FPGA Implementation of Sine and Cosine Generators Using the CORDIC Algorithm

Size: px
Start display at page:

Download "FPGA Implementation of Sine and Cosine Generators Using the CORDIC Algorithm"

Transcription

1 FPGA Implemetato of Se ad Cose Geerators Usg the CORDIC Algorthm Taya Vladmrova ad Has Tggeler Surrey Space Cetre Uversty of Surrey, Guldford, Surrey, GU 5XH Tel: +44() Fax: +44() Abstract: Ths paper s cocered wth FPGA mplemetato of CORDIC schemes for fast ad slco area effcet computato of the se ad cose fuctos. The results of theoretcal vestgato to redudat CORDIC are preseted. Summary of CORDIC sythess results based o Actel ad XILINX FPGAs s gve. Fally applcatos of CORDIC se ad cose geerators small satelltes are dscussed. Keywords: CORDIC, se, cose, FPGA, sythess, redudat sged-dgt system. 1. Itroducto The ame CORDIC stads for Coordate Rotato Dgtal Computer. Volder [Vold59] developed the uderlyg method of computg the rotato of a vector a Cartesa coordate system ad evaluatg the legth ad agle of a vector. The CORDIC method was later expaded for multplcato, dvso, logarthm, expoetal ad hyperbolc fuctos. The varous fucto computatos were summarsed to a ufed techque [Walt71]. The resultg vector z of the rotato of a vector [ x y ] T, by a agle θ Cartesa coordates ca be computed by the followg matrx operato [Prs98]: x cosθ sθ x = (1) y sθ cosθ y Usg the detty: cos θ = ta θ ad factorg out cos θ equato (1) ca be modfed as follows: x 1 1 taθ x = () y 1+ ta θ taθ 1 y I the CORDIC method, the rotato by a agle θ s mplemeted as a teratve process, cosstg of mcro-rotatos durg whch the tal vector s rotated by predetermed step agles α. Ay agle θ ca be represeted to a certa accuracy by a set of step agles α. Specfyg a drecto of rotato or sg σ, the sum of the step agles α approxmates a gve agle θ as follows: = = 1 θ, { 1,1 } σ α σ (3) 1

2 The sg of the dfferece betwee the agle θ ad the partal sum of step agles 1 θ σ α cotrols the sg j j σ of the step agles α. A auxlary varable z s j = troduced that cotas the accumulated partal sum of step agles ad s used to determe the sg of the ext mcro-rotato. To smplfy the computato of the matrx product gve by (), the step agles α are chose such that ta α represets a seres of powers of : ta = α, =, 1,,..., 1 (4) The CORDIC method ca be employed two dfferet modes, kow as the rotato mode ad the vectorg mode. I the rotato mode, the co-ordate compoets of a vector ad a agle of rotato are gve ad the co-ordate compoets of the orgal vector, after rotato through a gve agle, are computed. I the vectorg mode, the co-ordate compoets of a vector are gve ad the magtude ad agular argumet of the orgal vector are computed [Vold59]. The rotato mode of the CORDIC algorthm has three puts that are talsed to the co-ordate compoets of the vector x, y ad the agle of rotato z = θ ad s descrbed by the followg terato equatos: x + 1 = x yσ + 1 = y + xσ (5) + 1 = σ arcta y z z 1 f z < where σ = ad =,1,,..., 1 (6) + 1 f z The outputs of the rotato mode x, y ad z are gve by the followg expressos, x ad y beg the co-ordates of the rotated (by the agle θ ) vector: x = K x cos z y s ) y ( z = K ( y cos z + x s z z = 1 ) where K = 1+ (7) = A CORDIC mcro-rotato s ot a pure rotato but a rotato-exteso. The costat K, gve by (7), s referred to as a scale factor, ad represets the crease magtude of the vector durg the rotato process. Whe the umber of teratos/mcro-rotatos s fxed the scale factor s a costat approachg the value of as the umber of teratos goes to fty. The elemetary fuctos se ad cose ca be computed usg the rotato mode of the CORDIC algorthm f the tal vector s of ut legth ad s alged wth the abscssa. The computato of s θ ad cos θ s based o equatos (5) ad (6) wth put values x =, y ad z = θ. The outputs after teratos are as follows: 1 =

