COMPARATIVE STUDY OF ENCODERS FOR PARALLEL-TYPE ADCS

Size: px
Start display at page:

Download "COMPARATIVE STUDY OF ENCODERS FOR PARALLEL-TYPE ADCS"

Transcription

1 OMPAATIVE TUDY OF ENODE FO PAALLEL-TYPE AD Pul Pereir (,3), Jorge. Fernndes (2,3) () Deptº Engª Eletroténi, Esol uperior de Tenologi, I.P..B., Av. Empresário, stelo Brno, Portugl Phone (35) Fx (35) e-mil: (2) Instituto uperior Ténio / (3) INE-ID Liso,. Alves edol, 9, Liso, Portugl Phone (35) Fx (35) e-mil: jorge.fernndes@ines-id.pt Astrt - Anlog-to-digitl onverters (ADs) for high smpling frequenies hve prllel-type rhitetures, where the output of the omprtors is the so-lled thermometer ode. The digitl prt of suh ADs n e just n enoder from the thermometer ode to inry ode; however, different types of error n e implemented to enhne the AD overll performne. In this pper we ompre three different types of enoders: without error ; with error ; with n th order error. These enoders re ompred with respet to the yield of the AD with the different enoders, ssuming the use of n identil nlog prt. The yield is defined y four different riteri: monotoniity, sene of missing odes, nd either integrl or differentil nonlinerity elow speified vlue. Keywords - Anlogue-to-digitl onverters, Prllel ADs, Thermometer ode-to-inry s, Digitl error.. INTODUTION High-speed ADs with smpling frequenies in the tens (or hundreds) of MHz hve prllel-type rhitetures. The fullprllel rhiteture is the most used rhiteture when speed is the most importnt requirement, lthough this rhiteture requires lrge numer of omprtors (2 N - for N its), whih leds to lrge re nd power onsumption []. Due to this onstrint nd lso to the omprtors offset voltge, the resolution is usully limited to 8 it. Alterntives to the fullprllel rhiteture re the two-step nd the pipeline rhitetures [], where full-prllel ADs with redued numer of its re used s su-iruits. The omprtors offset voltge (V O ) is of prtiulr importne euse it is responsile for the stti nonidel This work ws supported y Fundção pr iêni e Tenologi (Portugl) through projets PAXI 2/2./TIT/66/95, PAXI 3300/99 nd POI (Qudro omunitário de Apoio III). The uthors re with INE-ID-Liso ( reserh enter of Instituto uperior Ténio). performne: it imposes limits on the integrl nd differentil nonlinerity nd my originte missing odes or even e responsile for non-monotoni ehviour []. V O, due to mismthes, is rndom vrile whih is ssumed to hve norml distriution []. Although, V O n e redued y iruit design tehniques, this usully leds to n inrese of power onsumption nd re. The lthed omprtors output is the so-lled thermometer ode ( 000 ) nd the offset voltge of the omprtors n use ules (e.g., 00 ) in this thermometer ode originting errors in the output ode. The enoders n e designed to orret lrge errors, tht pper s non-monotoniity of the trnsfer hrteristi of the AD or s missing odes, or even to redue lrge nonlinerity errors, using digitl shemes. We hve divided the enoders into three different lsses: without error ; with error, where simple ules n e orreted with minor modifition to the si enoder; nd with n th order error, where ules n e orreted. The n th order error enoders onsidered re the Mngelsdorf [2], the it swpping [3], nd the Wlle tree [4] enoders. The first two use thermometer ode orretor, etween the omprtors nd the enoder input to mke the of the thermometer ode. A different pproh is used y the Wlle tree method: insted of orreting the thermometer ode the numer of s is evluted. These different lsses of enoders re ompred for the ses N=8 its nd N=4 its. These re prtil vlues, respetively, for full-prllel ADs nd for su-onverters in two-step rhiteture. The expeted yields of n AD re ompred, for different enoders, using s riteri the requirement of monotoniity, sene of missing odes, nd either integrl or differentil nonlinerity elow speifi vlue. 2. PAALLEL-TYPE AD In full-prllel AD (Fig. ) the input voltge v I is pplied to nk of lthed omprtors, where it is ompred with

2 the eqully sped referene voltge levels otined from the resistor ldder. The referene voltge levels re the trnsition voltge levels in the trnsfer hrteristi of n idel AD (Fig. ). All the omprtors with referene voltge elow v I exhiit logi level nd ll the omprtors with referene voltge ove v I exhiit the omplementry logi level resulting in the so lled thermometer ode ( 000 ). This ode is enoded to inry form y n enoder lok. The full-prllel AD uses 2 N - omprtors (hene lrge re nd high power onsumption); lterntive rhitetures like two-step prllel (Fig.2), re used to redue the numer of omprtors. Two-step or multi-step rhitetures usully use full-prllel AD su-iruits. The sujet of AD hrteriztion is still under disussion. In this pper we will onsider tht the idel AD trnsfer hrteristi hs equl ode in widths, nd the stti prmeters used re defined s follows [5]: V V 3 V 2 V v I Lth Lth Lth E N O D E (MB) 2 (LB) ode V V 2 V 3 v I 3. DIFFEENT TYPE OF ENODE () Bsi The simplest iruit to onvert the thermometer ode to inry ode uses deoder with XO gtes to detet the 0J trnsition in the thermometer ode nd then ddress OM for the proper inry enoding (Fig. 3). The OM n e implemented with NMO trnsistors nd pull-up devies (pull-up resistors re represented in Fig. 4). This enoder does not perform ny error, so ule in the thermometer ode will use tht t lest three lines re ddressed in the OM, resulting in the AND of the orresponding three output odes. This will use nonmonoti trnsfer hrteristi for the AD nd the existene of missing ode. (2) with st Order Error orretion By using 3-input XO gtes, insted of the 2-input XO gtes in the simple enoder just presented, errors (ules) in the thermometer ode n e prtilly orreted, thus voiding the ddressing of severl lines in the OM: non-monotoni trnsfer hrteristi is voided ut missing ode my our. ENODE T7 T6 B2 Fig. - Full-prllel AD nd trnsfer hrteristi. v in T5 omprtor Bnk T4 OM B B0 T3 v IN /H AD M it M it (MB) T DA M it T AD L it L it (LB) Fig. 3 - Bsi. Fig. 2- Two-step prllel AD. Monotoni AD: the output odes do not derese (inrese) for uniformly inresing (deresing) input signl, disregrding rndom noise. Missing ode (M): ode tht does not our for ny vlue of the input signl. Integrl nonlinerity (INL): Mximum differene etween the idel nd the tul ode trnsition levels fter orreting for gin nd offset. Differentil nonlinerity (DNL): Mximum differene etween the idel nd the rel ode in width. Fig. 4 - NMO OM with pull-up resistors.

