EE 505. CMOS and BiCMOS Data Conversion Circuits

Size: px
Start display at page:

Download "EE 505. CMOS and BiCMOS Data Conversion Circuits"

Transcription

1 EE 505 CMOS ad BiCMOS Data Coversio Circuits

2 Course Iformatio: Lecture Istructor: Rady Geiger 2133 Coover Voice: WEB: Laboratory Istructor: TBD

3 Course Iformatio: Aalog VLSI Circuit Desig Course Descriptio: Lecture: MW 2:10-3:30 Rm 1226 Howe Labs: W 8:00 10:50 Rm 2046 Coover Course WEB Site: Theory, desig ad applicatios of data coversio circuits (A/D ad D/A coverters) icludig: architectures, characterizatio, quatizatio effects, coversio algorithms, spectral performace, elemet matchig, desig for yield, ad practical comparators, implemetatio issues.

4 Course Iformatio: Key Referece Texts: Data Coverters, Maloberti, Spriger, 2007 Aalog-to-Digital Coversio 3 rd Editio by Marcel Pelgrom, Spriger, 2016

5 Course Iformatio: Other Referece Texts: Advaced Data Coverters by G. Magaaro, Cambridge, 2012 CMOS Itegrated Aalog-to-Digital ad Digital-to-Aalog Coverters 2 d Editio by Rudy va de Plassche, Kluwer, 2003

6 Course Iformatio: Other Referece Texts: Priciples of Data Coversio System Desig by B. Razavi, IEEE Press, 1995 High Speed Data Coverters by A. Ali, IET, 2016

7 Course Iformatio: Other Referece Texts: CMOS Mixed-Sigal Circuit Desig, 2 d Editio, R. Jacob Baker, Wiley, 2008

8 Course Iformatio: Other Referece Texts: Data Coversio Hadbook by Aalog Devices, 2005 The Art of Aalog Layout by A. Hastigs, Pretice Hall, 2001

9 Course Iformatio: Other Referece Texts: Uderstadig Delta-Sigma Data Coverters by R. Schreier ad G. Temes, Wiley, 2005 by Pava, Schreier ad Temes, Wiley, 2017 Delta-Sigma Data Coverters Theory, Desig, ad Simulatio edited by S. Norsworthy, R. Schreier ad G. Temes, Wiley, 1997

10 Course Iformatio: Referece Texts: Oversamplig Delta-Sigma Data Coverters: Theory, Desig, ad Simulatio 1st Editio By Cady ad Temes, CMOS Aalog Circuit Desig by Alle ad Holberg, Oxford, 2011.

11 Course Iformatio: Referece Texts: CMOS: Circuit Desig, Layout, ad Simulatio Secod Editio by J. Baker, Wiley, Fudametals of Microelectroics by B. Razavi, McGraw Hill, 2008

12 Course Iformatio: Referece Texts: Desig of Aalog CMOS Itegrated Circuits 2 d editio by B. Razavi, McGraw Hill, 2016 Aalysis ad Desig of Aalog Itegrated Circuits-Fifth Editio Gray,Hurst,Lewis ad Meyer, Wiley, 2009 Aalog Itegrated Circuit Desig -2 d Editio by D. Johs ad K. Marti, Wiley, 2011

13 Referece Materials: Course Iformatio:

14 Course Iformatio: Gradig: Poits will be allocated for several differet parts of the course. A letter grade will be assiged based upo the total poits accumulated. The poits allocated for differet parts of the course are as listed below: 1 Exam 100 pts 1 Fial 100 pts. Homework 100 pts.total Lab ad Lab Reports 100 pts.total Desig Project 100 pts. Note: I the evet that oe of the exams is ot give, the weight of the remaiig exam will be icreased to somewhere betwee 100 pts ad 200 pts.

15 Course Iformatio: Desig Project: The desig project will be assiged by mid-term. Additioal details about the desig project will be give after relevat material is covered i class. The optio will exist to have this project fabricated through the MOSIS program. The desig should be ready for fabricatio ad post-layout simulatios are to be icluded as a part of the project.

16 Course Iformatio: I ecourage you to take advatage of the system o campus to commuicate about ay issues that arise i the course. I typically check my e- mail several times a day. Please try to iclude EE 505" i the subject field of ay message that you sed so that they stad out from what is ofte large volumes of routie messages.

17 Topical Coverage Data Coverter Operatio, Characterizatio ad Specificatios Trasfer Characteristics Noise Spectral characterizatio Compoet Matchig ad Yield Nyquist-Rate Data Coverter Desig DACs Architectures Buildig Blocks Aalysis, Simulatio, ad Yield ADCs Architectures Buildig Blocks Aalysis, Simulatio, ad Yield Over-Sampled Data Coverters Operatio Architectures Buildig Blocks

18 Sigals Types of sigals: Cotiuous amplitude vs discrete amplitude Cotiuous time vs discrete time Fiite resolutio vs ifiite resolutio Probability of ay cotiuous-amplitude sigal value beig exactly equal to a specific value is 0 Probability of ay time beig exactly equal to a specific time value is 0 If x is a cotiuous variable (time, voltage, curret,.) the i the cotext of data coverters, there is o distictio i the followig sets of umbers (x 1,x 2 ) [x 1,x 2 ] (x 1,x 2 ] [x 1,x 2 ) It may be more coveiet to iclude boudary poits whe usig programs such as Matlab to characterize data coverters but results should ot deped upo whether ed poits are icluded or excluded

19 Sigals Digital represetatios (may exist) uary (thermometer), biary, decimal, gray (RBC),BCD, hexadecimal, I the cotext of data coverters, the digital represetatio is almost always represeted by sets whose elemets are {0,1} Biary ad occasioally uary are ivariable the codes that are used whe buildig ADCs ad DACs Uless specifically stated to the cotrary, it will be assumed throughout this course that the iput or output codes i a data coverter are biary

