MODELING MEMORY ERRORS IN PIPELINED ANALOG-TO-DIGITAL CONVERTERS
|
|
- Martha Webb
- 7 years ago
- Views:
Transcription
1 MODELING MEMORY ERRORS IN PIPELINED ANALOG-TO-DIGITAL CONVERTERS John P. Keane, Paul J. Hurst, and Stephen H. Lews Dept. of Electrcal and Computer Eng. Unversty of Calforna, Davs, CA 9566, USA. emal: ABSTRACT Swtched-capactor mplementatons of ppelned ADCs contan several sources of memory errors, ncludng capactor delectrc absorpton/relaxaton, ncomplete stage reset at hgh clock rates, and parastc capactance effects when op amps are shared between subsequent ppelne stages. Ths paper descrbes these sources of memory errors and presents a unfed model for ther effect. The dependence of these errors on crcut parameters and ADC samplng rate s also dscussed. The effect of these errors on ADC lnearty s then analyzed, showng how memory errors can lmt the performance of a ppelned ADC. KEY WORDS Analogue crcuts, analog-dgtal converson, swtched capactor crcuts, modellng, delectrc materals. Introducton A conventonal ppelned analog-to-dgtal converter ADC) archtecture s shown n Fgure. It conssts of an nput sample-and-hold amplfer SHA) and N ppelned stages. All sgnals shown are normalzed to the reference voltage V ref e.g. x 0 = V n /V ref where V n s the ADC nput voltage). The SHA samples the nput sgnal x 0 at a unform rate f s = /T where T s the sample perod, multples t by a gan G 0 where G 0 n general) and holds the output x as the nput of the frst ppelne stage. Each ppelne stage conssts of an analog-to-dgtal sub-converter ADSC), a dgtal-to-analog sub-converter DASC) and a SHA wth nomnal gan G. The ADSC generates a dgtal estmaton d of the stage nput x. Usng a DASC, d s converted to an analog sgnal K d that s subtracted from x to form the resdue y. y = x K d ) Ths resdue s multpled by a gan G and then sampled and held as the nput x + to the next ppelne stage. In general, the fnal stage N s smply an ADSC as only ts decson output d N, and not ts amplfed resdue, s requred. The transfer functon of each stage can be wrtten x + = G x K d ) ) The ADC produces a dgtal output x 0 [k] that s a x 0 x 0 G 0 SHA x T/H d d d d N Stage x Stage x 3 x Stage x + x N Stage N x y x G T/H x + ADSC K d d DASC Fgure. Ppelne ADC dgtal estmate of the normalzed analog nput x 0 [k] = V n kt)/v ref. Ths output s generated usng dgtal logc to calculate a weghted sum of the stage decson outputs d = d, d,..., d N ) T usng the weghts w = w, w,...,w N ) T x 0 = w T d = w d 3) = By referrng each DASC output to the ADC nput, the deal values of these weghts are computed as w = K 4) G j j=0 If the deal weghts n 4) are used n 3) then, assumng all components are deal and noseless, x 0 = x 0 + q 5) where the quantzaton error q s gven by N q = K N d N x N ) 6) G j j=0 As addtonal stages are added to the ppelne, q becomes smaller, and the resoluton of the converter ncreases. In order to smplfy the dgtal calculaton n 3), the gans K and G are usually desgned to be nteger powers of. In practce, accurate nterstage gan s dffcult to ensure n a modern VLSI process due to factors such as capactor msmatch and lmted op amp gan. In order to correct
2 for lnearty errors that would result from nterstage gan naccuracy, calbraton technques[, ] can be used. Whle these calbraton technques allow correcton for nterstage gan errors, they assume that each stage of the ppelne s memoryless. In practce, ths assumpton may not be true, and memory effects can cause lnearty errors that are not correctable usng these conventonal technques. Ths paper descrbes some sources of these memory errors and the effect that they have on ppelned ADC lnearty. Sources of Memory Effect. Capactor delectrc absorpton / relaxaton effects The memory effect of delectrc relaxaton on successveapproxmaton ADCs was descrbed n [3]. Recently ths effect was also shown to lmt the lnearty of a ppelned ADC[4] for a process usng a slcon ntrde SN) delectrc rather than conventonal slcon doxde SO ) delectrc for capactors. SN capactors have the advantage of much hgher densty than SO capactors and so have the potental to reduce de cost when large capactors are requred. However, these capactors exhbt a much larger memory effect due to capactor delectrc absorpton/relaxaton. Ths phenomenon s essentally due to carrers beng trapped n the delectrc and slowly released over tme and can be observed [4, 5] by the followng threephase procedure:. Charge a capactor to a voltage V C = V nt.. At tme