Enforcement of Opacity Security Properties Using Insertion Functions

Size: px
Start display at page:

Download "Enforcement of Opacity Security Properties Using Insertion Functions"

Transcription

1 Enforcmnt of Opcity Scurity Proprtis Using Insrtion Functions Yi-Chin Wu nd Stéphn Lfortun EECS Dprtmnt, Univrsity of Michign CDC 12 Dc. 13 th, 2012

2 Motivtion Scurity nd privcy concrns in onlin srvics Opcity : whthr th scrt informtion of th systm cn infrrd y outsid osrvrs Exmpl: Loction-sd srvics Smrt phon Nvigtion rqusts Srvr Intrudr 2

3 Rltd Work Notions of opcity [Mzré t l 04 ], [Bryns t l. 05], [Soori t l. 07], [Bryns t l. 08] Vrifiction of opcity [Soori t l. 08, 09], [Cssz t l. 09], [Lin 11] Enforcmnt of opcity [Duril t l. 08], [Soori t l. 08], [Cssz t l. 09] 3

4 Contriution Enforc opcity using insrtion functions Systm G Proj. P Systm s Output Bhvior dditionl osrvl vnts Insrtion Function Modifid Bhvior Intrudr i-nforcing proprty All Insrtion Structur (AIS) Synthsis of i-nforcing insrtion functions 4

5 Automton Modl 0 uo E O ={,} X 0 L(G,X 0 ) := {s E : ( i X 0 ) [f(i,s) is dfind]} E = E o E uo P()= if E o ; P()=ε if E uo {ε} 5

6 Wht Is Th Opcity Prolm? Th systm is prtilly osrvl Th systm hs scrt initil stt, currnt stt, sulngug, initil-nd-finl stt Th intrudr knows th systm structur 6

7 Wht Is Th Opcity Prolm? Th systm is prtilly osrvl Th systm hs scrt initil stt, currnt stt, sulngug, initil-nd-finl stt Th intrudr knows th systm structur Th scrt is opqu if for vry scrt hvior, thr is nonscrt hvior tht is osrvtionlly-quivlnt 6

8 Currnt Stt Opcity Dfinition (Currnt-Stt Opcity) Givn G = (X,E,f,X 0 ), st of scrt stts X S, nd st of non-scrt stts X NS, th utomton is currnt-stt opqu if i X 0, t L(G,i) such tht f(i,t) X S, j X 0, t L(G,j) such tht (i) f(j,t ) X NS, (ii) P(t)=P(t ). Th systm is opqu Th systm is not opqu 1 0 c 2 E O ={} c 2 E O ={,} 3 4 Scrt stts Nonscrt stts 7

9 Systm G: 0 uo Vrify Currnt-Stt Opcity opqu E O ={,} Not opqu 8

10 Systm G: 0 uo Vrify Currnt-Stt Opcity E O ={,} Not opqu Currnt-Stt Estimtor: 1 3 0,2 4 8

11 Systm G: 0 uo Vrify Currnt-Stt Opcity E O ={,} Not opqu Currnt-Stt Estimtor: 1 3 0,2 4 Enforc Opcity Opcity Enforcmnt Prolm How cn w nforc th scrt to opqu? 8

12 Existing Opcity Enforcmnt Mchnisms Suprvisory control Only prtil systm hvior is llowd [Duril t l. 10][Soori t l. 12] Dynmic osrvr Crt nw osrvl hvior [Cssz t l. 09] 9

13 Existing Opcity Enforcmnt Mchnisms Suprvisory control Only prtil systm hvior is llowd [Duril t l. 10][Soori t l. 12] Dynmic osrvr Crt nw osrvl hvior [Cssz t l. 09] W nforc opcity such tht All systm hvior is llowd to occur No nw osrvl hvior is crtd 9

14 Our Approch: Insrtion Functions Systm G Proj. P Systm s Output Bhvior dditionl osrvl vnts Insrtion Function Modifid Bhvior Intrudr A monitoring intrfc Insrt xtr osrvl vnts Intrudr cnnot distinguish twn insrtd vnts nd systm s osrvl vnts 10

15 I-Enforcing Proprty for Insrtion Functions dmissil Systm G Proj. P Systm s Output Bhvior dditionl osrvl vnts Insrtion Function Modifid Bhvior Intrudr Admissil: llows ll systm s output hvior 11

16 I-Enforcing Proprty for Insrtion Functions dmissil sf Systm G Proj. P Systm s Output Bhvior dditionl osrvl vnts Insrtion Function Modifid Bhvior Intrudr Admissil: llows ll systm s output hvior Sf: hvior ftr insrtion must look lik xisting non-scrt strings 11

17 I-Enforcing Proprty for Insrtion Functions dmissil sf Systm G Proj. P Systm s Output Bhvior dditionl osrvl vnts Insrtion Function Modifid Bhvior Intrudr Admissil: llows ll systm s output hvior Sf: hvior ftr insrtion must look lik xisting non-scrt strings i-nforcing = dmissil + sf i-nforcl opcity proprty 11

18 Is Opcity Alwys i-nforcl? E O ={,} 12

19 Is Opcity Alwys i-nforcl? nonscrt E O ={,} 12

20 Is Opcity Alwys i-nforcl? E O ={,} nonscrt scrt 12

21 Is Opcity Alwys i-nforcl? E O ={,} nonscrt scrt I-nforcility Vrifiction Prolm Is opcity i-nforcl? 12

22 Is Opcity Alwys i-nforcl? E O ={,} nonscrt scrt I-nforcility Vrifiction Prolm Is opcity i-nforcl? Insrtion Function Synthsis Prolm How to synthsiz n i-nforcing insrtion function?