20 Types: Data Coverters A/D (Aalog to Digital) Coverts Aalog Iput to a Digital Output D/A (Digital to Aalog) Coverts a Digital Iput to a Aalog Output A/D is the world s most widely used mixed-sigal compoet D/A is ofte icluded i a FB path of a A/D A/D ad D/A fields will remai hot idefiitely techology advaces make data coverter desig more challegig embedded applicatios desigs ofte very applicatio depedet

21 Data Coverters Data coverters are ratio-metric devices ad outputs are all relative to a referece (i.e. traceability to a primary or secodary stadard is ot a issue) Ca be thought of as a amplifier where the output is a ratio-metric versio of the iput Uits of output of ADC are dimesioless ad uits of iput to DAC are dimesioless Uits of iput to ADC ca be arbitrary ad uits of output of DAC ca be arbitrary

22 The comparator is the basic aalog to digital coversio elemet i all ADCs The switch is the basic digital to aalog coversio elemet i all DACs Data coverters icorporate oe or more basic ADC or DAC cells Desig of comparator or switch is ofte critical i data coverters Performace of data coverters ofte depedet upo performace of comparator, switch, ad matchig Data Coverters Electroic Data Coversio Process: V d V OUT d d V OUT V OUT V OXX 0 d V OUT V XX 0 d 1 V d ADC DAC

23 D/A Coverters Basic structure: DAC X REF Basic structure with differetial outputs:: XIN DAC X + OUT X - OUT X + REF X - REF

24 D/A Coverters Notatio: DAC DAC X REF DAC X REF Referece always exists eve i ot explicitly show

25 D/A Coverters DAC (assumig biary codig) =<b -1,b -1,...b 1,b 0> b 0 is the Least Sigificat Bit (LSB) b -1 is the Most Sigificat Bit (MSB) Note: some authors use differet idex otatio A Ideal DAC is characterized at low frequecies by its static performace

26 D/A Coverters DAC =<b -1,b -1,...b 1,b 0> A Ideal DAC trasfer characteristic (3-bits) (Nyquist Rate) X - REF <0 0 0> <0 0 1> <0 1 0> <0 11> <1 0 0> <1 010> <1 1 0> <1 1 1> C 0 C 1 C 2 C 3 C 4 C 5 C 6 C 7 Code C k is used to represet the decimal equivalet of the biary umber <b -1.. b 0 )>

27 D/A Coverters DAC =<b -1,b -1,...b 1,b 0> A Ideal DAC trasfer characteristic (3-bits) X REF 7 X REF 8 C 0 C 1 C 2 C 3 C 4 C 5 C 6 C 7

28 D/A Coverters =<b -1,b -1,...b 1,b 0> DAC A Ideal DAC trasfer characteristic (3-bits) X - REF C 0 C 1 C 2 C 3 C 4 C 5 C 6 C 7 All poits of this ideal DAC lie o a straight lie

29 D/A Coverters DAC C 0 C 1 C 2 C 3 C 4 C 5 C 6 C 7 Most D/A ideally have a liear relatioship betwee biary iput ad aalog output Output represets a discrete set of cotiuous variables Typically this umber, N, is a itegral power of 2, i.e. N=2 is always dimesioless could have may differet dimesios A ideal oliear characteristic is also possible (waveform geeratio ad compadig) Will assume a liear trasfer characteristic is desired uless specifically stated to the cotrary

30 D/A Coverters DAC C 0 C 1 C 2 C 3 C 4 C 5 C 6 C 7 For this ideal DAC b b b b b X =X b-j =XREF j OUT REF -1 j=1 Number of outputs gets very large for large Spacig betwee outputs is X REF /2 ad gets very small (relative to X REF ) for large

31 D/A Coverters DAC X REF X LSB X REF 2 C 0 C 1 C 2 C 3 C 4 C 5 C 6 C 7 Ideal steps all equal ad termed the LSB X LSB gets very small for small X REF ad large e.g. If X REF =1V ad =16, the N=2 16 =65,536, X LSB =15.25μV

32 D/A Coverters DAC A alterate ideal 3-bit DAC C 0 C 1 C 2 C 3 C4 C5 C6 C7 Irrespective of which form is cosidered, the icremet i the output for oe Boolea bit chage i the iput is X LSB ad the total rage is 1LSB less tha X REF

33 Applicatios of DACs Waveform Geeratio Voltage Geeratio Aalog Trim or Calibratio Idustrial Cotrol Systems Feedback Elemet i ADCs.

34 Waveform Geeratio with DACs Ramp (Saw-tooth) Geerator Period CLK Geerator -bit Biary Couter D/A A Example: For =3 X REF X REF t Example: For large X REF t

35 Waveform Geeratio with DACs Sie Wave Geerator Period CLK Geerator -bit Biary or Couter D/A A m ROM RAM Example: For =3 X REF X REF t Distortio of the desired waveforms occurs due to both time ad amplitude quatizatio Ofte a filter precedes or follows the buffer amplifier to smooth the output waveform

36 A/D Coverters Basic structure: ADC X REF Iput rage is X REF Basic structure with differetial iputs/refereces: ADC X + IN ADC X - IN X + REF X - REF Iput rage is X + REF - X - REF X + REF X - REF Typically Iput rage is 2(X + REF - X - REF)

37 A/D Coverters Notatio: ADC ADC X REF ADC X REF Referece always exists eve i ot explicitly show

38 A/D Coverters (assumig biary codig) ADC =<d -1,d -2,...d 0> d 0 is the Least Sigificat Bit (LSB) d -1 is the Most Sigificat Bit (MSB) A Ideal ADC is characterized at low frequecies by its static performace

39 A/D Coverters A Ideal ADC trasfer characteristic (3-bits) (Nyquist Rate) ADC =<d -1,d -2,...d 0> C 7 <1 1 1> X LSB X = 2 REF C 6 <1 1 0> C 5 <1 01> C 4 C 3 <1 0 0> <0 11> X LSB C 2 <0 1 0> C 1 C 0 <0 0 1> <0 0 0> X REF X REF -X LSB

