.7).(3.5):-- (5.3).(7.5):-- (8.9).(2.3):-- (8.2).(9.3):-- (2.3).(4.5):-- (2.4).(3.5):-- '+.+.' ='.(+)+. (.5).(9.3):-- (5.7).(3.5):-- (8.9).(2.3):-- SPS 2 6

7 4..25 Group Minimiztion - Groups shre dt - Gte Logic: Two-Level Simplifiction lock igrm Truth Tle N N 2 F = F 2 < F 3 > F F 2 F 3 SPS 4 7

8 c c c5 c6 c4 c2 c3 c c c2 c3 c4 c5 c6 to 7 segment control signl decoder Gte Logic:Two-Level Simplifiction = : F < : F 2 > : F 3 F = '.'.'.' + '..' '..' (isolted '' ) F2 = F3 = '.'. + '. + '...'.' + ' + ' F = F2'. F3' Notice: Equlity comprisons of inry numers require more complicted logic functions thn less or greter comprisons ecuse isolted cells complicte coverge. SPS 5 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Exmple: / 7-segment X X X X X X = ' ' = ' ' + + ' 2 = + ' + 3 = ' ' + ' + ' + ' 4 = ' ' + ' 5 = + ' ' + ' + ' 6 = + ' + ' + ' x x x x x x x x x x x x x x X X X X X X SPS 6 8

9 4..25 Group minimiztion = ' ' = ' + ' ' + 2 = + ' + 3 = ' ' + ' + ' + ' 4 = ' ' + ' 5 = + ' ' + ' + ' 6 = + ' + ' + ' totl: 5 minterms 9 minterms+ 6 shred = + ' + + ' ' + ' = ' + ' ' + + ' ' 2 = ' + ' + ' ' + + ' 3 = ' ' + '+ ' + ' 4 = ' ' + ' 5 = + ' ' + ' + ' 6 = + ' + ' + ' totl: 8 minterms 2 minterms (6)+6 shred 2 X 2 X X X X X X X X X X X SPS 7 oolen lger mthemtic foundtion of logic 9

10 4..25 Reminding of sic Lws. losure (cz: uzávěr množiny): set S is sid to e closed with respect to inry opertor if whenever y x = z S; for ll x, y S Ex: opertion + or * in S= Z ={ -2, -,,, 2...} 2. ssocitive lw: inry opertor on set S is sid to e ssocitive whenever (x y) z = x (y z) for ll x, y, z, S Ex: in Z ssoc. +, ut non-ssoc. -, (7-5)- 7-(5-) 3. ommuttive lw: x y = y x for ll x, y S 4. Identity element: set S is sid to hve n identity element if there exists n element e S; e x = x e = x for every x S Ex) The element with +, or with * on the set of integers. 5. Inverse element x y = e Ex) In Z, + (-) = 6. istriutive lw opertor is sid to e distriutive over opertor whenever x (y z) = (x y) (x z) SPS 9 George oole (85-864) orn to working clss prents in the English industril town of Lincoln, tught himself mthemtics. pproched logic in new wy reducing it to simple lger incorported logic into mthemtics; xiomtic foundtions of oolen lgeric systems ws performed y Huntington (94) In 93, "oolen lger" ws first suggested y Sheffer s nme for the system specified y Huntington postultes. ( Note: Sheffer invented Sheffer s stroke k NN opertion) The crter oole on the Moon SPS [Source: 2

11 4..25 oolen lger oolen lger n lgeric structure on set of elements with two inry opertions + nd. provided the following (Huntington) postultes re stisfied :. losure with respective to () the opertor + () the opertor. 2. n identity element with respect to () +, designted y : x + = + x = x. ()., designted y : x = x = x. 3. ommuttive with respect to () + : x + y = y + x () with respect to : x y = y x. 4. istriutive over () + : x (y+z) = (x y)+(x z) (). : x+(y.z) = (x+y).(x+z) 5. For every element x, there exists n element x (clled the complement of x) such tht () x + x = nd () x x =. 6. There exists t lest two elements x, y such tht x y. Note: Huntington postultes do not include the ssocitive lw ut it cn e derived SPS 2 Switching lger vs. Multiple Vlued oolen lger oolen lger is termed Switching lger when = {, } When > 2, the system is multiple vlued. Exmple: M = {(,, 2, 3), #, &} # & SPS 22

12 4..25 oolen Logic k Switching lger oolen logic is six-tuple (, +,., ',, ), where is set of elements {, }, is minimum element in nd mximum element in ' is unry opertion (complement) two inry opertions. + re defined on, stisfying xioms: xiom () () ommuttivity cz:komuttivit + = + = Identity cz:neutrlit + = = istriutivity cz:istriutivit + ( c) = ( + ) ( + c) ( + c) = + c omplementtion cz: Komplementrit + = = nnihiltor cz:gresivit + = = (Note: oolen logic is one exmple of oolen lger.. ) SPS 23 Eng: ulity of xioms Nme of lw OR version N version ommuttivity + = + = Identity + = = istriutivity + ( c) = ( + ) ( + c) ( + c) = + c omplementtion + = = nnihiltion + = = Idempotency + = = oule negtion ( ) = socitivity + ( + c) = ( + ) + c ( c) = ( ) c emorgn s (x + y) = x. y (x.y) = x + y sorption + ( ) = ( + ) = Merging (x y) + (x y ) = x (x + y) (x + y ) = x SPS 24 2

13 4..25 z: ulit xiomů teorémů Vlstnost N OR OR N Komuttivit + = + = Identit + = = istriutivit + ( c) = ( + ) ( + c) ( + c) = + c Komplementrit + = = gresivit + = = Idempotence + = = vojí negce ( ) = socitivit + ( + c) = ( + ) + c ( c) = ( ) c emorgn ( + ) = ( ) = + sorpce + ( ) = ( + ) = Sloučení (x y) + (x y ) = x (x + y) (x + y ) = x SPS 25 socitivity Lw: Multi-input Gtes c c Y=(.).c =..c c c Y=(+)+c = ++c c c c c Y=((.).c) = (..c) Y=((+)+c) = (++c) SPS 26 3