3 x = K x cosθ y sθ ) = K cosθ (8) ( ( y cosθ + x sθ ) K y = K = z = sθ A addtoal operato of dvso s requred to obta the values of s θ ad cosθ from (8) as a result of the crease magtude of the vector by the factor K durg rotato. However, sce the scale factor s a costat for a gve umber of teratos, the operato of dvso ca be elmated by settg the magtude of the tal vector to the recprocal value of the scale factor,.e. x = 1 K. I ths paper we cosder computato of se ad cose of a agle θ (rad), where θ s a -bt bary fracto ad satsfes θ π. We compute s θ ad cosθ dow to the -th bary posto. I secto dfferet approaches to CORDIC mplemetato are summarsed. Secto 3 s dedcated to fast CORDIC methods. Secto 4 dscusses a redudat adder for fast CORDIC mplemetato ad ts realsato XILINX XC4. Secto 5 presets CORDIC sythess results targetg Actel ad XILINX FPGAs ad usg dfferet sythess tools. Secto 6 dscusses two satellte applcatos of CORDIC se ad cose geerators. Fally secto 7 cotas cocludg remarks.. Approaches to CORDIC Hardware Implemetato The CORDIC algorthm ca be mplemeted hardware usg three approaches: a sequetal approach - the structure s ufolded tme, a parallel approach - the structure s ufolded space or a combato of the two. These three approaches ad the resultg structures are also referred to the lterature as teratve, cascaded ad cascaded fuso, respectvely. A sequetal CORDIC desg performs oe terato per clock cycle ad cossts of three -bt adders/subtractors, two sg extedg shfters, a look-up table (LUT) for the step agle costats ad a fte state mache. A parallel CORDIC desg s smlar to a array multpler structure cosstg of rows of adders/subtractors, wth hardwred shfts ad costats. Parallel CORDIC ca be mplemeted the form of purely combatoal arrays or ca be ppeled depedg o the sze of the desg ad the requested data rate. A combed CORDIC desg s based o a sequetal structure where the logc for several successve teratos s cascaded ad s executed wth oe clock cycle [Wag95]. The umber of fused successve terato stages determes the order of a combed CORDIC desg. Fgure 1 summarses the structures used hardware mplemetato of the CORDIC algorthm. Sce algebrac addto s the ma operato the CORDIC algorthm, the effcecy of the hardware mplemetato of the algorthm depeds sgfcatly o the type of adder used. Adders based o the covetoal two-dgt bary system have tme delay depedet o the bt legth ad the best case of fast herarchcal adder structures the tme delay for executo of oe terato s of logarthmc order O (log ) [Prs98]. The tme delay of the operato of addto ca be made depedet o the bt legth by usg redudat adders that accept operads 3

4 represeted redudat sged-dgt (RSD) bary system. Numbers RSD system are represeted usg a three-dgt set {, 1, 1} ad may have several RDS represetatos, hece the ame redudat. Fgure 1. CORDIC hardware mplemetatos Bt-seral ad bary adders have bee used sequetal CORDIC mplemetatos [Adr98], all types of adders have bee tred cascaded CORDIC desgs bt-seral adders, carry-save adders, bary adders, redudat adders, combatos of both bary ad redudat adders [Adr98, Tmm9]. Obvously, a combato of sequetal approach ad bt-seral adders wll result the slowest desg wth mmal area, parallel approach ad redudat adders the fastest desg wth maxmal area. A trade-off betwee area ad speed would determe the rght mplemetato approach for a gve applcato. 3. Redudat CORDIC Schemes The troducto of the RSD system to the teral computato of the CORDIC method s cosdered to be oe of the most effectve ways to accelerate the algorthm [Erce87, Taka91, Tmm9, Bake76]. Cascaded desgs of redudat CORDIC schemes have outperformed array mplemetatos of CORDIC based o carry-save adders accordg to a comparatve study of these methods [Tmm9]. However, the straghtforward applcato of the RSD represetato to the CORDIC algorthm gves rse to problems that compromse the effcecy of the algorthm, as follows: Coverters from s complemet represetato to RSD ad vce versa are requred. The coverso from s complemet to RSD s straghtforward, however the coverso from RSD to s complemet requres a extra addto operato over -bt. 4