23 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 1] i-vrifir Currnt-Stt Estimtor: 1 3 0, ,2 4 13

24 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 1] i-vrifir Currnt-Stt Estimtor: 1 3 0, ,2 4 13

25 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 1] i-vrifir Currnt-Stt Estimtor: 1 3 0,2 4 {,}* 1 3 0,2 4 {,}* {,}* {,}* 13

26 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 1] i-vrifir Currnt-Stt Estimtor: 1 3 0,2 4 d {,}* 1 3 0,2 4 {,}* {,}* {,}* 13

27 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 1] i-vrifir Currnt-Stt Estimtor: 1 3 0,2 4 d {,}* 1 3 0,2 4 {,}* {,}* {,}* Nondtrministic, sf i-vrifir 13

28 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Tst 1] L( )) = P(L(G))? * No : No i-nforcing insrtion function xists. Stop. Ys : Go to Stp 2 nd complt th AIS *Should includd in th ppr 14

29 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Tst 1] L( )) = P(L(G))? * Ys : Go to Stp 2 nd complt th AIS *Should includd in th ppr 14

30 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 2] Unfoldd i-vrifir V u dtrminiztion nd unfolding i-vrifir i-vrifir 15

31 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 2] Unfoldd i-vrifir V u dtrminiztion nd unfolding systm output * insrtion choics i-vrifir i-vrifir * * ll dtrministic, sf insrtions 15

32 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 2] Unfoldd i-vrifir V u dtrminiztion nd unfolding systm output * insrtion choics i-vrifir i-vrifir * * ll dtrministic, sf insrtions 15

33 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 2] Unfoldd i-vrifir V u dtrminiztion nd unfolding systm output * insrtion choics i-vrifir i-vrifir * * ll dtrministic, sf insrtions 15

34 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 2] Unfoldd i-vrifir V u dtrminiztion nd unfolding systm output * insrtion choics i-vrifir i-vrifir * * ll dtrministic, sf insrtions 15

35 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 2] Unfoldd i-vrifir V u dtrminiztion nd unfolding systm output * insrtion choics i-vrifir i-vrifir * * ll dtrministic, sf insrtions 15

36 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 3] Th AIS * * * Unfoldd i-vrifir 16

37 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 3] Th AIS * * * Unfoldd i-vrifir 16

38 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 3] Th AIS * uncontrolll * * Unfoldd i-vrifir 16

39 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 3] Th AIS * uncontrolll * * Unfoldd i-vrifir 16

40 Th All-Insrtion Structur (AIS) Enumrt ll i-nforcing insrtion functions [Stp 3] Th AIS * Suprml controlll sulngug * * * * Unfoldd i-vrifir * ll dtrministic i-nforcing insrtions 16

41 Th AIS Construction Workflow G i-vrifir L1=?L2 N y Unfoldd i-vrifir AIS Not i-nforcl Th AIS numrts ll i-nforcing insrtion functions 17

42 Th AIS Construction Workflow G i-vrifir L1=?L2 N y Unfoldd i-vrifir AIS Φ? N y Not i-nforcl Not i-nforcl i-nforcl Th AIS numrts ll i-nforcing insrtion functions Thorm (I-Enforcility) An opcity proprty is i-nforcl iff th AIS is not th mpty utomton 17

43 Synthsis of I-Enforcing Insrtion Functions Givn th AIS c ((c*)*)* (c*)*c (c*)*c (c*)*c * (*(c)*)* * ((c*)*)* (c*)*c 18

44 Synthsis of I-Enforcing Insrtion Functions Givn th AIS Slct on insrtion t vry circl stt c ((c*)*)* (c*)*c (c*)*c (c*)*c * (*(c)*)* * ((c*)*)* - On insrtion choic (c*)*c 18

45 Synthsis of I-Enforcing Insrtion Functions Givn th AIS Slct on insrtion t vry circl stt c c c c (*(c)*)* - On insrtion choic - On insrtion string c 18

46 Synthsis of I-Enforcing Insrtion Functions Givn th AIS Slct on insrtion t vry circl stt Trnslt into th insrtion utomton insrtion + systm output systm output /c c/cc / /c / / / /c 18

47 Insrtion Enforcmnt Mchnism Systm G Proj. P Systm s Output Bhvior dditionl osrvl vnts Insrtion Function Modifid Bhvior Intrudr 19

48 Insrtion Enforcmnt Mchnism Systm G Proj. P Systm s Output Bhvior dditionl osrvl vnts Insrtion Function Modifid Bhvior Intrudr Insrtion utomtom / / / / / A finit ncoding of n insrtion function 19

49 Conclusion A nw opcity nforcmnt mchnism using insrtion functions Chrctriztion of th i-nforcility proprty An lgorithmic procdur to chck i-nforcility An lgorithmic procdur to synthsiz i-nforcing insrtion functions Futur work Improv th lgorithm for AIS construction Optiml insrtion function Loction-sd srvic pplictions 20

Last time: introduced our first computational model the DFA.

Last time: introduced our first computational model the DFA. Lctur 7 Homwork #7: 2.2.1, 2.2.2, 2.2.3 (hnd in c nd d), Misc: Givn: M, NFA Prov: (q,xy) * (p,y) iff (q,x) * (p,) (follow proof don in clss tody) Lst tim: introducd our first computtionl modl th DFA. Tody

More information

FSA. CmSc 365 Theory of Computation. Finite State Automata and Regular Expressions (Chapter 2, Section 2.3) ALPHABET operations: U, concatenation, *

FSA. CmSc 365 Theory of Computation. Finite State Automata and Regular Expressions (Chapter 2, Section 2.3) ALPHABET operations: U, concatenation, * CmSc 365 Thory of Computtion Finit Stt Automt nd Rgulr Exprssions (Chptr 2, Sction 2.3) ALPHABET oprtions: U, conctntion, * otin otin Strings Form Rgulr xprssions dscri Closd undr U, conctntion nd * (if

More information

Notes on Finite Automata Department of Computer Science Professor Goldberg Textbooks: Introduction to the Theory of Computation by Michael Sipser

Notes on Finite Automata Department of Computer Science Professor Goldberg Textbooks: Introduction to the Theory of Computation by Michael Sipser Nots on Finit Automt Dprtmnt of Computr Scinc Profssor Goldrg Txtooks: Introduction to th Thory of Computtion y Michl Sipsr Elmnts of th Thory of Computtion y H. Lwis nd C. Ppdimitriou Ths nots contin

More information

CSE303 - Introduction to the Theory of Computing Sample Solutions for Exercises on Finite Automata

CSE303 - Introduction to the Theory of Computing Sample Solutions for Exercises on Finite Automata CSE303 - Introduction to th Thory of Computing Smpl Solutions for Exrciss on Finit Automt Exrcis 2.1.1 A dtrministic finit utomton M ccpts th mpty string (i.., L(M)) if nd only if its initil stt is finl

