Polyphase Filters. Section 12.4 Porat 1/39
|
|
- Silvester Noah Campbell
- 7 years ago
- Views:
Transcription
1 Polyphase Flters Secto.4 Porat /39
2 .4 Polyphase Flters Polyphase s a way of dog saplg-rate coverso that leads to very effcet pleetatos. But ore tha that, t leads to very geeral vewpots that are useful buldg flter baks. Before we delve to the ath we ca see a lot just by lookg at the structure of the flterg. Of course, we WI eed to do the ath, too, though. /39
3 x Effcet FIR Flterg for Decato Flterg : x x h Decato : x x x h 3 x3 h3 x 3 h4 x4 h5 x5 h6 h7 x6 x7 h8 x8 Do t Copute x 3 Do t Copute 3/39
4 x h6 h9 h Effcet FIR Flterg for Decato x x x x3 x4 x5 x6 x7 x8 x9 x x x Orgal Flter h h h h3 h4 h5 x 3 3 x 3 gets splt to 3 subflters: Polyphase For of FIR Decato x 3 4 x x4 x7 x h h6 x x5 x8 x h h4 Σ x 3 3 x 3 x3 x6 x9 x h h3 Advatage: Decate the Flter 4/39
5 Ieffcet Drect For of FIR Decato x x x h h h h3 h4 h5 3 x 3 x 3 Effcet Polyphase For of FIR Decato Outputs are the Sae x x4 x7 h h6 x x x x x5 x8 h h4 Σ x 3 x 3 x x3 x6 x9 h h3 5/39
6 Exaple of Polyphase Flters for Decato Cosder egth- Flter w/ 4 : h: h h h h3 h4 h5 h6 h7 h8 h9. egth of Polyphase Flters: cel{legth/} cel{/4} 3 : p : h h4 h8 p : h h5 h9 p : h h6 p 3 : h3 h7 x : x x4 x8 x x6. x : x- x3 x7 x x5. x : x- x x6 x x4. x 3 : x-3 x x5 x9 x3. 6/39
7 Exaple of Polyphase Flters for Decato pt. atlab Code Create put sgal ad flter x:; h ; Drect For Ieffcet yflterh,,x; Copute flter output y_decy:4:ed Throw away ueeded output saples Pad eros to ake legth equal to teger ultple of Polyphase For Effcet Select polyphase flters ph:4:ed ph:4:ed ph3:4:ed p3h4:4:ed Select polyphase sgals Put a ero frot to provde the xx:4:ed x-3, x-, ad x- ters x x4:4:ed x x3:4:ed x3 x:4:ed flter each polyphase copoet ad add together y_poly_decflterp,,xflterp,,xflterp,,xflterp3,,x3 7/39
8 Effcet FIR Flterg for Iterpolato Iterpolat o : x x h x 3 h6 h7 h8 h9 h x x x x3 3 6 x 3 7 x 3 8 x 3 9 x 3 x 3 h x 3 8/39
9 Effcet FIR Flterg for Iterpolato Iterpolat o : x x h x 3 x x x x3 3 6 x 3 7 x 3 8 x 3 9 x 3 x 3 x 3 9/39
10 Effcet FIR Flterg for Iterpolato Orgal Flter h h h h3 h4 h5 3 gets splt to 3 subflters: x x x x3 The put goes to each subflter Polyphase For of FIR Iterpolato 6 9 Advatage Flter the Iterpolate h h3 h h4 h h5 x 3 7 x 3 8 x 3 x 3 x 3 x 3 The output coes fro alteratg betwee the subflter outputs /39
11 .4. ultrate Idettes These provde aalyss trcks useful whe dealg wth atheatcal aalyss of ultrate systes. The questo geeral s: How ca we terchage the order of flterg w/ decato/expaso? Decato Idetty Ths detty asserts equalty betwee the followg systes: x H y x H y Ca prove ths ether the Te-Doa or Z-Doa /39
12 TD Proof of Decato Idetty For the frst syste: x y w * h For the secod syste: x G H k w x y H h k x v k h k w k y k g h h /, f /, otherwse teger By Eq..5! /39
13 3/39 TD Proof of Decato Idetty cot. The k k x k h v y Sae as for Syste # " Proved!!! Thus k l k x k h l x l g g x v * Use!