14 = - SoP +. =. +. =.(+) =. = (omment: lso covers.).(+) = - PoS.(+) =. +. = +. = (omment: lso covers +) + '. = + SPS + '. =(+')(+) (distriutive+) =.(+) = + (omment: ' is redundnt) + '.' = +' + '.' =(+')(+') (distriutive+) =.(+') = +' Vrints of sorption Lw 27 SPS Generl sorption Lw omment: we cn sustitute ny oolen functions for or in sorption lws Let f(x), g(x): n e ny logic function then f(x)+f(x).g(x) = f(x) f(x) + f(x).g(x) = f(x). + f(x).g(x) = f(x).(+g(x)) = f(x). = f(x) f(x).(f(x)+g(x)) = f(x) f(x).(f(x)+g(x)) = f(x). f(x) + f(x).g(x)= f(x) + f(x).g(x) = f(x) f(x) + f(x)'.g(x) = f(x)+g(x) f(x) + f(x)'.g(x) =(f(x)+f(x)')(f(x)+g(x)) (distriutive+) =.(f(x)+g(x)) = f(x)+g(x) Generlly, we cn sustitute logic functions insted of vriles in ny oolen lw 28 4

15 4..25 Non-Equlity (XOR) = xor =.'+'. Equlity = ( xor )' =.+'.' < (less thn, Inhiition of ) < =.' <= (less thn or equl, Impliction), <= = +' > (greter thn, Inhiition of ) > =.' >= (greter thn or equl, Impliction), >= = +' SPS erived Opertion 29 SPS Exmple: 2 it comprtor Y = >= = ( ).( >=)+> (sustitute logic expressions of derived functions) = ('.'+.)(+')+.' (st prenthesis nd the lst element contin the sme vriles thus we pply distriutive +) = (('.'+.)+.').((+') +.' ) (we remove prentheses y commuttive lw) = ('.'+.+.').((+') +.' ) (sorption lw) = ('.'+.+.').((+') +.' ) = ('.'+ ).((+') +.' ) (sorption lw gin) = ('+ ).((+') +.' ) (distriutive lw) = ('+ ).(+') +('+ )..' = ('+ ).(+') +'..' +..' (idempotency) = ('+ ).(+') +.' The sme result s 8th slide of the st lecture 3 5

16 4..25 SPS () ( + )' =.' emorgn s Theorem () (.)' = ' + ' Generlized emorgn's Theorem () ( + + z)' =.' z' () (. z)' = ' + ' + z' The crter de Morgn on the Moon [Source of the imge: (lso with the crter) Exmple : (. +.c +.c) y gtes = ((. +.c +.c)')' = ((.)'.(.c)'.(.c)')' [nnd gtes only] = (('+').('+c').('+c'))' = ('+')'+('+c')'+('+c')' = ((('+')'+('+c')'+('+c')')')' [nor-gtes only] 3 emorgn's theorem follows from KM e Morgn's theorem is SoP nd PoS coverge of sme elements SoP: etector of primes F(w,x,y,z) = S(,2,3,5,7,,3) PoS: etector of non-primes F'(w,x,y,z) = S(,4,6,8,9,,2,4,5) ('+).('++').('+'+).('++).'+.'.+..'+.'.' SPS 32 6

17 4..25 emorgn Equivlents of Gtes Opertion NN N OR NOR N OR XOR XNOR Y Y=( ) SPS 33 Negtion ules Externl Inverter Externl Inverter Integrted Inverter Integrted Inverter input logic circuit = output logic circuit + = oule negtion + = + = N + NOT = NN + = NN + NOT = N OR + NOT = NOR + = NOR + NOT = OR Note: Functions re equl from logic point of view, for further informtion, see hzrds in the next lecture. SPS 34 7

18 4..25 Exmple From NN gtes to NOR y emorgn : c c c c SPS 35 Identity & nnihiltor Lws cz:neutrlit & gresivit Gte s ontrol Element of Switch 8

19 4..25 Enle ctive-high Gte s ontrol Element of Switch. =.= Enle = Enle = t in Enle ctive-low t out t in disled mode t out= t in. =.= Enle=, not Enle = t out Enle=, not Enle = t in t out disled mode t in t out t in t out= Enle ctive-low t in Enle = + = += t in Enle = t in SPS t out disled mode t out t out= 37 Tri stte uffer Enle ctive-high Enle = Enle = t in t out t in high impednce t in t out Enle ctive-low Enle= not Enle = Enle= not Enle = t in t out t in t out t in high impednce SPS 38 9

20 ounter HL Hrdwre escription Lnguges for forml description nd design of electronic circuits Motivtion exmple- econ MHL2 - equtions (/3) STOP STRT STOP M "MHL2" = "" Equtions: SPS 4 2

21 4..25 Motivtion exmple- econ MHL2 - circuit (2/3) ircuit SPS 4 Motivtion exmple- econ MHL2 - VHL (3/3) "MHL2" = "" lirry ieee; use ieee.std_logic_64.ll; entity KOM is port ( X, X, X2, X3, X4, X5 : in std_logic; STOP, Y : out std_logic); end entity; rchitecture ehviorl of KOM is signl,, c, d, e, f : std_logic; signl Y, Y, Y, Y : std_logic; egin -- prezncime signly, n rozdil od progrmovni se v hrdwru nevytvori nove promenne <= X5; <= X4; c <= X3; d <= X2; e <= X; f <= X; Y <= (e nd f) or (not c nd e) or (not c nd f) or (d nd f); Y <= (not e nd f) or (c nd not d nd f) or (not c nd d nd f) or (c nd not d nd not e); Y <= (c nd e) or (d nd e) or f; Y <= ''; -- Spojíme, ztím tkto, příště si ukážeme lepší konstrukce -- Y <= (not nd not nd Y) -- =, = or (not nd nd Y) -- =, = or ( nd not nd Y) -- =, = or ( nd nd Y); -- =, = STOP <= nd nd f; end rchitecture; SPS 42 2

22 4..25 VHL VHSI (Very High Speed Integrted ircuit) HL Wht is VHL? design entry lnguge design simultion nd modeling lnguge netlist lnguge for expressing circuit connectivity etween hierrchy of locks stndrd lnguge SPS 44 22

23 4..25 VHL history 98: Initited in 98 y US o ( U.S eprtment of efense ) to ddress the hrdwre life-cycle crisis (note: d ws lso initited y o in 97) : evelopment of seline lnguge y Intermetrics, IM nd Texs Instruments 986: ll rights trnsferred to IEEE (red I-Triple-E) - Institute of Electricl nd Electronics Engineers * 987: IEEE Stndrd VHL 987 o requires comprehensive VHL descriptions supplied with every SI delivered to the o. 994: Revised stndrd (nmed VHL ) 29: Revised Stndrd (nmed VHL 76-28) SPS 45 Verilog versus VHL World mrket divided etween VHL & Verilog Verilog - introduced in 983 s simultion lnguge, fterwrds extended with support for synthesis dded. VHL mostly in Europe Verilog dominnt in US VHL More generl lnguge More high-level constructs etter extendiility y pckges Verilog: Not s generl s VHL etter for low-level circuits Simpler thn VHL, especilly for nd Jv progrmmers, ut it cn lso confuse them y illusion on similrity to SPS 46 23