40 A/D Coverters A Ideal ADC trasfer characteristic (3-bits) ADC =<d -1,d -2,...d 0> X REF X LSB X = 2 REF 7X LSB C 7 6X LSB C 6 5X LSB C 5 4X LSB 3X LSB C 4 C 3 X LSB 2X LSB C 2 X LSB C 1 C 0 X REF -X LSB X REF The secod vertical axis, labeled is the iterpreted value of

41 A/D Coverters ADC C 7 C 6 C 5 C 4 C 3 C 2 C 1 C 0 For this ideal ADC d-1 d-2 d-3 d1 d X 0 OUT= XREF X - X = ε OUT IN where ε is small (typically less tha 1LSB) d -j XIN= XREF - ε j j=1 2 Number of bis gets very large for large Spacig betwee break poits is X REF /2 ad gets very small for large X REF ε is the quatizatio error ad is iheret i ay ADC

42 A/D Coverters ADC C 7 C 6 C 5 C 4 C 3 C 2 C 1 C 0 Trasitio Poits X T1 X T2 X T3 X T4 X T5 X T6 X T7 X REF Actual values of where trasitios occur are termed trasitio poits or break poits For a ideal -bit ADC, there are 2-1 trasitio poits Ideally the trasitio poits are all separated by 1 LSB -- X LSB =X REF /2 Ideally the trasitio poits are uiformly spaced I a actual ADC, the trasitio poits will deviate a little from their ideal locatio Labelig Covetio: We will defie the trasitio poit X Tk to be the break poit where the trasitio i the code output to code C k occurs. This seemigly obvious orderig of break poits becomes ambiguous, though, whe more tha oe break poits cause a trasitio to code C k which ca occur i some oideal ADCs

43 A/D Coverters Quatizatio Errors ADC A ideal ADC C 7 C 6 X T1 =X LSB C 5 C 4 C 3 C 2 C 1 C 0 X LSB 2X LSB 3X LSB 4X LSB 5X LSB 6X LSB 7X LSB Q XOUT -XIN ε Q X T1 X T2 X T3 X T4 X T5 X T6 X T7 X REF X T1 X T2 X T3 X T4 X T5 X T6 X T7 X REF -X LSB Magitude of ε Q bouded by X LSB for 0< X LSB < X REF

44 A/D Coverters Quatizatio Errors ADC Aother Ideal ADC X T1 =X LSB /2 C 7 C 6 C 5 C 4 C 3 C 2 C 1 C 0 X LSB 2X LSB 3X LSB 4X LSB 5X LSB 6X LSB 7X LSB Q XOUT -XIN X T1 X T2 X T3 X T4 X T5 X T6 X T7 ε Q X REF X T1 X T2 X T3 X T4 X T5 X T6 X T7 X REF Magitude of ε Q bouded by ½ X LSB

45 A/D Coverters ADC C 7 C 6 C 5 C 4 C 3 C 2 C 1 C 0 Quatizatio Errors X REF d d d d d ε = Q XREF -1 XIN The oly way to reduce p-p quatizatio errors is to icrease umber of levels A lower boud o the quatizatio errors i 0 < < X REF is ±½ X LSB The static performace of a ADC is completely determied by the fiite sequece of the trasitio poits < X T1, X T1 >

46 A/D Coverters May types: Successive Approximatio Register (SAR) Pipelied Sigma-Delta Flash Sigle-slope Dual-slope Wide rages of performace: Speed Resolutio Power Cost Large umber of vedors of catalog parts: Texas Istrumets Aalog Devices (Liear Techology) Maxim Embedded applicatios probably much larger: May SoCs cotai a large umber of data coverters of with varyig performace

47 A/D Coverters What types are really used? Cosider catalog parts from oe vedor Aalog Devices (Ja 2017) Flash 2 SAR 233 Pipelied 242 Sigma-Delta 81 Total 559

48 What do ADCs cost?

49 What do ADCs cost? Resolutio? 3 bits to 24 bits (oe at 32 bits)

50 $2.58 i 1000 s

51

52 $120 i 1000 s

53

54

55

56 Performace Characterizatio of Data Coverters ADC DAC A large umber of parameters are used to characterize a data coverter Performace parameters of iterest deped strogly o the applicatio Very small umber of parameters of iterest i may/most applicatios Catalog data coverters are geerally iteded to satisfy a wide rage of applicatios ad thus have much more striget requiremets placd o their performace Custom applicatio-specific data coverter will geerally perform much better tha a catalog part i the same

57 Performace Characterizatio of Data Coverters Static characteristics Resolutio Least Sigificat Bit (LSB) Offset ad Gai Errors Absolute Accuracy Relative Accuracy Itegral Noliearity (INL) Differetial Noliearity (DNL) Mootoicity (DAC) Missig Codes (ADC) Quatizatio Noise Low-f Spurious Free Dyamic Rage (SFDR) Low-f Total Harmoic Distortio (THD) Effective Number of Bits (ENOB) Power Dissipatio

58 Performace Characterizatio of Data Coverters Dyamic characteristics Coversio Time or Coversio Rate (ADC) Settlig time or Clock Rate (DAC) Samplig Time Ucertaity (aperture ucertaity or aperture jitter) Dyamic Rage Spurious Free Dyamic Rage (SFDR) Total Harmoic Distortio (THD) Sigal to Noise Ratio (SNR) Sigal to Noise ad Distortio Ratio (SNDR) Sparkle Characteristics Effective Number of Bits (ENOB)

59 Dyamic characteristics Degradatio of dyamic performace parameters ofte due to oideal effects i time-domai performace Dyamic characteristics of high resolutio data coverters ofte challegig to measure, to simulate, to uderstad source of cotributios, ad to miimize Example: A -bit ADC would ofte require SFDR at the 6+6 bit level or better. Thus, cosiderig a 14-bit ADC, the SFDR would be expected to be at the -90dB level or better. If the iput to the ADC is a 1V p-p siusoidal waveform, the secod harmoic term would eed to be at the 90 db / 20dB 10 32μV level. A 32uV level is about 1part i 30,000. Sigals at this level are difficult to accurately simulate i the presece of a 1V level sigal. For example, covergece parameters i simulators ad sample (strobe) poits used i data acquisitio adversely affect simulatio results ad observig the time domai waveforms that cotribute to oliearity at this level ad relatioships betwee these waveforms ad the sources of oliearity is ofte difficult to visualize. Simulatio errors that are at the 20dB level or worse ca occur if the simulatio eviromet is ot correctly established.

