Pointer Analysis. Outline: What is pointer analysis Intraprocedural pointer analysis Interprocedural pointer analysis. Andersen and Steensgaard

Size: px
Start display at page:

Download "Pointer Analysis. Outline: What is pointer analysis Intraprocedural pointer analysis Interprocedural pointer analysis. Andersen and Steensgaard"

Transcription

1 Poiner anaysis

2 Poiner Anaysis Ouine: Wha is oiner anaysis Inrarocedura oiner anaysis Inerrocedura oiner anaysis Andersen and Seensgaard

3 Poiner and Aias Anaysis Aiases: wo exressions ha denoe he same memory ocaion. Aiases are inroduced by: oiners ca-by-reference array indexing C unions

4 Usefu for wha? Imrove he recision of anayses ha require knowing wha is modified or referenced (eg cons ro, CSE ) Eiminae redundan oads/sores and dead sores. x := *;... y := *; // reace wih y := x? *x :=...; // is *x dead? Paraeizaion of code can recursive cas o quick_sor be run in arae? Yes, rovided ha hey reference disinc regions of he array. Idenify objecs o be racked in error deecion oos x.ock();... y.unock(); // same objec as x?

5 Kinds of aias informaion Poins-o informaion (mus or may versions) a rogram oin, comue a se of airs of he form! x, where oins o x. can reresen his informaion in a oins-o grah Aias airs a each rogram oin, comue he se of of a airs (e 1,e 2 ) where e 1 and e 2 mus/may reference he same memory. x y z Sorage shae anaysis a each rogram oin, comue an absrac descriion of he oiner srucure.

6 Inrarocedura Poins-o Anaysis Wan o comue may-oins-o informaion Laice:

7 Fow funcions in x := k F x := k (in) = ou in x := a + b F x := a+b (in) = ou

8 Fow funcions in x := y F x := y (in) = ou in x := &y F x := &y (in) = ou

9 Fow funcions in x := *y F x := *y (in) = ou in *x := y F *x := y (in) = ou

10 Inrarocedura Poins-o Anaysis Fow funcions:

11 Poiners o dynamicay-aocaed memory Hande saemens of he form: x := new T One idea: generae a new variabe each ime he new saemen is anayzed o sand for he new ocaion:

12 Exame := new Cons := := new Cons * := :=

13 Exame soved := new Cons := V1 V1 V2 := new Cons V1 V2 V1 V2 V3 * := V1 V2 V1 V2 V3 := V1 V2 V1 V2 V3

14 Wha wen wrong? Laice infiniey a! We were esseniay running he rogram Insead, we need o summarize he infiniey many aocaed objecs in a finie way New Idea: inroduce summary nodes, which wi sand for a whoe cass of aocaed objecs.

15 Wha wen wrong? Exame: For each new saemen wih abe L, inroduce a summary node oc L, which sands for he memory aocaed by saemen L. Summary nodes can use oher crierion for merging.

16 Exame revisied : := new Cons := : := new Cons * := :=

17 Exame revisied & soved : := new Cons Ier 1 Ier 2 Ier 3 := : := new Cons * := :=

18 Exame revisied & soved : := new Cons Ier 1 Ier 2 Ier 3 := : := new Cons * := :=

19 Array aiasing, and oiners o arrays Array indexing can cause aiasing: a[i] aiases b[j] if: a aiases b and i = j a and b overa, and i = j + k, where k is he amoun of overa. Can have oiners o eemens of an array := &a[i];...; ++; How can arrays be modeed? Coud rea he whoe array as one ocaion. Coud ry o reason abou he array index exressions: array deendence anaysis.

20 Fieds Can summarize fieds using er fied summary for each fied F, kee a oins-o node caed F ha summarizes a ossibe vaues ha can ever be sored in F Can aso use aocaion sies for each fied F, and each aocaion sie S, kee a oins-o node caed (F, S) ha summarizes a ossibe vaues ha can ever be sored in he fied F of objecs aocaed a sie S.

21 Summary We jus saw: inrarocedura oins-o anaysis handing dynamicay aocaed memory handing oiners o arrays Bu, inrarocedura oiner anaysis is no enough. Sharing daa srucures across muie rocedures is one he big benefis of oiners: insead of assing he whoe daa srucures around, jus ass oiners o hem (eg C ass by reference). So oiners end u oining o srucures shared across rocedures. If you don do an inerroc anaysis, you have o make conservaive assumions funcions enries and funcion cas.

22 Conservaive aroximaion on enry Say we don have inerrocedura oiner anaysis. Wha shoud he informaion be a he inu of he foowing rocedure: goba g; void (x,y) { x y g... }

23 Conservaive aroximaion on enry Here are a few souions: goba g; void (x,y) { }... x y g ocaions from aoc sies rior o his invocaion x,y,g & ocaions from aoc sies rior o his invocaion They are a very conservaive! We can ry o do beer.

24 Inerrocedura oiner anaysis Main difficuy in erforming inerrocedura oiner anaysis is scaing One can use a o-down summary based aroach (Wison & Lam 95), bu even hese are hard o scae

25 Exame revisied : := new Cons Cos: sace: sore one fac a each rog oin ime: ieraion Ier 1 Ier 2 Ier 3 := : := new Cons * := :=

26 New idea: sore one daafow fac Sore one daafow fac for he whoe rogram Each saemen udaes his one daafow fac use he revious fow funcions, bu now hey ake he whoe rogram daafow fac, and reurn an udaed version of i. Process each saemen once, ignoring he order of he saemens This is caed a fow-insensiive anaysis.

27 Fow insensiive oiner anaysis : := new Cons := : := new Cons * := :=

28 Fow insensiive oiner anaysis : := new Cons := : := new Cons * := :=

29 Fow sensiive vs. insensiive : := new Cons Fow-sensiive Son Fow-insensiive Son := : := new Cons * := :=

30 Wha wen wrong? Wha haened o he ink beween and? Can do srong udaes anymore! Need o remove a he ki ses from he fow funcions. Wha haened o he sef oo on? We si have o ierae!