t = 0, dscharge the capactor by shortng ts termnals together untl tme t = t Allow the capactor to float untl tme t = t f. The charge trapped n the delectrc durng phase above tends to gather back on the capactor plates durng phase 3, deally followng a logarthmc law[3, 4] V C t f, t 0 ) = γv nt 7) where γ = κlnt f /t 0 ) and κ s determned by the ntensty of the trap/release process n the delectrc. A common swtched-capactor mplementaton[6] of the ppelne stage n Fg. s shown n smplfed form n Fg. a). Although a sngle-ended crcut s shown for smplcty, n practce a fully dfferental crcut would be used. A tmng dagram for the clocks and used n ths stage are shown n Fg. b). Although short nonoverlappng tmes are often used between clock phases n practce[6], these are not shown here for smplcty and the duratons t and t of and, respectvely are both chosen to be T/. If the op amp s deal.e. a, offset=0) and there s no memory effect, then the output at the end of phase can be found from C f V + kt + T/) = C f + C n )V kt) 8) C n V DASC kt + T/) The parastc capactor C p has no effect n 8) as the nvertng termnal of the op amp s at the ground potental at the V t C n V DASC a) t C f C p a V + kt-t/ kt kt+t/ k+)t b) Fgure. a)swtched-capactor ppelne stage usng flparound archtecture, b) tmng dagram and are nonoverlappng). end of each phase. In ths paper, the tme ndex n e.g. d [n]) s used to denote the sgnals correspondng to a gven ADC nput sample and converson cycle. Although the stages n a ppelned ADC operate at dfferent tmes on resdues that correspond to one ADC nput sample, any sgnal that refers to the converson of the same nput sample wll be gven the same ndex. For example, although each stage output decson d [n] for a gven nput sample s generated at a dfferent tme, d [n] refers to the decson of stage correspondng to the converson of the nput sample x 0 [n] = x 0 nt). Usng ths conventon, defne the normalzed varables x [n] = V kt)/v ref 9) x + [n] = V + kt + T/)/V ref 0) d [n] = V DASC kt + T/)/K DASC ) where K DASC s the DASC gan n volts. In a typcal ppelned ADC[6], all stages, ncludng the SHA, have a delay of T/. In ths case k = n+0.5 n 9), 0) and ). Substtutng these expressons nto 8) gves x + [n] = G x [n] K d [n]) ) G = + C n /C f ) 3) K = K DASC /V ref )C n / C n + C f ) 4) Hence, for deal lnear capactors wthout memory effect, ths swtched-capactor ppelne stage can be modeled as shown n Fg. wth the transfer functon gven n ). In [4] a smple model for the memory effect caused by capactor delectrc absorpton/relaxaton was gven, representng the phenomenon as a smple one-tap fnte-mpulse response effect. In ths case, only relaxaton due to charge absorbed n the mmedately precedng phase s consdered. t
3 Usng normalzed varables, the stage transfer functon ncludng ths memory effect can be wrtten as: x + [n] = G x [n] K d [n]) 5) +γ f x + [n ] + γ n G K d [n ] V φ C C C n f φ C n f V V φ DASC DASC C p V m a V out where G and K are the same as for ) above. Here γ n and γ f model the memory effect of C n and C f, respectvely. From 7), Stage Stage γ n = κ n lnt/t ) = κ n ln) 6) γ f = κ f lnt/t ) = κ f ln) as t = T/ n ths case. If both C f and C n use the same delectrc materal κ n κ f and so γ n γ f. From 6), t can be seen that n ths case the magntude γ of ths memory effect s ndependent of the samplng rate f s = /T.. Incomplete stage reset effects In general, the perod T s chosen to be long enough to allow settlng n each phase of the resdue amplfer operaton to the requred accuracy of the converter. For hgh speed operaton, ths requres large swtches wth low onresstance, and a hgh-speed op amp to mnmze the settlng tme durng the amplfcaton phase. Incomplete settlng durng can be modeled as errors n the nterstage gan G and the DASC gan K f the settlng s lnear []. Such errors can be compensated for usng calbraton technques for nterstage gan errors [, ]. However, ncomplete settlng durng the reset phase causes memory-effect errors that can not be corrected usng these approaches. To llustrate ths pont, consder agan the ppelne stage n Fg.. Let the voltages on the capactors C n and C f be V Cn and V Cf, respectvely. Ideally, V Cn = V Cf = V at the end of. However, f the duraton of s too short to allow complete resettng of the voltages across C n and C f, V Cn and V Cf at the end of depend n part on the voltage changes that the crcut tres to mpress durng. From Fg. b), phase of sample perod k begns at tme t = kt T/ and ends at