60 Characterizatio of Data Coverter Performace Almost all ADC architectures will work perfectly if oideal effects are igored!! Most data coverter desig effort ivolves maagig oideal properties of compoets Devil is ofte i the detail whe desigig a ADC Critical to kow how to accurately characterize a ADC What may appear to be mior differeces i performace are ofte differetiators i both the marketplace ad i the profit potetial of a part

61 Performace Characterizatio of Data Coverters What is meat by low frequecy? Operatio at frequecies so low that further decreases i frequecy cause o further chages i a parameter of iterest Low frequecy operatio is ofte termed Pseudostatic operatio

62 Low-frequecy or Pseudo-Static Performace Parameter Pseudo-Static Regio f

63 Performace Characterizatio Resolutio Number of distict aalog levels i a ADC Number of digital output codes i A/D I most cases this is a power of 2 If a coverter ca resolve 2 levels, the we term it a -bit coverter 2 aalog outputs for a -bit DAC 2-1 trasitio poits for a -bit ADC Resolutio is ofte determied by architecture ad thus ot measured Effective resolutio ca be defied ad measured If N levels ca be resolved for a DAC the logn EQ= log2 If N-1 trasitio poits i a ADC, the logn EQ= log2

64 Performace Characterizatio Least Sigificat Bit Assume N = 2 Geerally Defied by Maufacturer to be X LSB =X REF /N Effective Value of LSB ca be Measured For DAC: X LSB is equal to the maximum icremet i the output for a sigle bit chage i the Boolea iput For ADC: X LSB is equal to the maximum distace betwee two adjacet trasitio poits

65 Ed of Lecture 1

EE 435. Lecture 26. Data Converters. Architectures. Characterization

EE 435. Lecture 26. Data Converters. Architectures. Characterization EE 435 Lecture 26 Data Coverters Architectures Characterizatio . Review from last lecture. Data Coverters Types: A/D (Aalog to Digital) Coverts Aalog Iput to a Digital Output D/A (Digital to Aalog) Coverts

More information

Appendix D. Controller Implementation

Appendix D. Controller Implementation COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Appedix D Cotroller Implemetatio Cotroller Implemetatios Combiatioal logic (sigle-cycle); Fiite state machie (multi-cycle, pipelied);

More information

EE 435. Lecture 27. Data Converters. INL of DAC and ADC Differential Nonlinearity Spectral Performance

EE 435. Lecture 27. Data Converters. INL of DAC and ADC Differential Nonlinearity Spectral Performance EE 435 Lecture 27 Data Converters INL of DAC and ADC Differential Nonlinearity Spectral Performance . Review from last lecture. Data Converter Architectures Many more data converter architectures have

More information

Elementary Educational Computer

Elementary Educational Computer Chapter 5 Elemetary Educatioal Computer. Geeral structure of the Elemetary Educatioal Computer (EEC) The EEC coforms to the 5 uits structure defied by vo Neuma's model (.) All uits are preseted i a simplified

More information

Performance Plus Software Parameter Definitions

Performance Plus Software Parameter Definitions Performace Plus+ Software Parameter Defiitios/ Performace Plus Software Parameter Defiitios Chapma Techical Note-TG-5 paramete.doc ev-0-03 Performace Plus+ Software Parameter Defiitios/2 Backgroud ad Defiitios

More information

Math 10C Long Range Plans

Math 10C Long Range Plans Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will

More information

IMP: Superposer Integrated Morphometrics Package Superposition Tool

IMP: Superposer Integrated Morphometrics Package Superposition Tool IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College

More information

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro

More information

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS APPLICATION NOTE PACE175AE BUILT-IN UNCTIONS About This Note This applicatio brief is iteded to explai ad demostrate the use of the special fuctios that are built ito the PACE175AE processor. These powerful

More information

Lecture 5. Counting Sort / Radix Sort

Lecture 5. Counting Sort / Radix Sort Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018

More information

Improving Template Based Spike Detection

Improving Template Based Spike Detection Improvig Template Based Spike Detectio Kirk Smith, Member - IEEE Portlad State Uiversity petra@ee.pdx.edu Abstract Template matchig algorithms like SSE, Covolutio ad Maximum Likelihood are well kow for

More information

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5 Morga Kaufma Publishers 26 February, 28 COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 5 Set-Associative Cache Architecture Performace Summary Whe CPU performace icreases:

More information

Designing a learning system

Designing a learning system CS 75 Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@cs.pitt.edu 539 Seott Square, x-5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please try

More information

Τεχνολογία Λογισμικού

Τεχνολογία Λογισμικού ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών Τεχνολογία Λογισμικού, 7ο/9ο εξάμηνο 2018-2019 Τεχνολογία Λογισμικού Ν.Παπασπύρου, Αν.Καθ. ΣΗΜΜΥ, ickie@softlab.tua,gr

More information

Lecture 1: Introduction

Lecture 1: Introduction Lecture 1: Itroductio g Class orgaizatio Istructor cotact Course objectives ad outcomes Lectures outlie Laboratory outlie Gradig system Tetative schedule g Lab schedule g Itelliget sesor systems (ISS)