5 The value of the drecto operator 1,, 1 sce t depeds o the sg of z that s represeted as a redudat. The sg evaluato of a redudat umber requres detecto of the sg of the most sgfcat ozero bary dgt ad the worst case eeds specto of all dgts whch s a very slow procedure. I redudat CORDIC o rotato-exteso takes place for some step agles sce zero s a vald choce for the drecto operator σ. Ths makes the scale σ s selected from the dgt set { } factor K operad depedet ad ot a costat value ay more. Two approaches have bee proposed to elmate the vared scale factor effect: the scale factor s calculated durg computato ad the fucto values are corrected wth t at the ed of the rotato process [Erce87] or the scale factor s compesated durg the terato process va troducto of specal teratos [Taka91, Tmm9]. A alteratve approach to evaluato of rotato operators σ s to predct ther values by decomposg the agle of rotato advace [Bake76]. A comparso of the latecy of covetoal CORDIC ad dfferet modfcatos of redudat CORDIC has bee carred out wth all desgs beg of array type [Marx99]. The latecy of the desgs expressed as a fucto of the bt-legth s gve Table 1 [Marx99], where τ - delay of a full adder; τ (log ) - the upper boud of a -bt o-redudat fast addto; δ - delay of a redudat adder, depedet of the bt-legth; m - a arbtrary teger the correctg method [Taka91] where a correcto terato s performed every m -th step. The termato algorthm orgally proposed by [Che7] allows quttg the terato process as early as possble, modfed Booth ecodg ca be used for the same purpose [Tmm9]. Table 1. Latecy expressos of CORDIC mplemetatos Name No-redudat method Double rotato method [Taka91] Correctg method [Taka91] Predcto method [Tmm9] Predcto wth termato method [Tmm9] Latecy expresso as a fucto of the bt legth τ log τ + δ + τ log ( ( + 1) m )( τ + δ ) + ( ( + 1) m + log ) ( τ + δ ) log3 1 log τ log δ + τ + log3(( + 1) 1 log + τ log δ log( ) δ ( + 1) + τ + Fgure [Marx99] shows graphcally the latecy of the CORDIC mplemetatos usg estmated delays for XC4XL ad a rato r δ τ =. It suggests that a predcto techque combed wth a termato method [Tmm9] mght lead to a fastest FPGA mplemetato. 5

6 Fgure. Estmated latecy of CORDIC mplemetatos XC4XL 4. Redudat Adder Implemetato I RSD represetato, a umber Y ca be vewed as the dfferece betwee two * ** postve bary umbers Y ad Y as follows: * ** Y = y = ( y y ) = = * ** wth, { 1, } y (9) y The covetoal oe-bt full adder assumes postve weghts to all of ts three bary puts ad two bary outputs. Such adders ca be geeralsed to four types of adder cells by mposg postve ad egatve weghts to the bary put/output termals [Hwa79]. The addto of two redudat sged-dgt umbers Y ad Z ca be performed by cascadg two levels of geeralsed full adders of types 1 ad as show Fgure 3. The ma drawback of ths computato scheme wth two umbers redudat form s the amout of hardware, whch s twce that the carry-save case [Vad9]. Fgure 3. Redudat sged dgt adder [Vad9] 6

7 The rpple-carry adder ad the redudat sg-dgt adder have bee mplemeted XILINX 41XL ad compared terms of speed ad area [Marx99]. The rpplecarry adder uses the XILINX dedcated carry logc ad takes.5 cofgurable logc blocks (CLBs) per bt. The smallest redudat adder that has bee acheved XILINX 41XL requres two CLBs per bt. Fgure 4 [Marx99] llustrates the mappg for the mmal area redudat adder, where S1_geerator comprses the * logc that geerates the S output ad Sa_geerator comprses the logc that 1 ( + 1) Fgure 4. A mmum-area mappg of a redudat adder oto XC41XL ** geerates the S output. The latecy results are show Fgure 5 [Marx99], where the rpple-carry adder s referred to as RCA ad the redudat adder s referred to as ISDA. As ca be see from Fgure 5, the delay of the rpple-carry adder s early equvalet to the delay of the redudat adder for bt-legths below 16 bts, however, for bt-legth above 3 bts the redudat adder gves sgfcat ga performace. Fgure 5. Latecy comparso betwee a rpple-carry adder ad a redudat adder XILINX 41XL. 7