t = kt. If lnear settlng can be assumed, then V Cf kt) = V kt) γ f ) + γ f V Cf kt T ) 7) V Cn kt) = V kt) γ n ) + γ n V Cn kt T ) 8) where the factors γ f and γ n are constants determned by the duraton t of and the settlng tme constants of C f and C n. A fracton of the prevous capactor voltage s retaned after the reset phase n 7) and 8) resultng n a memory effect smlar to that descrbed n Secton.. In ths case, the values of γ f and γ n can be reduced by operatng at a lower rate f s = /T, whch ncreases t. If the op amp s deal and complete settlng s assumed durng, then the stage transfer functon n ths case s Fgure 3. Swtched-capactor ppelne stage usng shared op amp. V out = V + durng and V out = V + durng. gven by 5), but wth G = γ f ) + γ n )C n /C f. 9) Hence, the stage output x + [n] depends on ts prevous output x + [n ] and DASC nput d [n ], whch consttutes a memory effect that can not be corrected for usng nterstage gan calbraton technques..3 Op amp sharng effects As descrbed above, the ppelne stage shown n Fg. operates n two phases. Durng the reset phase the capactors are reset by chargng them to the stage nput voltage V. Durng the amplfcaton phase, the op amp s used to perform the subtractng and amplfcaton functons of the stage. In general, subsequent stages of the ppelne operate n opposte phases,.e. whle stage amplfes, stage s reset and vce-versa. Snce the op amp s only requred durng the amplfcaton phase, op amp sharng between subsequent stages has been proposed [7] n order to reduce the number of requred op amps by a factor of and hence reduce power and area requrements. The structure used n [7] s shown n Fg. 3. Comparng to Fg. shows that Fg. 3 contans two cascaded ppelne stages that share a sngle op amp. Durng the op amp generates the output V out = V + of stage, whle drvng the nput of stage. Durng, the op amp generates the output V out = V + of stage and drves subsequent stage 3. Addtonal swtches are added to the negatve op amp nput termnal the summng node), to allow t to be swtched between the two stages. Whle ths archtecture can yeld sgnfcant power savngs, t can result n memory effects due to the parastc capactance C p at the negatve termnal of the op amp, as noted n [7]. Ths memory effect occurs when the op amp gan a s lmted and so the voltage V m = V out /a at the end of the amplfyng phase s sgnfcant. At the end of, the op amp output V out = V + kt T/), so ts nput s V m = V + kt T/)/a. At t = kt + T/ the op amp s swtched from stage to stage and the capactor C p njects a charge C p V + kt T/)/a at the summng node of the second stage whle t s generatng V out = V + kt) durng. The transfer functon of stage can be calculated as
4 x [n] x + [n] x + [n-] G z - β x G + [n-] + K d [n] K d [n-] + + Stage Stage a) the output of each odd stage. Therefore, ths memory effect can be modeled entrely n the even stages wthout changng the output of a ppelned ADC that conssts of a cascade of the structures shown n Fg. 3. In ths case, an equvalent model for the ADC transfer functon s gven when each stage s modeled as x + [n] = G x [n] K d [n]) + γ x + [n ] 7) ~ ~ x [n] x + [n] x + [n-] G z - G + K d [n] K d [n-] + + Stage Stage b) βg + z - x + [n-] and γ = 0 for odd and γ = G β for even. Eqn. 5) shows that the memory effect n ths case s ndependent of the samplng rate f s = /T but can be reduced by ncreasng the op amp gan a and/or decreasng the parastc capactance C p. Alternatvely, an addtonal clock phase can be added to reset C p after, but ths adds extra complexty and may reduce the maxmum converson rate. Fgure 4. Model of opamp sharng x + [n] = G + x + [n] K + d + [n]) 0) C f + C n + C p /a G + = ) C f + C f + C n + C p )/a ) KDASC C n K + = C f + C n + C p /a ) V ref Snce the output x + [n] n 0) depends only on the current nput x + [n] and stage decson d + [n], no memory effect results. The parastc capactor C p causes only a change n the factors K and G. Now consder stage when the op amp s swtched from generatng V out = V + kt) durng to generatng V out = V + kt +T/) durng. In ths case, the capactor C p njects a charge C p V + kt)/a onto the summng node of stage durng. The transfer functon of stage can be calculated as x + [n] = G x [n] K d [n]) + β x + [n ] 3) C f + C n G = 4) C f + C f + C n + C p )/a C p /a β = 5) C f + C f + C n + C p )/a ) KDASC C n K = 6) C f + C n V ref Snce the output x + of stage n 3) depends on the prevous output x + of stage, a memory effect exsts that can