More information

Minimum Spanning Trees

Minimum Spanning Trees Minimum Spnning Trs Minimum Spnning Trs Problm A town hs st of houss nd st of rods A rod conncts nd only houss A rod conncting houss u nd v hs rpir cost w(u, v) Gol: Rpir nough (nd no mor) rods such tht:

More information

Walk Like a Mathematician Learning Task:

Walk Like a Mathematician Learning Task: Gori Dprtmnt of Euction Wlk Lik Mthmticin Lrnin Tsk: Mtrics llow us to prform mny usful mthmticl tsks which orinrily rquir lr numbr of computtions. Som typs of problms which cn b on fficintly with mtrics

More information

Generalized Robust Diagnosability of Discrete Event Systems

Generalized Robust Diagnosability of Discrete Event Systems Prprints of th 18th IFAC World Congrss Milno (Itly) August 28 - Sptmr 2, 2011 Gnrlizd Roust Dignosility of Disrt Evnt Systms Lilin K. Crvlho Mros V. Morir João C. Bsilio Univrsidd Fdrl do Rio d Jniro,

More information

Propositional Logic. Combinatorial Problem Solving (CPS) Albert Oliveras Enric Rodríguez-Carbonell. May 17, 2018

Propositional Logic. Combinatorial Problem Solving (CPS) Albert Oliveras Enric Rodríguez-Carbonell. May 17, 2018 Propositional Logic Combinatorial Problm Solving (CPS) Albrt Olivras Enric Rodríguz-Carbonll May 17, 2018 Ovrviw of th sssion Dfinition of Propositional Logic Gnral Concpts in Logic Rduction to SAT CNFs

More information

Non-Deterministic Finite Automata

Non-Deterministic Finite Automata Non-Deterministic Finite Automt http://users.comlb.ox.c.uk/luke. ong/teching/moc/nf2up.pdf 1 Nondeterministic Finite Automton (NFA) Alphbet ={} q1 q2 2 Alphbet ={} Two choices q1 q2 3 Alphbet ={} Two choices

More information

The Course covers: Lexical Analysis Syntax Analysis Semantic Analysis Runtime environments Code Generation Code Optimization. CS 540 Spring 2013 GMU 2

The Course covers: Lexical Analysis Syntax Analysis Semantic Analysis Runtime environments Code Generation Code Optimization. CS 540 Spring 2013 GMU 2 CS 540 Spring 2013 Th Cours covrs: Lxicl Anlysis Syntx Anlysis Smntic Anlysis Runtim nvironmnts Cod Gnrtion Cod Optimiztion CS 540 Spring 2013 GMU 2 Pr-rquisit courss Strong progrmming ckground in C, C++

More information

CSE 373: More on graphs; DFS and BFS. Michael Lee Wednesday, Feb 14, 2018

CSE 373: More on graphs; DFS and BFS. Michael Lee Wednesday, Feb 14, 2018 CSE 373: Mor on grphs; DFS n BFS Mihl L Wnsy, F 14, 2018 1 Wrmup Wrmup: Disuss with your nighor: Rmin your nighor: wht is simpl grph? Suppos w hv simpl, irt grph with x nos. Wht is th mximum numr of gs

More information

Non Deterministic Automata. Formal Languages and Automata - Yonsei CS 1

Non Deterministic Automata. Formal Languages and Automata - Yonsei CS 1 Non Deterministic Automt Forml Lnguges nd Automt - Yonsei CS 1 Nondeterministic Finite Accepter (NFA) We llow set of possible moves insted of A unique move. Alphbet = {} Two choices q 1 q2 Forml Lnguges

More information

Winter 2016 COMP-250: Introduction to Computer Science. Lecture 23, April 5, 2016

Winter 2016 COMP-250: Introduction to Computer Science. Lecture 23, April 5, 2016 Wintr 2016 COMP-250: Introduction to Computr Scinc Lctur 23, April 5, 2016 Commnt out input siz 2) Writ ny lgorithm tht runs in tim Θ(n 2 log 2 n) in wors cs. Explin why this is its running tim. I don

More information

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture:

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture: Lctur 11 Wvs in Priodic Potntils Tody: 1. Invrs lttic dfinition in 1D.. rphicl rprsnttion of priodic nd -priodic functions using th -xis nd invrs lttic vctors. 3. Sris solutions to th priodic potntil Hmiltonin

More information

INTEGRALS. Chapter 7. d dx. 7.1 Overview Let d dx F (x) = f (x). Then, we write f ( x)

INTEGRALS. Chapter 7. d dx. 7.1 Overview Let d dx F (x) = f (x). Then, we write f ( x) Chptr 7 INTEGRALS 7. Ovrviw 7.. Lt d d F () f (). Thn, w writ f ( ) d F () + C. Ths intgrls r clld indfinit intgrls or gnrl intgrls, C is clld constnt of intgrtion. All ths intgrls diffr y constnt. 7..

More information

Formal Concept Analysis

Formal Concept Analysis Forml Conpt Anlysis Conpt intnts s losd sts Closur Systms nd Implitions 4 Closur Systms 0.06.005 Nxt-Closur ws dvlopd y B. Gntr (984). Lt M = {,..., n}. A M is ltilly smllr thn B M, if B A if th smllst

More information

Non-Deterministic Finite Automata. Fall 2018 Costas Busch - RPI 1

Non-Deterministic Finite Automata. Fall 2018 Costas Busch - RPI 1 Non-Deterministic Finite Automt Fll 2018 Costs Busch - RPI 1 Nondeterministic Finite Automton (NFA) Alphbet ={} q q2 1 q 0 q 3 Fll 2018 Costs Busch - RPI 2 Nondeterministic Finite Automton (NFA) Alphbet

More information

Problem solving by search

Problem solving by search Prolm solving y srh Tomáš voo Dprtmnt o Cyrntis, Vision or Roots n Autonomous ystms Mrh 5, 208 / 3 Outlin rh prolm. tt sp grphs. rh trs. trtgis, whih tr rnhs to hoos? trtgy/algorithm proprtis? Progrmming

More information

Outline. Circuits. Euler paths/circuits 4/25/12. Part 10. Graphs. Euler s bridge problem (Bridges of Konigsberg Problem)

Outline. Circuits. Euler paths/circuits 4/25/12. Part 10. Graphs. Euler s bridge problem (Bridges of Konigsberg Problem) 4/25/12 Outlin Prt 10. Grphs CS 200 Algorithms n Dt Struturs Introution Trminology Implmnting Grphs Grph Trvrsls Topologil Sorting Shortst Pths Spnning Trs Minimum Spnning Trs Ciruits 1 2 Eulr s rig prolm

More information

Paths. Connectivity. Euler and Hamilton Paths. Planar graphs.