8 5. Expermetal Results We have mplemeted teratve ad cascaded se ad cose CORDIC-based geerators Actel ad XILINX FPGAs usg fast bary adders. The umber of the teratos all desgs was equal to the bt-legth. The bt-legths used were 1, 14, 16, 4 ad 3 bt for the teratve desgs ad 1, 14 ad 16 bt for the cascaded desgs. All of the cascaded desgs were o-ppeled. Redudat CORDIC desgs have ot bee attempted vew of the fdgs about fourfold area crease ad o sgfcat performace ga for bt-legths below 3 bts secto 4 above. Sythess results terms of module cout ad speed are summarsed Table 3 ad 4 where results for both area ad delay optmsed desgs are preseted. Four dfferet sythess tools have bee used Actmap 3.5.4, Syplfy 5.1.4, Spectrum 5.69 ad XILINX Foudato Seres Express 1.5. The speed estmates the two rghtmost colums of the tables are based o back-aotated delays ad dcate the value of the maxmal data rate acheved ad the maxmal clock frequecy. The expermetal results show that module cout ad operatg speed deped sgfcatly o the used sythess tool. The Actel-based desgs are faster tha the XILINX-based oes, however the Actel FPGAs are ot dese eough to accommodate cascaded desgs wth bt-legths hgher tha 16 bts. A 3-bt 1.9 Msps teratve se/cose geerator ca be mplemeted a small FPGA (Actel SX16-3). The most area-cosumg compoet of the teratve desgs s the sg extedg Barrel shfter, shftg over programmable shft-wdth, further optmsato should focus o more area-ecoomcal Barrel shfter desg. A 16-bt cascaded desg s ot possble to be ftted a XC41XL devce, ths s ot surprsg, the parallel mplemetato approach s a trade-off of area for speed where the area crease s of quadratc order wth respect to the bt-legth O ( ). A 1-bt o-ppeled cascaded CORDIC rus at.3 Msps (Actel SX16-3) - ths performace s comparable wth the performace of a 1-bt look-up table accordg to our LUT sythess results preseted Table 5. Table 3. Summary of CORDIC sythess results based o ACTEL FPGAs. Desgs A54SX16-3 Legth Actmap bts Area/Delay 4 Syplfy Area/Delay 4 Spectrum Area/Delay 4 Speed Data rate Frequecy s Msps MHz Iteratve 1 4/574 37/ / Iteratve / /414 48/ Iteratve /958 44/46 51/ Iteratve 4 117/ /77 995/ Iteratve / /1 1419/ Cascaded / / / Cascaded / / / Cascaded / / /

9 Table 4. Summary of CORDIC sythess results based o XILINX FPGAs Desg Legth Foudato 7 bts Express 1.5 Area/Delay Target Devce Speed Data rate Frequecy s Msps MHz Iteratve 1 16/139 XC41XL Iteratve /145 XC41XL Iteratve 16 16/178 XC41XL Iteratve 4 317/376 XC46XL Iteratve 3 56/66 XC46XL Cascaded 1 1/1 XC41XL Cascaded 14 88/88 XC41XL Cascaded /378 XC46XL Table 5. LUT sythess results Desg A54SX16-3 Legth Actmap bts Area/Delay 4 Syplfy Area/Delay 4 Speed s Frequecy MHz LUT 1 513/ / LUT / / Note 1: All sythess tools operated a "push-butto" fasho wth maxmum optmsato eabled were avalable. Note : Speed estmate based o Vtal smulato usg typcal operatg codtos. Note 3: Estmate frequecy gve by Syplfy Note 4: All module cout gve by Place ad Route software. Note 5: Actel Netlst Selected Note 6: Syplfy Netlst Selected Note 7: Foudato Express buld Note 8: ---- Sythess results ot avalable 6. Applcato Two applcatos of CORDIC se/cose geerators satellte data processg systems have bee vestgated atttude determato ad drect dgtal sythess. The Earth s Magetc Feld s a very computatoally tesve procedure satellte atttude determato ad s usually mplemeted software. A hardware structure based o CORDIC modules has bee proposed [Vlac99] for the calculato of the Legedre polyomals - the frst step of the teratoal geomagetc referece feld (IGRF) model [Wert85]. It cossts of four blocks comprsg CORDIC modules for se/cose as well as other fuctos ad a cotrol block. The delay of the hardware structure was estmated based o a 3-bt teratve CORDIC module mplemeted XC485XL. It was compared wth the delay of a C-program rug o a Petum 333 MHz computer for fve dfferet values of the costats m ad l. The 9