14 4/39 ZD Proof of Decato Idetty For the secod syste: G H X V Y H X V!! where Now W W j e #"! / π But / / / } { W H W X W V V Y Use!! By ZT Result for Decato
15 5/39 ZD Proof of Decato Idetty cot. { } / / X H W X H H W X Y Whch s clearly the sae thg that the frst syste gves: H X {X } Y H {X }
16 Expaso Idetty Ths detty asserts equalty betwee the followg systes: x H w y x v H y Wll gve oly Z-Doa proof here. 6/39
17 7/39 ZD Proof of Expaso Idetty H x y w Frst syste gves: H X W The H X W W Y v Secod syste gves: H x y X X V The H X H V Y Sae!
18 .4. Polyphase Represetato of Decato Now we re-vst ths topc ad do t atheatcally Basc ath Idea: Re-wrte covoluto su s dex & apulate to get parallel flters: x Recall Decato: H y Output gve by.7 as y h x!!! Wrte su s dex block for a coo trck : teger Block Se Couts Blocks Couts Saples Isde a Block 8/39
19 9/39.4. Polyphase Rep of Dec cot. Block-Based Idexg: 3 Each row s dexed forward teger Forward Idexg
20 .4. Polyphase Rep of Dec cot. Use Block Idexg!!!: y h x h h x #&& " &! & x Su up sde each block Su up all Block Results!!!! Su all eleets the th posto of each block /39
21 .4. Polyphase Rep of Dec cot. Now, let s terpret ths: Defe for each, - p h th Polyphase Copoet of h Exaple : h: p p p {., 7, } {4,, } {.5,.7, } Each oe s a decated verso of h & the versos are staggered < See Fg..5> /39
22 Fg..5 fro Porat s Book /39
23 .4. Polyphase Rep of Dec cot. What have we doe? Splt up h to subsequeces where the th subsequece s a decated-by- verso of h Why the ae Polyphase? Recall: Te-Shft TD Phase-Shft FD h e jθ H f θ " Polyphase 3/39
24 4/39.4. Polyphase Rep of Dec cot. Now let s chop up the put slarly: x u Dffers Fro Before: Each row s dexed backward Backward Idexg
25 5/39.4. Polyphase Rep of Dec cot. Now back to the atheatcal developet. Puttg these re-dexed versos to!!!!: { } * u p u p y x u h p x h y To Ipleet Polyphase Decato Chop up flter to sub-flters Chop up sgal to sub-sgals Flter each sub-sgal w/ a sub-flter Add outputs pot-by-pot
26 .4. Polyphase Rep of Dec cot. Two equvalet ways to thk of ths: Frst Way show for 3: Note that Decato occurs Before Flterg Effcet!!! <Ths s Fg..6 fro Porat s Book> 6/39
27 .4. Polyphase Rep of Dec cot. Secod Way to Vew It show for 3: <Ths s Fg..7 fro Porat s Book> 7/39
28 .4. Polyphase Rep of Dec cot. Now we re-aalye ths set-up, but the Z-Doa. Why?.It provdes further aalyss sght. Z-Doa results ofte provde sght to how to: Derve other results Desg Polyphase Flters Etc. 8/39
29 .4. Polyphase Rep of Dec cot. Frst. soe te-doa trckery: How do we get back h fro the p???. Isert - eros betwee each saple. e the up usg delays 3. Add the up Recall Exaple: p p p {., {4, {.5,, 7, }.7, } } {., { 4, {.5,,,,,,, 7,,.7,,,, Expaso!,,,,,,,,, } } } {.,,, 7,,,,, } {, 4,,,,,,, } {,,.5,,,.7,,, } h {., 4,.5, 7,,.7,,, } 9/39
30 3/39.4. Polyphase Rep of Dec cot. Thus. } { p h So. Z-Doa we have: P H Delay Expad Now flter/decate looks lke ths: H X Y V X P H X V
31 .4. Polyphase Rep of Dec cot. ad after we get: Y { V } { P X } #&&&& "&&&&! P P P U { } { X } #&& "&&&! U X X H V By the Decato Idetty By Defto Sgal s Polyphase Copoets Y.whch s the Z-Doa Descrpto of the polyphase decato structure. We have ow developed two dfferet dervatos of the polyphase structure. 3/39
32 .4.3 Polyphase Rep of Expaso Recall Expaso: x H y Output gve by.9 as y x h Re-Idex usg: l #&"&! "backwards" teger l Block Idex l I-Block Idex dexes backward through block 3/39
33 33/39 l Polyphase Rep of Exp cot. l teger "backwards" l #&"&! Expaso Re-Idex Table
34 34/ Polyphase Rep of Exp cot. Usg ths re-dexg gves l h x l h x l y h x y & &! & #& " & &! & #& " for each l, ths dexg just reads dow a colu of the Expaso Re-Idex Table For each l such that l we defe: l y v l h q l l } { q x v l l
35 .4.3 Polyphase Rep of Exp cot. To see ths dexg structure, look at a exaple wth 3: l v v v y y y 3 y y y y5 y4 y3 y8 y7 y6 35/39
36 .4.3 Polyphase Rep of Exp cot. Now how do we get y fro the v l s?? If we terpolate each v l sequece we get 3. y 3 y y3 y6 y y y4 y7 y y y5 y8 Now delay these terpolated sequeces y 3 y y3 y6 y y y4 y7 y y y5 y8 y 3 y y y y y y3 y4 y5 y6 y7 y8 To get y: add up the delayed, terpolated copoets!! 36/39
37 .4.3 Polyphase Rep of Exp cot. Fro ths we see that we ca wrte y l { v l } l Recall: vl { x ql} Ths leads to the followg polyphase pleetato for expaso: Note: Expaso Occurs After Flterg Effcet!! 37/39