Paths. Connectivity. Euler and Hamilton Paths. Planar graphs. Pths.. Eulr n Hmilton Pths.. Pth D. A pth rom s to t is squn o gs {x 0, x 1 }, {x 1, x 2 },... {x n 1, x n }, whr x 0 = s, n x n = t. D. Th lngth o pth is th numr o gs in it. {, } {, } {, } {, } {, } {,

More information

DFA (Deterministic Finite Automata) q a

DFA (Deterministic Finite Automata) q a Big pictur All lngugs Dcidl Turing mchins NP P Contxt-fr Contxt-fr grmmrs, push-down utomt Rgulr Automt, non-dtrministic utomt, rgulr xprssions DFA (Dtrministic Finit Automt) 0 q 0 0 0 0 q DFA (Dtrministic

More information

Basic Polyhedral theory

Basic Polyhedral theory Basic Polyhdral thory Th st P = { A b} is calld a polyhdron. Lmma 1. Eithr th systm A = b, b 0, 0 has a solution or thr is a vctorπ such that π A 0, πb < 0 Thr cass, if solution in top row dos not ist

More information

On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery

On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery On th Rol of Fitnss, Prcision, Gnrliztion n Simplicity in Procss Discovry Joos Buijs Bouwijn vn Dongn Wil vn r Alst http://www.win.tu.nl/coslog/ Avncs in Procss ining ny procss iscovry n conformnc chcking

More information

We will see what is meant by standard form very shortly

We will see what is meant by standard form very shortly THEOREM: For fesible liner progrm in its stndrd form, the optimum vlue of the objective over its nonempty fesible region is () either unbounded or (b) is chievble t lest t one extreme point of the fesible

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

Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example

Outline. 1 Introduction. 2 Min-Cost Spanning Trees. 4 Example Outlin Computr Sin 33 Computtion o Minimum-Cost Spnnin Trs Prim's Alorithm Introution Mik Joson Dprtmnt o Computr Sin Univrsity o Clry Ltur #33 3 Alorithm Gnrl Constrution Mik Joson (Univrsity o Clry)

More information

12/3/12. Outline. Part 10. Graphs. Circuits. Euler paths/circuits. Euler s bridge problem (Bridges of Konigsberg Problem)

12/3/12. Outline. Part 10. Graphs. Circuits. Euler paths/circuits. Euler s bridge problem (Bridges of Konigsberg Problem) 12/3/12 Outlin Prt 10. Grphs CS 200 Algorithms n Dt Struturs Introution Trminology Implmnting Grphs Grph Trvrsls Topologil Sorting Shortst Pths Spnning Trs Minimum Spnning Trs Ciruits 1 Ciruits Cyl 2 Eulr

More information

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)} Introution Computr Sin & Enginring 423/823 Dsign n Anlysis of Algorithms Ltur 03 Elmntry Grph Algorithms (Chptr 22) Stphn Sott (Apt from Vinohnrn N. Vriym) I Grphs r strt t typs tht r pplil to numrous

More information

5/9/13. Part 10. Graphs. Outline. Circuits. Introduction Terminology Implementing Graphs

5/9/13. Part 10. Graphs. Outline. Circuits. Introduction Terminology Implementing Graphs Prt 10. Grphs CS 200 Algorithms n Dt Struturs 1 Introution Trminology Implmnting Grphs Outlin Grph Trvrsls Topologil Sorting Shortst Pths Spnning Trs Minimum Spnning Trs Ciruits 2 Ciruits Cyl A spil yl

More information

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)}

V={A,B,C,D,E} E={ (A,D),(A,E),(B,D), (B,E),(C,D),(C,E)} s s of s Computr Sin & Enginring 423/823 Dsign n Anlysis of Ltur 03 (Chptr 22) Stphn Sott (Apt from Vinohnrn N. Vriym) s of s s r strt t typs tht r pplil to numrous prolms Cn ptur ntitis, rltionships twn

More information

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers. Mehryar Mohri Courant Institute and Google Research

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers. Mehryar Mohri Courant Institute and Google Research Speech Recognition Lecture 2: Finite Automt nd Finite-Stte Trnsducers Mehryr Mohri Cournt Institute nd Google Reserch mohri@cims.nyu.com Preliminries Finite lphet Σ, empty string. Set of ll strings over

More information

Deterministic Finite Automata

Deterministic Finite Automata Finite Automt Deterministic Finite Automt H. Geuvers nd J. Rot Institute for Computing nd Informtion Sciences Version: fll 2016 J. Rot Version: fll 2016 Tlen en Automten 1 / 21 Outline Finite Automt Finite

More information

QUESTIONS BEGIN HERE!

QUESTIONS BEGIN HERE! Points miss: Stunt's Nm: Totl sor: /100 points Est Tnnss Stt Univrsity Dprtmnt of Computr n Informtion Sins CSCI 710 (Trnoff) Disrt Struturs TEST for Fll Smstr, 00 R this for strtin! This tst is los ook

More information

Non Deterministic Automata. Linz: Nondeterministic Finite Accepters, page 51

Non Deterministic Automata. Linz: Nondeterministic Finite Accepters, page 51 Non Deterministic Automt Linz: Nondeterministic Finite Accepters, pge 51 1 Nondeterministic Finite Accepter (NFA) Alphbet ={} q 1 q2 q 0 q 3 2 Nondeterministic Finite Accepter (NFA) Alphbet ={} Two choices

More information

Finite Automata. d: Q S Q. Finite automaton is M=(Q, S, d, q 0, F) Ex: an FA that accepts all odd-length strings of zeros: q 0 q 1. q i. q k.