10 mprovemet speed was 44% for m = l = 1, 37% for m = l = 15, 3% for m = l =, 8% for m = l = 5 ad 3% for m = l = 36 [Vlac99]. Drect dgtal sythess (DDS) geerates a ew frequecy based upo a orgal referece frequecy. Vrtually all DDS archtectures clude a lookup table that performs a se computato fucto for geeratg susodal output sgals. For comparso purposes we have desged ad sythessed a LUT that s a mproved verso of the modfed Sutherlad archtecture [Vak96] (Table 5). It ca be see that a 1-bt cascaded o-ppeled CORDIC (Table 3) acheves the same data rate of Msps as the 1-bt mproved LUT desg. However, addto to that the CORDIC desg provdes both fuctos se ad cose at the same tme ad also ts speed ca be accelerated further f ppelg s troduced to reach a data rate of about 5 Msps. 7. Coclusos Ths paper presets theoretcal ad practcal aspects of mplemetg se/cose CORDIC-based geerators FPGAs. The ma results ca be summarsed as follows: A trade-off speed/area wll determe the rght structural approach to CORDIC FPGA mplemetato for a applcato. A 3-bt 1.9 Msps teratve CORDIC ca be mplemeted a small FPGA (Actel SX16-3). A 1-bt o-ppeled cascaded CORDIC rus at.3 Msps (Actel SX16-3) that s comparable to a LUT. Module cout ad operatg speed deped sgfcatly o the used sythess tool. Curret rad-tolerat FPGAs are ot dese eough for the cascaded ad redudat approaches. Smulato has show that the redudat adder ca mprove the effcecy of CORDIC FPGA mplemetatos for bt-legths hgher tha 3-bt. 8. Refereces [Adr98] R.Adraka. A Survey of CORDIC Algorthms for FPGA Based Computers Proc. Of the 1998 CM/SIGDA Sxth Iteratoal Symposum o FPGAs, February 1998, Moterey, CA, pp [Bake76] P.W.Baker. Suggesto for a Bary Cose Geerator, IEEE Trasactos o Computers, February, 1975, pp [Che7] T.C.Che. Automatc Computato of Expoetals, Logarthms, Ratos ad Square Roots, IBM J. Res.Developmet, July, 1997, pp [Erce87] M.D.Ercegovac, T.Lag. Fast Cose/Se Implemetato Usg CORDIC Iteratos, IEEE Tras. O Comput., vol.4, 9, 1987, pp. -6 1

11 [Marx99] M.Marx. FPGA Implemetato of s(x) ad cos(x) Geerators Usg the CORDIC Algorthm, Fal Year Project Report, School of Electroc Egeerg, Uversty of Surrey, Gudford, UK, [Prs98] P.Prsch. Archtectures for Dgtal Sgal Processg, Joh Wley & Sos, [Taka91] N.Takag. Redudat CORDIC Methods wth a Costat Scale Factor for Se ad Cose Computato, IEEE Tras. O Comput., vol. 4, 9, 1991, pp [Tmm9] D.Tmmerma, H.Hah, B.J.Hostcka. Low Latecy Tme CORDIC Algorthms, IEEE Trasactos o Comput., vol.41, 8, 199, pp [Tmm91] D.Tmmerma, H.Hah, B.J.Hostcka, B.Rx. A New Addto Scheme ad Fast Scalg Factor Compesato Methods for CORDIC algorthms, Itegrato the VLSI Joural, vol. 11, 1, 1991, pp [Vad9] A.Vademeulebroecke, E.Vazeledhem, et al. A New Carry-Free Dvso Algorthm ad ts Applcato to a Sgle Chp 14-b RSA Processor, IEEE Joural of Sold-State Crcuts, vol.5, 3, 199, pp [Vak96] J.Vakka. Methods of Mappg from Phase to Se Ampltude Drect Dgtal Sythess, Proc of the 1996 IEEE Iteratoal Frequecy Cotrol Symposum, 1996, pp [Vlac99] A.Vlachos. Desg ad Implemetato of CORDIC Modules for ADCS, MSc Project Report, School of Electroc Egeerg, Uversty of Surrey, Gudford, UK, [Vold59] J.Volder. The CORDIC Computg Techque, IRE Tras. Comput., Sept. 1959, pp [Walt71] J.S. Walther. A Ufed Algorthm for Elemetary Fuctos, Proc. AFIPS Sprg Jot Computer Coferece, pp , [Wag96] S.Wag, V.Pur. A Ufed Vew of CORDIC Processor Desg, Applcato Specfc Processors, Ed. By Earl E. Swatzlader, Jr., Kluwer Academc Press, 1996, pp [Wert85] J. Wertz. Spacecraft Atttude Determato ad Cotrol, D.Rdel Publshg Compay, Lodo,