3 (3) with n th Order Error orretion A higher order error enoder using the Mngelsdorf [2], the it swpping [3] or the Wlle tree [4] methods n improve the, with respet to the previous sheme, nd n perform of higher order ules, depending on the method hosen. () Mngelsdorf method The Mngelsdorf method orrets the thermometer ode efore enoding. The output of eh omprtor is ompred with the outputs of the two djent omprtors nd hnged if it is different from oth. Eh omprtor orreted output is T N : previous; insted of orreting the thermometer ode nd then using si enoder, it uses single lok where oth opertions re performed. An exmple for three it enoder is presented in Fig.6 where the si ell is summing iruit of s. The truth tle of this si ell is presented in Tle where, nd, re the its to e summed, is the sum result nd is the rry. If the result is presented with the formt then it represents the numer of ones t the input in inry form. T7 T6 T5 T4 B 0 (LB) T N = TN TN + TN TN + + TN TN+ where T N : is omprtor output without. This method orrets one digit ule in ny position (e.g., ) ut not ules tht re more thn one digit long (e.g., ). T3 T2 T B B 2 (MB) Fig.6: Wlle Tree (exmple for 3 it). () Bit swpping method The it swpping method lso orrets the thermometer ode nd then uses si enoder. The logi opertion represented in Fig.5 is performed etween eh two onseutive omprtors nd ules in the thermometer ode desend if they re s or send if they re 0s. Tle I - umming lok truth tle A2 A Q2 Q A A2 Q Q Fig. 5 - Bit swpping lthing element nd truth tle. In order to perform of ules it is neessry to use n olumns of it swpping lthes, nd therefore the order of error to e performed should e refully evluted. () Wlle tree method If the it swpping method is implemented with its mximum order (2 N ) the lgorithm is equivlent to ounting the numer of s. The Wlle tree method implements the sme lgorithm diretly, without moving the s through the thermometer ode (this method is used to implement high speed multipliers in omputer rithmeti units). The Wlle tree method is different from the The tree struture mens tht there is n inrese in the numer of stges with the numer of its; however the internl nodes re ll low pitne node ensuring fst response. 4. OMPAATIVE TUDY To ompre the three lsses of enoders we hve simulted two full-prllel ADs (with N= 8 it nd N= 4 it) imposing n offset t eh trnsition point of the trnsfer hrteristi. This offset is rndom vrile with norml distriution [], nd models the offset voltge of the lthed omprtor whih is ssumed to hve zero men nd to e independent for different omprtors. The most signifint results re presented in Fig. 7, where the yield is the perentge of ADs tht meet the speifi riteri. The results for the monotoniity riterion re otined from 2000 smples; for the missing ode nd INL.5 LB riteri the results re otined fter disrding the smples tht hve filed the monotoniity riteri.

4 The results plotted for n th order error enoder orrespond to the highest order (2 N ) level. Our omprtive study leds to the following generl onlusions: I. The non-monotoni ehviour n e totlly eliminted y the use of n n th order error enoder. For the si nd the error enoders the yield redution due to non-monotoniity is signifint, even for very low vlues of the offset voltge. II. The missing ode riteri shows tht the n th order error enoder n gin e very effetive in improving the yield. The si enoder n e pprently etter thn the enoder with error, ut it should e noted tht we hve only onsidered the yield due to sene of missing odes fter exluding the nonmonotoni ADs. III. The results onerning INL nd DNL led to the sme generl onlusions. For nonlinerity lower thn the n th order error enoder shows slight improvement over the other enoders, ut it should e noted tht these results re sed on the smples tht hve pssed the monotoniity riterion; for nonlinerity greter thn, the n th order error enoder shows signifint improvement over the other enoders, nd this improvement is more importnt for higher resolution or () () () 0.00 (d) (e) (f) Fig.7 - ADs for () N=8 it, Monotoniity; () N=4 it, Monotoniity; () N=8 it, Missing odes; (d) N=4 it, Missing odes; (e) N=8 it, INL.5 LB; nd (f) N=4 it, INL.5 LB

5 higher σ(v O )/V LB. These onlusions show tht is desirle to use error enoder, Mngelsdorf enoder, or low order it swpping enoder, euse the yield is signifintly enhned without high ost with respet to the simple enoder. The Wlle tree enoder or higher order it swpping enoder improve the AD for ll the riteri nd n e justifile when the resolution inreses (lower V LB nd higher numer of trnsition levels), or when the offset voltge inreses. The hoie etween these two enoders depend on the order of error. If higher order error is needed the Wlle tree enoder is the most suitle, ut it my e too ostly for high resolutions. In this se, medium (3 rd or 4 th order) order error using the it swpping enoder n e the est solution, sine it is good ompromise etween effiieny nd dded iruitry. 5. ONLUION In this pper we onsider three types of thermometer-toinry ode enoders: without error ; with error nd with n th order error. These enoders re studied nd ompred with respet to the expeted yield of the AD, with the different enoders, defined y four different riteri: monotoniity, sene of missing odes, nd either integrl or differentil nonlinerity elow speifi vlue. The results show tht low order error produes signifint inrese in yield with smll inrese in iruitry. For medium resolution ADs, the Wlle tree is the most effetive euse it leds to n inrese of performne (not only of the yield) with eptle dded iruitry. For higher resolution ADs the Wlle tree enoder n eome too ostly nd the use of medium order error using it swpping n e ompromise etween improved performne without to muh dded iruitry. AKNOWLEDGEMENT The uthors re grteful to Prof. M. Medeiros ilv nd Eng. Edgr Aluquerque for helpful disussions onerning the work reported here. EFEENE [] J. Fernndes, onversores A/D om Arquiteturs de Tipo Prlelo, PhD Thesis, IT, [2]. Mngelsdorf A 400-MHz Input Flsh onverter with Error orretion, IEEE J. olid-tte ir., vol. 25, pp. 84-9, Fe [3] V.E. Gruts, Y..Yu, E.O.Tr nd T.Ymguhi, A Dul 4-it 2-Gs/s Full Nyquist Anlog-to-Digitl onverter Using 70-ps ilion Bipolr Tehnology with Boroseni-Poly Proess nd oupling-bse Implnt, IEEE J. olid-tte ir., vol.24, pp , Apr [4] F.Kess,.Knn, B.Hohet nd M.Delerq New Enoding heme for High-peed Flsh ADs, in Pro. IEEE Int. ymp. iruits yst., June 997. [5] IEEE tndrd 057, IEEE tndrd for Digitizing Wveforms eorders, De. 994.