38 .4.3 Polyphase Rep of Exp cot. A equvalet alterate for of ths processg s 38/39
39 Skp.4.4 Shows how to do polyphase ethod for ratoal rate chage of / But brefly to chage the rate by factor of / Iterpolate Decate x h h y whch s equvalet to x h y Q: How to pleet ths effcetly usg polyphase deas? If terested: see Ch.3 of Oppehe & o reserve 39/39
Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.
Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E
More informationT = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :
Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of
More information6.7 Network analysis. 6.7.1 Introduction. References - Network analysis. Topological analysis
6.7 Network aalyss Le data that explctly store topologcal formato are called etwork data. Besdes spatal operatos, several methods of spatal aalyss are applcable to etwork data. Fgure: Network data Refereces
More informationANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data
ANOVA Notes Page Aalss of Varace for a Oe-Wa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there
More informationAverage Price Ratios
Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or
More informationA Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time
Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral
More informationSequences and Series
Secto 9. Sequeces d Seres You c thk of sequece s fucto whose dom s the set of postve tegers. f ( ), f (), f (),... f ( ),... Defto of Sequece A fte sequece s fucto whose dom s the set of postve tegers.
More informationA Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining
A Fast Clusterg Algorth to Cluster Very Large Categorcal Data Sets Data Mg Zhexue Huag * Cooperatve Research Cetre for Advaced Coputatoal Systes CSIRO Matheatcal ad Iforato Sceces GPO Box 664, Caberra
More informationThe analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0
Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may
More informationA Comparative Study for Email Classification
A Coparatve Study for Eal Classfcato Seogwook You ad Des McLeod Uversty of Souther Calfora, Los Ageles, CA 90089 USA Abstract - Eal has becoe oe of the fastest ad ost ecoocal fors of coucato. However,
More information1. The Time Value of Money
Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg
More informationNumerical Methods with MS Excel
TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how
More informationCHAPTER 2. Time Value of Money 6-1
CHAPTER 2 Tme Value of Moey 6- Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 6-2 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show
More informationProject 3 Weight analysis
The Faculty of Power ad Aeroautcal Egeerg Arcraft Desg Departet Project 3 Weght aalyss Ths project cossts of two parts. Frst part cludes fuselage teror (cockpt) coceptual desg. Secod part cludes etoed
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationChapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =
Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are
More informationAbraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract
Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected
More informationThree Dimensional Interpolation of Video Signals
Three Dmesoal Iterpolato of Vdeo Sgals Elham Shahfard March 0 th 006 Outle A Bref reve of prevous tals Dgtal Iterpolato Bascs Upsamplg D Flter Desg Issues Ifte Impulse Respose Fte Impulse Respose Desged
More informationChapter Eight. f : R R
Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,
More informationOptimal multi-degree reduction of Bézier curves with constraints of endpoints continuity
Computer Aded Geometrc Desg 19 (2002 365 377 wwwelsevercom/locate/comad Optmal mult-degree reducto of Bézer curves wth costrats of edpots cotuty Guo-Dog Che, Guo-J Wag State Key Laboratory of CAD&CG, Isttute
More informationNumerical Comparisons of Quality Control Charts for Variables
Global Vrtual Coferece Aprl, 8. - 2. 203 Nuercal Coparsos of Qualty Cotrol Charts for Varables J.F. Muñoz-Rosas, M.N. Pérez-Aróstegu Uversty of Graada Facultad de Cecas Ecoócas y Epresarales Graada, pa
More informationof the relationship between time and the value of money.
TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp
More informationOnline Appendix: Measured Aggregate Gains from International Trade
Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,
More informationChapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization
Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve
More informationBanking (Early Repayment of Housing Loans) Order, 5762 2002 1
akg (Early Repaymet of Housg Loas) Order, 5762 2002 y vrtue of the power vested me uder Secto 3 of the akg Ordace 94 (hereafter, the Ordace ), followg cosultato wth the Commttee, ad wth the approval of
More informationMeasuring the Quality of Credit Scoring Models
Measur the Qualty of Credt cor Models Mart Řezáč Dept. of Matheatcs ad tatstcs, Faculty of cece, Masaryk Uversty CCC XI, Edurh Auust 009 Cotet. Itroducto 3. Good/ad clet defto 4 3. Measur the qualty 6
More information10/19/2011. Financial Mathematics. Lecture 24 Annuities. Ana NoraEvans 403 Kerchof AnaNEvans@virginia.edu http://people.virginia.
Math 40 Lecture 24 Autes Facal Mathematcs How ready do you feel for the quz o Frday: A) Brg t o B) I wll be by Frday C) I eed aother week D) I eed aother moth Aa NoraEvas 403 Kerchof AaNEvas@vrga.edu http://people.vrga.edu/~as5k/
More informationOn formula to compute primes and the n th prime
Joural's Ttle, Vol., 00, o., - O formula to compute prmes ad the th prme Issam Kaddoura Lebaese Iteratoal Uversty Faculty of Arts ad ceces, Lebao Emal: ssam.addoura@lu.edu.lb amh Abdul-Nab Lebaese Iteratoal
More informationn. We know that the sum of squares of p independent standard normal variables has a chi square distribution with p degrees of freedom.
UMEÅ UNIVERSITET Matematsk-statstska sttutoe Multvarat dataaalys för tekologer MSTB0 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multvarat dataaalys för tekologer B, 5 poäg.
More informationFuzzy Task Assignment Model of Web Services Supplier in Collaborative Development Environment
, pp.199-210 http://dx.do.org/10.14257/uesst.2015.8.6.19 Fuzzy Task Assget Model of Web Servces Suppler Collaboratve Developet Evroet Su Ja 1,2, Peg Xu-ya 1, *, Xu Yg 1,3, Wag Pe-e 2 ad Ma Na- 4,2 1. College
More informationSecurity Analysis of RAPP: An RFID Authentication Protocol based on Permutation
Securty Aalyss of RAPP: A RFID Authetcato Protocol based o Permutato Wag Shao-hu,,, Ha Zhje,, Lu Sujua,, Che Da-we, {College of Computer, Najg Uversty of Posts ad Telecommucatos, Najg 004, Cha Jagsu Hgh
More informationHow To Value An Annuity
Future Value of a Auty After payg all your blls, you have $200 left each payday (at the ed of each moth) that you wll put to savgs order to save up a dow paymet for a house. If you vest ths moey at 5%
More informationFINANCIAL MATHEMATICS 12 MARCH 2014
FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.
More informationIDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki
IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 16315-1355, ehra, Ira sedgh@eetd.ktu.ac.r,
More informationClassic Problems at a Glance using the TVM Solver
C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the
More informationON SLANT HELICES AND GENERAL HELICES IN EUCLIDEAN n -SPACE. Yusuf YAYLI 1, Evren ZIPLAR 2. yayli@science.ankara.edu.tr. evrenziplar@yahoo.