31 Fow insensiive oiner anaysis: fixed : := new Cons := : := new Cons * := :=

32 Fow insensiive oiner anaysis: fixed This is Andersen s agorihm 94 Fina resu : := new Cons Ier 1 Ier 2 Ier 3 := : := new Cons * := := L2 L1 L2

33 Fow sensiive vs. insensiive, again : := new Cons Fow-sensiive Son Fow-insensiive Son := : := new Cons * := :=

34 Fow insensiive oss of recision Fow insensiive anaysis eads o oss of recision! main() { x := &y; }... x := &z; Fow insensiive anaysis es us ha x may oin o z here! However: uses ess memory (memory can be a big boeneck o running on arge rograms) runs faser

35 In Cass Exercise! : := new Cons : q := new Cons * = q r = &q *q = r *q = s = r s = *r = s

36 In Cass Exercise! soved : := new Cons : q := new Cons * = q r = &q *q = r *q = q r s s = r s = *r = s

37 Wors case comexiy of Andersen x y *x = y x y a b c d e f a b c d e f Wors case: N 2 er saemen, so a eas N 3 for he whoe rogram. Andersen is in fac O(N 3 )

38 New idea: one successor er node Make each node have ony one successor. This is an invarian ha we wan o mainain. x y *x = y x y a,b,c d,e,f a,b,c d,e,f

39 More genera case for *x = y x y *x = y

40 More genera case for *x = y x y x y x y *x = y

41 Handing: x = *y x y x = *y

42 Handing: x = *y x y x y x y x = *y

43 Handing: x = y (wha abou y = x?) x y x = y Handing: x = &y x y x = &y

44 Handing: x = y (wha abou y = x?) x y x y x y x = y ge he same for y = x Handing: x = &y x y x = &y x y x y,

45 Our favorie exame, once more! : := new Cons 1 := 2 : := new Cons 3 * := 4 := 5

46 Our favorie exame, once more! : := new Cons := : := new Cons * := 4 := 5,

47 Fow insensiive oss of recision : := new Cons Fow-sensiive Subse-based Fow-insensiive Subse-based Fow-insensiive Unificaionbased := : := new Cons * :=, :=

48 Anoher exame bar() { 1 i := &a; 2 j := &b; 3 foo(&i); 4 foo(&j); // i ns o wha? *i :=...; } void foo(in* ) { rinf( %d,*); }

49 Anoher exame bar() { 1 i := &a; 2 j := &b; 3 foo(&i); 4 foo(&j); // i ns o wha? *i :=...; i 1 2 a i a 4 j b 3 i a j b } void foo(in* ) { rinf( %d,*); } i a j b i,j a,b

50 Amos inear ime Time comexiy: O(Nα(N, N)) inverse Ackermann funcion So sow-growing, i is basicay inear in racice For he curious: node merging imemened using UNION-FIND srucure, which aows se union wih amorized cos of O(α(N, N)) er o. Take CSE 202 o earn more!

51 In Cass Exercise! : := new Cons : q := new Cons * = q r = &q *q = r *q = s = r s = *r = s

52 In Cass Exercise! soved : := new Cons : q := new Cons Seensgaard q,,s2 * = q r s r = &q *q = r s = r *q = s = Andersen q *r = s r s

53 Advanced Poiner Anaysis Combine fow-sensiive/fow-insensiive Cever daa-srucure design Conex-sensiiviy

Pointer Analysis. Pointer analysis. Pointer and Alias Analysis. Useful for what? Intraprocedural Points-to Analysis. Kinds of alias information

Pointer Analysis. Pointer analysis. Pointer and Alias Analysis. Useful for what? Intraprocedural Points-to Analysis. Kinds of alias information Poer Anasis Poer anasis Oue: Wha is oer anasis Inrarocedura oer anasis Inerrocedura oer anasis Andersen and Seensgaard Poer and Aias Anasis Aiases: wo eressions ha denoe he same memor ocaion. Aiases are

More information

Data Structures and Algorithms

Data Structures and Algorithms Daa Srucures and Algorihms The maerial for his lecure is drawn, in ar, from The Pracice of Programming (Kernighan & Pike) Chaer 2 1 Goals of his Lecure Hel you learn (or refresh your memory) abou: Common

More information

Parallel Flow-Sensitive Pointer Analysis by Graph-Rewriting

Parallel Flow-Sensitive Pointer Analysis by Graph-Rewriting Parae Fow-Sensitive Pointer Anaysis by Grah-Rewriting Vaivaswatha Nagaraj R. Govindarajan Indian Institute of Science, Bangaore PACT 2013 2 Outine Introduction Background Fow-sensitive grah-rewriting formuation

More information

Sam knows that his MP3 player has 40% of its battery life left and that the battery charges by an additional 12 percentage points every 15 minutes.

Sam knows that his MP3 player has 40% of its battery life left and that the battery charges by an additional 12 percentage points every 15 minutes. 8.F Baery Charging Task Sam wans o ake his MP3 player and his video game player on a car rip. An hour before hey plan o leave, he realized ha he forgo o charge he baeries las nigh. A ha poin, he plugged

More information

Data Structures and Algorithms. The material for this lecture is drawn, in part, from The Practice of Programming (Kernighan & Pike) Chapter 2

Data Structures and Algorithms. The material for this lecture is drawn, in part, from The Practice of Programming (Kernighan & Pike) Chapter 2 Daa Srucures and Algorihms The maerial for his lecure is drawn, in par, from The Pracice of Programming (Kernighan & Pike) Chaper 2 1 Moivaing Quoaion Every program depends on algorihms and daa srucures,

More information

Petri Nets for Object-Oriented Modeling

Petri Nets for Object-Oriented Modeling Peri Nes for Objec-Oriened Modeling Sefan Wi Absrac Ensuring he correcness of concurren rograms is difficul since common aroaches for rogram design do no rovide aroriae mehods This aer gives a brief inroducion

More information

COMP26120: Algorithms and Imperative Programming

COMP26120: Algorithms and Imperative Programming COMP26120 ecure C3 1/48 COMP26120: Algorihms and Imperaive Programming ecure C3: C - Recursive Daa Srucures Pee Jinks School of Compuer Science, Universiy of Mancheser Auumn 2011 COMP26120 ecure C3 2/48

More information

Efficient Computation of Parameterized Pointer Information for Interprocedural Analyses

Efficient Computation of Parameterized Pointer Information for Interprocedural Analyses Efficien Comuaion of Parameerized Poiner Informaion for Inerrocedural Analyses Donlin Lian and Mary Jean Harrold Collee of Comuin Georia Insiue of Technoloy Alana, GA 30332, USA {dlian,harrold}@cc.aech.edu

More information

A Principled Approach to. MILP Modeling. Columbia University, August Carnegie Mellon University. Workshop on MIP. John Hooker.