Finite Automata. d: Q S Q. Finite automaton is M=(Q, S, d, q 0, F) Ex: an FA that accepts all odd-length strings of zeros: q 0 q 1. q i. q k. Finit Automt Bsic id: FA is mchin tht chngs stts whil procssing symols, on t tim. Finit st of stts: Q = {q 0, q 1, q 3,..., q k } Trnsition function: Initil stt: Finl stts: d: Q S Q q 0 Q F Q Finit utomton

More information

a,b a 1 a 2 a 3 a,b 1 a,b a,b 2 3 a,b a,b a 2 a,b CS Determinisitic Finite Automata 1

a,b a 1 a 2 a 3 a,b 1 a,b a,b 2 3 a,b a,b a 2 a,b CS Determinisitic Finite Automata 1 CS4 45- Determinisitic Finite Automt -: Genertors vs. Checkers Regulr expressions re one wy to specify forml lnguge String Genertor Genertes strings in the lnguge Deterministic Finite Automt (DFA) re nother

More information

Similarity Search. The Binary Branch Distance. Nikolaus Augsten.

Similarity Search. The Binary Branch Distance. Nikolaus Augsten. Similrity Srh Th Binry Brnh Distn Nikolus Augstn nikolus.ugstn@sg..t Dpt. of Computr Sins Univrsity of Slzurg http://rsrh.uni-slzurg.t Vrsion Jnury 11, 2017 Wintrsmstr 2016/2017 Augstn (Univ. Slzurg) Similrity

More information

Chapter 16. 1) is a particular point on the graph of the function. 1. y, where x y 1

Chapter 16. 1) is a particular point on the graph of the function. 1. y, where x y 1 Prctic qustions W now tht th prmtr p is dirctl rltd to th mplitud; thrfor, w cn find tht p. cos d [ sin ] sin sin Not: Evn though ou might not now how to find th prmtr in prt, it is lws dvisl to procd

More information

ECE602 Exam 1 April 5, You must show ALL of your work for full credit.

ECE602 Exam 1 April 5, You must show ALL of your work for full credit. ECE62 Exam April 5, 27 Nam: Solution Scor: / This xam is closd-book. You must show ALL of your work for full crdit. Plas rad th qustions carfully. Plas chck your answrs carfully. Calculators may NOT b

More information

Fingerprint idea. Assume:

Fingerprint idea. Assume: Fingerprint ide Assume: We cn compute fingerprint f(p) of P in O(m) time. If f(p) f(t[s.. s+m 1]), then P T[s.. s+m 1] We cn compre fingerprints in O(1) We cn compute f = f(t[s+1.. s+m]) from f(t[s.. s+m

More information

Verification of Initial-State Opacity in Petri Nets

Verification of Initial-State Opacity in Petri Nets Verifiction of Initil-Stte Opcity in Petri Nets Yin Tong 1, Zhiwu Li, Crl Setzu 3 nd Alessndro Giu 4 Astrct A Petri net system is sid to e initil-stte opque if its initil stte remins opque to n externl

More information

Why the Junction Tree Algorithm? The Junction Tree Algorithm. Clique Potential Representation. Overview. Chris Williams 1.

Why the Junction Tree Algorithm? The Junction Tree Algorithm. Clique Potential Representation. Overview. Chris Williams 1. Why th Juntion Tr lgorithm? Th Juntion Tr lgorithm hris Willims 1 Shool of Informtis, Univrsity of Einurgh Otor 2009 Th JT is gnrl-purpos lgorithm for omputing (onitionl) mrginls on grphs. It os this y

More information

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura

Module graph.py. 1 Introduction. 2 Graph basics. 3 Module graph.py. 3.1 Objects. CS 231 Naomi Nishimura Moul grph.py CS 231 Nomi Nishimur 1 Introution Just lik th Python list n th Python itionry provi wys of storing, ssing, n moifying t, grph n viw s wy of storing, ssing, n moifying t. Bus Python os not

More information

Math 61 : Discrete Structures Final Exam Instructor: Ciprian Manolescu. You have 180 minutes.

Math 61 : Discrete Structures Final Exam Instructor: Ciprian Manolescu. You have 180 minutes. Nm: UCA ID Numr: Stion lttr: th 61 : Disrt Struturs Finl Exm Instrutor: Ciprin nolsu You hv 180 minuts. No ooks, nots or lultors r llow. Do not us your own srth ppr. 1. (2 points h) Tru/Fls: Cirl th right

More information

Sybil Attacks and Defenses

Sybil Attacks and Defenses CPSC 426/526 Syil Attks n Dfnss Ennn Zhi Computr Sin Dprtmnt Yl Univrsity Rll: L-5 Rputtion systms: - Why w n rputtion/trust systms - Wht is glol rputtion mol - Wht is prsonliz rputtion mol - Cs stuis:

More information

On Decentralized Observability of Discrete Event Systems

On Decentralized Observability of Discrete Event Systems 2011 50th IEEE Conference on Decision nd Control nd Europen Control Conference (CDC-ECC) Orlndo, FL, USA, Decemer 12-15, 2011 On Decentrlized Oservility of Discrete Event Systems M.P. Csino, A. Giu, C.

More information

Novel Logical Method for Security Analysis of Electronic Payment Protocols. Technology, No.47, Yanwachi street, Changsha, Hunan, China

Novel Logical Method for Security Analysis of Electronic Payment Protocols. Technology, No.47, Yanwachi street, Changsha, Hunan, China Novl Logicl thod for Scurity Anlysis of Elctronic Pymnt Protocols Yi Liu, Xingtong Liu, Li Zhng, Jin Wng nd hojing Tng ollg of Elctronic Scinc nd Enginring, Ntionl Univrsity of Dfns Tchnology, No.47, Ynwchi

More information

The Z transform techniques

The Z transform techniques h Z trnfor tchniqu h Z trnfor h th rol in dicrt yt tht th Lplc trnfor h in nlyi of continuou yt. h Z trnfor i th principl nlyticl tool for ingl-loop dicrt-ti yt. h Z trnfor h Z trnfor i to dicrt-ti yt

More information

a b c cat CAT A B C Aa Bb Cc cat cat Lesson 1 (Part 1) Verbal lesson: Capital Letters Make The Same Sound Lesson 1 (Part 1) continued...

a b c cat CAT A B C Aa Bb Cc cat cat Lesson 1 (Part 1) Verbal lesson: Capital Letters Make The Same Sound Lesson 1 (Part 1) continued... Progrssiv Printing T.M. CPITLS g 4½+ Th sy, fun (n FR!) wy to tch cpitl lttrs. ook : C o - For Kinrgrtn or First Gr (not for pr-school). - Tchs tht cpitl lttrs mk th sm souns s th littl lttrs. - Tchs th

