The realization of low order FSM method and its application Jiai He1,a, Xiangyang Liu1,b, Chengquan Pei2,3,c

Size: px
Start display at page:

Download "The realization of low order FSM method and its application Jiai He1,a, Xiangyang Liu1,b, Chengquan Pei2,3,c"

Transcription

1 3rd Inernionl Conferene on Mhinery, Meril nd Informion ehnology ppliion (ICMMI 05 he relizion of low order FSM mehod nd i ppliion Jii He,, Xingyng Liu,b, Chengqun Pei,3, Shool of Compuer nd Communiion, Lnzhou Univ. of eh, Lnzhou , Chin; Xi n Jioong Univeriy, Shni, i n, 70000, Chin; 3 Xi n Iniue of Opi nd Preiion Mehni of CS, hni, i n, 70000, Chin. hejii@lu.n, blingyng06@6.om, Pq49669@6.om Keyword: α ble diribuion; FLOS; low order FSM lgorihm br. Cyloionry iil hrerii i he min heory of udying non-ionry periodi ignl. Signl nd noie re lwy umed h obey Guin diribuion model in rdiionl ignl proeing. he environmen of non-guin pule i inreing omple, degrdion problem of yem performne n be olved by building α ble diribuion heory model. Firly, hi pper inrodue he α ble diribuion o eime FSM yli perum nd propoe yle perum nlyi under diree ignl. hen, he verifiion i given by king M ommuniion ignl on Mlb plform. he reul how h in omple environmen, he low-level FSM lgorihm whih i bed on α ble diribuion h beer performne on robune nd noie immuniy. Finlly, he lgorihm propoed in hi pper i pplied o onru hrerii prmeer, whih i imporn for blind ignl eprion nd idenifiion. Ⅰ.Inroduion he Guin umpion bed on he enrl limi heorem onform o he norml iuion, nd i deripion i imple, nlyi nd proeing re lo onvenien, mny priniple nd mehod of rdiionl reerh re re ofen umed o be Guin model, uh ignl hrerii nlyi, ignl filering, prmeer eimion nd deeion, yem idenifiion e., nd Guin diribuion model i lwy populr in he field. Bu in pril ppliion, he ignl we mee re ofen been ompnied by ome noie whih hve low probbiliy nd loud mgniude. If we ue Guin diribuion model o deribe uh proee, he ignl proeor performne will be ignifinly degrded for he noie nno mh wih he model well. herefore, we hve o onider uing non-guin ignl noie model nd deign proeing yem, o o mke hem in line wih ignl noie hrerii beer. α ble diribuion i kind of generlized Guin diribuion[-3], nd i i lo diribuion h n ify generlized enrl limi heorem. I beome he mo poenil nd rive model o deribe rndom ignl whih hve more ignifin pike pule wveform nd hiker il probbiliy deniy nion for i beer robune performne. α ble diribuion n deribe vriou degree of impule noie, whih i bed on eing differen hrerii prmeer, no mer he noie i ymmeril or ymmeril. hi rile promoe he FSM lgorihm o α ble diribuion, mke he imulion onfirmion o FSM lgorihm under he ble diribuion, nd preen he yem blok digrm. I provide new wy for ignl eprion, reogniion nd erion under impule noie environmen. Ⅱ.Frionl lower order yli perl deniy he iil momen of ignl onin welh of feure informion.frionl lower order ii heory(flos [4,5]i powerl ool for udying α ble diribuion. ( (, Hypohei, i rel SS diribued rndom proe whoe hrerii inde i α,nd poiion prmeer i 0.I rdiionl eond-order uoorrelion nion i: 05. he uhor - Publihed by lni Pre 830