24 4..25 VHL / Verilog Google trend VHL Verilog SPS 47 escriing omintionl Logic Using tflow esign Style EE 545 Introduction to VHL 24

25 4..25 Register Trnsfer Level (RTL) esign escription Tody s Topic omintionl Logic omintionl Logic Registers SPS 49 VHL esign Styles VHL esign Styles dtflow oncurrent sttements structurl omponents nd interconnects ehviorl Sequentil sttements Registers Stte mchines Test enches Register Trnsfer Level (RTL) stype SPS 5 25

26 prolemtic or impossile implementtion in hrdwre VHL dt types VHL for Specifiction VHL for Simultion file I/O flot 3.45 it, integer 234 string "234" VHL for Synthesis types derived from std_logic type chrcter '' oolen SPS 5 Multi-Vlued Logic Representtions MVL - 9 more sttes of signls MVL - 9 Unitilized U Wek H on t re - Wek L Forcing Wek Unknown W Forcing High Impednce Z Forcing Unknown X Wired Or/nd Impossile implement in yclone II SPS 52 26

27 4..25 it versus std_logic it vlues ''nd '' oolen - vlues TRUE FLSE only conditions std_logic MVL-9: enumrted type with vlues defined y literls '', '', 'X', 'Z','U', '-', 'L', 'H', 'W' Wrning: std_logic nd it re mutully unconvertile SPS 53 sic rules for source code. VHL is not cse sensitive, 2. VHL does not understnd dicritics, not even in comments. 3. Identifier must strt with letter, lst chrcter cnnot e n underscore, two connected underscores re not llowed 4. ll sttements end with semi-colon 5. omments precede with (--) The rest of line is treted s comment. rrige return termintes comment. 6. No method for commenting lock SPS 54 27

28 4..25 The most importnt rules Keep things simple, simple progrms re the est Prtition the design (ivide et Imper) Prefer known sfe-constructions; VHL is strongly type lnguge, mny times stricter thn Jv or Pscl Keep in mind tht synthesizle VHL code is not clssic progrm ut it descries circuits. To keep circuits simple, check results of your work (in Qurtus II: Locte->Locte in RTL View, Locte in Technology Mp View...) SPS 55 28

ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT, OAKLAND UNIVERSITY ECE-2700: Digital Logic Design Fall Notes - Unit 1

ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT, OAKLAND UNIVERSITY ECE-2700: Digital Logic Design Fall Notes - Unit 1 INTRODUTION TO LOGI IRUITS Notes - Unit 1 OOLEN LGER This is the oundtion or designing nd nlyzing digitl systems. It dels with the cse where vriles ssume only one o two vlues: TRUE (usully represented

More information

Digital Control of Electric Drives

Digital Control of Electric Drives igitl Control o Electric rives Logic Circuits - Comintionl Boolen Alger, escription Form Czech Technicl University in Prgue Fculty o Electricl Engineering Ver.. J. Zdenek Logic Comintionl Circuit Logic

More information

ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT, OAKLAND UNIVERSITY ECE-378: Computer Hardware Design Winter Notes - Unit 1

ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT, OAKLAND UNIVERSITY ECE-378: Computer Hardware Design Winter Notes - Unit 1 ELETRIL ND OMPUTER ENGINEERING DEPRTMENT, OKLND UNIVERSIT EE-78: omputer Hrdwre Design Winter 016 INTRODUTION TO LOGI IRUITS Notes - Unit 1 OOLEN LGER This is the oundtion or designing nd nlyzing digitl

More information

Boolean algebra.

Boolean algebra. http://en.wikipedi.org/wiki/elementry_boolen_lger Boolen lger www.tudorgir.com Computer science is not out computers, it is out computtion nd informtion. computtion informtion computer informtion Turing

More information

Boolean Algebra. Boolean Algebra

Boolean Algebra. Boolean Algebra Boolen Alger Boolen Alger A Boolen lger is set B of vlues together with: - two inry opertions, commonly denoted y + nd, - unry opertion, usully denoted y ˉ or ~ or, - two elements usully clled zero nd

More information

ECE223. R eouven Elbaz Office room: DC3576

ECE223. R eouven Elbaz Office room: DC3576 ECE223 R eouven Elz reouven@uwterloo.c Office room: DC3576 Outline Decoders Decoders with Enle VHDL Exmple Multiplexers Multiplexers with Enle VHDL Exmple From Decoder to Multiplexer 3-stte Gtes Multiplexers

More information

Combinational Logic. Precedence. Quick Quiz 25/9/12. Schematics à Boolean Expression. 3 Representations of Logic Functions. Dr. Hayden So.

Combinational Logic. Precedence. Quick Quiz 25/9/12. Schematics à Boolean Expression. 3 Representations of Logic Functions. Dr. Hayden So. 5/9/ Comintionl Logic ENGG05 st Semester, 0 Dr. Hyden So Representtions of Logic Functions Recll tht ny complex logic function cn e expressed in wys: Truth Tle, Boolen Expression, Schemtics Only Truth

More information

CS12N: The Coming Revolution in Computer Architecture Laboratory 2 Preparation

CS12N: The Coming Revolution in Computer Architecture Laboratory 2 Preparation CS2N: The Coming Revolution in Computer Architecture Lortory 2 Preprtion Ojectives:. Understnd the principle of sttic CMOS gte circuits 2. Build simple logic gtes from MOS trnsistors 3. Evlute these gtes

More information

CS 330 Formal Methods and Models

CS 330 Formal Methods and Models CS 330 Forml Methods nd Models Dn Richrds, George Mson University, Spring 2017 Quiz Solutions Quiz 1, Propositionl Logic Dte: Ferury 2 1. Prove ((( p q) q) p) is tutology () (3pts) y truth tle. p q p q

More information

Fast Boolean Algebra

Fast Boolean Algebra Fst Boolen Alger ELEC 267 notes with the overurden removed A fst wy to lern enough to get the prel done honorly Printed; 3//5 Slide Modified; Jnury 3, 25 John Knight Digitl Circuits p. Fst Boolen Alger

More information

expression simply by forming an OR of the ANDs of all input variables for which the output is

expression simply by forming an OR of the ANDs of all input variables for which the output is 2.4 Logic Minimiztion nd Krnugh Mps As we found ove, given truth tle, it is lwys possile to write down correct logic expression simply y forming n OR of the ANDs of ll input vriles for which the output

More information

Unit 4. Combinational Circuits

Unit 4. Combinational Circuits Unit 4. Comintionl Ciruits Digitl Eletroni Ciruits (Ciruitos Eletrónios Digitles) E.T.S.I. Informáti Universidd de Sevill 5/10/2012 Jorge Jun 2010, 2011, 2012 You re free to opy, distriute