Lecture 6. CMOS Static & Dynamic Logic Gates. Static CMOS Circuit. PMOS Transistors in Series/Parallel Connection

Lecture 6. CMOS Static & Dynamic Logic Gates. Static CMOS Circuit. PMOS Transistors in Series/Parallel Connection NMOS Trnsistors in Series/Prllel onnetion Leture 6 MOS Stti & ynmi Logi Gtes Trnsistors n e thought s swith ontrolled y its gte signl NMOS swith loses when swith ontrol input is high Peter heung eprtment

More information

Unit 4. Combinational Circuits

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

More information

8 THREE PHASE A.C. CIRCUITS

8 THREE PHASE A.C. CIRCUITS 8 THREE PHSE.. IRUITS The signls in hpter 7 were sinusoidl lternting voltges nd urrents of the so-lled single se type. n emf of suh type n e esily generted y rotting single loop of ondutor (or single winding),

More information

NON-DETERMINISTIC FSA

NON-DETERMINISTIC FSA Tw o types of non-determinism: NON-DETERMINISTIC FS () Multiple strt-sttes; strt-sttes S Q. The lnguge L(M) ={x:x tkes M from some strt-stte to some finl-stte nd ll of x is proessed}. The string x = is

More information

Lecture Notes No. 10

Lecture Notes No. 10 2.6 System Identifition, Estimtion, nd Lerning Leture otes o. Mrh 3, 26 6 Model Struture of Liner ime Invrint Systems 6. Model Struture In representing dynmil system, the first step is to find n pproprite

More information

Instructions. An 8.5 x 11 Cheat Sheet may also be used as an aid for this test. MUST be original handwriting.

Instructions. An 8.5 x 11 Cheat Sheet may also be used as an aid for this test. MUST be original handwriting. ID: B CSE 2021 Computer Orgniztion Midterm Test (Fll 2009) Instrutions This is losed ook, 80 minutes exm. The MIPS referene sheet my e used s n id for this test. An 8.5 x 11 Chet Sheet my lso e used s

More information

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014 S 224 DIGITAL LOGI & STATE MAHINE DESIGN SPRING 214 DUE : Mrh 27, 214 HOMEWORK III READ : Relte portions of hpters VII n VIII ASSIGNMENT : There re three questions. Solve ll homework n exm prolems s shown

More information

Exercise 3 Logic Control

Exercise 3 Logic Control Exerise 3 Logi Control OBJECTIVE The ojetive of this exerise is giving n introdution to pplition of Logi Control System (LCS). Tody, LCS is implemented through Progrmmle Logi Controller (PLC) whih is lled

More information

SOLUTIONS TO ASSIGNMENT NO The given nonrecursive signal processing structure is shown as

SOLUTIONS TO ASSIGNMENT NO The given nonrecursive signal processing structure is shown as SOLUTIONS TO ASSIGNMENT NO.1 3. The given nonreursive signl proessing struture is shown s X 1 1 2 3 4 5 Y 1 2 3 4 5 X 2 There re two ritil pths, one from X 1 to Y nd the other from X 2 to Y. The itertion

More information

6.3.2 Spectroscopy. N Goalby chemrevise.org 1 NO 2 CH 3. CH 3 C a. NMR spectroscopy. Different types of NMR

6.3.2 Spectroscopy. N Goalby chemrevise.org 1 NO 2 CH 3. CH 3 C a. NMR spectroscopy. Different types of NMR 6.. Spetrosopy NMR spetrosopy Different types of NMR NMR spetrosopy involves intertion of mterils with the lowenergy rdiowve region of the eletromgneti spetrum NMR spetrosopy is the sme tehnology s tht

More information

Appendix C Partial discharges. 1. Relationship Between Measured and Actual Discharge Quantities

Appendix C Partial discharges. 1. Relationship Between Measured and Actual Discharge Quantities Appendi Prtil dishrges. Reltionship Between Mesured nd Atul Dishrge Quntities A dishrging smple my e simply represented y the euilent iruit in Figure. The pplied lternting oltge V is inresed until the

More information

PAIR OF LINEAR EQUATIONS IN TWO VARIABLES

PAIR OF LINEAR EQUATIONS IN TWO VARIABLES PAIR OF LINEAR EQUATIONS IN TWO VARIABLES. Two liner equtions in the sme two vriles re lled pir of liner equtions in two vriles. The most generl form of pir of liner equtions is x + y + 0 x + y + 0 where,,,,,,

More information

Dorf, R.C., Wan, Z. T- Equivalent Networks The Electrical Engineering Handbook Ed. Richard C. Dorf Boca Raton: CRC Press LLC, 2000

Dorf, R.C., Wan, Z. T- Equivalent Networks The Electrical Engineering Handbook Ed. Richard C. Dorf Boca Raton: CRC Press LLC, 2000 orf, R.C., Wn,. T- Equivlent Networks The Eletril Engineering Hndook Ed. Rihrd C. orf Bo Rton: CRC Press LLC, 000 9 T P Equivlent Networks hen Wn University of Cliforni, vis Rihrd C. orf University of

More information

Iowa Training Systems Trial Snus Hill Winery Madrid, IA

Iowa Training Systems Trial Snus Hill Winery Madrid, IA Iow Trining Systems Tril Snus Hill Winery Mdrid, IA Din R. Cohrn nd Gil R. Nonneke Deprtment of Hortiulture, Iow Stte University Bkground nd Rtionle: Over the lst severl yers, five sttes hve een evluting