A Principled Approach to. MILP Modeling. Columbia University, August Carnegie Mellon University. Workshop on MIP. John Hooker. Slide A Principled Approach o MILP Modeling John Hooer Carnegie Mellon Universiy Worshop on MIP Columbia Universiy, Augus 008 Proposal MILP modeling is an ar, bu i need no be unprincipled. Slide Proposal

More information

4. Minimax and planning problems

4. Minimax and planning problems CS/ECE/ISyE 524 Inroducion o Opimizaion Spring 2017 18 4. Minima and planning problems ˆ Opimizing piecewise linear funcions ˆ Minima problems ˆ Eample: Chebyshev cener ˆ Muli-period planning problems

More information

NEWTON S SECOND LAW OF MOTION

NEWTON S SECOND LAW OF MOTION Course and Secion Dae Names NEWTON S SECOND LAW OF MOTION The acceleraion of an objec is defined as he rae of change of elociy. If he elociy changes by an amoun in a ime, hen he aerage acceleraion during

More information

Midterm Exam Announcements

Midterm Exam Announcements Miderm Exam Noe: This was a challenging exam. CSCI 4: Principles o Programming Languages Lecure 1: Excepions Insrucor: Dan Barowy Miderm Exam Scores 18 16 14 12 10 needs improvemen 8 6 4 2 0 0-49 50-59

More information

index.pdf March 17,

index.pdf March 17, index.pdf March 17, 2013 1 ITI 1121. Introduction to omputing II Marce Turcotte Schoo of Eectrica Engineering and omputer Science Linked List (Part 2) Tai pointer ouby inked ist ummy node Version of March

More information

AML710 CAD LECTURE 11 SPACE CURVES. Space Curves Intrinsic properties Synthetic curves

AML710 CAD LECTURE 11 SPACE CURVES. Space Curves Intrinsic properties Synthetic curves AML7 CAD LECTURE Space Curves Inrinsic properies Synheic curves A curve which may pass hrough any region of hreedimensional space, as conrased o a plane curve which mus lie on a single plane. Space curves

More information

Announcements For The Logic of Boolean Connectives Truth Tables, Tautologies & Logical Truths. Outline. Introduction Truth Functions

Announcements For The Logic of Boolean Connectives Truth Tables, Tautologies & Logical Truths. Outline. Introduction Truth Functions Announcemens For 02.05.09 The Logic o Boolean Connecives Truh Tables, Tauologies & Logical Truhs 1 HW3 is due nex Tuesday William Sarr 02.05.09 William Sarr The Logic o Boolean Connecives (Phil 201.02)

More information

MOTION DETECTORS GRAPH MATCHING LAB PRE-LAB QUESTIONS

MOTION DETECTORS GRAPH MATCHING LAB PRE-LAB QUESTIONS NME: TE: LOK: MOTION ETETORS GRPH MTHING L PRE-L QUESTIONS 1. Read he insrucions, and answer he following quesions. Make sure you resae he quesion so I don hae o read he quesion o undersand he answer..

More information

Shortest Path Algorithms. Lecture I: Shortest Path Algorithms. Example. Graphs and Matrices. Setting: Dr Kieran T. Herley.

Shortest Path Algorithms. Lecture I: Shortest Path Algorithms. Example. Graphs and Matrices. Setting: Dr Kieran T. Herley. Shores Pah Algorihms Background Seing: Lecure I: Shores Pah Algorihms Dr Kieran T. Herle Deparmen of Compuer Science Universi College Cork Ocober 201 direced graph, real edge weighs Le he lengh of a pah

More information

Network management and QoS provisioning - QoS in Frame Relay. . packet switching with virtual circuit service (virtual circuits are bidirectional);

Network management and QoS provisioning - QoS in Frame Relay. . packet switching with virtual circuit service (virtual circuits are bidirectional); QoS in Frame Relay Frame relay characerisics are:. packe swiching wih virual circui service (virual circuis are bidirecional);. labels are called DLCI (Daa Link Connecion Idenifier);. for connecion is

More information

CENG 477 Introduction to Computer Graphics. Modeling Transformations

CENG 477 Introduction to Computer Graphics. Modeling Transformations CENG 477 Inroducion o Compuer Graphics Modeling Transformaions Modeling Transformaions Model coordinaes o World coordinaes: Model coordinaes: All shapes wih heir local coordinaes and sies. world World

More information

Coded Caching with Multiple File Requests

Coded Caching with Multiple File Requests Coded Caching wih Muliple File Requess Yi-Peng Wei Sennur Ulukus Deparmen of Elecrical and Compuer Engineering Universiy of Maryland College Park, MD 20742 ypwei@umd.edu ulukus@umd.edu Absrac We sudy a

More information

Definition and examples of time series

Definition and examples of time series Definiion and examples of ime series A ime series is a sequence of daa poins being recorded a specific imes. Formally, le,,p be a probabiliy space, and T an index se. A real valued sochasic process is

More information

Implementing Ray Casting in Tetrahedral Meshes with Programmable Graphics Hardware (Technical Report)

Implementing Ray Casting in Tetrahedral Meshes with Programmable Graphics Hardware (Technical Report) Implemening Ray Casing in Terahedral Meshes wih Programmable Graphics Hardware (Technical Repor) Marin Kraus, Thomas Erl March 28, 2002 1 Inroducion Alhough cell-projecion, e.g., [3, 2], and resampling,

More information

Nearest Neighbor Learning

Nearest Neighbor Learning Nearest Neighbor Learning Cassify based on oca simiarity Ranges from simpe nearest neighbor to case-based and anaogica reasoning Use oca information near the current query instance to decide the cassification