VLSI Implementation of High-Performance CORDIC-Based Vector Interpolator in Power-Aware 3-D Graphic Systems

VLSI Implementation of High-Performance CORDIC-Based Vector Interpolator in Power-Aware 3-D Graphic Systems Proceedgs of the 6th WSEAS Iteratoal Coferece o Istrumetato, Measuremet, Crcuts & Systems, Hagzhou, Cha, Aprl 5-7, 7 7 VLSI Implemetato of Hgh-Performace CORDIC-Based Vector Iterpolator Power-Aware 3-D

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

A Multiplier-Free Residue to Weighted Converter. for the Moduli Set {3 n 2, 3 n 1, 3 n }

A Multiplier-Free Residue to Weighted Converter. for the Moduli Set {3 n 2, 3 n 1, 3 n } Cotemporary Egeerg Sceces, Vol., 8, o., 7-8 A Multpler-Free Resdue to Weghted Coverter for the Modul Set {,, } Amr Sabbagh Molahosse ad Mehd Hossezadeh Islamc Azad Uversty, Scece ad Research Brach, Tehra,

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

Newton s Power Flow algorithm

Newton s Power Flow algorithm Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other

More information

Low Power Modulo 2 n +1 Adder Based on Carry Save Diminished-One Number System

Low Power Modulo 2 n +1 Adder Based on Carry Save Diminished-One Number System Amerca Joural of Appled Sceces 5 (4: 3-39, 8 ISSN 546-939 8 Scece Publcatos Low Power Modulo + Adder Based o Carry Save Dmshed-Oe Number System Somayeh Tmarch, Omd Kavehe, ad Keva Nav Departmet of Electrcal

More information

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers. PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

Hybrid RNS-to-Binary Converter for the Moduli Set {2 2n, 2 n -1, 2 n +1, 2 n+1-1}

Hybrid RNS-to-Binary Converter for the Moduli Set {2 2n, 2 n -1, 2 n +1, 2 n+1-1} Research Joural of Appled Sceces, Egeerg ad echology 6(): 07-0, 0 ISSN: 00-759; e-issn: 00-767 Mawell Scetfc Orgazato, 0 Submtted: November, 0 Accepted: Jauary 9, 0 ublshed: July 5, 0 Hybrd RNS-to-Bary

More information

Scaling Function Based on Chinese Remainder Theorem Applied to a Recursive Filter Design

Scaling Function Based on Chinese Remainder Theorem Applied to a Recursive Filter Design SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol., No. 3, October 04, 365-377 UDC: 6.37.54:004.383.3]:5.64 DOI: 0.98/SJEE40306S Scalg Fucto Based o Chese Remader Theorem Appled to a Recursve Flter Desg Negova

More information

Investigating Cellular Automata

Investigating Cellular Automata Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte

More information

New Arithmetic Residue to Binary Converters

New Arithmetic Residue to Binary Converters IJCSES Iteratoal Joural of Computer Sceces ad Egeerg Systems, Vol., No.4, October 007 CSES Iteratoal c007 ISSN 0973-4406 95 New Arthmetc Resdue to Bary Coerters Amr Sabbagh MOLAHOSSEINI ad Kea NAVI Departmet

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

Transforms that are commonly used are separable

Transforms that are commonly used are separable Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Chapter 11 Systematic Sampling

Chapter 11 Systematic Sampling Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Lecture 2 - What are component and system reliability and how it can be improved?