More information

Note 12. Introduction to Digital Control Systems

Note 12. Introduction to Digital Control Systems Note Introduction to Digitl Control Systems Deprtment of Mechnicl Engineering, University Of Ssktchewn, 57 Cmpus Drive, Ssktoon, SK S7N 5A9, Cnd . Introduction A digitl control system is one in which the

More information

Reduction of Switching Noise in Digital CMOS Circuits by Pin Swapping of Library Cells 1

Reduction of Switching Noise in Digital CMOS Circuits by Pin Swapping of Library Cells 1 Redution of Swithing Noise in Digitl CMOS Ciruits y Pin Swpping of Lirry Cells 1 Pilr Prr, Antonio Aost, nd Mnuel Vleni Instituto de Miroeletróni de Sevill-CNM / Universidd de Sevill Avd. Rein Meredes

More information

6.3.2 Spectroscopy. N Goalby chemrevise.org 1 NO 2 H 3 CH3 C. NMR spectroscopy. Different types of NMR

6.3.2 Spectroscopy. N Goalby chemrevise.org 1 NO 2 H 3 CH3 C. NMR spectroscopy. Different types of NMR 6.. Spetrosopy NMR spetrosopy Different types of NMR NMR spetrosopy involves intertion of mterils with the lowenergy rdiowve region of the eletromgneti spetrum NMR spetrosopy is the sme tehnology s tht

More information

Algorithms & Data Structures Homework 8 HS 18 Exercise Class (Room & TA): Submitted by: Peer Feedback by: Points:

Algorithms & Data Structures Homework 8 HS 18 Exercise Class (Room & TA): Submitted by: Peer Feedback by: Points: Eidgenössishe Tehnishe Hohshule Zürih Eole polytehnique fédérle de Zurih Politenio federle di Zurigo Federl Institute of Tehnology t Zurih Deprtement of Computer Siene. Novemer 0 Mrkus Püshel, Dvid Steurer

More information

AP Calculus BC Chapter 8: Integration Techniques, L Hopital s Rule and Improper Integrals

AP Calculus BC Chapter 8: Integration Techniques, L Hopital s Rule and Improper Integrals AP Clulus BC Chpter 8: Integrtion Tehniques, L Hopitl s Rule nd Improper Integrls 8. Bsi Integrtion Rules In this setion we will review vrious integrtion strtegies. Strtegies: I. Seprte the integrnd into

More information

THE INFLUENCE OF MODEL RESOLUTION ON AN EXPRESSION OF THE ATMOSPHERIC BOUNDARY LAYER IN A SINGLE-COLUMN MODEL

THE INFLUENCE OF MODEL RESOLUTION ON AN EXPRESSION OF THE ATMOSPHERIC BOUNDARY LAYER IN A SINGLE-COLUMN MODEL THE INFLUENCE OF MODEL RESOLUTION ON AN EXPRESSION OF THE ATMOSPHERIC BOUNDARY LAYER IN A SINGLE-COLUMN MODEL P3.1 Kot Iwmur*, Hiroto Kitgw Jpn Meteorologil Ageny 1. INTRODUCTION Jpn Meteorologil Ageny

More information

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides.

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides. 1 PYTHAGORAS THEOREM 1 1 Pythgors Theorem In this setion we will present geometri proof of the fmous theorem of Pythgors. Given right ngled tringle, the squre of the hypotenuse is equl to the sum of the

More information

A Study on the Properties of Rational Triangles

A Study on the Properties of Rational Triangles Interntionl Journl of Mthemtis Reserh. ISSN 0976-5840 Volume 6, Numer (04), pp. 8-9 Interntionl Reserh Pulition House http://www.irphouse.om Study on the Properties of Rtionl Tringles M. Q. lm, M.R. Hssn

More information

Generalization of 2-Corner Frequency Source Models Used in SMSIM

Generalization of 2-Corner Frequency Source Models Used in SMSIM Generliztion o 2-Corner Frequeny Soure Models Used in SMSIM Dvid M. Boore 26 Mrh 213, orreted Figure 1 nd 2 legends on 5 April 213, dditionl smll orretions on 29 My 213 Mny o the soure spetr models ville

More information

Engr354: Digital Logic Circuits

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

More information

CS12N: The Coming Revolution in Computer Architecture Laboratory 2 Preparation

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

More information

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

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

More information

University of Sioux Falls. MAT204/205 Calculus I/II

University of Sioux Falls. MAT204/205 Calculus I/II University of Sioux Flls MAT204/205 Clulus I/II Conepts ddressed: Clulus Textook: Thoms Clulus, 11 th ed., Weir, Hss, Giordno 1. Use stndrd differentition nd integrtion tehniques. Differentition tehniques

More information

NEW CIRCUITS OF HIGH-VOLTAGE PULSE GENERATORS WITH INDUCTIVE-CAPACITIVE ENERGY STORAGE

NEW CIRCUITS OF HIGH-VOLTAGE PULSE GENERATORS WITH INDUCTIVE-CAPACITIVE ENERGY STORAGE NEW CIRCUITS OF HIGH-VOLTAGE PULSE GENERATORS WITH INDUCTIVE-CAPACITIVE ENERGY STORAGE V.S. Gordeev, G.A. Myskov Russin Federl Nuler Center All-Russi Sientifi Reserh Institute of Experimentl Physis (RFNC-VNIIEF)

More information

Part 4. Integration (with Proofs)

Part 4. Integration (with Proofs) Prt 4. Integrtion (with Proofs) 4.1 Definition Definition A prtition P of [, b] is finite set of points {x 0, x 1,..., x n } with = x 0 < x 1

More information

Project 6: Minigoals Towards Simplifying and Rewriting Expressions

Project 6: Minigoals Towards Simplifying and Rewriting Expressions MAT 51 Wldis Projet 6: Minigols Towrds Simplifying nd Rewriting Expressions The distriutive property nd like terms You hve proly lerned in previous lsses out dding like terms ut one prolem with the wy

More information

Solutions to Assignment 1

Solutions to Assignment 1 MTHE 237 Fll 2015 Solutions to Assignment 1 Problem 1 Find the order of the differentil eqution: t d3 y dt 3 +t2 y = os(t. Is the differentil eqution liner? Is the eqution homogeneous? b Repet the bove

More information

Probability. b a b. a b 32.