More information

Chapter 3. Floating Point Arithmetic

Chapter 3. Floating Point Arithmetic COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 3 Floatig Poit Arithmetic Review - Multiplicatio 0 1 1 0 = 6 multiplicad 32-bit ALU shift product right multiplier add

More information

South Slave Divisional Education Council. Math 10C

South Slave Divisional Education Council. Math 10C South Slave Divisioal Educatio Coucil Math 10C Curriculum Package February 2012 12 Strad: Measuremet Geeral Outcome: Develop spatial sese ad proportioal reasoig It is expected that studets will: 1. Solve

More information

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter The Processor Part A path Desig Itroductio CPU performace factors Istructio cout Determied by ISA ad compiler. CPI ad

More information

Data diverse software fault tolerance techniques

Data diverse software fault tolerance techniques Data diverse software fault tolerace techiques Complemets desig diversity by compesatig for desig diversity s s limitatios Ivolves obtaiig a related set of poits i the program data space, executig the

More information

Chapter 3 Classification of FFT Processor Algorithms

Chapter 3 Classification of FFT Processor Algorithms Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As

More information

EE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control

EE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control EE 459/500 HDL Based Digital Desig with Programmable Logic Lecture 13 Cotrol ad Sequecig: Hardwired ad Microprogrammed Cotrol Refereces: Chapter s 4,5 from textbook Chapter 7 of M.M. Mao ad C.R. Kime,

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

From last week. Lecture 5. Outline. Principles of programming languages

From last week. Lecture 5. Outline. Principles of programming languages Priciples of programmig laguages From last week Lecture 5 http://few.vu.l/~silvis/ppl/2007 Natalia Silvis-Cividjia e-mail: silvis@few.vu.l ML has o assigmet. Explai how to access a old bidig? Is & for

More information

Outline. Applications of FFT in Communications. Fundamental FFT Algorithms. FFT Circuit Design Architectures. Conclusions

Outline. Applications of FFT in Communications. Fundamental FFT Algorithms. FFT Circuit Design Architectures. Conclusions FFT Circuit Desig Outlie Applicatios of FFT i Commuicatios Fudametal FFT Algorithms FFT Circuit Desig Architectures Coclusios DAB Receiver Tuer OFDM Demodulator Chael Decoder Mpeg Audio Decoder 56/5/ 4/48

More information

Orientation. Orientation 10/28/15

Orientation. Orientation 10/28/15 Orietatio Orietatio We will defie orietatio to mea a object s istataeous rotatioal cofiguratio Thik of it as the rotatioal equivalet of positio 1 Represetig Positios Cartesia coordiates (x,y,z) are a easy

More information

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 )

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 ) EE26: Digital Desig, Sprig 28 3/6/8 EE 26: Itroductio to Digital Desig Combiatioal Datapath Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa Combiatioal Logic Blocks Multiplexer Ecoders/Decoders

More information

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation Improvemet of the Orthogoal Code Covolutio Capabilities Usig FPGA Implemetatio Naima Kaabouch, Member, IEEE, Apara Dhirde, Member, IEEE, Saleh Faruque, Member, IEEE Departmet of Electrical Egieerig, Uiversity

More information

Module Instantiation. Finite State Machines. Two Types of FSMs. Finite State Machines. Given submodule mux32two: Instantiation of mux32two

Module Instantiation. Finite State Machines. Two Types of FSMs. Finite State Machines. Given submodule mux32two: Instantiation of mux32two Give submodule mux32two: 2-to- MUX module mux32two (iput [3:] i,i, iput sel, output [3:] out); Module Istatiatio Fiite Machies esig methodology for sequetial logic -- idetify distict s -- create trasitio

More information

The VSS CCD photometry spreadsheet

The VSS CCD photometry spreadsheet The VSS CCD photometry spreadsheet Itroductio This Excel spreadsheet has bee developed ad tested by the BAA VSS for aalysig results files produced by the multi-image CCD photometry procedure i AIP4Wi v2.

More information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

More information

Lower Bounds for Sorting

Lower Bounds for Sorting Liear Sortig Topics Covered: Lower Bouds for Sortig Coutig Sort Radix Sort Bucket Sort Lower Bouds for Sortig Compariso vs. o-compariso sortig Decisio tree model Worst case lower boud Compariso Sortig

More information

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions U.C. Berkeley CS170 : Algorithms Midterm 1 Solutios Lecturers: Sajam Garg ad Prasad Raghavedra Feb 1, 017 Midterm 1 Solutios 1. (4 poits) For the directed graph below, fid all the strogly coected compoets

More information

FPGA IMPLEMENTATION OF BASE-N LOGARITHM. Salvador E. Tropea

FPGA IMPLEMENTATION OF BASE-N LOGARITHM. Salvador E. Tropea FPGA IMPLEMENTATION OF BASE-N LOGARITHM Salvador E. Tropea Electróica e Iformática Istituto Nacioal de Tecología Idustrial Bueos Aires, Argetia email: salvador@iti.gov.ar ABSTRACT I this work, we preset

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 19 Query Optimizatio Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Query optimizatio Coducted by a query optimizer i a DBMS Goal:

More information

Modern Systems Analysis and Design Seventh Edition

Modern Systems Analysis and Design Seventh Edition Moder Systems Aalysis ad Desig Seveth Editio Jeffrey A. Hoffer Joey F. George Joseph S. Valacich Desigig Databases Learig Objectives ü Cocisely defie each of the followig key database desig terms: relatio,

More information

Announcements. Reading. Project #4 is on the web. Homework #1. Midterm #2. Chapter 4 ( ) Note policy about project #3 missing components