More information

UX260 QUICK START GUIDE

UX260 QUICK START GUIDE UX260 QUICK START GUIDE Transferring Music Playing Music Blueooh Pairing Taking a Picure/ Recording a Video www.lgusa.com Geing o Know Your Phone Camera BACK SIDE Lef Sof Key Speakerphone Key Talk Key

More information

M y. Image Warping. Targil 7 : Image Warping. Image Warping. 2D Geometric Transformations. image filtering: change range of image g(x) = T(f(x))

M y. Image Warping. Targil 7 : Image Warping. Image Warping. 2D Geometric Transformations. image filtering: change range of image g(x) = T(f(x)) Hebrew Universi Image Processing - 6 Image Warping Hebrew Universi Image Processing - 6 argil 7 : Image Warping D Geomeric ransormaions hp://www.jere-marin.com Man slides rom Seve Seiz and Aleei Eros Image

More information

A Matching Algorithm for Content-Based Image Retrieval

A Matching Algorithm for Content-Based Image Retrieval A Maching Algorihm for Conen-Based Image Rerieval Sue J. Cho Deparmen of Compuer Science Seoul Naional Universiy Seoul, Korea Absrac Conen-based image rerieval sysem rerieves an image from a daabase using

More information

Simple example. Analysis of programs with pointers. Points-to relation. Program model. Points-to graph. Ordering on points-to relation

Simple example. Analysis of programs with pointers. Points-to relation. Program model. Points-to graph. Ordering on points-to relation Simle eamle Analsis of rograms with ointers := 5 tr := @ *tr := 9 := rogram S1 S2 S3 S4 deendences What are the deendences in this rogram? Problem: just looking at variable names will not give ou the correct

More information

Dimensions. Electrical connection

Dimensions. Electrical connection SU8//40a/0//6a Eecrica connecion Reease dae: 204-0- 4:04 Dae of issue: 204-0- 80888_eng.xm 9 Teach Ura H Pwr Sd Off.D T.Off On.D LO DO Ura HPwr Sd Off.D T.Off On.D LO DO Ura = Ura HPwr = High-Power Sd

More information

Using CANopen Slave Driver

Using CANopen Slave Driver CAN Bus User Manual Using CANopen Slave Driver V1. Table of Conens 1. SDO Communicaion... 1 2. PDO Communicaion... 1 3. TPDO Reading and RPDO Wriing... 2 4. RPDO Reading... 3 5. CANopen Communicaion Parameer

More information

Flow graph/networks MAX FLOW APPLICATIONS. Flow constraints. Max flow problem 4/26/12

Flow graph/networks MAX FLOW APPLICATIONS. Flow constraints. Max flow problem 4/26/12 4// low graph/nework MX LOW PPLIION 30, pring 0 avid Kauchak low nework direced, weighed graph (V, ) poiive edge weigh indicaing he capaciy (generally, aume ineger) conain a ingle ource V wih no incoming

More information

NRMI: Natural and Efficient Middleware

NRMI: Natural and Efficient Middleware NRMI: Naural and Efficien Middleware Eli Tilevich and Yannis Smaragdakis Cener for Experimenal Research in Compuer Sysems (CERCS), College of Compuing, Georgia Tech {ilevich, yannis}@cc.gaech.edu Absrac

More information

geometric transformations

geometric transformations geomeric ranformaion comuer grahic ranform 28 fabio ellacini linear algebra review marice noaion baic oeraion mari-vecor mulilicaion comuer grahic ranform 28 fabio ellacini 2 marice noaion for marice and

More information

Design of IP Networks with End-to. to- End Performance Guarantees

Design of IP Networks with End-to. to- End Performance Guarantees Design of IP Networks with End-to to- End Performance Guarantees Irena Atov and Richard J. Harris* ( Swinburne University of Technoogy & *Massey University) Presentation Outine Introduction Mutiservice

More information

4.1 3D GEOMETRIC TRANSFORMATIONS

4.1 3D GEOMETRIC TRANSFORMATIONS MODULE IV MCA - 3 COMPUTER GRAPHICS ADMN 29- Dep. of Compuer Science And Applicaions, SJCET, Palai 94 4. 3D GEOMETRIC TRANSFORMATIONS Mehods for geomeric ransformaions and objec modeling in hree dimensions

More information

Traditional Rendering (Ray Tracing and Radiosity)

Traditional Rendering (Ray Tracing and Radiosity) Tradiional Rendering (Ray Tracing and Radiosiy) CS 517 Fall 2002 Compuer Science Cornell Universiy Bidirecional Reflecance (BRDF) λ direcional diffuse specular θ uniform diffuse τ σ BRDF Bidirecional Reflecance

More information

Projection & Interaction

Projection & Interaction Projecion & Ineracion Algebra of projecion Canonical viewing volume rackball inerface ransform Hierarchies Preview of Assignmen #2 Lecure 8 Comp 236 Spring 25 Projecions Our lives are grealy simplified

More information

The Roots of Lisp paul graham

The Roots of Lisp paul graham The Roos of Lisp paul graham Draf, January 18, 2002. In 1960, John McCarhy published a remarkable paper in which he did for programming somehing like wha Euclid did for geomery. 1 He showed how, given

More information

Ray Tracing II. Improving Raytracing Speed. Improving Computational Complexity. Raytracing Computational Complexity

Ray Tracing II. Improving Raytracing Speed. Improving Computational Complexity. Raytracing Computational Complexity Ra Tracing II Iproving Raracing Speed Copuer Graphics Ra Tracing II 2005 Fabio Pellacini 1 Copuer Graphics Ra Tracing II 2005 Fabio Pellacini 2 Raracing Copuaional Coplei ra-scene inersecion is epensive

More information

4 Error Control. 4.1 Issues with Reliable Protocols

4 Error Control. 4.1 Issues with Reliable Protocols 4 Error Conrol Jus abou all communicaion sysems aemp o ensure ha he daa ges o he oher end of he link wihou errors. Since i s impossible o build an error-free physical layer (alhough some shor links can