2 ( If ( τ τ = E[ ( τ ( τ +τ ] R, ( i yloionry ignl h mee he following relion ( i yle R, = R +, ( he eond-order iil momen of he ignl i no ei when i i mied wih ble diribuion impule noie. herefore, frionl lower order yle ionry ignl n be defined (3: (, ; +, = (, ( +, = ( +, ; + +, R b E b R b (, ( (3 = In ddiion:, 0 < < /. ording o Fourier erie epnion: e jπe R (, b, = R (, ;, be d + (4 mong hem ε i yli frequeny. he power perum nd uoorrelion nion re Fourier rnform pir, we define he frionl lower order yli perl deniy following : j ( ;, ( τ;, = τ (5 e e π fτ S f b R b e d he moduled ignl i yloionrii. hrough nlyi he perl orrelion ruure of he ignl, we n omplee vriey of ignl proeing k, uh blind ignl deeion, modulion reogniion nd lifiion, prmeer eimion nd blind equlizion e.. Ⅲ. Low-level FSM lgorihm ( ume i eond-order yloionry proe, he yli perum bed on oninuou FSM lgorihm h he following epreion[6,7]: jπ + (, ( X f u e du = (6 α α α S (, f = X,, f X f + (7 We obin he orreponding diree FSM lgorihm following [8] : ( M / α α α SX (, f = X,, f f vf X f vf (8 M v= M / Where X (, f mong hem ( k moohing, F = N ( h he following form: N (, = ( ( j k= 0 X f k k e π f ( k i window for he enuion d, f = MF i he widh of he perum i he frequeny domin mpling widh, i he ime domin mpling widh,, N = +. he eond-order iil momen of nd N i he ol number of mple in he he ignl i no ei when i i mied wih ble diribuion impule noie. herefore, he low-level iil feure under ble diribuion i inrodued in(4 (5, he reul following: X, f + u e j π = du p, = (9 Diree e [9] : ( α α α SX, (,,, p f = X f + X f (0 83

3 mong hem ( ( M / α ( α ( α X (, =,,, p f M v= ( M / S f X f vf X f vf ( ( X, f h following form: N ( (, = ( ( j k= 0 X f k k e ll of he prmeer re onien wih he Guin model. Ⅳ. Emple nlyi o( We ke modulion ignl f = π f perum: ( If i omple ignl, +/ ( jπ (, = ( o( π f / F v e du π f ( k for emple o nlyze low-level irulion + + ( jπ ( jπ F, v = o π f e du = o π f e du f + f p ( ( Sf, p f = lim lim F, f F, f + df f 0 f f f herefore: (4 If ( i rel ignl, ( + ( + jπ jπ, = o π = ( gn ( o( π (5 F v f f e du f f e du 0< p, = P, 0<,Mke he following hnge: f g f ( ( = ( g( = We n obin he me perl nion wih he plurl form by urning he rel form ino plurl form. o( In he diree e, f = π f (,ume i omple ignl, i he mpling ' ' ume: f ( = f ( + j f (, herefore f ( Re f ( N j, ( ( F f = k f k e inervl, k = 0 herefore: π f ( k = π ( M N j p f ( k, p(, (. ( ( S f M ( M k = 0 v= S f = k f k f k e N j p f + ( k ( k f ( k f ( ks e k = 0 he yem blok digrm i hown in figure : ( (3 (6 (7 83

4 S X α (, f - k= - + Figure low-level FSM lgorihm yem relizion digrm Ⅴ. Feure erion bed on low-level yle perum I i no diffiul o ge oher moduled ignl' yli perum epreion under ble diribuion hrough he bove mhemil nlyi. we n ele he following hrerii prmeer bed on low-level yli perum : = he mpliude of yle perum f+ / S ( f = 0 : S ( f = 0 = k Q(/ Q*( / /4 = + / BPSK p S S / ( f = 0 = k Q(/ Q*( / / = + / OQPSK MSK p S = he mpliude of yle perum / S ( f = f : S / ( f = f = k Q(/ Q*( / /4 = / BPSK MPSK p S = 3he mpliude of yle perum / S ( f = f : S = f+ / BPSK ( f = 0 = 0 = / S OQPSK / MSK ( f = f = 0 = / = / SBPSK / MPSK ( f = f = kp Q(/ Q*( / /4S S / ( f = f = k Q(/ Q*( / / OQPSK MSK p S k p From he bove heoreil nlyi, we know h i onn reled o p, nd build up following hrerii prmeer bed on low-level yli perl: = = = S ( f = f S ( f = f = = = f + S S ( f = f ( f = 0 hrough nlyi, ignl' hrerii prmeer hown in ble : ble he hrerii prmeer of differen modulion ignl BPSK QPSK/8PSK MSK/OQPSK Q(/ Q*( / Q(/ Q*( / Q(/ Q*( / Q(/ Q*( / 0 0 he reul howing in he ble, he hrerii prmeer of BPSK nd MPSK i me, nd i lwy more hn, while he hrerii prmeer of MSK nd OQPSK i 0. We n epre he BPSK/MPSK nd MSK/OQPSK by eing he hrehold h =. Similrly, for he hrerii prmeer, he BPSK nd QPSK/8PSK lo n be epred by eing he hrehold h =. We n idenify differen moduled ignl by deigning he bove lifiion, whih ruure i imple, bu h good performne in omple environmen. herefore, he low order yli perum nlyi of he modulion ignl in hi pper offer new pproh o modulion reogniion under α ble diribuion. Ⅵ. Compuer imulion We mke imulion for M modulion ignl, he rrier frequeny f = 00kHz, he mpling frequeny i 600KHz, he ymbol re i 56, nd he bebnd ignl i ingle inuoidl nion. We hooe onvenionl eond-order orrelion nlyi mehod when miing impule noie nd Guin noie (SNR = 5dB. he imulion reul figure nd figure3.mking imulion for M modulion ignl by uing he lgorihm propoed in hi pper, he imulion reul figure 833