More information

On the Maximally-Permissive Range Control Problem in Partially-Observed Discrete Event Systems

On the Maximally-Permissive Range Control Problem in Partially-Observed Discrete Event Systems On the Mximlly-Permissie Rnge Control Prolem in Prtilly-Osered Disrete Eent Systems Xing Yin nd Stéphne Lfortune EECS Deprtment, Uniersity of Mihign 55th IEEE CDC, De 2-4, 206, Ls Vegs, USA X.Yin & S.Lfortune

More information

5.1 Definitions and Examples 5.2 Deterministic Pushdown Automata

5.1 Definitions and Examples 5.2 Deterministic Pushdown Automata CSC4510 AUTOMATA 5.1 Definitions nd Exmples 5.2 Deterministic Pushdown Automt Definitions nd Exmples A lnguge cn be generted by CFG if nd only if it cn be ccepted by pushdown utomton. A pushdown utomton

More information

A Symbolic Approach to Control via Approximate Bisimulations

A Symbolic Approach to Control via Approximate Bisimulations A Symolic Approch to Control vi Approximte Bisimultions Antoine Girrd Lortoire Jen Kuntzmnn, Université Joseph Fourier Grenole, Frnce Interntionl Symposium on Innovtive Mthemticl Modelling Tokyo, Jpn,

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

Reinforcement learning II

Reinforcement learning II CS 1675 Introduction to Mchine Lerning Lecture 26 Reinforcement lerning II Milos Huskrecht milos@cs.pitt.edu 5329 Sennott Squre Reinforcement lerning Bsics: Input x Lerner Output Reinforcement r Critic

More information

Hybrid Control and Switched Systems. Lecture #2 How to describe a hybrid system? Formal models for hybrid system

Hybrid Control and Switched Systems. Lecture #2 How to describe a hybrid system? Formal models for hybrid system Hyrid Control nd Switched Systems Lecture #2 How to descrie hyrid system? Forml models for hyrid system João P. Hespnh University of Cliforni t Snt Brr Summry. Forml models for hyrid systems: Finite utomt

More information

Graph Isomorphism. Graphs - II. Cayley s Formula. Planar Graphs. Outline. Is K 5 planar? The number of labeled trees on n nodes is n n-2

Graph Isomorphism. Graphs - II. Cayley s Formula. Planar Graphs. Outline. Is K 5 planar? The number of labeled trees on n nodes is n n-2 Grt Thortil Is In Computr Sin Vitor Amhik CS 15-251 Ltur 9 Grphs - II Crngi Mllon Univrsity Grph Isomorphism finition. Two simpl grphs G n H r isomorphi G H if thr is vrtx ijtion V H ->V G tht prsrvs jny

More information

Constructive Geometric Constraint Solving

Constructive Geometric Constraint Solving Construtiv Gomtri Constrint Solving Antoni Soto i Rir Dprtmnt Llngutgs i Sistms Inormàtis Univrsitt Politèni Ctluny Brlon, Sptmr 2002 CGCS p.1/37 Prliminris CGCS p.2/37 Gomtri onstrint prolm C 2 D L BC

More information

Chemical Physics II. More Stat. Thermo Kinetics Protein Folding...

Chemical Physics II. More Stat. Thermo Kinetics Protein Folding... Chmical Physics II Mor Stat. Thrmo Kintics Protin Folding... http://www.nmc.ctc.com/imags/projct/proj15thumb.jpg http://nuclarwaponarchiv.org/usa/tsts/ukgrabl2.jpg http://www.photolib.noaa.gov/corps/imags/big/corp1417.jpg

More information

CONTINUITY AND DIFFERENTIABILITY

CONTINUITY AND DIFFERENTIABILITY MCD CONTINUITY AND DIFFERENTIABILITY NCERT Solvd mpls upto th sction 5 (Introduction) nd 5 (Continuity) : Empl : Chck th continuity of th function f givn by f() = + t = Empl : Emin whthr th function f

More information

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers

Speech Recognition Lecture 2: Finite Automata and Finite-State Transducers Speech Recognition Lecture 2: Finite Automt nd Finite-Stte Trnsducers Eugene Weinstein Google, NYU Cournt Institute eugenew@cs.nyu.edu Slide Credit: Mehryr Mohri Preliminries Finite lphet, empty string.

More information

Graphs. CSC 1300 Discrete Structures Villanova University. Villanova CSC Dr Papalaskari

Graphs. CSC 1300 Discrete Structures Villanova University. Villanova CSC Dr Papalaskari Grphs CSC 1300 Disrt Struturs Villnov Univrsity Grphs Grphs r isrt struturs onsis?ng of vr?s n gs tht onnt ths vr?s. Grphs n us to mol: omputr systms/ntworks mthm?l rl?ons logi iruit lyout jos/prosss f

More information

COMP108 Algorithmic Foundations

COMP108 Algorithmic Foundations Grdy mthods Prudn Wong http://www.s.liv..uk/~pwong/thing/omp108/01617 Coin Chng Prolm Suppos w hv 3 typs of oins 10p 0p 50p Minimum numr of oins to mk 0.8, 1.0, 1.? Grdy mthod Lrning outoms Undrstnd wht

More information

Centrum voor Wiskunde en Informatica REPORTRAPPORT. Supervisory control for nondeterministic systems

Centrum voor Wiskunde en Informatica REPORTRAPPORT. Supervisory control for nondeterministic systems Centrum voor Wiskunde en Informtic REPORTRAPPORT Supervisory control for nondeterministic systems A. Overkmp Deprtment of Opertions Reserch, Sttistics, nd System Theory BS-R9411 1994 Supervisory Control

More information

Let's start with an example:

Let's start with an example: Finite Automt Let's strt with n exmple: Here you see leled circles tht re sttes, nd leled rrows tht re trnsitions. One of the sttes is mrked "strt". One of the sttes hs doule circle; this is terminl stte

More information

CS September 2018

CS September 2018 Loil los Distriut Systms 06. Loil los Assin squn numrs to msss All ooprtin prosss n r on orr o vnts vs. physil los: rport tim o y Assum no ntrl tim sour Eh systm mintins its own lol lo No totl orrin o

More information

Lecture contents. Bloch theorem k-vector Brillouin zone Almost free-electron model Bands Effective mass Holes. NNSE 508 EM Lecture #9