More information

Optics and Light. Presentation

Optics and Light. Presentation Opics and Ligh Presenaion Opics and Ligh Wha comes o mind when you hear he words opics and ligh? Wha is an opical illusion? Opical illusions can use color, ligh and paerns o creae images ha can be

More information

Introduction to Data-Driven Animation: Programming with Motion Capture Jehee Lee

Introduction to Data-Driven Animation: Programming with Motion Capture Jehee Lee Inroducion o Daa-Driven Animaion: Programming wih Moion Caure Jehee Lee Seoul Naional Universiy Daa-Driven Animaion wih Moion Caure Programming wih Moion Caure Why is i difficul? Encomass a lo of heerogeneous

More information

IDEF3 Process Description Capture Method

IDEF3 Process Description Capture Method IDEF3 Process Descripion Capure Mehod IDEF3 is par of he IDEF family of mehods developmen funded by he US Air Force o provide modelling suppor for sysems engineering and enerprise inegraion 2 IDEF3 Mehod

More information

PART 1 REFERENCE INFORMATION CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONITOR

PART 1 REFERENCE INFORMATION CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONITOR . ~ PART 1 c 0 \,).,,.,, REFERENCE NFORMATON CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONTOR n CONTROL DATA 6400 Compuer Sysems, sysem funcions are normally handled by he Monior locaed in a Peripheral

More information

THE MOLDFLOW ANALYSE OF THE INJECTION PARTS, AND THE IMPROVEMENT OF THE INJECTION PROCESS.

THE MOLDFLOW ANALYSE OF THE INJECTION PARTS, AND THE IMPROVEMENT OF THE INJECTION PROCESS. THE MOLDFLOW ANALYSE OF THE INJECTION PARTS, AND THE IMPROVEMENT OF THE INJECTION PROCESS. Togan V.C. 1,2, Ioniţă Gh. 1 1 Vaahia University, Facuty of Materia Engineering, Mechatronics and Robotics, 18-24,

More information

Forgot to compute the new centroids (-1); error in centroid computations (-1); incorrect clustering results (-2 points); more than 2 errors: 0 points.

Forgot to compute the new centroids (-1); error in centroid computations (-1); incorrect clustering results (-2 points); more than 2 errors: 0 points. Probem 1 a. K means is ony capabe of discovering shapes that are convex poygons [1] Cannot discover X shape because X is not convex. [1] DBSCAN can discover X shape. [1] b. K-means is prototype based and

More information

Chapter Six Chapter Six

Chapter Six Chapter Six Chaper Si Chaper Si 0 CHAPTER SIX ConcepTess and Answers and Commens for Secion.. Which of he following graphs (a) (d) could represen an aniderivaive of he funcion shown in Figure.? Figure. (a) (b) (c)

More information

BI-TEMPORAL INDEXING

BI-TEMPORAL INDEXING BI-TEMPORAL INDEXING Mirella M. Moro Uniersidade Federal do Rio Grande do Sul Poro Alegre, RS, Brazil hp://www.inf.ufrgs.br/~mirella/ Vassilis J. Tsoras Uniersiy of California, Rierside Rierside, CA 92521,

More information

A new method for 3-dimensional roadway design using visualization techniques

A new method for 3-dimensional roadway design using visualization techniques Urban Transor XIII: Urban Transor and he Environmen in he 2s Cenury 23 A new mehod for 3-dimensional roadway design using visualizaion echniques G. Karri & M. K. Jha Dearmen of Civil Engineering, Morgan

More information

Spline Curves. Color Interpolation. Normal Interpolation. Last Time? Today. glshademodel (GL_SMOOTH); Adjacency Data Structures. Mesh Simplification

Spline Curves. Color Interpolation. Normal Interpolation. Last Time? Today. glshademodel (GL_SMOOTH); Adjacency Data Structures. Mesh Simplification Las Time? Adjacency Daa Srucures Spline Curves Geomeric & opologic informaion Dynamic allocaion Efficiency of access Mesh Simplificaion edge collapse/verex spli geomorphs progressive ransmission view-dependen

More information

Maximum Flows: Polynomial Algorithms

Maximum Flows: Polynomial Algorithms Maximum Flow: Polynomial Algorihm Algorihm Augmening pah Algorihm - Labeling Algorihm - Capaciy Scaling Algorihm - Shore Augmening Pah Algorihm Preflow-Puh Algorihm - FIFO Preflow-Puh Algorihm - Highe

More information

CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL

CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL Klečka Jan Docoral Degree Programme (1), FEEC BUT E-mail: xkleck01@sud.feec.vubr.cz Supervised by: Horák Karel E-mail: horak@feec.vubr.cz

More information

Assignment 2. Due Monday Feb. 12, 10:00pm.

Assignment 2. Due Monday Feb. 12, 10:00pm. Faculy of rs and Science Universiy of Torono CSC 358 - Inroducion o Compuer Neworks, Winer 218, LEC11 ssignmen 2 Due Monday Feb. 12, 1:pm. 1 Quesion 1 (2 Poins): Go-ack n RQ In his quesion, we review how

More information

Dimensions. Electrical connection

Dimensions. Electrical connection SU8-6/40a/0//6a Eecrica connecion Reease dae: 20-09-6 :4 Dae of issue: 20-- 8084_eng.xm 9 SENS H.Res Norm H.Spd Off.D T.Off On.D LO DO H.Res Norm H.Spd Off.D T.Off On.D LO DO H.Res = High Resouion Norm

More information

Handling uncertainty in semantic information retrieval process

Handling uncertainty in semantic information retrieval process Handling uncerainy in semanic informaion rerieval process Chkiwa Mounira 1, Jedidi Anis 1 and Faiez Gargouri 1 1 Mulimedia, InfoRmaion sysems and Advanced Compuing Laboraory Sfax Universiy, Tunisia m.chkiwa@gmail.com,

More information

understood as processors that match AST patterns of the source language and translate them into patterns in the target language.

understood as processors that match AST patterns of the source language and translate them into patterns in the target language. A Basic Compier At a fundamenta eve compiers can be understood as processors that match AST patterns of the source anguage and transate them into patterns in the target anguage. Here we wi ook at a basic