Announcements. Reading. Project #4 is on the web. Homework #1. Midterm #2. Chapter 4 ( ) Note policy about project #3 missing components Aoucemets Readig Chapter 4 (4.1-4.2) Project #4 is o the web ote policy about project #3 missig compoets Homework #1 Due 11/6/01 Chapter 6: 4, 12, 24, 37 Midterm #2 11/8/01 i class 1 Project #4 otes IPv6Iit,

More information

DATA STRUCTURES. amortized analysis binomial heaps Fibonacci heaps union-find. Data structures. Appetizer. Appetizer

DATA STRUCTURES. amortized analysis binomial heaps Fibonacci heaps union-find. Data structures. Appetizer. Appetizer Data structures DATA STRUCTURES Static problems. Give a iput, produce a output. Ex. Sortig, FFT, edit distace, shortest paths, MST, max-flow,... amortized aalysis biomial heaps Fiboacci heaps uio-fid Dyamic

More information

Fast Fourier Transform (FFT) Algorithms

Fast Fourier Transform (FFT) Algorithms Fast Fourier Trasform FFT Algorithms Relatio to the z-trasform elsewhere, ozero, z x z X x [ ] 2 ~ elsewhere,, ~ e j x X x x π j e z z X X π 2 ~ The DFS X represets evely spaced samples of the z- trasform

More information

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig

More information

Data Structures and Algorithms. Analysis of Algorithms

Data Structures and Algorithms. Analysis of Algorithms Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output

More information

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 5 Fuctios for All Subtasks Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 5.1 void Fuctios 5.2 Call-By-Referece Parameters 5.3 Usig Procedural Abstractio 5.4 Testig ad Debuggig

More information

ECE4050 Data Structures and Algorithms. Lecture 6: Searching

ECE4050 Data Structures and Algorithms. Lecture 6: Searching ECE4050 Data Structures ad Algorithms Lecture 6: Searchig 1 Search Give: Distict keys k 1, k 2,, k ad collectio L of records of the form (k 1, I 1 ), (k 2, I 2 ),, (k, I ) where I j is the iformatio associated

More information

The Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana

The Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana The Closest Lie to a Data Set i the Plae David Gurey Southeaster Louisiaa Uiversity Hammod, Louisiaa ABSTRACT This paper looks at three differet measures of distace betwee a lie ad a data set i the plae:

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

Fundamentals of. Chapter 1. Microprocessor and Microcontroller. Dr. Farid Farahmand. Updated: Tuesday, January 16, 2018

Fundamentals of. Chapter 1. Microprocessor and Microcontroller. Dr. Farid Farahmand. Updated: Tuesday, January 16, 2018 Fudametals of Chapter 1 Microprocessor ad Microcotroller Dr. Farid Farahmad Updated: Tuesday, Jauary 16, 2018 Evolutio First came trasistors Itegrated circuits SSI (Small-Scale Itegratio) to ULSI Very

More information

Chapter 4 The Datapath

Chapter 4 The Datapath The Ageda Chapter 4 The Datapath Based o slides McGraw-Hill Additioal material 24/25/26 Lewis/Marti Additioal material 28 Roth Additioal material 2 Taylor Additioal material 2 Farmer Tae the elemets that

More information

End Semester Examination CSE, III Yr. (I Sem), 30002: Computer Organization

End Semester Examination CSE, III Yr. (I Sem), 30002: Computer Organization Ed Semester Examiatio 2013-14 CSE, III Yr. (I Sem), 30002: Computer Orgaizatio Istructios: GROUP -A 1. Write the questio paper group (A, B, C, D), o frot page top of aswer book, as per what is metioed

More information

AN OPTIMIZATION NETWORK FOR MATRIX INVERSION

AN OPTIMIZATION NETWORK FOR MATRIX INVERSION 397 AN OPTIMIZATION NETWORK FOR MATRIX INVERSION Ju-Seog Jag, S~ Youg Lee, ad Sag-Yug Shi Korea Advaced Istitute of Sciece ad Techology, P.O. Box 150, Cheogryag, Seoul, Korea ABSTRACT Iverse matrix calculatio

More information

Computer Systems - HS

Computer Systems - HS What have we leared so far? Computer Systems High Level ENGG1203 2d Semester, 2017-18 Applicatios Sigals Systems & Cotrol Systems Computer & Embedded Systems Digital Logic Combiatioal Logic Sequetial Logic

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

CS : Programming for Non-Majors, Summer 2007 Programming Project #3: Two Little Calculations Due by 12:00pm (noon) Wednesday June

CS : Programming for Non-Majors, Summer 2007 Programming Project #3: Two Little Calculations Due by 12:00pm (noon) Wednesday June CS 1313 010: Programmig for No-Majors, Summer 2007 Programmig Project #3: Two Little Calculatios Due by 12:00pm (oo) Wedesday Jue 27 2007 This third assigmet will give you experiece writig programs that

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

EE123 Digital Signal Processing

EE123 Digital Signal Processing Last Time EE Digital Sigal Processig Lecture 7 Block Covolutio, Overlap ad Add, FFT Discrete Fourier Trasform Properties of the Liear covolutio through circular Today Liear covolutio with Overlap ad add

More information

NTH, GEOMETRIC, AND TELESCOPING TEST

NTH, GEOMETRIC, AND TELESCOPING TEST NTH, GEOMETRIC, AND TELESCOPING TEST Sectio 9. Calculus BC AP/Dual, Revised 08 viet.dag@humbleisd.et /4/08 0:0 PM 9.: th, Geometric, ad Telescopig Test SUMMARY OF TESTS FOR SERIES Lookig at the first few

More information

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4

More information

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits Egieerig Letters, :, EL Reversible Realizatio of Quaterary Decoder, Multiplexer, ad Demultiplexer Circuits Mozammel H.. Kha, Member, ENG bstract quaterary reversible circuit is more compact tha the correspodig