Lecture 2 - What are component and system reliability and how it can be improved? Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected

More information

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i. CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

Median as a Weighted Arithmetic Mean of All Sample Observations

Median as a Weighted Arithmetic Mean of All Sample Observations Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin

d dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace

More information

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK Ram Rzayev Cyberetc Isttute of the Natoal Scece Academy of Azerbaa Republc ramrza@yahoo.com Aygu Alasgarova Khazar

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Solution of General Dual Fuzzy Linear Systems. Using ABS Algorithm

Solution of General Dual Fuzzy Linear Systems. Using ABS Algorithm Appled Mathematcal Sceces, Vol 6, 0, o 4, 63-7 Soluto of Geeral Dual Fuzzy Lear Systems Usg ABS Algorthm M A Farborz Aragh * ad M M ossezadeh Departmet of Mathematcs, Islamc Azad Uversty Cetral ehra Brach,

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

This lecture and the next. Why Sorting? Sorting Algorithms so far. Why Sorting? (2) Selection Sort. Heap Sort. Heapsort

This lecture and the next. Why Sorting? Sorting Algorithms so far. Why Sorting? (2) Selection Sort. Heap Sort. Heapsort Ths lecture ad the ext Heapsort Heap data structure ad prorty queue ADT Qucksort a popular algorthm, very fast o average Why Sortg? Whe doubt, sort oe of the prcples of algorthm desg. Sortg used as a subroute

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel

More information

Sequential Approach to Covariance Correction for P-Field Simulation

Sequential Approach to Covariance Correction for P-Field Simulation Sequetal Approach to Covarace Correcto for P-Feld Smulato Chad Neufeld ad Clayto V. Deutsch Oe well kow artfact of the probablty feld (p-feld smulato algorthm s a too large covarace ear codtog data. Prevously,

More information

Some Notes on the Probability Space of Statistical Surveys

Some Notes on the Probability Space of Statistical Surveys Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty

More information

Chapter 8. Inferences about More Than Two Population Central Values

Chapter 8. Inferences about More Than Two Population Central Values Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

On Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros

On Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros It. Joural of Math. Aalyss, Vol. 7, 2013, o. 20, 983-988 HIKARI Ltd, www.m-hkar.com O Modfed Iterval Symmetrc Sgle-Step Procedure ISS2-5D for the Smultaeous Icluso of Polyomal Zeros 1 Nora Jamalud, 1 Masor

More information

1. BLAST (Karlin Altschul) Statistics

1. BLAST (Karlin Altschul) Statistics Parwse seuece algmet global ad local Multple seuece algmet Substtuto matrces Database searchg global local BLAST Seuece statstcs Evolutoary tree recostructo Gee Fdg Prote structure predcto RNA structure

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

MA/CSSE 473 Day 27. Dynamic programming

MA/CSSE 473 Day 27. Dynamic programming MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)

More information

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

More information

Bayes (Naïve or not) Classifiers: Generative Approach

Bayes (Naïve or not) Classifiers: Generative Approach Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

To use adaptive cluster sampling we must first make some definitions of the sampling universe:

To use adaptive cluster sampling we must first make some definitions of the sampling universe: 8.3 ADAPTIVE SAMPLING Most of the methods dscussed samplg theory are lmted to samplg desgs hch the selecto of the samples ca be doe before the survey, so that oe of the decsos about samplg deped ay ay

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

Carbonyl Groups. University of Chemical Technology, Beijing , PR China;

Carbonyl Groups. University of Chemical Technology, Beijing , PR China; Electroc Supplemetary Materal (ESI) for Physcal Chemstry Chemcal Physcs Ths joural s The Ower Socetes 0 Supportg Iformato A Theoretcal Study of Structure-Solublty Correlatos of Carbo Doxde Polymers Cotag

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET

AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET Abstract. The Permaet versus Determat problem s the followg: Gve a matrx X of determates over a feld of characterstc dfferet from

More information

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek

Investigation of Partially Conditional RP Model with Response Error. Ed Stanek Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a

More information

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler Mathematcal ad Computatoal Applcatos, Vol. 8, No. 3, pp. 293-300, 203 BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS Aysegul Ayuz Dascoglu ad Nese Isler Departmet of Mathematcs,