More information

Where We Are. Incorrect Programs. Non-Context-Free Syntax. Goals of Semantic Analysis. Type Information CS412/CS413

Where We Are. Incorrect Programs. Non-Context-Free Syntax. Goals of Semantic Analysis. Type Information CS412/CS413 Where We Are CS412/CS413 Iroduco o Copers T Teebau Lecure 12: Sbo Tabes Februar 20, 2007 Source code (characer srea) Toke srea Absrac sa ree (AST) Decoraed AST f (b == 0) a = b; f ( b == 0 ) a = b ; f

More information

Quantitative macro models feature an infinite number of periods A more realistic (?) view of time

Quantitative macro models feature an infinite number of periods A more realistic (?) view of time INFINIE-HORIZON CONSUMPION-SAVINGS MODEL SEPEMBER, Inroducion BASICS Quaniaive macro models feaure an infinie number of periods A more realisic (?) view of ime Infinie number of periods A meaphor for many

More information

Lecture 7: Objects (Chapter 15) CS 1110 Introduction to Computing Using Python

Lecture 7: Objects (Chapter 15) CS 1110 Introduction to Computing Using Python htt://www.cs.cornell.edu/courses/cs1110/2018s Lecture 7: Objects (Chater 15) CS 1110 Introduction to Comuting Using Python [E. Andersen, A. Bracy, D. Gries, L. Lee, S. Marschner, C. Van Loan, W. White]

More information

Numerical Solution of ODE

Numerical Solution of ODE Numerical Soluion of ODE Euler and Implici Euler resar; wih(deools): wih(plos): The package ploools conains more funcions for ploing, especially a funcion o draw a single line: wih(ploools): wih(linearalgebra):

More information

1 œ DRUM SET KEY. 8 Odd Meter Clave Conor Guilfoyle. Cowbell (neck) Cymbal. Hi-hat. Floor tom (shell) Clave block. Cowbell (mouth) Hi tom.

1 œ DRUM SET KEY. 8 Odd Meter Clave Conor Guilfoyle. Cowbell (neck) Cymbal. Hi-hat. Floor tom (shell) Clave block. Cowbell (mouth) Hi tom. DRUM SET KEY Hi-ha Cmbal Clave block Cowbell (mouh) 0 Cowbell (neck) Floor om (shell) Hi om Mid om Snare Floor om Snare cross sick or clave block Bass drum Hi-ha wih foo 8 Odd Meer Clave Conor Guilfole

More information

Language Identification for Texts Written in Transliteration

Language Identification for Texts Written in Transliteration Language Identification for Texts Written in Transiteration Andrey Chepovskiy, Sergey Gusev, Margarita Kurbatova Higher Schoo of Economics, Data Anaysis and Artificia Inteigence Department, Pokrovskiy

More information

Mobile App Recommendation: Maximize the Total App Downloads

Mobile App Recommendation: Maximize the Total App Downloads Mobie App Recommendation: Maximize the Tota App Downoads Zhuohua Chen Schoo of Economics and Management Tsinghua University chenzhh3.12@sem.tsinghua.edu.cn Yinghui (Catherine) Yang Graduate Schoo of Management

More information

Piecewise Linear Models

Piecewise Linear Models 6-6 Applied Operaions Research Piecewise Linear Models Deparmen of Mahemaics and Saisics The Universi of Melbourne This presenaion has been made in accordance wih he provisions of Par VB of he coprigh

More information

Image Content Representation

Image Content Representation Image Conen Represenaion Represenaion for curves and shapes regions relaionships beween regions E.G.M. Perakis Image Represenaion & Recogniion 1 Reliable Represenaion Uniqueness: mus uniquely specify an

More information

MOBILE COMPUTING. Wi-Fi 9/20/15. CSE 40814/60814 Fall Wi-Fi:

MOBILE COMPUTING. Wi-Fi 9/20/15. CSE 40814/60814 Fall Wi-Fi: MOBILE COMPUTING CSE 40814/60814 Fall 2015 Wi-Fi Wi-Fi: name is NOT an abbreviaion play on Hi-Fi (high fideliy) Wireless Local Area Nework (WLAN) echnology WLAN and Wi-Fi ofen used synonymous Typically

More information

MOBILE COMPUTING 3/18/18. Wi-Fi IEEE. CSE 40814/60814 Spring 2018

MOBILE COMPUTING 3/18/18. Wi-Fi IEEE. CSE 40814/60814 Spring 2018 MOBILE COMPUTING CSE 40814/60814 Spring 2018 Wi-Fi Wi-Fi: name is NOT an abbreviaion play on Hi-Fi (high fideliy) Wireless Local Area Nework (WLAN) echnology WLAN and Wi-Fi ofen used synonymous Typically

More information

Functions. 6.1 Modular Programming. 6.2 Defining and Calling Functions. Gaddis: 6.1-5,7-10,13,15-16 and 7.7

Functions. 6.1 Modular Programming. 6.2 Defining and Calling Functions. Gaddis: 6.1-5,7-10,13,15-16 and 7.7 Functions Unit 6 Gaddis: 6.1-5,7-10,13,15-16 and 7.7 CS 1428 Spring 2018 Ji Seaman 6.1 Moduar Programming Moduar programming: breaking a program up into smaer, manageabe components (modues) Function: a

More information

Chapter 4 Sequential Instructions

Chapter 4 Sequential Instructions Chaper 4 Sequenial Insrucions The sequenial insrucions of FBs-PLC shown in his chaper are also lised in secion 3.. Please refer o Chaper, "PLC Ladder diagram and he Coding rules of Mnemonic insrucion",

More information

Jacobian Range Space

Jacobian Range Space Kinematic Redundanc A manipuator ma have more DOFs than are necessar to contro a desired variabe What do ou do w/ the etra DOFs? However, even if the manipuator has enough DOFs, it ma sti be unabe to contro

More information

Introduction to Computer Graphics 10. Curves and Surfaces

Introduction to Computer Graphics 10. Curves and Surfaces Inroduion o Comuer Grahis. Curves and Surfaes I-Chen Lin, Assisan Professor Naional Chiao Tung Univ, Taiwan Tebook: E.Angel, Ineraive Comuer Grahis, 5 h Ed., Addison Wesley Ref: Hearn and aker, Comuer