More information

Introduction to Electrical & Electronic Engineering ENGG1203

Introduction to Electrical & Electronic Engineering ENGG1203 Introduction to Electricl & Electronic Engineering ENGG23 2 nd Semester, 27-8 Dr. Hden Kwok-H So Deprtment of Electricl nd Electronic Engineering Astrction DIGITAL LOGIC 2 Digitl Astrction n Astrct ll

More information

CS 330 Formal Methods and Models Dana Richards, George Mason University, Spring 2016 Quiz Solutions

CS 330 Formal Methods and Models Dana Richards, George Mason University, Spring 2016 Quiz Solutions CS 330 Forml Methods nd Models Dn Richrds, George Mson University, Spring 2016 Quiz Solutions Quiz 1, Propositionl Logic Dte: Ferury 9 1. (4pts) ((p q) (q r)) (p r), prove tutology using truth tles. p

More information

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3

I1 = I2 I1 = I2 + I3 I1 + I2 = I3 + I4 I 3 2 The Prllel Circuit Electric Circuits: Figure 2- elow show ttery nd multiple resistors rrnged in prllel. Ech resistor receives portion of the current from the ttery sed on its resistnce. The split is

More information

Convert the NFA into DFA

Convert the NFA into DFA Convert the NF into F For ech NF we cn find F ccepting the sme lnguge. The numer of sttes of the F could e exponentil in the numer of sttes of the NF, ut in prctice this worst cse occurs rrely. lgorithm:

More information

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true.

1. For each of the following theorems, give a two or three sentence sketch of how the proof goes or why it is not true. York University CSE 2 Unit 3. DFA Clsses Converting etween DFA, NFA, Regulr Expressions, nd Extended Regulr Expressions Instructor: Jeff Edmonds Don t chet y looking t these nswers premturely.. For ech

More information

Overview of Today s Lecture:

Overview of Today s Lecture: CPS 4 Computer Orgniztion nd Progrmming Lecture : Boolen Alger & gtes. Roert Wgner CPS4 BA. RW Fll 2 Overview of Tody s Lecture: Truth tles, Boolen functions, Gtes nd Circuits Krnugh mps for simplifying

More information

Minimal DFA. minimal DFA for L starting from any other

Minimal DFA. minimal DFA for L starting from any other Miniml DFA Among the mny DFAs ccepting the sme regulr lnguge L, there is exctly one (up to renming of sttes) which hs the smllest possile numer of sttes. Moreover, it is possile to otin tht miniml DFA

More information

CS 330 Formal Methods and Models

CS 330 Formal Methods and Models CS 0 Forml Methods nd Models Dn Richrds, George Mson University, Fll 2016 Quiz Solutions Quiz 1, Propositionl Logic Dte: Septemer 8 1. Prove q (q p) p q p () (4pts) with truth tle. p q p q p (q p) p q

More information

Homework 3 Solutions

Homework 3 Solutions CS 341: Foundtions of Computer Science II Prof. Mrvin Nkym Homework 3 Solutions 1. Give NFAs with the specified numer of sttes recognizing ech of the following lnguges. In ll cses, the lphet is Σ = {,1}.

More information

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER LANGUAGES AND COMPUTATION ANSWERS

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER LANGUAGES AND COMPUTATION ANSWERS The University of Nottinghm SCHOOL OF COMPUTER SCIENCE LEVEL 2 MODULE, SPRING SEMESTER 2016 2017 LNGUGES ND COMPUTTION NSWERS Time llowed TWO hours Cndidtes my complete the front cover of their nswer ook

More information

CISC 4090 Theory of Computation

CISC 4090 Theory of Computation 9/6/28 Stereotypicl computer CISC 49 Theory of Computtion Finite stte mchines & Regulr lnguges Professor Dniel Leeds dleeds@fordhm.edu JMH 332 Centrl processing unit (CPU) performs ll the instructions

More information

Coalgebra, Lecture 15: Equations for Deterministic Automata

Coalgebra, Lecture 15: Equations for Deterministic Automata Colger, Lecture 15: Equtions for Deterministic Automt Julin Slmnc (nd Jurrin Rot) Decemer 19, 2016 In this lecture, we will study the concept of equtions for deterministic utomt. The notes re self contined

More information

Lecture 2 : Propositions DRAFT

Lecture 2 : Propositions DRAFT CS/Mth 240: Introduction to Discrete Mthemtics 1/20/2010 Lecture 2 : Propositions Instructor: Dieter vn Melkeeek Scrie: Dlior Zelený DRAFT Lst time we nlyzed vrious mze solving lgorithms in order to illustrte

More information

Chapter 2 Finite Automata

Chapter 2 Finite Automata Chpter 2 Finite Automt 28 2.1 Introduction Finite utomt: first model of the notion of effective procedure. (They lso hve mny other pplictions). The concept of finite utomton cn e derived y exmining wht

More information

CS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University

CS415 Compilers. Lexical Analysis and. These slides are based on slides copyrighted by Keith Cooper, Ken Kennedy & Linda Torczon at Rice University CS415 Compilers Lexicl Anlysis nd These slides re sed on slides copyrighted y Keith Cooper, Ken Kennedy & Lind Torczon t Rice University First Progrmming Project Instruction Scheduling Project hs een posted

More information

Lecture 08: Feb. 08, 2019

Lecture 08: Feb. 08, 2019 4CS4-6:Theory of Computtion(Closure on Reg. Lngs., regex to NDFA, DFA to regex) Prof. K.R. Chowdhry Lecture 08: Fe. 08, 2019 : Professor of CS Disclimer: These notes hve not een sujected to the usul scrutiny

More information

Parse trees, ambiguity, and Chomsky normal form

Parse trees, ambiguity, and Chomsky normal form Prse trees, miguity, nd Chomsky norml form In this lecture we will discuss few importnt notions connected with contextfree grmmrs, including prse trees, miguity, nd specil form for context-free grmmrs

More information

INF1383 -Bancos de Dados

INF1383 -Bancos de Dados 3//0 INF383 -ncos de Ddos Prof. Sérgio Lifschitz DI PUC-Rio Eng. Computção, Sistems de Informção e Ciênci d Computção LGER RELCIONL lguns slides sedos ou modificdos dos originis em Elmsri nd Nvthe, Fundmentls

More information

Reasoning and programming. Lecture 5: Invariants and Logic. Boolean expressions. Reasoning. Examples

Reasoning and programming. Lecture 5: Invariants and Logic. Boolean expressions. Reasoning. Examples Chir of Softwre Engineering Resoning nd progrmming Einführung in die Progrmmierung Introduction to Progrmming Prof. Dr. Bertrnd Meyer Octoer 2006 Ferury 2007 Lecture 5: Invrints nd Logic Logic is the sis