Probability. b a b. a b 32. Proility If n event n hppen in '' wys nd fil in '' wys, nd eh of these wys is eqully likely, then proility or the hne, or its hppening is, nd tht of its filing is eg, If in lottery there re prizes nd lnks,

More information

TOPIC: LINEAR ALGEBRA MATRICES

TOPIC: LINEAR ALGEBRA MATRICES Interntionl Blurete LECTUE NOTES for FUTHE MATHEMATICS Dr TOPIC: LINEA ALGEBA MATICES. DEFINITION OF A MATIX MATIX OPEATIONS.. THE DETEMINANT deta THE INVESE A -... SYSTEMS OF LINEA EQUATIONS. 8. THE AUGMENTED

More information

Lecture 6: Coding theory

Lecture 6: Coding theory Leture 6: Coing theory Biology 429 Crl Bergstrom Ferury 4, 2008 Soures: This leture loosely follows Cover n Thoms Chpter 5 n Yeung Chpter 3. As usul, some of the text n equtions re tken iretly from those

More information

, g. Exercise 1. Generator polynomials of a convolutional code, given in binary form, are g. Solution 1.

, g. Exercise 1. Generator polynomials of a convolutional code, given in binary form, are g. Solution 1. Exerise Genertor polynomils of onvolutionl ode, given in binry form, re g, g j g. ) Sketh the enoding iruit. b) Sketh the stte digrm. ) Find the trnsfer funtion T. d) Wht is the minimum free distne of

More information

Chapter 4 State-Space Planning

Chapter 4 State-Space Planning Leture slides for Automted Plnning: Theory nd Prtie Chpter 4 Stte-Spe Plnning Dn S. Nu CMSC 722, AI Plnning University of Mrylnd, Spring 2008 1 Motivtion Nerly ll plnning proedures re serh proedures Different

More information

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

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

More information

Arrow s Impossibility Theorem

Arrow s Impossibility Theorem Rep Fun Gme Properties Arrow s Theorem Arrow s Impossiility Theorem Leture 12 Arrow s Impossiility Theorem Leture 12, Slide 1 Rep Fun Gme Properties Arrow s Theorem Leture Overview 1 Rep 2 Fun Gme 3 Properties

More information

Algorithm Design and Analysis

Algorithm Design and Analysis Algorithm Design nd Anlysis LECTURE 5 Supplement Greedy Algorithms Cont d Minimizing lteness Ching (NOT overed in leture) Adm Smith 9/8/10 A. Smith; sed on slides y E. Demine, C. Leiserson, S. Rskhodnikov,

More information

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of:

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of: 22: Union Fin CS 473u - Algorithms - Spring 2005 April 14, 2005 1 Union-Fin We wnt to mintin olletion of sets, uner the opertions of: 1. MkeSet(x) - rete set tht ontins the single element x. 2. Fin(x)

More information

Section 1.3 Triangles

Section 1.3 Triangles Se 1.3 Tringles 21 Setion 1.3 Tringles LELING TRINGLE The line segments tht form tringle re lled the sides of the tringle. Eh pir of sides forms n ngle, lled n interior ngle, nd eh tringle hs three interior

More information

1B40 Practical Skills

1B40 Practical Skills B40 Prcticl Skills Comining uncertinties from severl quntities error propgtion We usully encounter situtions where the result of n experiment is given in terms of two (or more) quntities. We then need

More information

The Area of a Triangle

The Area of a Triangle The e of Tingle tkhlid June 1, 015 1 Intodution In this tile we will e disussing the vious methods used fo detemining the e of tingle. Let [X] denote the e of X. Using se nd Height To stt off, the simplest

More information

CS 491G Combinatorial Optimization Lecture Notes

CS 491G Combinatorial Optimization Lecture Notes CS 491G Comintoril Optimiztion Leture Notes Dvi Owen July 30, August 1 1 Mthings Figure 1: two possile mthings in simple grph. Definition 1 Given grph G = V, E, mthing is olletion of eges M suh tht e i,

More information

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 )

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 ) Neessry n suient onitions for some two vrile orthogonl esigns in orer 44 C. Koukouvinos, M. Mitrouli y, n Jennifer Seerry z Deite to Professor Anne Penfol Street Astrt We give new lgorithm whih llows us

More information

Continuous Random Variables

Continuous Random Variables CPSC 53 Systems Modeling nd Simultion Continuous Rndom Vriles Dr. Anirn Mhnti Deprtment of Computer Science University of Clgry mhnti@cpsc.uclgry.c Definitions A rndom vrile is sid to e continuous if there

More information

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution Tehnishe Universität Münhen Winter term 29/ I7 Prof. J. Esprz / J. Křetínský / M. Luttenerger. Ferur 2 Solution Automt nd Forml Lnguges Homework 2 Due 5..29. Exerise 2. Let A e the following finite utomton:

More information

MATRIX INVERSE ON CONNEX PARALLEL ARCHITECTURE

MATRIX INVERSE ON CONNEX PARALLEL ARCHITECTURE U.P.B. Si. Bull., Series C, Vol. 75, Iss. 2, ISSN 86 354 MATRIX INVERSE ON CONNEX PARALLEL ARCHITECTURE An-Mri CALFA, Gheorghe ŞTEFAN 2 Designed for emedded omputtion in system on hip design, the Connex

More information

Statistics in medicine

Statistics in medicine Sttistis in meiine Workshop 1: Sreening n ignosti test evlution Septemer 22, 2016 10:00 AM to 11:50 AM Hope 110 Ftm Shel, MD, MS, MPH, PhD Assistnt Professor Chroni Epiemiology Deprtment Yle Shool of Puli

More information

Behavior Composition in the Presence of Failure

Behavior Composition in the Presence of Failure Behvior Composition in the Presene of Filure Sestin Srdin RMIT University, Melourne, Austrli Fio Ptrizi & Giuseppe De Giomo Spienz Univ. Rom, Itly KR 08, Sept. 2008, Sydney Austrli Introdution There re

More information

Table of Content. c 1 / 5

Table of Content. c 1 / 5 Tehnil Informtion - t nd t Temperture for Controlger 03-2018 en Tble of Content Introdution....................................................................... 2 Definitions for t nd t..............................................................