ON SLANT HELICES AND ENERAL HELICES IN EUCLIDEAN -SPACE Yusuf YAYLI Evre ZIPLAR Departmet of Mathematcs Faculty of Scece Uversty of Akara Tadoğa Akara Turkey yayl@sceceakaraedutr Departmet of Mathematcs
More informationSECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES
SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,
More informationPlastic Number: Construction and Applications
Scet f c 0 Advaced Advaced Scetfc 0 December,.. 0 Plastc Number: Costructo ad Applcatos Lua Marohć Polytechc of Zagreb, 0000 Zagreb, Croata lua.marohc@tvz.hr Thaa Strmeč Polytechc of Zagreb, 0000 Zagreb,
More informationFractal-Structured Karatsuba`s Algorithm for Binary Field Multiplication: FK
Fractal-Structured Karatsuba`s Algorthm for Bary Feld Multplcato: FK *The authors are worg at the Isttute of Mathematcs The Academy of Sceces of DPR Korea. **Address : U Jog dstrct Kwahadog Number Pyogyag
More informationDECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT
ESTYLF08, Cuecas Meras (Meres - Lagreo), 7-9 de Septembre de 2008 DECISION MAKING WITH THE OWA OPERATOR IN SPORT MANAGEMENT José M. Mergó Aa M. Gl-Lafuete Departmet of Busess Admstrato, Uversty of Barceloa
More informationThe Binomial Multi- Section Transformer
4/15/21 The Bioial Multisectio Matchig Trasforer.doc 1/17 The Bioial Multi- Sectio Trasforer Recall that a ulti-sectio atchig etwork ca be described usig the theory of sall reflectios as: where: Γ ( ω
More informationStatistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology
I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50
More information10.5 Future Value and Present Value of a General Annuity Due
Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the
More informationAPPENDIX III THE ENVELOPE PROPERTY
Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful
More informationAn Effectiveness of Integrated Portfolio in Bancassurance
A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606-850 Japa arya@eryoto-uacp Itroducto As s well ow the
More informationCompressive Sensing over Strongly Connected Digraph and Its Application in Traffic Monitoring
Compressve Sesg over Strogly Coected Dgraph ad Its Applcato Traffc Motorg Xao Q, Yogca Wag, Yuexua Wag, Lwe Xu Isttute for Iterdscplary Iformato Sceces, Tsghua Uversty, Bejg, Cha {qxao3, kyo.c}@gmal.com,
More informationRUSSIAN ROULETTE AND PARTICLE SPLITTING
RUSSAN ROULETTE AND PARTCLE SPLTTNG M. Ragheb 3/7/203 NTRODUCTON To stuatos are ecoutered partcle trasport smulatos:. a multplyg medum, a partcle such as a eutro a cosmc ray partcle or a photo may geerate
More informationEfficient Traceback of DoS Attacks using Small Worlds in MANET
Effcet Traceback of DoS Attacks usg Small Worlds MANET Yog Km, Vshal Sakhla, Ahmed Helmy Departmet. of Electrcal Egeerg, Uversty of Souther Calfora, U.S.A {yogkm, sakhla, helmy}@ceg.usc.edu Abstract Moble
More informationThe Digital Signature Scheme MQQ-SIG
The Dgtal Sgature Scheme MQQ-SIG Itellectual Property Statemet ad Techcal Descrpto Frst publshed: 10 October 2010, Last update: 20 December 2010 Dalo Glgorosk 1 ad Rue Stesmo Ødegård 2 ad Rue Erled Jese
More informationECONOMICS. Calculating loan interest no. 3.758
F A M & A N H S E E S EONOMS alculatig loa iterest o. 3.758 y Nora L. Dalsted ad Paul H. Gutierrez Quick Facts... The aual percetage rate provides a coo basis to copare iterest charges associated with
More informationCommon p-belief: The General Case
GAMES AND ECONOMIC BEHAVIOR 8, 738 997 ARTICLE NO. GA97053 Commo p-belef: The Geeral Case Atsush Kaj* ad Stephe Morrs Departmet of Ecoomcs, Uersty of Pesylaa Receved February, 995 We develop belef operators
More informationGreen Master based on MapReduce Cluster
Gree Master based o MapReduce Cluster Mg-Zh Wu, Yu-Chag L, We-Tsog Lee, Yu-Su L, Fog-Hao Lu Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of Electrcal Egeerg Tamkag Uversty, Tawa, ROC Dept of
More informationA Novel Method in Scam Detection and Prevention using Data Mining Approaches
A Novel Method Scam Detecto ad Preveto usg Data Mg Approaches Maryam Mokhtar, Mohammad Saraee, Alreza Haghsheas Departmet of Electrcal ad Computer Egeerg Isfaha Uversty of Techology, Isfaha, Ira Mokhtar@ec.ut.ac.r,
More informationMathematics of Finance
CATE Mathematcs of ace.. TODUCTO ths chapter we wll dscuss mathematcal methods ad formulae whch are helpful busess ad persoal face. Oe of the fudametal cocepts the mathematcs of face s the tme value of
More informationMulti-Channel Pricing for Financial Services
0-7695-435-9/0 $7.00 (c) 00 IEEE Proceedgs of the 35th Aual Hawa Iteratoal Coferece o yste ceces (HIC-35 0) 0-7695-435-9/0 $7.00 00 IEEE Proceedgs of the 35th Hawa Iteratoal Coferece o yste ceces - 00
More informationConstrained Cubic Spline Interpolation for Chemical Engineering Applications
Costraed Cubc Sple Iterpolato or Chemcal Egeerg Applcatos b CJC Kruger Summar Cubc sple terpolato s a useul techque to terpolate betwee kow data pots due to ts stable ad smooth characterstcs. Uortuatel
More informationFast, Secure Encryption for Indexing in a Column-Oriented DBMS
Fast, Secure Ecrypto for Idexg a Colum-Oreted DBMS Tgja Ge, Sta Zdok Brow Uversty {tge, sbz}@cs.brow.edu Abstract Networked formato systems requre strog securty guaratees because of the ew threats that
More informationThe Gompertz-Makeham distribution. Fredrik Norström. Supervisor: Yuri Belyaev
The Gompertz-Makeham dstrbuto by Fredrk Norström Master s thess Mathematcal Statstcs, Umeå Uversty, 997 Supervsor: Yur Belyaev Abstract Ths work s about the Gompertz-Makeham dstrbuto. The dstrbuto has
More informationHow do bookmakers (or FdJ 1 ) ALWAYS manage to win?