More information

First Midterm Examination

First Midterm Examination 24-25 Fll Semester First Midterm Exmintion ) Give the stte digrm of DFA tht recognizes the lnguge A over lphet Σ = {, } where A = {w w contins or } 2) The following DFA recognizes the lnguge B over lphet

More information

CMPSCI 250: Introduction to Computation. Lecture #31: What DFA s Can and Can t Do David Mix Barrington 9 April 2014

CMPSCI 250: Introduction to Computation. Lecture #31: What DFA s Can and Can t Do David Mix Barrington 9 April 2014 CMPSCI 250: Introduction to Computtion Lecture #31: Wht DFA s Cn nd Cn t Do Dvid Mix Brrington 9 April 2014 Wht DFA s Cn nd Cn t Do Deterministic Finite Automt Forml Definition of DFA s Exmples of DFA

More information

Boolean Algebra. Boolean Algebras

Boolean Algebra. Boolean Algebras Boolen Algebr Boolen Algebrs A Boolen lgebr is set B of vlues together with: - two binry opertions, commonly denoted by + nd, - unry opertion, usully denoted by or ~ or, - two elements usully clled zero

More information

Review of Gaussian Quadrature method

Review of Gaussian Quadrature method Review of Gussin Qudrture method Nsser M. Asi Spring 006 compiled on Sundy Decemer 1, 017 t 09:1 PM 1 The prolem To find numericl vlue for the integrl of rel vlued function of rel vrile over specific rnge

More information

CS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS

CS 310 (sec 20) - Winter Final Exam (solutions) SOLUTIONS CS 310 (sec 20) - Winter 2003 - Finl Exm (solutions) SOLUTIONS 1. (Logic) Use truth tles to prove the following logicl equivlences: () p q (p p) (q q) () p q (p q) (p q) () p q p q p p q q (q q) (p p)

More information

Bases for Vector Spaces

Bases for Vector Spaces Bses for Vector Spces 2-26-25 A set is independent if, roughly speking, there is no redundncy in the set: You cn t uild ny vector in the set s liner comintion of the others A set spns if you cn uild everything

More information

Worked out examples Finite Automata

Worked out examples Finite Automata Worked out exmples Finite Automt Exmple Design Finite Stte Automton which reds inry string nd ccepts only those tht end with. Since we re in the topic of Non Deterministic Finite Automt (NFA), we will

More information

Designing finite automata II

Designing finite automata II Designing finite utomt II Prolem: Design DFA A such tht L(A) consists of ll strings of nd which re of length 3n, for n = 0, 1, 2, (1) Determine wht to rememer out the input string Assign stte to ech of

More information

5. (±±) Λ = fw j w is string of even lengthg [ 00 = f11,00g 7. (11 [ 00)± Λ = fw j w egins with either 11 or 00g 8. (0 [ ffl)1 Λ = 01 Λ [ 1 Λ 9.

5. (±±) Λ = fw j w is string of even lengthg [ 00 = f11,00g 7. (11 [ 00)± Λ = fw j w egins with either 11 or 00g 8. (0 [ ffl)1 Λ = 01 Λ [ 1 Λ 9. Regulr Expressions, Pumping Lemm, Right Liner Grmmrs Ling 106 Mrch 25, 2002 1 Regulr Expressions A regulr expression descries or genertes lnguge: it is kind of shorthnd for listing the memers of lnguge.

More information

p-adic Egyptian Fractions

p-adic Egyptian Fractions p-adic Egyptin Frctions Contents 1 Introduction 1 2 Trditionl Egyptin Frctions nd Greedy Algorithm 2 3 Set-up 3 4 p-greedy Algorithm 5 5 p-egyptin Trditionl 10 6 Conclusion 1 Introduction An Egyptin frction

More information

Genetic Programming. Outline. Evolutionary Strategies. Evolutionary strategies Genetic programming Summary

Genetic Programming. Outline. Evolutionary Strategies. Evolutionary strategies Genetic programming Summary Outline Genetic Progrmming Evolutionry strtegies Genetic progrmming Summry Bsed on the mteril provided y Professor Michel Negnevitsky Evolutionry Strtegies An pproch simulting nturl evolution ws proposed

More information

CM10196 Topic 4: Functions and Relations

CM10196 Topic 4: Functions and Relations CM096 Topic 4: Functions nd Reltions Guy McCusker W. Functions nd reltions Perhps the most widely used notion in ll of mthemtics is tht of function. Informlly, function is n opertion which tkes n input

More information

Good Review book ( ) ( ) ( )

Good Review book ( ) ( ) ( ) 7/31/2011 34 Boolen (Switching) Algebr Review Good Review book BeBop to the Boolen Boogie: An Unconventionl Guide to Electronics, 2 nd ed. by Clive Mxwell Hightext Publictions Inc. from Amzon.com for pprox.

More information

Mathematics Number: Logarithms

Mathematics Number: Logarithms plce of mind F A C U L T Y O F E D U C A T I O N Deprtment of Curriculum nd Pedgogy Mthemtics Numer: Logrithms Science nd Mthemtics Eduction Reserch Group Supported y UBC Teching nd Lerning Enhncement

More information

NFAs and Regular Expressions. NFA-ε, continued. Recall. Last class: Today: Fun:

NFAs and Regular Expressions. NFA-ε, continued. Recall. Last class: Today: Fun: CMPU 240 Lnguge Theory nd Computtion Spring 2019 NFAs nd Regulr Expressions Lst clss: Introduced nondeterministic finite utomt with -trnsitions Tody: Prove n NFA- is no more powerful thn n NFA Introduce

More information

Lecture 3: Equivalence Relations

Lecture 3: Equivalence Relations Mthcmp Crsh Course Instructor: Pdric Brtlett Lecture 3: Equivlence Reltions Week 1 Mthcmp 2014 In our lst three tlks of this clss, we shift the focus of our tlks from proof techniques to proof concepts

More information

CS 330 Formal Methods and Models

CS 330 Formal Methods and Models CS 330 Forml Methods nd Models Dn Richrds, section 003, George Mson University, Fll 2017 Quiz Solutions Quiz 1, Propositionl Logic Dte: Septemer 7 1. Prove (p q) (p q), () (5pts) using truth tles. p q

More information

Handout: Natural deduction for first order logic

Handout: Natural deduction for first order logic MATH 457 Introduction to Mthemticl Logic Spring 2016 Dr Json Rute Hndout: Nturl deduction for first order logic We will extend our nturl deduction rules for sententil logic to first order logic These notes

More information

M A T H F A L L CORRECTION. Algebra I 2 1 / 0 9 / U N I V E R S I T Y O F T O R O N T O

M A T H F A L L CORRECTION. Algebra I 2 1 / 0 9 / U N I V E R S I T Y O F T O R O N T O M A T H 2 4 0 F A L L 2 0 1 4 HOMEWORK ASSIGNMENT #1 CORRECTION Alger I 2 1 / 0 9 / 2 0 1 4 U N I V E R S I T Y O F T O R O N T O 1. Suppose nd re nonzero elements of field F. Using only the field xioms,

More information

Lecture 3. Introduction digital logic. Notes. Notes. Notes. Representations. February Bern University of Applied Sciences.