More information

Algorithm Design and Analysis

Algorithm Design and Analysis Algorithm Design nd Anlysis LECTURE 8 Mx. lteness ont d Optiml Ching Adm Smith 9/12/2008 A. Smith; sed on slides y E. Demine, C. Leiserson, S. Rskhodnikov, K. Wyne Sheduling to Minimizing Lteness Minimizing

More information

Technology Mapping Method for Low Power Consumption and High Performance in General-Synchronous Framework

Technology Mapping Method for Low Power Consumption and High Performance in General-Synchronous Framework R-17 SASIMI 015 Proeeings Tehnology Mpping Metho for Low Power Consumption n High Performne in Generl-Synhronous Frmework Junki Kwguhi Yukihie Kohir Shool of Computer Siene, the University of Aizu Aizu-Wkmtsu

More information

Nondeterministic Automata vs Deterministic Automata

Nondeterministic Automata vs Deterministic Automata Nondeterministi Automt vs Deterministi Automt We lerned tht NFA is onvenient model for showing the reltionships mong regulr grmmrs, FA, nd regulr expressions, nd designing them. However, we know tht n

More information

Maintaining Mathematical Proficiency

Maintaining Mathematical Proficiency Nme Dte hpter 9 Mintining Mthemtil Profiieny Simplify the epression. 1. 500. 189 3. 5 4. 4 3 5. 11 5 6. 8 Solve the proportion. 9 3 14 7. = 8. = 9. 1 7 5 4 = 4 10. 0 6 = 11. 7 4 10 = 1. 5 9 15 3 = 5 +

More information

System Validation (IN4387) November 2, 2012, 14:00-17:00

System Validation (IN4387) November 2, 2012, 14:00-17:00 System Vlidtion (IN4387) Novemer 2, 2012, 14:00-17:00 Importnt Notes. The exmintion omprises 5 question in 4 pges. Give omplete explntion nd do not onfine yourself to giving the finl nswer. Good luk! Exerise

More information

Linear Algebra Introduction

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

More information

Introduction to Olympiad Inequalities

Introduction to Olympiad Inequalities Introdution to Olympid Inequlities Edutionl Studies Progrm HSSP Msshusetts Institute of Tehnology Snj Simonovikj Spring 207 Contents Wrm up nd Am-Gm inequlity 2. Elementry inequlities......................

More information

On Implicative and Strong Implicative Filters of Lattice Wajsberg Algebras

On Implicative and Strong Implicative Filters of Lattice Wajsberg Algebras Glol Journl of Mthemtil Sienes: Theory nd Prtil. ISSN 974-32 Volume 9, Numer 3 (27), pp. 387-397 Interntionl Reserh Pulition House http://www.irphouse.om On Implitive nd Strong Implitive Filters of Lttie

More information

EECS 141 Due 04/19/02, 5pm, in 558 Cory

EECS 141 Due 04/19/02, 5pm, in 558 Cory UIVERSITY OF CALIFORIA College of Engineering Deprtment of Electricl Engineering nd Computer Sciences Lst modified on April 8, 2002 y Tufn Krlr (tufn@eecs.erkeley.edu) Jn M. Rey, Andrei Vldemirescu Homework

More information

Trigonometry Revision Sheet Q5 of Paper 2

Trigonometry Revision Sheet Q5 of Paper 2 Trigonometry Revision Sheet Q of Pper The Bsis - The Trigonometry setion is ll out tringles. We will normlly e given some of the sides or ngles of tringle nd we use formule nd rules to find the others.

More information

Activities. 4.1 Pythagoras' Theorem 4.2 Spirals 4.3 Clinometers 4.4 Radar 4.5 Posting Parcels 4.6 Interlocking Pipes 4.7 Sine Rule Notes and Solutions

Activities. 4.1 Pythagoras' Theorem 4.2 Spirals 4.3 Clinometers 4.4 Radar 4.5 Posting Parcels 4.6 Interlocking Pipes 4.7 Sine Rule Notes and Solutions MEP: Demonstrtion Projet UNIT 4: Trigonometry UNIT 4 Trigonometry tivities tivities 4. Pythgors' Theorem 4.2 Spirls 4.3 linometers 4.4 Rdr 4.5 Posting Prels 4.6 Interloking Pipes 4.7 Sine Rule Notes nd

More information

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point GCSE C Emple 7 Work out 9 Give your nswer in its simplest form Numers n inies Reiprote mens invert or turn upsie own The reiprol of is 9 9 Mke sure you only invert the frtion you re iviing y 7 You multiply

More information

a) Read over steps (1)- (4) below and sketch the path of the cycle on a P V plot on the graph below. Label all appropriate points.

a) Read over steps (1)- (4) below and sketch the path of the cycle on a P V plot on the graph below. Label all appropriate points. Prole 3: Crnot Cyle of n Idel Gs In this prole, the strting pressure P nd volue of n idel gs in stte, re given he rtio R = / > of the volues of the sttes nd is given Finlly onstnt γ = 5/3 is given You

More information

Context model automata for text compression (published in The ComputerJournal, 41 (7), , 1998)

Context model automata for text compression (published in The ComputerJournal, 41 (7), , 1998) Context model utomt for text ompression (pulished in The ComputerJournl, 41 (7), 474-485, 1998) Psi Fränti Deprtment of Computer Siene, University of Joensuu, Box 111, FIN-80101 Joensuu, FINLAND Timo Htkk

More information

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4.