How do bookakers (or FdJ ALWAYS aage to w? Itroducto otatos & varables Bookaker's beeft eected value 4 4 Bookaker's strateges5 4 The hoest bookaker 6 4 "real lfe" bookaker 6 4 La FdJ 8 5 How ca we estate
More informationLoad Balancing Algorithm based Virtual Machine Dynamic Migration Scheme for Datacenter Application with Optical Networks
0 7th Iteratoal ICST Coferece o Commucatos ad Networkg Cha (CHINACOM) Load Balacg Algorthm based Vrtual Mache Dyamc Mgrato Scheme for Dataceter Applcato wth Optcal Networks Xyu Zhag, Yogl Zhao, X Su, Ruyg
More informationISyE 512 Chapter 7. Control Charts for Attributes. Instructor: Prof. Kaibo Liu. Department of Industrial and Systems Engineering UW-Madison
ISyE 512 Chapter 7 Cotrol Charts for Attrbutes Istructor: Prof. Kabo Lu Departmet of Idustral ad Systems Egeerg UW-Madso Emal: klu8@wsc.edu Offce: Room 3017 (Mechacal Egeerg Buldg) 1 Lst of Topcs Chapter
More informationApplications of Support Vector Machine Based on Boolean Kernel to Spam Filtering
Moder Appled Scece October, 2009 Applcatos of Support Vector Mache Based o Boolea Kerel to Spam Flterg Shugag Lu & Keb Cu School of Computer scece ad techology, North Cha Electrc Power Uversty Hebe 071003,
More informationCurve Fitting and Solution of Equation
UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed
More informationECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil
ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable
More informationADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN
Colloquum Bometrcum 4 ADAPTATION OF SHAPIRO-WILK TEST TO THE CASE OF KNOWN MEAN Zofa Hausz, Joaa Tarasńska Departmet of Appled Mathematcs ad Computer Scece Uversty of Lfe Sceces Lubl Akademcka 3, -95 Lubl
More informationThe Time Value of Money
The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : $24.00, FV of $24 @ 6%: FV = $24 (1+0.06) 388 = $158.08 bllo Opto 1 0 1 2 3 4 5 t ($519.37) 0 0 0 0 $1,000 Opto
More informationA New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree
, pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal
More informationLocally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases
Locally Adaptve Dmesoalty educto for Idexg Large Tme Seres Databases Kaushk Chakrabart Eamo Keogh Sharad Mehrotra Mchael Pazza Mcrosoft esearch Uv. of Calfora Uv. of Calfora Uv. of Calfora edmod, WA 985
More informationIntegrating Production Scheduling and Maintenance: Practical Implications
Proceedgs of the 2012 Iteratoal Coferece o Idustral Egeerg ad Operatos Maagemet Istabul, Turkey, uly 3 6, 2012 Itegratg Producto Schedulg ad Mateace: Practcal Implcatos Lath A. Hadd ad Umar M. Al-Turk
More informationSession 4: Descriptive statistics and exporting Stata results
Itrduct t Stata Jrd Muñz (UAB) Sess 4: Descrptve statstcs ad exprtg Stata results I ths sess we are gg t wrk wth descrptve statstcs Stata. Frst, we preset a shrt trduct t the very basc statstcal ctets
More informationOptimal Packetization Interval for VoIP Applications Over IEEE 802.16 Networks
Optmal Packetzato Iterval for VoIP Applcatos Over IEEE 802.16 Networks Sheha Perera Harsha Srsea Krzysztof Pawlkowsk Departmet of Electrcal & Computer Egeerg Uversty of Caterbury New Zealad sheha@elec.caterbury.ac.z
More informationOn Savings Accounts in Semimartingale Term Structure Models
O Savgs Accouts Semmartgale Term Structure Models Frak Döberle Mart Schwezer moeyshelf.com Techsche Uverstät Berl Bockehemer Ladstraße 55 Fachberech Mathematk, MA 7 4 D 6325 Frakfurt am Ma Straße des 17.
More informationANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS. Janne Peisa Ericsson Research 02420 Jorvas, Finland. Michael Meyer Ericsson Research, Germany
ANALYTICAL MODEL FOR TCP FILE TRANSFERS OVER UMTS Jae Pesa Erco Research 4 Jorvas, Flad Mchael Meyer Erco Research, Germay Abstract Ths paper proposes a farly complex model to aalyze the performace of
More informationSimple Linear Regression
Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8
More informationAutomated Event Registration System in Corporation
teratoal Joural of Advaces Computer Scece ad Techology JACST), Vol., No., Pages : 0-0 0) Specal ssue of CACST 0 - Held durg 09-0 May, 0 Malaysa Automated Evet Regstrato System Corporato Zafer Al-Makhadmee
More informationDIRAC s BRA AND KET NOTATION. 1 From inner products to bra-kets 1
DIRAC s BRA AND KET NOTATION B. Zwebach October 7, 2013 Cotets 1 From er products to bra-kets 1 2 Operators revsted 5 2.1 Projecto Operators..................................... 6 2.2 Adjot of a lear operator.................................
More informationThe Present Value of an Annuity
Module 4.4 Page 492 of 944. Module 4.4: The Preset Value of a Auty Here we wll lear about a very mportat formula: the preset value of a auty. Ths formula s used wheever there s a seres of detcal paymets
More informationSHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN
SHAPIRO-WILK TEST FOR NORMALITY WITH KNOWN MEAN Wojcech Zelńsk Departmet of Ecoometrcs ad Statstcs Warsaw Uversty of Lfe Sceces Nowoursyowska 66, -787 Warszawa e-mal: wojtekzelsk@statystykafo Zofa Hausz,
More informationPOSTRACK: A Low Cost Real-Time Motion Tracking System for VR Application
POSTRACK: A Low Cost Real-Te Moto Trackg Syste for VR Applcato Jaeyog Chug, Nagyu K, Gerard Joughyu K, ad Cha-Mo Park VR Laboratory, Departet of Coputer Scece ad Egeerg, Pohag Uversty of Scece ad Techology
More informationTHE McELIECE CRYPTOSYSTEM WITH ARRAY CODES. MATRİS KODLAR İLE McELIECE ŞİFRELEME SİSTEMİ
SAÜ e Blmler Dergs, 5 Clt, 2 Sayı, THE McELIECE CRYPTOSYSTEM WITH ARRAY CODES Vedat ŞİAP* *Departmet of Mathematcs, aculty of Scece ad Art, Sakarya Uversty, 5487, Serdva, Sakarya-TURKEY vedatsap@gmalcom
More informationA DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS
L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS Kwe-Jay L Departmet of Electrcal Egeerg ad Computer Scece
More informationCyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), January Edition, 2011
Cyber Jourals: Multdscplary Jourals cece ad Techology, Joural of elected Areas Telecommucatos (JAT), Jauary dto, 2011 A ovel rtual etwork Mappg Algorthm for Cost Mmzg ZHAG hu-l, QIU Xue-sog tate Key Laboratory
More informationA Parallel Transmission Remote Backup System
2012 2d Iteratoal Coferece o Idustral Techology ad Maagemet (ICITM 2012) IPCSIT vol 49 (2012) (2012) IACSIT Press, Sgapore DOI: 107763/IPCSIT2012V495 2 A Parallel Trasmsso Remote Backup System Che Yu College
More informationLoad and Resistance Factor Design (LRFD)
53:134 Structural Desg II Load ad Resstace Factor Desg (LRFD) Specfcatos ad Buldg Codes: Structural steel desg of buldgs the US s prcpally based o the specfcatos of the Amerca Isttute of Steel Costructo
More informationSpeeding up k-means Clustering by Bootstrap Averaging
Speedg up -meas Clusterg by Bootstrap Averagg Ia Davdso ad Ashw Satyaarayaa Computer Scece Dept, SUNY Albay, NY, USA,. {davdso, ashw}@cs.albay.edu Abstract K-meas clusterg s oe of the most popular clusterg
More informationThe simple linear Regression Model
The smple lear Regresso Model Correlato coeffcet s o-parametrc ad just dcates that two varables are assocated wth oe aother, but t does ot gve a deas of the kd of relatoshp. Regresso models help vestgatg
More informationOn Cheeger-type inequalities for weighted graphs