Lecture contents. Bloch theorem k-vector Brillouin zone Almost free-electron model Bands Effective mass Holes. NNSE 508 EM Lecture #9 Lctur contnts Bloch thorm -vctor Brillouin zon Almost fr-lctron modl Bnds ffctiv mss Hols Trnsltionl symmtry: Bloch thorm On-lctron Schrödingr qution ch stt cn ccommo up to lctrons: If Vr is priodic function:

More information

Learning Regular Languages over Large Alphabets

Learning Regular Languages over Large Alphabets Irini-Eleftheri Mens VERIMAG, University of Grenoble-Alpes Lerning Regulr Lnguges over Lrge Alphbets 10 October 2017 Jury Members Oded Mler Directeur de thèse Lurent Fribourg Exminteur Dn Angluin Rpporteur

More information

Elliptical motion, gravity, etc

Elliptical motion, gravity, etc FW Physics 130 G:\130 lctur\ch 13 Elliticl motion.docx g 1 of 7 11/3/010; 6:40 PM; Lst rintd 11/3/010 6:40:00 PM Fig. 1 Elliticl motion, grvity, tc minor xis mjor xis F 1 =A F =B C - D, mjor nd minor xs

More information

Chapter 1, Part 1. Regular Languages. CSC527, Chapter 1, Part 1 c 2012 Mitsunori Ogihara 1

Chapter 1, Part 1. Regular Languages. CSC527, Chapter 1, Part 1 c 2012 Mitsunori Ogihara 1 Chpter 1, Prt 1 Regulr Lnguges CSC527, Chpter 1, Prt 1 c 2012 Mitsunori Ogihr 1 Finite Automt A finite utomton is system for processing ny finite sequence of symols, where the symols re chosen from finite

More information

Section 3: Antiderivatives of Formulas

Section 3: Antiderivatives of Formulas Chptr Th Intgrl Appli Clculus 96 Sction : Antirivtivs of Formuls Now w cn put th is of rs n ntirivtivs togthr to gt wy of vluting finit intgrls tht is ct n oftn sy. To vlut finit intgrl f(t) t, w cn fin

More information

TuLiP: A Software Toolbox for Receding Horizon Temporal Logic Planning & Computer Lab 2

TuLiP: A Software Toolbox for Receding Horizon Temporal Logic Planning & Computer Lab 2 TuLiP: A Softwar Toolbox for Rcding Horizon Tmporal Logic Planning & Computr Lab 2 Nok Wongpiromsarn Richard M. Murray Ufuk Topcu EECI, 21 March 2013 Outlin Ky Faturs of TuLiP Embddd control softwar synthsis

More information

Kleene s Theorem. Kleene s Theorem. Kleene s Theorem. Kleene s Theorem. Kleene s Theorem. Kleene s Theorem 2/16/15

Kleene s Theorem. Kleene s Theorem. Kleene s Theorem. Kleene s Theorem. Kleene s Theorem. Kleene s Theorem 2/16/15 Models of Comput:on Lecture #8 Chpter 7 con:nued Any lnguge tht e defined y regulr expression, finite utomton, or trnsi:on grph cn e defined y ll three methods We prove this y showing tht ny lnguge defined

More information

Seven-Segment Display Driver

Seven-Segment Display Driver 7-Smnt Disply Drivr, Ron s in 7-Smnt Disply Drivr, Ron s in Prolm 62. 00 0 0 00 0000 000 00 000 0 000 00 0 00 00 0 0 0 000 00 0 00 BCD Diits in inry Dsin Drivr Loi 4 inputs, 7 outputs 7 mps, h with 6 on

More information

CS375: Logic and Theory of Computing

CS375: Logic and Theory of Computing CS375: Logic nd Theory of Computing Fuhu (Frnk) Cheng Deprtment of Computer Science University of Kentucky 1 Tble of Contents: Week 1: Preliminries (set lgebr, reltions, functions) (red Chpters 1-4) Weeks

More information

State Minimization for DFAs

State Minimization for DFAs Stte Minimiztion for DFAs Red K & S 2.7 Do Homework 10. Consider: Stte Minimiztion 4 5 Is this miniml mchine? Step (1): Get rid of unrechle sttes. Stte Minimiztion 6, Stte is unrechle. Step (2): Get rid

More information

CS 461, Lecture 17. Today s Outline. Example Run

CS 461, Lecture 17. Today s Outline. Example Run Prim s Algorithm CS 461, Ltur 17 Jr Si Univrsity o Nw Mxio In Prim s lgorithm, th st A mintin y th lgorithm orms singl tr. Th tr strts rom n ritrry root vrtx n grows until it spns ll th vrtis in V At h

More information

, between the vertical lines x a and x b. Given a demand curve, having price as a function of quantity, p f (x) at height k is the curve f ( x,

, between the vertical lines x a and x b. Given a demand curve, having price as a function of quantity, p f (x) at height k is the curve f ( x, Clculus for Businss nd Socil Scincs - Prof D Yun Finl Em Rviw vrsion 5/9/7 Chck wbsit for ny postd typos nd updts Pls rport ny typos This rviw sht contins summris of nw topics only (This rviw sht dos hv

More information

CSE 373. Graphs 1: Concepts, Depth/Breadth-First Search reading: Weiss Ch. 9. slides created by Marty Stepp

CSE 373. Graphs 1: Concepts, Depth/Breadth-First Search reading: Weiss Ch. 9. slides created by Marty Stepp CSE 373 Grphs 1: Conpts, Dpth/Brth-First Srh ring: Wiss Ch. 9 slis rt y Mrty Stpp http://www.s.wshington.u/373/ Univrsity o Wshington, ll rights rsrv. 1 Wht is grph? 56 Tokyo Sttl Soul 128 16 30 181 140

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

Recursively Enumerable and Recursive. Languages

Recursively Enumerable and Recursive. Languages Recursively Enumerble nd Recursive nguges 1 Recll Definition (clss 19.pdf) Definition 10.4, inz, 6 th, pge 279 et S be set of strings. An enumertion procedure for Turing Mchine tht genertes ll strings

More information

Petri Nets. Rebecca Albrecht. Seminar: Automata Theory Chair of Software Engeneering

Petri Nets. Rebecca Albrecht. Seminar: Automata Theory Chair of Software Engeneering Petri Nets Ree Alreht Seminr: Automt Theory Chir of Softwre Engeneering Overview 1. Motivtion: Why not just using finite utomt for everything? Wht re Petri Nets nd when do we use them? 2. Introdution:

More information

In the previous two chapters, we clarified what it means for a problem to be decidable or undecidable.