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4. Mth 5 Tutoril Week 1 - Jnury 1 1 Nme Setion Tutoril Worksheet 1. Find ll solutions to the liner system by following the given steps x + y + z = x + y + z = 4. y + z = Step 1. Write down the rgumented mtrix

More information

AP CALCULUS Test #6: Unit #6 Basic Integration and Applications

AP CALCULUS Test #6: Unit #6 Basic Integration and Applications AP CALCULUS Test #6: Unit #6 Bsi Integrtion nd Applitions A GRAPHING CALCULATOR IS REQUIRED FOR SOME PROBLEMS OR PARTS OF PROBLEMS IN THIS PART OF THE EXAMINATION. () The ext numeril vlue of the orret

More information

CHENG Chun Chor Litwin The Hong Kong Institute of Education

CHENG Chun Chor Litwin The Hong Kong Institute of Education PE-hing Mi terntionl onferene IV: novtion of Mthemtis Tehing nd Lerning through Lesson Study- onnetion etween ssessment nd Sujet Mtter HENG hun hor Litwin The Hong Kong stitute of Edution Report on using

More information

#A42 INTEGERS 11 (2011) ON THE CONDITIONED BINOMIAL COEFFICIENTS

#A42 INTEGERS 11 (2011) ON THE CONDITIONED BINOMIAL COEFFICIENTS #A42 INTEGERS 11 (2011 ON THE CONDITIONED BINOMIAL COEFFICIENTS Liqun To Shool of Mthemtil Sienes, Luoyng Norml University, Luoyng, Chin lqto@lynuedun Reeived: 12/24/10, Revised: 5/11/11, Aepted: 5/16/11,

More information

Graph States EPIT Mehdi Mhalla (Calgary, Canada) Simon Perdrix (Grenoble, France)

Graph States EPIT Mehdi Mhalla (Calgary, Canada) Simon Perdrix (Grenoble, France) Grph Sttes EPIT 2005 Mehdi Mhll (Clgry, Cnd) Simon Perdrix (Grenole, Frne) simon.perdrix@img.fr Grph Stte: Introdution A grph-sed representtion of the entnglement of some (lrge) quntum stte. Verties: quits

More information

Discrete Structures Lecture 11

Discrete Structures Lecture 11 Introdution Good morning. In this setion we study funtions. A funtion is mpping from one set to nother set or, perhps, from one set to itself. We study the properties of funtions. A mpping my not e funtion.

More information

Electronic Circuits I Revision after midterm

Electronic Circuits I Revision after midterm Eletroni Ciruits I Revision fter miterm Dr. Ahme ElShfee, ACU : Fll 2018, Eletroni Ciruits I -1 / 14 - MCQ1 # Question If the frequeny of the input voltge in Figure 2 36 is inrese, the output voltge will

More information

Arrow s Impossibility Theorem

Arrow s Impossibility Theorem Rep Voting Prdoxes Properties Arrow s Theorem Arrow s Impossiility Theorem Leture 12 Arrow s Impossiility Theorem Leture 12, Slide 1 Rep Voting Prdoxes Properties Arrow s Theorem Leture Overview 1 Rep

More information

5. Every rational number have either terminating or repeating (recurring) decimal representation.

5. Every rational number have either terminating or repeating (recurring) decimal representation. CHAPTER NUMBER SYSTEMS Points to Rememer :. Numer used for ounting,,,,... re known s Nturl numers.. All nturl numers together with zero i.e. 0,,,,,... re known s whole numers.. All nturl numers, zero nd

More information

Solutions - Homework 1 (Due date: September 9:30 am) Presentation and clarity are very important!

Solutions - Homework 1 (Due date: September 9:30 am) Presentation and clarity are very important! ECE-238L: Computer Logi Design Fll 23 Solutions - Homework (Due dte: Septemer 2th @ 9:3 m) Presenttion nd lrity re very importnt! PROBLEM (5 PTS) ) Simpliy the ollowing untions using ONLY Boolen Alger

More information

Electromagnetism Notes, NYU Spring 2018

Electromagnetism Notes, NYU Spring 2018 Eletromgnetism Notes, NYU Spring 208 April 2, 208 Ation formultion of EM. Free field desription Let us first onsider the free EM field, i.e. in the bsene of ny hrges or urrents. To tret this s mehnil system

More information

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b CS 294-2 9/11/04 Quntum Ciruit Model, Solovy-Kitev Theorem, BQP Fll 2004 Leture 4 1 Quntum Ciruit Model 1.1 Clssil Ciruits - Universl Gte Sets A lssil iruit implements multi-output oolen funtion f : {0,1}

More information

Polyphase Systems. Objectives 23.1 INTRODUCTION

Polyphase Systems. Objectives 23.1 INTRODUCTION Polyphse Systems 23 Ojetives eome fmilir with the opertion of threephse genertor nd the mgnitude nd phse reltionship etween the three phse voltges. e le to lulte the voltges nd urrents for three-phse Y-onneted

More information

3.15 NMR spectroscopy Different types of NMR There are two main types of NMR 1. C 13 NMR 2. H (proton) NMR

3.15 NMR spectroscopy Different types of NMR There are two main types of NMR 1. C 13 NMR 2. H (proton) NMR .5 NMR spetrosopy Different types of NMR There re two min types of NMR. NMR. (proton) NMR There is only round % in orgni moleules ut modern NMR mhines re sensitive enough to give full spetr for The spetr

More information

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then.

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then. pril 8, 2017 Mth 9 Geometry Solving vetor prolems Prolem Prove tht if vetors nd stisfy, then Solution 1 onsider the vetor ddition prllelogrm shown in the Figure Sine its digonls hve equl length,, the prllelogrm

More information

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix tries Definition of tri mtri is regulr rry of numers enlosed inside rkets SCHOOL OF ENGINEERING & UIL ENVIRONEN Emple he following re ll mtries: ), ) 9, themtis ), d) tries Definition of tri Size of tri

More information

Lossless Compression Lossy Compression

Lossless Compression Lossy Compression Administrivi CSE 39 Introdution to Dt Compression Spring 23 Leture : Introdution to Dt Compression Entropy Prefix Codes Instrutor Prof. Alexnder Mohr mohr@s.sunys.edu offie hours: TBA We http://mnl.s.sunys.edu/lss/se39/24-fll/

More information

Computing data with spreadsheets. Enter the following into the corresponding cells: A1: n B1: triangle C1: sqrt

Computing data with spreadsheets. Enter the following into the corresponding cells: A1: n B1: triangle C1: sqrt Computing dt with spredsheets Exmple: Computing tringulr numers nd their squre roots. Rell, we showed 1 ` 2 ` `n npn ` 1q{2. Enter the following into the orresponding ells: A1: n B1: tringle C1: sqrt A2:

More information

Logarithms LOGARITHMS.