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists

More information

Structuring Redundancy for Fault Tolerance. CSE 598D: Fault Tolerant Software

Structuring Redundancy for Fault Tolerance. CSE 598D: Fault Tolerant Software Structurig Redudacy for Fault Tolerace CSE 598D: Fault Tolerat Software What do we wat to achieve? Versios Damage Assessmet Versio 1 Error Detectio Iputs Versio 2 Voter Outputs State Restoratio Cotiued

More information

Computers and Scientific Thinking

Computers and Scientific Thinking Computers ad Scietific Thikig David Reed, Creighto Uiversity Chapter 15 JavaScript Strigs 1 Strigs as Objects so far, your iteractive Web pages have maipulated strigs i simple ways use text box to iput

More information

1. SWITCHING FUNDAMENTALS

1. SWITCHING FUNDAMENTALS . SWITCING FUNDMENTLS Switchig is the provisio of a o-demad coectio betwee two ed poits. Two distict switchig techiques are employed i commuicatio etwors-- circuit switchig ad pacet switchig. Circuit switchig

More information

Appendix A. Use of Operators in ARPS

Appendix A. Use of Operators in ARPS A Appedix A. Use of Operators i ARPS The methodology for solvig the equatios of hydrodyamics i either differetial or itegral form usig grid-poit techiques (fiite differece, fiite volume, fiite elemet)

More information

1 Enterprise Modeler

1 Enterprise Modeler 1 Eterprise Modeler Itroductio I BaaERP, a Busiess Cotrol Model ad a Eterprise Structure Model for multi-site cofiguratios are itroduced. Eterprise Structure Model Busiess Cotrol Models Busiess Fuctio

More information

MOTIF XF Extension Owner s Manual

MOTIF XF Extension Owner s Manual MOTIF XF Extesio Ower s Maual Table of Cotets About MOTIF XF Extesio...2 What Extesio ca do...2 Auto settig of Audio Driver... 2 Auto settigs of Remote Device... 2 Project templates with Iput/ Output Bus

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19 CIS Data Structures ad Algorithms with Java Sprig 09 Stacks, Queues, ad Heaps Moday, February 8 / Tuesday, February 9 Stacks ad Queues Recall the stack ad queue ADTs (abstract data types from lecture.

More information

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein 068.670 Subliear Time Algorithms November, 0 Lecture 6 Lecturer: Roitt Rubifeld Scribes: Che Ziv, Eliav Buchik, Ophir Arie, Joatha Gradstei Lesso overview. Usig the oracle reductio framework for approximatig

More information

3D Model Retrieval Method Based on Sample Prediction

3D Model Retrieval Method Based on Sample Prediction 20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer

More information

n Industrial inspection n Laser gauging n Low light applications n Spectroscopy Figure 1. IL-C Sensor Block Diagram Pixel Reset Drain

n Industrial inspection n Laser gauging n Low light applications n Spectroscopy Figure 1. IL-C Sensor Block Diagram Pixel Reset Drain L I N E S C A N C A M E R A S DALSA CL-C6 Cameras Tall pixels (38: aspect ratio), tremedous dyamic rage, great full-well capacity ad a sigle output make the CL-C6 a outstadig performer i spectroscopic

More information

Learning to Shoot a Goal Lecture 8: Learning Models and Skills

Learning to Shoot a Goal Lecture 8: Learning Models and Skills Learig to Shoot a Goal Lecture 8: Learig Models ad Skills How do we acquire skill at shootig goals? CS 344R/393R: Robotics Bejami Kuipers Learig to Shoot a Goal The robot eeds to shoot the ball i the goal.

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

Chapter 4 Threads. Operating Systems: Internals and Design Principles. Ninth Edition By William Stallings

Chapter 4 Threads. Operating Systems: Internals and Design Principles. Ninth Edition By William Stallings Operatig Systems: Iterals ad Desig Priciples Chapter 4 Threads Nith Editio By William Stalligs Processes ad Threads Resource Owership Process icludes a virtual address space to hold the process image The

More information

Object based Pseudo-3D Conversion of 2D Videos

Object based Pseudo-3D Conversion of 2D Videos Object based Pseudo-3D Coversio of 2D Videos J. Jiag 1,2 ad G. Xiao 1 1 Southwest Uiversity; 2 Uiversity of Bradford ABSTRACT: I this paper, we describe a ew algorithm to costruct pseudo-3d videos out

More information

TELETERM M2 Series Programmable RTU s

TELETERM M2 Series Programmable RTU s Model C6xC ad C6xC Teleterm MR Radio RTU s DATASHEET Cofigurable Iputs ad Outputs 868MHz or 900MHz radio port 0/00 Etheret port o C6Cx ISaGRAF 6 Programmable microsd Card Loggig Low power operatio Two

More information

BOOLEAN MATHEMATICS: GENERAL THEORY

BOOLEAN MATHEMATICS: GENERAL THEORY CHAPTER 3 BOOLEAN MATHEMATICS: GENERAL THEORY 3.1 ISOMORPHIC PROPERTIES The ame Boolea Arithmetic was chose because it was discovered that literal Boolea Algebra could have a isomorphic umerical aspect.

More information

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0 Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity

More information

EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS

EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS I this uit of the course we ivestigate fittig a straight lie to measured (x, y) data pairs. The equatio we wat to fit

More information

Behavioral Modeling in Verilog

Behavioral Modeling in Verilog Behavioral Modelig i Verilog COE 202 Digital Logic Desig Dr. Muhamed Mudawar Kig Fahd Uiversity of Petroleum ad Mierals Presetatio Outlie Itroductio to Dataflow ad Behavioral Modelig Verilog Operators

More information

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a 4. [10] Usig a combiatorial argumet, prove that for 1: = 0 = Let A ad B be disjoit sets of cardiality each ad C = A B. How may subsets of C are there of cardiality. We are selectig elemets for such a subset

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

top() Applications of Stacks

top() Applications of Stacks CS22 Algorithms ad Data Structures MW :00 am - 2: pm, MSEC 0 Istructor: Xiao Qi Lecture 6: Stacks ad Queues Aoucemets Quiz results Homework 2 is available Due o September 29 th, 2004 www.cs.mt.edu~xqicoursescs22

More information

Sorting in Linear Time. Data Structures and Algorithms Andrei Bulatov

Sorting in Linear Time. Data Structures and Algorithms Andrei Bulatov Sortig i Liear Time Data Structures ad Algorithms Adrei Bulatov Algorithms Sortig i Liear Time 7-2 Compariso Sorts The oly test that all the algorithms we have cosidered so far is compariso The oly iformatio

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 26 Ehaced Data Models: Itroductio to Active, Temporal, Spatial, Multimedia, ad Deductive Databases Copyright 2016 Ramez Elmasri ad Shamkat B.