Lecture 3. Introduction digital logic. Notes. Notes. Notes. Representations. February Bern University of Applied Sciences. Lecture 3 Ferury 6 ern University of pplied ciences ev. f57fc 3. We hve seen tht circuit cn hve multiple (n) inputs, e.g.,, C, We hve lso seen tht circuit cn hve multiple (m) outputs, e.g. X, Y,, ; or

More information

FABER Formal Languages, Automata and Models of Computation

FABER Formal Languages, Automata and Models of Computation DVA337 FABER Forml Lnguges, Automt nd Models of Computtion Lecture 5 chool of Innovtion, Design nd Engineering Mälrdlen University 2015 1 Recp of lecture 4 y definition suset construction DFA NFA stte

More information

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton 25. Finite Automt AUTOMATA AND LANGUAGES A system of computtion tht only hs finite numer of possile sttes cn e modeled using finite utomton A finite utomton is often illustrted s stte digrm d d d. d q

More information

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018 Finite Automt Theory nd Forml Lnguges TMV027/DIT321 LP4 2018 Lecture 10 An Bove April 23rd 2018 Recp: Regulr Lnguges We cn convert between FA nd RE; Hence both FA nd RE ccept/generte regulr lnguges; More

More information

Engr354: Digital Logic Circuits

Engr354: Digital Logic Circuits Engr354: Digitl Logi Ciruits Chpter 4: Logi Optimiztion Curtis Nelson Logi Optimiztion In hpter 4 you will lern out: Synthesis of logi funtions; Anlysis of logi iruits; Tehniques for deriving minimum-ost

More information

EE 108A Lecture 2 (c) W. J. Dally and P. Levis 2

EE 108A Lecture 2 (c) W. J. Dally and P. Levis 2 EE08A Leture 2: Comintionl Logi Design EE 08A Leture 2 () 2005-2008 W. J. Dlly n P. Levis Announements Prof. Levis will hve no offie hours on Friy, Jn 8. Ls n setions hve een ssigne - see the we pge Register

More information

CS 301. Lecture 04 Regular Expressions. Stephen Checkoway. January 29, 2018

CS 301. Lecture 04 Regular Expressions. Stephen Checkoway. January 29, 2018 CS 301 Lecture 04 Regulr Expressions Stephen Checkowy Jnury 29, 2018 1 / 35 Review from lst time NFA N = (Q, Σ, δ, q 0, F ) where δ Q Σ P (Q) mps stte nd n lphet symol (or ) to set of sttes We run n NFA

More information

set is not closed under matrix [ multiplication, ] and does not form a group.

set is not closed under matrix [ multiplication, ] and does not form a group. Prolem 2.3: Which of the following collections of 2 2 mtrices with rel entries form groups under [ mtrix ] multipliction? i) Those of the form for which c d 2 Answer: The set of such mtrices is not closed

More information

(e) if x = y + z and a divides any two of the integers x, y, or z, then a divides the remaining integer

(e) if x = y + z and a divides any two of the integers x, y, or z, then a divides the remaining integer Divisibility In this note we introduce the notion of divisibility for two integers nd b then we discuss the division lgorithm. First we give forml definition nd note some properties of the division opertion.

More information

Lecture 6. Notes. Notes. Notes. Representations Z A B and A B R. BTE Electronics Fundamentals August Bern University of Applied Sciences

Lecture 6. Notes. Notes. Notes. Representations Z A B and A B R. BTE Electronics Fundamentals August Bern University of Applied Sciences Lecture 6 epresenttions epresenttions TE52 - Electronics Fundmentls ugust 24 ern University of pplied ciences ev. c2d5c88 6. Integers () sign-nd-mgnitude representtion The set of integers contins the Nturl

More information

Harvard University Computer Science 121 Midterm October 23, 2012

Harvard University Computer Science 121 Midterm October 23, 2012 Hrvrd University Computer Science 121 Midterm Octoer 23, 2012 This is closed-ook exmintion. You my use ny result from lecture, Sipser, prolem sets, or section, s long s you quote it clerly. The lphet is

More information

Fachgebiet Rechnersysteme1. 1. Boolean Algebra. 1. Boolean Algebra. Verification Technology. Content. 1.1 Boolean algebra basics (recap)

Fachgebiet Rechnersysteme1. 1. Boolean Algebra. 1. Boolean Algebra. Verification Technology. Content. 1.1 Boolean algebra basics (recap) . Boolen Alger Fchgeiet Rechnersysteme. Boolen Alger Veriiction Technology Content. Boolen lger sics (recp).2 Resoning out Boolen expressions . Boolen Alger 2 The prolem o logic veriiction: Show tht two

More information

Theory of Computation Regular Languages. (NTU EE) Regular Languages Fall / 38

Theory of Computation Regular Languages. (NTU EE) Regular Languages Fall / 38 Theory of Computtion Regulr Lnguges (NTU EE) Regulr Lnguges Fll 2017 1 / 38 Schemtic of Finite Automt control 0 0 1 0 1 1 1 0 Figure: Schemtic of Finite Automt A finite utomton hs finite set of control

More information

The Regulated and Riemann Integrals

The Regulated and Riemann Integrals Chpter 1 The Regulted nd Riemnn Integrls 1.1 Introduction We will consider severl different pproches to defining the definite integrl f(x) dx of function f(x). These definitions will ll ssign the sme vlue

More information

Assignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages

Assignment 1 Automata, Languages, and Computability. 1 Finite State Automata and Regular Languages Deprtment of Computer Science, Austrlin Ntionl University COMP2600 Forml Methods for Softwre Engineering Semester 2, 206 Assignment Automt, Lnguges, nd Computility Smple Solutions Finite Stte Automt nd

More information

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005 RLETON UNIVERSIT eprtment of Eletronis ELE 2607 Swithing iruits erury 28, 05; 0 pm.0 Prolems n Most Solutions, Set, 2005 Jn. 2, #8 n #0; Simplify, Prove Prolem. #8 Simplify + + + Reue to four letters (literls).