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

General Method for Calculating Chemical Equilibrium Composition

General Method for Calculating Chemical Equilibrium Composition AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt

More information

Quantization in Dynamic Smarandache Multi-Space

Quantization in Dynamic Smarandache Multi-Space Quatzato Dyamc Smaradache Mult-Space Fu Yuhua Cha Offshore Ol Research Ceter, Beg, 7, Cha (E-mal: fuyh@cooc.com.c ) Abstract: Dscussg the applcatos of Dyamc Smaradache Mult-Space (DSMS) Theory. Supposg

More information

The Double Rotation CORDIC Algorithm: New Results for VLSI Implementation of Fast Sine/Cosine Generation

The Double Rotation CORDIC Algorithm: New Results for VLSI Implementation of Fast Sine/Cosine Generation he Doble Rotato CORDIC Algorthm: New Reslts for VLSI Implemetato of Fast Se/Cose eerato ze-y Sg * Chch-S Che ** Mg-Cho Shh * * Departmet of Electrcal Egeerg ** Isttte of Egeerg Scece Chg Ha Uerst, Hsch,

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015 Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

PROJECTION PROBLEM FOR REGULAR POLYGONS Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

Physics 114 Exam 2 Fall Name:

Physics 114 Exam 2 Fall Name: Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout

More information

MOLECULAR VIBRATIONS

MOLECULAR VIBRATIONS MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal

More information

Reliability evaluation of distribution network based on improved non. sequential Monte Carlo method

Reliability evaluation of distribution network based on improved non. sequential Monte Carlo method 3rd Iteratoal Coferece o Mecatrocs, Robotcs ad Automato (ICMRA 205) Relablty evaluato of dstrbuto etwork based o mproved o sequetal Mote Carlo metod Je Zu, a, Cao L, b, Aog Tag, c Scool of Automato, Wua

More information

10.1 Approximation Algorithms

10.1 Approximation Algorithms 290 0. Approxmato Algorthms Let us exame a problem, where we are gve A groud set U wth m elemets A collecto of subsets of the groud set = {,, } s.t. t s a cover of U: = U The am s to fd a subcover, = U,

More information

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s

More information

Simulation Model for a Hardware Implementation of Modular Multiplication

Simulation Model for a Hardware Implementation of Modular Multiplication Smulato Model for a Hardware Implemetato of Modular Multplcato NADIA NEDJAH AND LUIZA DE MACEDO MOURELLE Departmet of de Systems Egeerg ad Computato, State Uversty of Ro de Jaero São Fracsco Xaver, 54,

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION

Fourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar

More information

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems [ype text] [ype text] [ype text] ISSN : 0974-7435 Volume 0 Issue 6 Boechology 204 Ida Joural FULL PPER BIJ, 0(6, 204 [927-9275] Research o scheme evaluato method of automato mechatroc systems BSRC Che

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

9.1 Introduction to the probit and logit models

9.1 Introduction to the probit and logit models EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos

More information

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).

f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s). CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The

More information

Overcoming Limitations of Sampling for Aggregation Queries

Overcoming Limitations of Sampling for Aggregation Queries CIS 6930 Approxmate Quer Processg Paper Presetato Sprg 2004 - Istructor: Dr Al Dobra Overcomg Lmtatos of Samplg for Aggregato Queres Authors: Surajt Chaudhur, Gautam Das, Maur Datar, Rajeev Motwa, ad Vvek

More information

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS

ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS ON THE MOTION OF PLANAR BARS SYSTEMS WITH CLEARANCES IN JOINTS Şl uv dr g Ja-Crsta GRIGORE, Uverstatea d Pteşt, strtîrgu dvale Nr Prof uv dr g Ncolae PANDREA, Uverstatea d Pteşt, strtîrgu dvale Nr Cof

More information

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames KLT Tracker Tracker. Detect Harrs corers the frst frame 2. For each Harrs corer compute moto betwee cosecutve frames (Algmet). 3. Lk moto vectors successve frames to get a track 4. Itroduce ew Harrs pots

More information

A New Method for Decision Making Based on Soft Matrix Theory

A New Method for Decision Making Based on Soft Matrix Theory Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer

More information