result n nonlnearty. Combnng 0) and 3) gves the model shown n Fg. 4a). Ths model shows that a delayed verson of the output of the even stage s added to the output of the odd stage. The model can be changed so that the memory effect s completely contaned wthng one stage, as shown n Fg. 4b). The key step n ths transformaton s to note that an even stage follows and processes 3 Lnearty errors due to memory effects To consder the effect on converter accuracy of the memory effect n each stage, consder the stage transfer functon. The result n 5), whch apples to the memory effects descrbed n Sectons. and., can be summarzed by the expresson: x + [n] = G x [n] K d [n]) 8) +γ x + [n ] + δ G K d [n ] where γ and δ correspond to the memory effects γ f and γ n of the stage output and DASC output of stage, respectvely. The memory effect descrbed n Secton.3 resulted n the equvalent stage transfer functon gven n 7). Ths s dentcal to 8) except that δ = 0 for all stages n ths case. Computng the z-transform of 8) and rearrangng terms gves X z) = δ z )K z)d z) 9) + G γ z )X + z) N where X z) and D z) are the z-transforms of x [n] and d [n], respectvely. The nput SHA stage 0) can also be modeled by 9) f t s consdered as a stage wth the DASC removed.e. D 0 z) = 0). From 9), X + z) s gven by G X + z) = X γ z z) δ z )K D z) ) 30) The stage model usng 30) s shown n Fg. 5a). For the case where δ = 0, as n the opamp sharng memory effect descrbed n Secton.3, the stage can be modeled as n Fg. 5b). For the case where δ = γ, as can often be assumed for the memory effects descrbed n Sectons. and., 30) can be rewrtten as X + z) = G γ z G K D z) 3)
5 K D z) K D z) K D z) - δ z - Y z) Y z) G a) G b) S z) - γ z - c) - γ z - - γ z - G Fgure 5. Ppelne stage model a) ncludng memory effect, b) wth δ = 0, c) wth δ = γ. and modeled as shown n Fg. 5c). In general, the fnal ppelne stage stage N) conssts of an ADSC only and hence does not suffer from the memory effects dscussed n Secton. In ths case, from 6), X N z) can be wrtten as N X N z) = K N D N z) Qz) G K 3) k=0 where Qz) s the z-transform of the ADC quantzaton error q[n]. Startng wth the defnton 3) for X N z), 9) s recursvely appled for = N, N,...,0 to gve [ ] X 0 z) = w D z) δ z ) γ k z ) = k=0 N Qz) γ k z ) 33) k=0 where w are the weghts as defned n 4) and δ N = 0 when the fnal stage s an ADSC only. If the expresson n 3) s used to calculate the dgtal output x 0 [n], ts z-transform X 0 z) s gven by X 0 z) = w D z) 34) = In ths case, combnng 33) and 34) gves X 0 z) X 0 z) = N k=0 γ + Qz) 35) kz ) ) δ z + w D z) N k= γ kz ) = The frst term n 35) corresponds to a lnearly fltered verson of the nput X 0 z). The second term corresponds to the quantzaton error of the converter. The last term contans a weghted sum of past stage decsons. Ths s the term that contrbutes nonlnearty to the overall ADC transfer functon. To demonstrate the effects of ths term on converter lnearty, frst consder only memory effect n the SHA.e. γ = 0 for > 0 and δ = 0 for all stages). Here, 35) becomes X 0 z) = X 0z) + Qz) 36) γ 0 z Note that 36) has only lnear flterng of X 0 z), and hence wll not degrade the lnearty of the converter, although t wll add a hgh-frequency pole to ts frequencydoman transfer functon, resultng n some low-pass flterng. Next, consder memory effect only n the SHA and frst ppelne stage.e. γ = 0 and δ = 0 for > ). Here 35) becomes X 0 z) X 0 z) = γ 0 z ) γ z 37) ) + K ) D z) δ γ G 0 γ z z + Qz) An addtonal pole has been added to the lnear flterng of the nput X 0 z) due to the memory effect of stage. The second term n 37) nvolves the prevous values of the frst stage decson d. Snce these past decsons are correlated wth the the current nput X 0 n general, nonlnearty n the form of spectral tones wll result from ths term. However, n the common case where δ γ, the second term n 37) wll almost dsappear and so the memory effect of the frst stage wll not cause sgnfcant ADC nonlnearty. Extendng the memory effect to two stages gves X 0 z) = X 0 z) =0 γ z ) + K D z) G 0 + K D z) G 0 G 38) δ γ γ )z + γ γ z ) γ z ) γ z ) ) z + Qz) δ γ γ z In ths case the output contans terms relatng to prevous decsons d and d of stage and stage, respectvely. Even n the case where γ = δ, terms relatng to prevous values of d wll reman, causng converter nonlnearty. Intutvely, the reason that memory effect n the frst stage does not cause nonlnearty when γ = δ s that the memory effect of the output x and the decson d combne to form an equvalent memory effect on the frst stage nput x only, as shown n Fg. 5c). Snce x contans no quantzaton error, only lnear flterng of the nput results. However, snce the output of each stage n a ppelned ADC s an amplfed verson of the quantzaton error of