More information

CS 152 Computer Architecture and Engineering. Lecture 7 - Memory Hierarchy-II

CS 152 Computer Architecture and Engineering. Lecture 7 - Memory Hierarchy-II CS 152 Compuer Archiecure and Engineering Lecure 7 - Memory Hierarchy-II Krse Asanovic Elecrical Engineering and Compuer Sciences Universiy of California a Berkeley hp://www.eecs.berkeley.edu/~krse hp://ins.eecs.berkeley.edu/~cs152

More information

AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION MAPPING

AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION MAPPING AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION MAPPING ANTENEH LEMMI ESHETE March, 0 SUPERVISORS: Dr. V. A. Toekin Dr. N. A. S. Hamm AUTOMATIC PARAMETER ESTIMATION IN MRF BASED SUPER RESOLUTION

More information

Extended Node-Arc Formulation for the K-Edge-Disjoint Hop-Constrained Network Design Problem

Extended Node-Arc Formulation for the K-Edge-Disjoint Hop-Constrained Network Design Problem Extended Node-Arc Formuation for the K-Edge-Disjoint Hop-Constrained Network Design Probem Quentin Botton Université cathoique de Louvain, Louvain Schoo of Management, (Begique) botton@poms.uc.ac.be Bernard

More information

Welcome - CSC 301. CSC 301- Foundations of Programming Languages

Welcome - CSC 301. CSC 301- Foundations of Programming Languages Wecome - CSC 301 CSC 301- Foundations of Programming Languages Instructor: Dr. Lutz Hame Emai: hame@cs.uri.edu Office: Tyer, Rm 251 Office Hours: TBA TA: TBA Assignments Assignment #0: Downoad & Read Syabus

More information

Michiel Helder and Marielle C.T.A Geurts. Hoofdkantoor PTT Post / Dutch Postal Services Headquarters

Michiel Helder and Marielle C.T.A Geurts. Hoofdkantoor PTT Post / Dutch Postal Services Headquarters SHORT TERM PREDICTIONS A MONITORING SYSTEM by Michiel Helder and Marielle C.T.A Geurs Hoofdkanoor PTT Pos / Duch Posal Services Headquarers Keywords macro ime series shor erm predicions ARIMA-models faciliy

More information

Geometry Transformation

Geometry Transformation Geomer Transformaion Januar 26 Prof. Gar Wang Dep. of Mechanical and Manufacuring Engineering Universi of Manioba Wh geomer ransformaion? Beer undersanding of he design Communicaion wih cusomers Generaing

More information

Optimal Crane Scheduling

Optimal Crane Scheduling Opimal Crane Scheduling Samid Hoda, John Hooker Laife Genc Kaya, Ben Peerson Carnegie Mellon Universiy Iiro Harjunkoski ABB Corporae Research EWO - 13 November 2007 1/16 Problem Track-mouned cranes move

More information

Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases

Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases Lmarks: A New Model for Similariy-Based Paern Querying in Time Series Daabases Chang-Shing Perng Haixun Wang Sylvia R. Zhang D. So Parker perng@cs.ucla.edu hxwang@cs.ucla.edu Sylvia Zhang@cle.com so@cs.ucla.edu

More information

Neural Network Enhancement of the Los Alamos Force Deployment Estimator

Neural Network Enhancement of the Los Alamos Force Deployment Estimator Missouri University of Science and Technoogy Schoars' Mine Eectrica and Computer Engineering Facuty Research & Creative Works Eectrica and Computer Engineering 1-1-1994 Neura Network Enhancement of the

More information

Outerjoins, Constraints, Triggers

Outerjoins, Constraints, Triggers Outerjoins, Constraints, Triggers Lecture #13 Autumn, 2001 Fa, 2001, LRX #13 Outerjoins, Constraints, Triggers HUST,Wuhan,China 358 Outerjoin R S = R S with danging tupes padded with nus and incuded in

More information

A New Supervised Clustering Algorithm Based on Min-Max Modular Network with Gaussian-Zero-Crossing Functions

A New Supervised Clustering Algorithm Based on Min-Max Modular Network with Gaussian-Zero-Crossing Functions 2006 Internationa Joint Conference on Neura Networks Sheraton Vancouver Wa Centre Hote, Vancouver, BC, Canada Juy 16-21, 2006 A New Supervised Custering Agorithm Based on Min-Max Moduar Network with Gaussian-Zero-Crossing

More information

Dimensions. Electrical connection

Dimensions. Electrical connection SU8-6/40a/02//6a Dimensions Eecrica connecion Reease dae: 208-0- 7:06 Dae of issue: 208-0- 808_eng.xm 9 SENS H.Res Norm H.Spd Off.D T.Off On.D LO DO H.Res Norm H.Spd Off.D T.Off On.D LO DO H.Res = High

More information

Exercise 3: Bluetooth BR/EDR

Exercise 3: Bluetooth BR/EDR Wireless Communicaions, M. Rupf. Exercise 3: Blueooh BR/EDR Problem 1: Blueooh Daa Raes. Consider he ACL packe 3-DH5 wih a maximum user payload of 1021 byes. a) Deermine he maximum achievable daa rae in

More information

Motor Control. 5. Control. Motor Control. Motor Control

Motor Control. 5. Control. Motor Control. Motor Control 5. Conrol In his chaper we will do: Feedback Conrol On/Off Conroller PID Conroller Moor Conrol Why use conrol a all? Correc or wrong? Supplying a cerain volage / pulsewidh will make he moor spin a a cerain

More information

Streamline Pathline Eulerian Lagrangian

Streamline Pathline Eulerian Lagrangian Sreamline Pahline Eulerian Lagrangian Sagnaion Poin Flow V V V = + = + = + o V xi y j a V V xi y j o Pahline and Sreakline Insananeous Sreamlines Pahlines Sreaklines Maerial Derivaive Acceleraion