More information

Control with binary code. William Sandqvist

Control with binary code. William Sandqvist Control with binry code Dec Bin He Oct 218 10 11011010 2 DA 16 332 8 E 1.1c Deciml to Binäry binry weights: 1024 512 256 128 64 32 16 8 4 2 1 71 10? 2 E 1.1c Deciml to Binäry binry weights: 1024 512 256

More information

DEFINITION The inner product of two functions f 1 and f 2 on an interval [a, b] is the number. ( f 1, f 2 ) b DEFINITION 11.1.

DEFINITION The inner product of two functions f 1 and f 2 on an interval [a, b] is the number. ( f 1, f 2 ) b DEFINITION 11.1. 398 CHAPTER 11 ORTHOGONAL FUNCTIONS AND FOURIER SERIES 11.1 ORTHOGONAL FUNCTIONS REVIEW MATERIAL The notions of generlized vectors nd vector spces cn e found in ny liner lger text. INTRODUCTION The concepts

More information

1 ELEMENTARY ALGEBRA and GEOMETRY READINESS DIAGNOSTIC TEST PRACTICE

1 ELEMENTARY ALGEBRA and GEOMETRY READINESS DIAGNOSTIC TEST PRACTICE ELEMENTARY ALGEBRA nd GEOMETRY READINESS DIAGNOSTIC TEST PRACTICE Directions: Study the exmples, work the prolems, then check your nswers t the end of ech topic. If you don t get the nswer given, check

More information

Formal languages, automata, and theory of computation

Formal languages, automata, and theory of computation Mälrdlen University TEN1 DVA337 2015 School of Innovtion, Design nd Engineering Forml lnguges, utomt, nd theory of computtion Thursdy, Novemer 5, 14:10-18:30 Techer: Dniel Hedin, phone 021-107052 The exm

More information

Grammar. Languages. Content 5/10/16. Automata and Languages. Regular Languages. Regular Languages

Grammar. Languages. Content 5/10/16. Automata and Languages. Regular Languages. Regular Languages 5//6 Grmmr Automt nd Lnguges Regulr Grmmr Context-free Grmmr Context-sensitive Grmmr Prof. Mohmed Hmd Softwre Engineering L. The University of Aizu Jpn Regulr Lnguges Context Free Lnguges Context Sensitive

More information

IST 4 Information and Logic

IST 4 Information and Logic IST 4 Informtion nd Logic T = tody x= hw#x out x= hw#x due mon tue wed thr 28 M1 oh 1 4 oh M1 11 oh oh 1 2 M2 18 oh oh 2 fri oh oh = office hours oh 25 oh M2 2 3 oh midterms oh Mx= MQx out 9 oh 3 T 4 oh

More information

Finite Automata-cont d

Finite Automata-cont d Automt Theory nd Forml Lnguges Professor Leslie Lnder Lecture # 6 Finite Automt-cont d The Pumping Lemm WEB SITE: http://ingwe.inghmton.edu/ ~lnder/cs573.html Septemer 18, 2000 Exmple 1 Consider L = {ww

More information

BİL 354 Veritabanı Sistemleri. Relational Algebra (İlişkisel Cebir)

BİL 354 Veritabanı Sistemleri. Relational Algebra (İlişkisel Cebir) BİL 354 Veritnı Sistemleri Reltionl lger (İlişkisel Ceir) Reltionl Queries Query lnguges: llow mnipultion nd retrievl of dt from dtse. Reltionl model supports simple, powerful QLs: Strong forml foundtion

More information

Fault Modeling. EE5375 ADD II Prof. MacDonald

Fault Modeling. EE5375 ADD II Prof. MacDonald Fult Modeling EE5375 ADD II Prof. McDonld Stuck At Fult Models l Modeling of physicl defects (fults) simplify to logicl fult l stuck high or low represents mny physicl defects esy to simulte technology

More information

Math 4310 Solutions to homework 1 Due 9/1/16

Math 4310 Solutions to homework 1 Due 9/1/16 Mth 4310 Solutions to homework 1 Due 9/1/16 1. Use the Eucliden lgorithm to find the following gretest common divisors. () gcd(252, 180) = 36 (b) gcd(513, 187) = 1 (c) gcd(7684, 4148) = 68 252 = 180 1

More information

STRUCTURE OF CONCURRENCY Ryszard Janicki. Department of Computing and Software McMaster University Hamilton, ON, L8S 4K1 Canada

STRUCTURE OF CONCURRENCY Ryszard Janicki. Department of Computing and Software McMaster University Hamilton, ON, L8S 4K1 Canada STRUCTURE OF CONCURRENCY Ryszrd Jnicki Deprtment of Computing nd Softwre McMster University Hmilton, ON, L8S 4K1 Cnd jnicki@mcmster.c 1 Introduction Wht is concurrency? How it cn e modelled? Wht re the

More information

First Midterm Examination

First Midterm Examination Çnky University Deprtment of Computer Engineering 203-204 Fll Semester First Midterm Exmintion ) Design DFA for ll strings over the lphet Σ = {,, c} in which there is no, no nd no cc. 2) Wht lnguge does

More information

Discrete Mathematics and Probability Theory Summer 2014 James Cook Note 17

Discrete Mathematics and Probability Theory Summer 2014 James Cook Note 17 CS 70 Discrete Mthemtics nd Proility Theory Summer 2014 Jmes Cook Note 17 I.I.D. Rndom Vriles Estimting the is of coin Question: We wnt to estimte the proportion p of Democrts in the US popultion, y tking

More information

Regular expressions, Finite Automata, transition graphs are all the same!!

Regular expressions, Finite Automata, transition graphs are all the same!! CSI 3104 /Winter 2011: Introduction to Forml Lnguges Chpter 7: Kleene s Theorem Chpter 7: Kleene s Theorem Regulr expressions, Finite Automt, trnsition grphs re ll the sme!! Dr. Neji Zgui CSI3104-W11 1

More information

Part 5 out of 5. Automata & languages. A primer on the Theory of Computation. Last week was all about. a superset of Regular Languages

Part 5 out of 5. Automata & languages. A primer on the Theory of Computation. Last week was all about. a superset of Regular Languages Automt & lnguges A primer on the Theory of Computtion Lurent Vnbever www.vnbever.eu Prt 5 out of 5 ETH Zürich (D-ITET) October, 19 2017 Lst week ws ll bout Context-Free Lnguges Context-Free Lnguges superset

More information

Linear Algebra Introduction

Linear Algebra Introduction Introdution Wht is Liner Alger out? Liner Alger is rnh of mthemtis whih emerged yers k nd ws one of the pioneer rnhes of mthemtis Though, initilly it strted with solving of the simple liner eqution x +