O Cheeger-type equaltes for weghted graphs Shmuel Fredlad Uversty of Illos at Chcago Departmet of Mathematcs 851 S. Morga St., Chcago, Illos 60607-7045 USA Rehard Nabbe Fakultät für Mathematk Uverstät
More informationReinsurance and the distribution of term insurance claims
Resurace ad the dstrbuto of term surace clams By Rchard Bruyel FIAA, FNZSA Preseted to the NZ Socety of Actuares Coferece Queestow - November 006 1 1 Itroducto Ths paper vestgates the effect of resurace
More informationDeveloping a Fuzzy Search Engine Based on Fuzzy Ontology and Semantic Search
0 IEEE Iteratoal Coferece o Fuzzy Systes Jue 7-30, 0, Tape, Tawa Developg a Fuzzy Search Ege Based o Fuzzy Otology ad Seatc Search Le-Fu La Chao-Ch Wu Pe-Yg L Dept. of Coputer Scece ad Iforato Egeerg Natoal
More informationLecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem
Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits
More informationRaport końcowy Zadanie nr 8:
Opracowae: Polsko- Japońska Wższa Szkoła Techk Komputerowch Wdzał amejscow Iformatk w tomu Raport końcow adae r 8: Przeprowadzee badań opracowae algortmów do projektu: adae 4 Idetfkacja zachowaa terakcj
More informationSTART Selected Topics in Assurance
SAR Selected opcs Assurace Related echologes able of Cotets Itroducto Relablty of Seres Systems of Idetcal ad Idepedet Compoets Numercal Examples he Case of Dfferet Compoet Relabltes Relablty of Parallel
More informationCHAPTER 4: NET PRESENT VALUE
EMBA 807 Corporate Fiace Dr. Rodey Boehe CHAPTER 4: NET PRESENT VALUE (Assiged probles are, 2, 7, 8,, 6, 23, 25, 28, 29, 3, 33, 36, 4, 42, 46, 50, ad 52) The title of this chapter ay be Net Preset Value,
More informationConversion of Non-Linear Strength Envelopes into Generalized Hoek-Brown Envelopes
Covero of No-Lear Stregth Evelope to Geeralzed Hoek-Brow Evelope Itroducto The power curve crtero commoly ued lmt-equlbrum lope tablty aaly to defe a o-lear tregth evelope (relatohp betwee hear tre, τ,
More informationThe paper presents Constant Rebalanced Portfolio first introduced by Thomas
Itroducto The paper presets Costat Rebalaced Portfolo frst troduced by Thomas Cover. There are several weakesses of ths approach. Oe s that t s extremely hard to fd the optmal weghts ad the secod weakess
More informationPerformance Attribution. Methodology Overview
erformace Attrbuto Methodology Overvew Faba SUAREZ March 2004 erformace Attrbuto Methodology 1.1 Itroducto erformace Attrbuto s a set of techques that performace aalysts use to expla why a portfolo's performace
More informationA system to extract social networks based on the processing of information obtained from Internet
A system to extract socal etworks based o the processg of formato obtaed from Iteret av CANALETA a, Pablo ROS a, Alex VALLEJO b, Davd VERNET c, ad Agustí ZABALLOS b a Grup de Recerca e Sstemes Dstrbuïts
More informationVectors & Newton's Laws I
Physics 6 Vectors & Newton's Laws I Introduction In this laboratory you will eplore a few aspects of Newton s Laws ug a force table in Part I and in Part II, force sensors and DataStudio. By establishing
More informationExample 2 Find the square root of 0. The only square root of 0 is 0 (since 0 is not positive or negative, so those choices don t exist here).
BEGINNING ALGEBRA Roots ad Radicals (revised summer, 00 Olso) Packet to Supplemet the Curret Textbook - Part Review of Square Roots & Irratioals (This portio ca be ay time before Part ad should mostly
More informationModels for Selecting an ERP System with Intuitionistic Trapezoidal Fuzzy Information
JOURNAL OF SOFWARE, VOL 5, NO 3, MARCH 00 75 Models for Selectg a ERP System wth Itutostc rapezodal Fuzzy Iformato Guwu We, Ru L Departmet of Ecoomcs ad Maagemet, Chogqg Uversty of Arts ad Sceces, Yogchua,
More information