6 INL LSB) INL LSB) INL LSB) Code a) Code b) 5 0 f=f 0 f=f Code c) Fgure 6. Smulated INL at -bt level for.5b/stage ppelne ADC wth γ = δ = a) for frst stage only, b) for frst stages, and c) for all stages wth nput full-scale snewave at frequences f /T sold) and f 0 dashed). the prevous stage, memory effects n subsequent stages wll nclude ths nonlnear quantzaton error and hence cause overall converter nonlnearty. Ths s shown when the memory effect s extended to stages n 38). Fg. 6 shows the smulated converter ntegral nonlnearty INL) at -bt level for a conventonal -stage.5 bt/stage ppelne ADC[6]. A.5 bt stage has nomnal gan G =, ADSC thresholds at ±V ref /4 and DASC levels of 0, ±V ref /. The test sgnal s an nput snewave wth ampltude 95% of full-scale at frequences f 0 = 0.007/T. sold) and f 0 dashed). The result n Fg. 6a) s for memory effect wth γ = δ = for the frst stage only. Snce the ADC s otherwse deal, the INL s almost zero n ths case. Ths verfes that memory effect n the frst stage when γ = δ does not cause nonlnearty. In Fg. 6b), the INL s shown where the frst two stages have a memory effect wth γ = δ = and all others are deal. The memory effect results n an INL of ±3.7 LSB. When the nput snewave frequency s doubled from f 0 to f 0, the wdth of the INL dscontnutes around the frst stage ADSC thresholds of ±V ref /4 ncreases. Hence, the measured INL s frequency-dependent. These dscontnutes are caused when an nput sample s mmedately preceded by a sample that s on the other sde of an ADSC threshold. In ths case, the DASC output jumps by one level, but a memory stll exsts of the prevous DASC output resultng n a memory error. When the nput frequency ncreases, the dfference between the current and last nput sample ncreases, so INL errors occur due to ths effect occur for codes further away from the ADSC thresholds. When the memory effect s extended to all stages, Fg. 6c) shows the resultng converter INL of ±5.3 LSBs. Ths slght ncrease shows that the memory error n ths case s domnated by the second stage. The INL n ths case can also be seen to vary slghtly wth the nput frequency. 4 Concluson Sources of memory effects n ppelned ADCs due to capactor delectrc absorpton/relaxaton, ncomplete stage reset, and op amp sharng have been descrbed and modeled. Whle errors due to ncomplete stage reset can be reduced by decreasng the ADC converson rate f s = /T, the other effects descrbed are ndependent of f s. These memory effects can lmt the ADC lnearty. However, for common cases, memory effects n the SHA and frst ppelne stage do not cause ADC nonlnearty. Ths allows some flexblty of the desgn of these stages, whch typcally domnate the power and area of a ppelned ADC due to nose consderatons. Acknowledgements Ths research was supported by UC MICRO Grant No and by NSF Grant No. CCR References [] A. N. Karancolas, H.-S. Lee, and K. L. Barcrana, A 5-b -Msample/s dgtally self-calbrated ppelne ADC, IEEE Journal of Sold-State Crcuts, 993, 8), [] J. Mng and S. H. Lews, An 8-bt 80-Msample/s ppelned analog-to-dgtal converter wth background calbraton, IEEE Journal of Sold-State Crcuts, 00, 360), [3] J. W. Fattaruso, M. de Wt, G. Warwar, K-S. Tan, and R. K. Hester, The effect of delectrc relaxaton on charge-redstrbuton A/D converters, IEEE Journal of Sold-State Crcuts, 990, 56), [4] A. Zanch, F. Tsay, and I. Papantonopoulos, Impact of capactor delectrc relaxaton on a 4-bt 70-MS/s ppelne ADC n 3-V BCMOS, IEEE Journal of Sold- State Crcuts, 003, 38), [5] H. Resnger, G. Stenlesberger, S. Jakschk, M. Gutsche, T. Hecht, M. Leonhard, U. Schroder, H. Sedl, and D. Schumann, A comparatve study of delectrc relaxaton losses n alternatve delectrcs, Int. Electron Devces Mtg. Tech. Dg., Washngton, D.C., USA, 00, [6] S. H. Lews, H. S. Fetterman, G. F. Gross Jr, R. Ramachandran, and T. R. Vswanathan, A 0-b 0- Msample/s analog-to-dgtal converter, IEEE Journal of Sold-State Crcuts, 99, 73), [7] K. Nagaraj, H. S. Fetterman, J. Andjar, S. H. Lews, and R. G. Rennnger, A 50-mW, 8-b, 5-Msamples/s parallel-ppelned A/D converter wth reduced number of amplfers, IEEE Journal of Sold-State Crcuts, 997, 33), 3 30.
RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.