Logarithms LOGARITHMS. Logrithms LOGARITHMS www.mthletis.om.u Logrithms LOGARITHMS Logrithms re nother method to lulte nd work with eponents. Answer these questions, efore working through this unit. I used to think: In the

More information

Outline. Theory-based Bayesian framework for property induction Causal structure induction

Outline. Theory-based Bayesian framework for property induction Causal structure induction Outline Theory-sed Byesin frmework for property indution Cusl struture indution Constrint-sed (ottom-up) lerning Theory-sed Byesin lerning The origins of usl knowledge Question: how do people relily ome

More information

Thermodynamics. Question 1. Question 2. Question 3 3/10/2010. Practice Questions PV TR PV T R

Thermodynamics. Question 1. Question 2. Question 3 3/10/2010. Practice Questions PV TR PV T R /10/010 Question 1 1 mole of idel gs is rought to finl stte F y one of three proesses tht hve different initil sttes s shown in the figure. Wht is true for the temperture hnge etween initil nd finl sttes?

More information

Comparing the Pre-image and Image of a Dilation

Comparing the Pre-image and Image of a Dilation hpter Summry Key Terms Postultes nd Theorems similr tringles (.1) inluded ngle (.2) inluded side (.2) geometri men (.) indiret mesurement (.6) ngle-ngle Similrity Theorem (.2) Side-Side-Side Similrity

More information

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

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS The University of ottinghm SCHOOL OF COMPUTR SCIC A LVL 2 MODUL, SPRIG SMSTR 2015 2016 MACHIS AD THIR LAGUAGS ASWRS Time llowed TWO hours Cndidtes my omplete the front over of their nswer ook nd sign their

More information

MA10207B: ANALYSIS SECOND SEMESTER OUTLINE NOTES

MA10207B: ANALYSIS SECOND SEMESTER OUTLINE NOTES MA10207B: ANALYSIS SECOND SEMESTER OUTLINE NOTES CHARLIE COLLIER UNIVERSITY OF BATH These notes hve been typeset by Chrlie Collier nd re bsed on the leture notes by Adrin Hill nd Thoms Cottrell. These

More information

Review of Gaussian Quadrature method

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

More information

Section 6.1 Definite Integral

Section 6.1 Definite Integral Section 6.1 Definite Integrl Suppose we wnt to find the re of region tht is not so nicely shped. For exmple, consider the function shown elow. The re elow the curve nd ove the x xis cnnot e determined

More information

= state, a = reading and q j

= state, a = reading and q j 4 Finite Automt CHAPTER 2 Finite Automt (FA) (i) Derterministi Finite Automt (DFA) A DFA, M Q, q,, F, Where, Q = set of sttes (finite) q Q = the strt/initil stte = input lphet (finite) (use only those

More information

PYTHAGORAS THEOREM WHAT S IN CHAPTER 1? IN THIS CHAPTER YOU WILL:

PYTHAGORAS THEOREM WHAT S IN CHAPTER 1? IN THIS CHAPTER YOU WILL: PYTHAGORAS THEOREM 1 WHAT S IN CHAPTER 1? 1 01 Squres, squre roots nd surds 1 02 Pythgors theorem 1 03 Finding the hypotenuse 1 04 Finding shorter side 1 05 Mixed prolems 1 06 Testing for right-ngled tringles

More information

u( t) + K 2 ( ) = 1 t > 0 Analyzing Damped Oscillations Problem (Meador, example 2-18, pp 44-48): Determine the equation of the following graph.

u( t) + K 2 ( ) = 1 t > 0 Analyzing Damped Oscillations Problem (Meador, example 2-18, pp 44-48): Determine the equation of the following graph. nlyzing Dmped Oscilltions Prolem (Medor, exmple 2-18, pp 44-48): Determine the eqution of the following grph. The eqution is ssumed to e of the following form f ( t) = K 1 u( t) + K 2 e!"t sin (#t + $

More information

Polynomials. Polynomials. Curriculum Ready ACMNA:

Polynomials. Polynomials. Curriculum Ready ACMNA: Polynomils Polynomils Curriulum Redy ACMNA: 66 www.mthletis.om Polynomils POLYNOMIALS A polynomil is mthemtil expression with one vrile whose powers re neither negtive nor frtions. The power in eh expression

More information

Polyphase Systems 22.1 INTRODUCTION

Polyphase Systems 22.1 INTRODUCTION 22 Polyphse Systems 22.1 INTRODUTION n genertor designed to develop single sinusoidl voltge for eh rottion of the shft (rotor) is referred to s single-phse genertor. If the numer of oils on the rotor is

More information

Calculus Cheat Sheet. Integrals Definitions. where F( x ) is an anti-derivative of f ( x ). Fundamental Theorem of Calculus. dx = f x dx g x dx

Calculus Cheat Sheet. Integrals Definitions. where F( x ) is an anti-derivative of f ( x ). Fundamental Theorem of Calculus. dx = f x dx g x dx Clulus Chet Sheet Integrls Definitions Definite Integrl: Suppose f ( ) is ontinuous Anti-Derivtive : An nti-derivtive of f ( ) on [, ]. Divide [, ] into n suintervls of is funtion, F( ), suh tht F = f.

More information

MAT 403 NOTES 4. f + f =

MAT 403 NOTES 4. f + f = MAT 403 NOTES 4 1. Fundmentl Theorem o Clulus We will proo more generl version o the FTC thn the textook. But just like the textook, we strt with the ollowing proposition. Let R[, ] e the set o Riemnn

More information

Industrial Electrical Engineering and Automation

Industrial Electrical Engineering and Automation CODEN:LUTEDX/(TEIE-719)/1-7/(7) Industril Electricl Engineering nd Automtion Estimtion of the Zero Sequence oltge on the D- side of Dy Trnsformer y Using One oltge Trnsformer on the D-side Frncesco Sull

More information

Nondeterministic Finite Automata

Nondeterministic Finite Automata Nondeterministi Finite utomt The Power of Guessing Tuesdy, Otoer 4, 2 Reding: Sipser.2 (first prt); Stoughton 3.3 3.5 S235 Lnguges nd utomt eprtment of omputer Siene Wellesley ollege Finite utomton (F)

More information