In the previous two chapters, we clarified what it means for a problem to be decidable or undecidable. Chaptr 7 Computational Complxity 7.1 Th Class P In th prvious two chaptrs, w clarifid what it mans for a problm to b dcidabl or undcidabl. In principl, if a problm is dcidabl, thn thr is an algorithm (i..,

More information

The size of subsequence automaton

The size of subsequence automaton Theoreticl Computer Science 4 (005) 79 84 www.elsevier.com/locte/tcs Note The size of susequence utomton Zdeněk Troníček,, Ayumi Shinohr,c Deprtment of Computer Science nd Engineering, FEE CTU in Prgue,

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

CS 275 Automata and Formal Language Theory

CS 275 Automata and Formal Language Theory CS 275 Automt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Problem (II) Chpter II.6.: Push Down Automt Remrk: This mteril is no longer tught nd not directly exm relevnt Anton Setzer (Bsed

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

Regular languages refresher

Regular languages refresher Regulr lnguges refresher 1 Regulr lnguges refresher Forml lnguges Alphet = finite set of letters Word = sequene of letter Lnguge = set of words Regulr lnguges defined equivlently y Regulr expressions Finite-stte

More information

Graphs. Graphs. Graphs: Basic Terminology. Directed Graphs. Dr Papalaskari 1

Graphs. Graphs. Graphs: Basic Terminology. Directed Graphs. Dr Papalaskari 1 CSC 00 Disrt Struturs : Introuon to Grph Thory Grphs Grphs CSC 00 Disrt Struturs Villnov Univrsity Grphs r isrt struturs onsisng o vrs n gs tht onnt ths vrs. Grphs n us to mol: omputr systms/ntworks mthml

More information

# 1 ' 10 ' 100. Decimal point = 4 hundred. = 6 tens (or sixty) = 5 ones (or five) = 2 tenths. = 7 hundredths.

# 1 ' 10 ' 100. Decimal point = 4 hundred. = 6 tens (or sixty) = 5 ones (or five) = 2 tenths. = 7 hundredths. How os it work? Pl vlu o imls rprsnt prts o whol numr or ojt # 0 000 Tns o thousns # 000 # 00 Thousns Hunrs Tns Ons # 0 Diml point st iml pl: ' 0 # 0 on tnth n iml pl: ' 0 # 00 on hunrth r iml pl: ' 0

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

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata

CS103B Handout 18 Winter 2007 February 28, 2007 Finite Automata CS103B ndout 18 Winter 2007 Ferury 28, 2007 Finite Automt Initil text y Mggie Johnson. Introduction Severl childrens gmes fit the following description: Pieces re set up on plying ord; dice re thrown or

More information

b. How many ternary words of length 23 with eight 0 s, nine 1 s and six 2 s?

b. How many ternary words of length 23 with eight 0 s, nine 1 s and six 2 s? MATH 3012 Finl Exm, My 4, 2006, WTT Stunt Nm n ID Numr 1. All our prts o this prolm r onrn with trnry strings o lngth n, i.., wors o lngth n with lttrs rom th lpht {0, 1, 2}.. How mny trnry wors o lngth

More information

Section: Other Models of Turing Machines. Definition: Two automata are equivalent if they accept the same language.

Section: Other Models of Turing Machines. Definition: Two automata are equivalent if they accept the same language. Section: Other Models of Turing Mchines Definition: Two utomt re equivlent if they ccept the sme lnguge. Turing Mchines with Sty Option Modify δ, Theorem Clss of stndrd TM s is equivlent to clss of TM

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

Chapter Five: Nondeterministic Finite Automata. Formal Language, chapter 5, slide 1

Chapter Five: Nondeterministic Finite Automata. Formal Language, chapter 5, slide 1 Chpter Five: Nondeterministic Finite Automt Forml Lnguge, chpter 5, slide 1 1 A DFA hs exctly one trnsition from every stte on every symol in the lphet. By relxing this requirement we get relted ut more

More information

A 4-state solution to the Firing Squad Synchronization Problem based on hybrid rule 60 and 102 cellular automata

A 4-state solution to the Firing Squad Synchronization Problem based on hybrid rule 60 and 102 cellular automata A 4-stt solution to th Firing Squ Synhroniztion Prolm s on hyri rul 60 n 102 llulr utomt LI Ning 1, LIANG Shi-li 1*, CUI Shung 1, XU Mi-ling 1, ZHANG Ling 2 (1. Dprtmnt o Physis, Northst Norml Univrsity,

More information

ME 522 PRINCIPLES OF ROBOTICS. FIRST MIDTERM EXAMINATION April 19, M. Kemal Özgören

ME 522 PRINCIPLES OF ROBOTICS. FIRST MIDTERM EXAMINATION April 19, M. Kemal Özgören ME 522 PINCIPLES OF OBOTICS FIST MIDTEM EXAMINATION April 9, 202 Nm Lst Nm M. Kml Özgörn 2 4 60 40 40 0 80 250 USEFUL FOMULAS cos( ) cos cos sin sin sin( ) sin cos cos sin sin y/ r, cos x/ r, r 0 tn 2(

More information

Weighted graphs -- reminder. Data Structures LECTURE 15. Shortest paths algorithms. Example: weighted graph. Two basic properties of shortest paths

Weighted graphs -- reminder. Data Structures LECTURE 15. Shortest paths algorithms. Example: weighted graph. Two basic properties of shortest paths Dt Strutur LECTURE Shortt pth lgorithm Proprti of hortt pth Bllmn-For lgorithm Dijktr lgorithm Chptr in th txtook (pp ). Wight grph -- rminr A wight grph i grph in whih g hv wight (ot) w(v i, v j ) >.

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

Deciding the value 1 problem for probabilistic leaktight automata

Deciding the value 1 problem for probabilistic leaktight automata Deciding the vlue 1 prolem for proilistic lektight utomt Nthnël Fijlkow, joint work with Hugo Gimert nd Youssouf Oulhdj LIAFA, Université Pris 7, Frnce, University of Wrsw, Polnd. LICS, Durovnik, Croti

More information