ICSV4 Carns Australa 9- July, 007 RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) yaoq.feng@yahoo.com Abstract
More informationLinear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits
Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationThe circuit shown on Figure 1 is called the common emitter amplifier circuit. The important subsystems of this circuit are:
polar Juncton Transstor rcuts Voltage and Power Amplfer rcuts ommon mtter Amplfer The crcut shown on Fgure 1 s called the common emtter amplfer crcut. The mportant subsystems of ths crcut are: 1. The basng
More informationINVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS
21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS
More informationSection C2: BJT Structure and Operational Modes
Secton 2: JT Structure and Operatonal Modes Recall that the semconductor dode s smply a pn juncton. Dependng on how the juncton s based, current may easly flow between the dode termnals (forward bas, v
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationFrequency Selective IQ Phase and IQ Amplitude Imbalance Adjustments for OFDM Direct Conversion Transmitters
Frequency Selectve IQ Phase and IQ Ampltude Imbalance Adjustments for OFDM Drect Converson ransmtters Edmund Coersmeer, Ernst Zelnsk Noka, Meesmannstrasse 103, 44807 Bochum, Germany edmund.coersmeer@noka.com,
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationLecture 3: Force of Interest, Real Interest Rate, Annuity
Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and
More informationQuantization Effects in Digital Filters
Quantzaton Effects n Dgtal Flters Dstrbuton of Truncaton Errors In two's complement representaton an exact number would have nfntely many bts (n general). When we lmt the number of bts to some fnte value
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationSPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:
SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and
More informationHow To Calculate The Accountng Perod Of Nequalty
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationNOTE: The Flatpak version has the same pinouts (Connection Diagram) as the Dual In-Line Package. *MR for LS160A and LS161A *SR for LS162A and LS163A
BCD DECADE COUNTERS/ 4-BIT BINARY COUNTERS The LS160A/ 161A/ 162A/ 163A are hgh-speed 4-bt synchronous counters. They are edge-trggered, synchronously presettable, and cascadable MSI buldng blocks for
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationSection 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
More informationOn the Optimal Control of a Cascade of Hydro-Electric Power Stations
On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More informationMultiple stage amplifiers
Multple stage amplfers Ams: Examne a few common 2-transstor amplfers: -- Dfferental amplfers -- Cascode amplfers -- Darlngton pars -- current mrrors Introduce formal methods for exactly analysng multple
More informationLecture 3: Annuity. Study annuities whose payments form a geometric progression or a arithmetic progression.
Lecture 3: Annuty Goals: Learn contnuous annuty and perpetuty. Study annutes whose payments form a geometrc progresson or a arthmetc progresson. Dscuss yeld rates. Introduce Amortzaton Suggested Textbook
More informationFaraday's Law of Induction
Introducton Faraday's Law o Inducton In ths lab, you wll study Faraday's Law o nducton usng a wand wth col whch swngs through a magnetc eld. You wll also examne converson o mechanc energy nto electrc energy
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More information1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationImplementation of Deutsch's Algorithm Using Mathcad
Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationRisk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008
Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More informationChapter 4 ECONOMIC DISPATCH AND UNIT COMMITMENT
Chapter 4 ECOOMIC DISATCH AD UIT COMMITMET ITRODUCTIO A power system has several power plants. Each power plant has several generatng unts. At any pont of tme, the total load n the system s met by the
More informationBERNSTEIN POLYNOMIALS
On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample
More informationCausal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
More informationTraffic State Estimation in the Traffic Management Center of Berlin
Traffc State Estmaton n the Traffc Management Center of Berln Authors: Peter Vortsch, PTV AG, Stumpfstrasse, D-763 Karlsruhe, Germany phone ++49/72/965/35, emal peter.vortsch@ptv.de Peter Möhl, PTV AG,
More informationAnswer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.
More informationFINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals
FINANCIAL MATHEMATICS A Practcal Gude for Actuares and other Busness Professonals Second Edton CHRIS RUCKMAN, FSA, MAAA JOE FRANCIS, FSA, MAAA, CFA Study Notes Prepared by Kevn Shand, FSA, FCIA Assstant
More informationIDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS
IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS Chrs Deeley* Last revsed: September 22, 200 * Chrs Deeley s a Senor Lecturer n the School of Accountng, Charles Sturt Unversty,
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationChapter 6 Inductance, Capacitance, and Mutual Inductance
Chapter 6 Inductance Capactance and Mutual Inductance 6. The nductor 6. The capactor 6.3 Seres-parallel combnatons of nductance and capactance 6.4 Mutual nductance 6.5 Closer look at mutual nductance Oerew
More informationCalculating the high frequency transmission line parameters of power cables
< ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,
More informationA hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
More information"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
More informationJet Engine. Figure 1 Jet engine
Jet Engne Prof. Dr. Mustafa Cavcar Anadolu Unversty, School of Cvl Avaton Esksehr, urkey GROSS HRUS INAKE MOMENUM DRAG NE HRUS Fgure 1 Jet engne he thrust for a turboet engne can be derved from Newton
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationChapter 31B - Transient Currents and Inductance
Chapter 31B - Transent Currents and Inductance A PowerPont Presentaton by Paul E. Tppens, Professor of Physcs Southern Polytechnc State Unversty 007 Objectves: After completng ths module, you should be
More informationMean Molecular Weight
Mean Molecular Weght The thermodynamc relatons between P, ρ, and T, as well as the calculaton of stellar opacty requres knowledge of the system s mean molecular weght defned as the mass per unt mole of
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationNumber of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000
Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from
More information1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
More informationTime Domain simulation of PD Propagation in XLPE Cables Considering Frequency Dependent Parameters
Internatonal Journal of Smart Grd and Clean Energy Tme Doman smulaton of PD Propagaton n XLPE Cables Consderng Frequency Dependent Parameters We Zhang a, Jan He b, Ln Tan b, Xuejun Lv b, Hong-Je L a *
More informationIMPACT ANALYSIS OF A CELLULAR PHONE
4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng
More information1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
More informationForecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network
700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School
More informationBrigid Mullany, Ph.D University of North Carolina, Charlotte
Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte
More informationVOLUME 5 BLAGOEVGRAD, BULGARIA SCIENTIFIC. Research ELECTRONIC ISSUE ISSN 1312-7535
VOLUME 5 007 BLAGOEVGRAD, BULGARIA SCIENTIFIC Research ISSN 131-7535 ELECTRONIC ISSUE IMPROVING FAIRNESS IN CDMA-HDR NETWORKS Valentn Hrstov Abstract. Improvng throughput and farness n Cellular Data Networks
More informationChapter 12 Inductors and AC Circuits
hapter Inductors and A rcuts awrence B. ees 6. You may make a sngle copy of ths document for personal use wthout wrtten permsson. Hstory oncepts from prevous physcs and math courses that you wll need for
More informationEfficient Striping Techniques for Variable Bit Rate Continuous Media File Servers æ
Effcent Strpng Technques for Varable Bt Rate Contnuous Meda Fle Servers æ Prashant J. Shenoy Harrck M. Vn Department of Computer Scence, Department of Computer Scences, Unversty of Massachusetts at Amherst
More informationThe Development of Web Log Mining Based on Improve-K-Means Clustering Analysis
The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationLogistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification
Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson
More informationTime Value of Money Module
Tme Value of Money Module O BJECTIVES After readng ths Module, you wll be able to: Understand smple nterest and compound nterest. 2 Compute and use the future value of a sngle sum. 3 Compute and use the
More informationwhere the coordinates are related to those in the old frame as follows.
Chapter 2 - Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of non-coplanar vectors Scalar product
More informationThe Effect of Mean Stress on Damage Predictions for Spectral Loading of Fiberglass Composite Coupons 1
EWEA, Specal Topc Conference 24: The Scence of Makng Torque from the Wnd, Delft, Aprl 9-2, 24, pp. 546-555. The Effect of Mean Stress on Damage Predctons for Spectral Loadng of Fberglass Composte Coupons
More informationA DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña
Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,
More information) of the Cell class is created containing information about events associated with the cell. Events are added to the Cell instance
Calbraton Method Instances of the Cell class (one nstance for each FMS cell) contan ADC raw data and methods assocated wth each partcular FMS cell. The calbraton method ncludes event selecton (Class Cell
More informationADC Integral Non-Linearity Testing with Low Linearity Monotonic Signals
ADC Integral Non-Lnearty Testng wth Low Lnearty onotonc Sgnals Bharath Vasan, Degang J Chen, Randall L Geger Department of Electrcal and Computer Engneerng Iowa State Unversty Ames, Iowa, USA bharath,dchen@astate.edu
More informationLaddered Multilevel DC/AC Inverters used in Solar Panel Energy Systems
Proceedngs of the nd Internatonal Conference on Computer Scence and Electroncs Engneerng (ICCSEE 03) Laddered Multlevel DC/AC Inverters used n Solar Panel Energy Systems Fang Ln Luo, Senor Member IEEE
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationRing structure of splines on triangulations
www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAM-Report 2014-48 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon
More informationUsing Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
More informationTexas Instruments 30X IIS Calculator
Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the
More informationVRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) 2127472, Fax: (370-5) 276 1380, Email: info@teltonika.
VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual
More informationSimple Interest Loans (Section 5.1) :
Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part
More informationThe University of Texas at Austin. Austin, Texas 78712. December 1987. Abstract. programs in which operations of dierent processes mayoverlap.