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

CMSC Computer Architecture Lecture 10: Caches. Prof. Yanjing Li University of Chicago

CMSC Computer Architecture Lecture 10: Caches. Prof. Yanjing Li University of Chicago CMSC 22200 Computer Architecture Lecture 10: Caches Prof. Yajig Li Uiversity of Chicago Midterm Recap Overview ad fudametal cocepts ISA Uarch Datapath, cotrol Sigle cycle, multi cycle Pipeliig Basic idea,

More information

. Perform a geometric (ray-optics) construction (i.e., draw in the rays on the diagram) to show where the final image is formed.

. Perform a geometric (ray-optics) construction (i.e., draw in the rays on the diagram) to show where the final image is formed. MASSACHUSETTS INSTITUTE of TECHNOLOGY Departmet of Electrical Egieerig ad Computer Sciece 6.161 Moder Optics Project Laboratory 6.637 Optical Sigals, Devices & Systems Problem Set No. 1 Geometric optics

More information

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by

More information

Protected points in ordered trees

Protected points in ordered trees Applied Mathematics Letters 008 56 50 www.elsevier.com/locate/aml Protected poits i ordered trees Gi-Sag Cheo a, Louis W. Shapiro b, a Departmet of Mathematics, Sugkyukwa Uiversity, Suwo 440-746, Republic

More information

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago CMSC 22200 Computer Architecture Lecture 12: Virtual Memory Prof. Yajig Li Uiversity of Chicago A System with Physical Memory Oly Examples: most Cray machies early PCs Memory early all embedded systems

More information

CIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8)

CIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8) CIS 11 Data Structures ad Algorithms with Java Fall 017 Big-Oh Notatio Tuesday, September 5 (Make-up Friday, September 8) Learig Goals Review Big-Oh ad lear big/small omega/theta otatios Practice solvig

More information

Today s objectives. CSE401: Introduction to Compiler Construction. What is a compiler? Administrative Details. Why study compilers?

Today s objectives. CSE401: Introduction to Compiler Construction. What is a compiler? Administrative Details. Why study compilers? CSE401: Itroductio to Compiler Costructio Larry Ruzzo Sprig 2004 Today s objectives Admiistrative details Defie compilers ad why we study them Defie the high-level structure of compilers Associate specific

More information

Term Project Report. This component works to detect gesture from the patient as a sign of emergency message and send it to the emergency manager.

Term Project Report. This component works to detect gesture from the patient as a sign of emergency message and send it to the emergency manager. CS2310 Fial Project Loghao Li Term Project Report Itroductio I this project, I worked o expadig exercise 4. What I focused o is makig the real gesture recogizig sesor ad desig proper gestures ad recogizig

More information

State-space feedback 6 challenges of pole placement

State-space feedback 6 challenges of pole placement State-space feedbac 6 challeges of pole placemet J Rossiter Itroductio The earlier videos itroduced the cocept of state feedbac ad demostrated that it moves the poles. x u x Kx Bu It was show that whe

More information

Politecnico di Milano Advanced Network Technologies Laboratory. Internet of Things. Projects

Politecnico di Milano Advanced Network Technologies Laboratory. Internet of Things. Projects Politecico di Milao Advaced Network Techologies Laboratory Iteret of Thigs Projects 2016-2017 Politecico di Milao Advaced Network Techologies Laboratory Geeral Rules Geeral Rules o Gradig 26/30 are assiged

More information

SMRT410 Megger Relay Test System

SMRT410 Megger Relay Test System SMRT410 Small, rugged, lightweight ad powerful Operate with or without a computer Ituitive maual operatio with Smart Touch View Iterface High curret, high power output (60 Amps/300 VA rms) per phase Flexible

More information

THIN LAYER ORIENTED MAGNETOSTATIC CALCULATION MODULE FOR ELMER FEM, BASED ON THE METHOD OF THE MOMENTS. Roman Szewczyk

THIN LAYER ORIENTED MAGNETOSTATIC CALCULATION MODULE FOR ELMER FEM, BASED ON THE METHOD OF THE MOMENTS. Roman Szewczyk THIN LAYER ORIENTED MAGNETOSTATIC CALCULATION MODULE FOR ELMER FEM, BASED ON THE METHOD OF THE MOMENTS Roma Szewczyk Istitute of Metrology ad Biomedical Egieerig, Warsaw Uiversity of Techology E-mail:

More information

APPLICATION NOTE. Automated Gain Flattening. 1. Experimental Setup. Scope and Overview

APPLICATION NOTE. Automated Gain Flattening. 1. Experimental Setup. Scope and Overview APPLICATION NOTE Automated Gai Flatteig Scope ad Overview A flat optical power spectrum is essetial for optical telecommuicatio sigals. This stems from a eed to balace the chael powers across large distaces.

More information

Octahedral Graph Scaling

Octahedral Graph Scaling Octahedral Graph Scalig Peter Russell Jauary 1, 2015 Abstract There is presetly o strog iterpretatio for the otio of -vertex graph scalig. This paper presets a ew defiitio for the term i the cotext of

More information