More information

CS 267: Automated Verification. Lecture 8: Automata Theoretic Model Checking. Instructor: Tevfik Bultan

CS 267: Automated Verification. Lecture 8: Automata Theoretic Model Checking. Instructor: Tevfik Bultan CS 267: Automted Verifiction Lecture 8: Automt Theoretic Model Checking Instructor: Tevfik Bultn LTL Properties Büchi utomt [Vrdi nd Wolper LICS 86] Büchi utomt: Finite stte utomt tht ccept infinite strings

More information

NFAs continued, Closure Properties of Regular Languages

NFAs continued, Closure Properties of Regular Languages lgorithms & Models of omputtion S/EE 374, Spring 209 NFs continued, losure Properties of Regulr Lnguges Lecture 5 Tuesdy, Jnury 29, 209 Regulr Lnguges, DFs, NFs Lnguges ccepted y DFs, NFs, nd regulr expressions

More information

Learning Goals. Relational Query Languages. Formal Relational Query Languages. Formal Query Languages: Relational Algebra and Relational Calculus

Learning Goals. Relational Query Languages. Formal Relational Query Languages. Formal Query Languages: Relational Algebra and Relational Calculus Forml Query Lnguges: Reltionl Alger nd Reltionl Clculus Chpter 4 Lerning Gols Given dtse ( set of tles ) you will e le to express dtse query in Reltionl Alger (RA), involving the sic opertors (selection,

More information

Name Ima Sample ASU ID

Name Ima Sample ASU ID Nme Im Smple ASU ID 2468024680 CSE 355 Test 1, Fll 2016 30 Septemer 2016, 8:35-9:25.m., LSA 191 Regrding of Midterms If you elieve tht your grde hs not een dded up correctly, return the entire pper to

More information

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4

Intermediate Math Circles Wednesday, November 14, 2018 Finite Automata II. Nickolas Rollick a b b. a b 4 Intermedite Mth Circles Wednesdy, Novemer 14, 2018 Finite Automt II Nickols Rollick nrollick@uwterloo.c Regulr Lnguges Lst time, we were introduced to the ide of DFA (deterministic finite utomton), one

More information

Theory of Computation Regular Languages

Theory of Computation Regular Languages Theory of Computtion Regulr Lnguges Bow-Yw Wng Acdemi Sinic Spring 2012 Bow-Yw Wng (Acdemi Sinic) Regulr Lnguges Spring 2012 1 / 38 Schemtic of Finite Automt control 0 0 1 0 1 1 1 0 Figure: Schemtic of

More information

Lecture 9: LTL and Büchi Automata

Lecture 9: LTL and Büchi Automata Lecture 9: LTL nd Büchi Automt 1 LTL Property Ptterns Quite often the requirements of system follow some simple ptterns. Sometimes we wnt to specify tht property should only hold in certin context, clled

More information

Myhill-Nerode Theorem

Myhill-Nerode Theorem Overview Myhill-Nerode Theorem Correspondence etween DA s nd MN reltions Cnonicl DA for L Computing cnonicl DFA Myhill-Nerode Theorem Deepk D Souz Deprtment of Computer Science nd Automtion Indin Institute

More information

Network Analysis and Synthesis. Chapter 5 Two port networks

Network Analysis and Synthesis. Chapter 5 Two port networks Network Anlsis nd Snthesis hpter 5 Two port networks . ntroduction A one port network is completel specified when the voltge current reltionship t the terminls of the port is given. A generl two port on

More information

Nondeterminism and Nodeterministic Automata

Nondeterminism and Nodeterministic Automata Nondeterminism nd Nodeterministic Automt 61 Nondeterminism nd Nondeterministic Automt The computtionl mchine models tht we lerned in the clss re deterministic in the sense tht the next move is uniquely

More information

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations.

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations. Lecture 3 3 Solving liner equtions In this lecture we will discuss lgorithms for solving systems of liner equtions Multiplictive identity Let us restrict ourselves to considering squre mtrices since one

More information

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique? XII. LINEAR ALGEBRA: SOLVING SYSTEMS OF EQUATIONS Tody we re going to tlk out solving systems of liner equtions. These re prolems tht give couple of equtions with couple of unknowns, like: 6= x + x 7=

More information

Resources. Introduction: Binding. Resource Types. Resource Sharing. The type of a resource denotes its ability to perform different operations

Resources. Introduction: Binding. Resource Types. Resource Sharing. The type of a resource denotes its ability to perform different operations Introduction: Binding Prt of 4-lecture introduction Scheduling Resource inding Are nd performnce estimtion Control unit synthesis This lecture covers Resources nd resource types Resource shring nd inding

More information

Lecture Solution of a System of Linear Equation

Lecture Solution of a System of Linear Equation ChE Lecture Notes, Dept. of Chemicl Engineering, Univ. of TN, Knoville - D. Keffer, 5/9/98 (updted /) Lecture 8- - Solution of System of Liner Eqution 8. Why is it importnt to e le to solve system of liner

More information

IST 4 Information and Logic

IST 4 Information and Logic IST 4 Informtion nd Logic mon tue wed thr fri sun T = tody 3 M1 oh 1 x= hw#x out 10 oh M1 17 oh oh 1 2 M2 oh oh x= hw#x due 24 oh oh 2 oh = office hours oh 1 oh M2 8 3 oh midterms oh oh Mx= MQx out 15

More information

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24 Mtrix lger Mtrix ddition, Sclr Multipliction nd rnsposition Mtrix lger Section.. Mtrix ddition, Sclr Multipliction nd rnsposition rectngulr rry of numers is clled mtrix ( the plurl is mtrices ) nd the

More information

308K. 1 Section 3.2. Zelaya Eufemia. 1. Example 1: Multiplication of Matrices: X Y Z R S R S X Y Z. By associativity we have to choices:

308K. 1 Section 3.2. Zelaya Eufemia. 1. Example 1: Multiplication of Matrices: X Y Z R S R S X Y Z. By associativity we have to choices: 8K Zely Eufemi Section 2 Exmple : Multipliction of Mtrices: X Y Z T c e d f 2 R S X Y Z 2 c e d f 2 R S 2 By ssocitivity we hve to choices: OR: X Y Z R S cr ds er fs X cy ez X dy fz 2 R S 2 Suggestion

More information

Lecture 3 ( ) (translated and slightly adapted from lecture notes by Martin Klazar)

Lecture 3 ( ) (translated and slightly adapted from lecture notes by Martin Klazar) Lecture 3 (5.3.2018) (trnslted nd slightly dpted from lecture notes by Mrtin Klzr) Riemnn integrl Now we define precisely the concept of the re, in prticulr, the re of figure U(, b, f) under the grph of

More information