Atomc Semantcs of Nonatomc Programs James H. Anderson Mohamed G. Gouda Department of Computer Scences The Unversty of Texas at Austn Austn, Texas 78712 December 1987 Abstract We argue that t s possble,
More informationHow To Understand The Results Of The German Meris Cloud And Water Vapour Product
Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller
More informationInterest Rate Forwards and Swaps
Interest Rate Forwards and Swaps Forward rate agreement (FRA) mxn FRA = agreement that fxes desgnated nterest rate coverng a perod of (n-m) months, startng n m months: Example: Depostor wants to fx rate
More informationHÜCKEL MOLECULAR ORBITAL THEORY
1 HÜCKEL MOLECULAR ORBITAL THEORY In general, the vast maorty polyatomc molecules can be thought of as consstng of a collecton of two electron bonds between pars of atoms. So the qualtatve pcture of σ
More informationPortfolio Loss Distribution
Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment
More informationOptimal Bidding Strategies for Generation Companies in a Day-Ahead Electricity Market with Risk Management Taken into Account
Amercan J. of Engneerng and Appled Scences (): 8-6, 009 ISSN 94-700 009 Scence Publcatons Optmal Bddng Strateges for Generaton Companes n a Day-Ahead Electrcty Market wth Rsk Management Taken nto Account
More informationPerformance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application
Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdo-dong,
More informationPSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12
14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationJoe Pimbley, unpublished, 2005. Yield Curve Calculations
Joe Pmbley, unpublshed, 005. Yeld Curve Calculatons Background: Everythng s dscount factors Yeld curve calculatons nclude valuaton of forward rate agreements (FRAs), swaps, nterest rate optons, and forward
More informationTraffic-light a stress test for life insurance provisions
MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang
More informationSolution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.
Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces
More informationComparison of Control Strategies for Shunt Active Power Filter under Different Load Conditions
Comparson of Control Strateges for Shunt Actve Power Flter under Dfferent Load Condtons Sanjay C. Patel 1, Tushar A. Patel 2 Lecturer, Electrcal Department, Government Polytechnc, alsad, Gujarat, Inda
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems
More informationSingle and multiple stage classifiers implementing logistic discrimination
Sngle and multple stage classfers mplementng logstc dscrmnaton Hélo Radke Bttencourt 1 Dens Alter de Olvera Moraes 2 Vctor Haertel 2 1 Pontfíca Unversdade Católca do Ro Grande do Sul - PUCRS Av. Ipranga,
More informationOn-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features
On-Lne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: ruoyu.l@skf.com
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More informationCorrelated Noise Modeling - An Implementation into HICUM
Correlated ose Modelng - An Implementaton nto HICUM A. Chakravorty, M. chroter, P. akalas, J. Herrcht Char for Electron Devces and Integrated Crcuts (CEDIC) Unversty of Technology Dresden Germany Dept.
More informationWe are now ready to answer the question: What are the possible cardinalities for finite fields?
Chapter 3 Fnte felds We have seen, n the prevous chapters, some examples of fnte felds. For example, the resdue class rng Z/pZ (when p s a prme) forms a feld wth p elements whch may be dentfed wth the
More informationA Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression
Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,
More informationSIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
More information7.5. Present Value of an Annuity. Investigate
7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on
More informationSection 5.3 Annuities, Future Value, and Sinking Funds
Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme
More informationA Design Method of High-availability and Low-optical-loss Optical Aggregation Network Architecture
A Desgn Method of Hgh-avalablty and Low-optcal-loss Optcal Aggregaton Network Archtecture Takehro Sato, Kuntaka Ashzawa, Kazumasa Tokuhash, Dasuke Ish, Satoru Okamoto and Naoak Yamanaka Dept. of Informaton
More informationAn Interest-Oriented Network Evolution Mechanism for Online Communities
An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne
More informationLaws of Electromagnetism
There are four laws of electromagnetsm: Laws of Electromagnetsm The law of Bot-Savart Ampere's law Force law Faraday's law magnetc feld generated by currents n wres the effect of a current on a loop of
More informationAnalysis of Reactivity Induced Accident for Control Rods Ejection with Loss of Cooling
Analyss of Reactvty Induced Accdent for Control Rods Ejecton wth Loss of Coolng Hend Mohammed El Sayed Saad 1, Hesham Mohammed Mohammed Mansour 2 Wahab 1 1. Nuclear and Radologcal Regulatory Authorty,
More informationAn Isolated Feedback Circuit for a Flyback Charging Circuit
Proceedngs of the 007 WSEAS Int. Conference on Crcuts, Systems, Sgnal and Telecommuncatons, Gold Coast, Australa, January 17-19, 007 35 An Isolated Feedback Crcut for a Flyback Chargng Crcut LI JIE, HUAG
More informationThe exergy approach in a legal framework
The exergy approach n a legal framewor Prof Danel Favrat Insttut des Scences de l'énerge, PFL Prof Danel Favrat 1 ÉCOL POLYTCHNIU FÉDÉRAL D LAUSANN Preamble Introducton of an energy concept ncludng a exergetc
More information