More information

Systems & Biomedical Engineering Department. Transformation

Systems & Biomedical Engineering Department. Transformation Sem & Biomedical Engineering Deparmen SBE 36B: Compuer Sem III Compuer Graphic Tranformaion Dr. Aman Eldeib Spring 28 Tranformaion Tranformaion i a fundamenal corner one of compuer graphic and i a cenral

More information

Effects needed for Realism. Ray Tracing. Ray Tracing: History. Outline. Foundations of Computer Graphics (Fall 2012)

Effects needed for Realism. Ray Tracing. Ray Tracing: History. Outline. Foundations of Computer Graphics (Fall 2012) Foundaions of ompuer Graphics (Fall 2012) S 184, Lecure 16: Ray Tracing hp://ins.eecs.berkeley.edu/~cs184 Effecs needed for Realism (Sof) Shadows Reflecions (Mirrors and Glossy) Transparency (Waer, Glass)

More information

Lecture 18: Mix net Voting Systems

Lecture 18: Mix net Voting Systems 6.897: Advanced Topics in Crypography Apr 9, 2004 Lecure 18: Mix ne Voing Sysems Scribed by: Yael Tauman Kalai 1 Inroducion In he previous lecure, we defined he noion of an elecronic voing sysem, and specified

More information

Automatic Calculation of Coverage Profiles for Coverage-based Testing

Automatic Calculation of Coverage Profiles for Coverage-based Testing Auomaic Calculaion of Coverage Profiles for Coverage-based Tesing Raimund Kirner 1 and Waler Haas 1 Vienna Universiy of Technology, Insiue of Compuer Engineering, Vienna, Ausria, raimund@vmars.uwien.ac.a

More information

MATH Differential Equations September 15, 2008 Project 1, Fall 2008 Due: September 24, 2008

MATH Differential Equations September 15, 2008 Project 1, Fall 2008 Due: September 24, 2008 MATH 5 - Differenial Equaions Sepember 15, 8 Projec 1, Fall 8 Due: Sepember 4, 8 Lab 1.3 - Logisics Populaion Models wih Harvesing For his projec we consider lab 1.3 of Differenial Equaions pages 146 o

More information

An improved distributed version of Han s method for distributed MPC of canal systems

An improved distributed version of Han s method for distributed MPC of canal systems Deft University of Technoogy Deft Center for Systems and Contro Technica report 10-013 An improved distributed version of Han s method for distributed MPC of cana systems M.D. Doan, T. Keviczky, and B.

More information

Representing Non-Manifold Shapes in Arbitrary Dimensions

Representing Non-Manifold Shapes in Arbitrary Dimensions Represening Non-Manifold Shapes in Arbirary Dimensions Leila De Floriani,2 and Annie Hui 2 DISI, Universiy of Genova, Via Dodecaneso, 35-646 Genova (Ialy). 2 Deparmen of Compuer Science, Universiy of Maryland,

More information

A Petrel Plugin for Surface Modeling

A Petrel Plugin for Surface Modeling A Petre Pugin for Surface Modeing R. M. Hassanpour, S. H. Derakhshan and C. V. Deutsch Structure and thickness uncertainty are important components of any uncertainty study. The exact ocations of the geoogica

More information

Why not experiment with the system itself? Ways to study a system System. Application areas. Different kinds of systems

Why not experiment with the system itself? Ways to study a system System. Application areas. Different kinds of systems Simulaion Wha is simulaion? Simple synonym: imiaion We are ineresed in sudying a Insead of experimening wih he iself we experimen wih a model of he Experimen wih he Acual Ways o sudy a Sysem Experimen

More information

Dimensions. Electrical connection

Dimensions. Electrical connection Eecrica connecion Pinou 8 9 SENS Ura H Pwr Sd Off.D T.Off On.D LO DO Ura HPwr Sd Off.D T.Off On.D LO DO Ura = Ura HPwr = High-Power Sd = Sandard Off.D = Off Deay T.Off = Timer off On.D = On Deay LO = Ligh

More information

Shape Analysis with Structural Invariant Checkers

Shape Analysis with Structural Invariant Checkers Shape Anaysis with Structura Invariant Checkers Bor-Yuh Evan Chang Xavier Riva George C. Necua University of Caifornia, Berkeey SAS 2007 Exampe: Typestate with shape anaysis Concrete Exampe Abstraction

More information

A METHOD OF MODELING DEFORMATION OF AN OBJECT EMPLOYING SURROUNDING VIDEO CAMERAS

A METHOD OF MODELING DEFORMATION OF AN OBJECT EMPLOYING SURROUNDING VIDEO CAMERAS A METHOD OF MODELING DEFORMATION OF AN OBJECT EMLOYING SURROUNDING IDEO CAMERAS Joo Kooi TAN, Seiji ISHIKAWA Deparmen of Mechanical and Conrol Engineering Kushu Insiue of Technolog, Japan ehelan@is.cnl.kuech.ac.jp,

More information

Distance Weighted Discrimination and Second Order Cone Programming

Distance Weighted Discrimination and Second Order Cone Programming Distance Weighted Discrimination and Second Order Cone Programming Hanwen Huang, Xiaosun Lu, Yufeng Liu, J. S. Marron, Perry Haaand Apri 3, 2012 1 Introduction This vignette demonstrates the utiity and

More information

It is easier to visualize plotting the curves of cos x and e x separately: > plot({cos(x),exp(x)},x = -5*Pi..Pi,y = );

It is easier to visualize plotting the curves of cos x and e x separately: > plot({cos(x),exp(x)},x = -5*Pi..Pi,y = ); Mah 467 Homework Se : some soluions > wih(deools): wih(plos): Warning, he name changecoords has been redefined Problem :..7 Find he fixed poins, deermine heir sabiliy, for x( ) = cos x e x > plo(cos(x)

More information

Xisa: Extensible Inductive Shape Analysis

Xisa: Extensible Inductive Shape Analysis Xisa: Extensibe Inductive Shape Anaysis Bor-Yuh Evan Chang U of Coorado, Bouder Xavier Riva INRIA/ENS Paris George C. Necua U of Caifornia, Berkeey Additiona Contributors: Vincent Laviron, James Hoey,

More information