5 4 nd figure 5(p=.Under Guin umpion, he yloionry of M ignl bed on eond-order yli perl heory figure 6. ording o heoreil nlyi we n obin h he mimum orrelion pek pper α = 0, α = ± f 0. Figure 5( nd Figure5(b how h wo pek pper he α = 0,nd he oher pek pper α = ± 30KHz.Figure3(, Figure3(b how he yle perum bed on he rdiionl mehod ffe perl ruure under edy noie diribuion. nd we ompre Figure 3 o Figure5, he lgorihm bed on he umpion of α ble diribuion h beer ni-noie performne hn Guin umpion. he reul how h he lgorihm bed on he umpion of ble diribuion i effeivene nd relibiliy. ( ξ = 0 (b f = 0 Figure M ignl yle perum wih mied noie Figure 3 Seionl view of he yli perum ( ξ = 0 (b f = 0 Figure 4 M ignl 3D yle perum Figure 5 Seionl view of he yli perum Ⅶ.Summry ording o he nlyi bove, i i ler h he ruure of yli perum on he lph ble diribuion i he me he ruure on he umpion of he Guin. Bu he mgniude i differen, whih minly depend on he hnge of p. If p =, he frionl yli perum i rnformed ino he eond-order yli perum. In ddiion, he nlyi of yloionriy feure bed on he umpion of non-guin h good ni-noie biliy.he yli frequeny or perl ruure n be regrded e of prmeer o idenify differen modulion ignl, whih i bed on he onluion h differen ommuniion ignl hve differen yli ruure. Beide, he heory n be pplied o he blind oure eprion, whih i of he hrer of pule noie. he yli frequeny or new vrible bed on yloionriy n be een hrerii prmeer o epre. In onluion, i i very meningl for enrihing he heory of ignl proeing o udy he hrer of yloionriy ignl under ble diribuion. knowledgemen hi work w uppored by he Nionl Nurl Siene Foundion of Chin (No.65603,nd he Nurl Siene Foundion of Gnu Provine(No.48RJZ08. For more informion on low order FSM mehod, end he emil o lingyng06@6.om. Referene [] Grdner W. Cyloionriy in Communiion nd Signl proeing[r]. New York. IEEE pre,994,pp:

6 [] Chen H L, Wng J, Zhng Y, e l. Reerh on prmeerizion of ble diribuion[c]//eleroni Engineering nd Informion Siene: Proeeding of he Inernionl Conferene of Eleroni Engineering nd Informion Siene 05 (ICEEIS 05, Jnury 7-8, 05, Hrbin, Chin. CRC Pre, 05: 405. [3] Niki C L, Sho M. Signl proeing wih lph-ble diribuion nd ppliion[m]. Wiley-Ineriene, 995. [4] Ling Y, Chen W. urvey on ompuing Lévy ble diribuion nd new MLB oolbo[j]. Signl Proeing, 03, 93(,pp: 4-5. [5] Cek M E. Cover ommuniion uing kewed α-ble diribuion[j]. Eleroni Leer, 04, 5(,pp: 6-8. [6] Grdner W. Meuremen of perl orrelion [J]. IEEE rn.ou Speeh, Signl Proeing. 986, 34(5,pp:-3. [7] him M,Cngrjh C N,Bull D R. Comple wvele domin imge ion bed on frionl lower order momen[c].ieee Inernionl Conferene on Informion Fuion 005, pp:55-5. [8] Willim.Grdner. Meuremen of Sperl Correlion [J].IEEE rnion on oui, Speeh nd Signl Proeing, O 986, Vo.34, No.5, pp:-3. [9] Grdner W. he perl orrelion heory of yloionry ime-erie [J]. Signl Proeing, 986,(, pp:

Dynamic Response of an Active Filter Using a Generalized Nonactive Power Theory

Dynamic Response of an Active Filter Using a Generalized Nonactive Power Theory Dynmi Repone of n Aive Filer Uing Generlized Nonive Power heory Yn Xu Leon M. olber John N. Chion Fng Z. Peng yxu3@uk.edu olber@uk.edu hion@uk.edu fzpeng@mu.edu he Univeriy of enneee Mihign Se Univeriy

More information

Interval Oscillation of Nonlinear Differential Equation with Damped Term

Interval Oscillation of Nonlinear Differential Equation with Damped Term Communiion in Informion Siene nd Mngemen Engineering Mr, Vo I, PP 7-4 Inerv Oiion of Noniner Differeni Equion wih Dmped Term Yun-Hui Zeng Deprmen of Mhemi nd Compuion Siene, Hengyng Norm Univeriy,Hunn,

More information

CSC 373: Algorithm Design and Analysis Lecture 9

CSC 373: Algorithm Design and Analysis Lecture 9 CSC 373: Algorihm Deign n Anlyi Leure 9 Alln Boroin Jnury 28, 2013 1 / 16 Leure 9: Announemen n Ouline Announemen Prolem e 1 ue hi Friy. Term Te 1 will e hel nex Mony, Fe in he uoril. Two nnounemen o follow

More information

LAPLACE TRANSFORMS. 1. Basic transforms

LAPLACE TRANSFORMS. 1. Basic transforms LAPLACE TRANSFORMS. Bic rnform In hi coure, Lplce Trnform will be inroduced nd heir properie exmined; ble of common rnform will be buil up; nd rnform will be ued o olve ome dierenil equion by rnforming

More information

Bisimulation, Games & Hennessy Milner logic p.1/32

Bisimulation, Games & Hennessy Milner logic p.1/32 Clil lnguge heory Biimulion, Gme & Henney Milner logi Leure 1 of Modelli Memii dei Proei Conorreni Pweł Sooińki Univeriy of Souhmon, UK I onerned rimrily wih lnguge, eg finie uom regulr lnguge; uhdown

More information

S Radio transmission and network access Exercise 1-2

S Radio transmission and network access Exercise 1-2 S-7.330 Rdio rnsmission nd nework ccess Exercise 1 - P1 In four-symbol digil sysem wih eqully probble symbols he pulses in he figure re used in rnsmission over AWGN-chnnel. s () s () s () s () 1 3 4 )

More information

Fall 2014 David Wagner 10/31 Notes. The min-cut problem. Examples

Fall 2014 David Wagner 10/31 Notes. The min-cut problem. Examples CS 7 Algorihm Fll 24 Dvid Wgner /3 Noe The min-u problem Le G = (V,E) be direed grph, wih oure verex V nd ink verex V. Aume h edge re lbelled wih o, whih n be modelled o funion : E N h oie non-negive inegrl

More information

Chapter Introduction. 2. Linear Combinations [4.1]

Chapter Introduction. 2. Linear Combinations [4.1] Chper 4 Inrouion Thi hper i ou generlizing he onep you lerne in hper o pe oher n hn R Mny opi in hi hper re heoreil n MATLAB will no e le o help you ou You will ee where MATLAB i ueful in hper 4 n how

More information

graph of unit step function t

graph of unit step function t .5 Piecewie coninuou forcing funcion...e.g. urning he forcing on nd off. The following Lplce rnform meril i ueful in yem where we urn forcing funcion on nd off, nd when we hve righ hnd ide "forcing funcion"

More information

International ejournals

International ejournals Avilble online ww.inernionlejournl.om Inernionl ejournl Inernionl Journl of Mhemil Siene, Tehnology nd Humniie 7 (0 8-8 The Mellin Type Inegrl Trnform (MTIT in he rnge (, Rmhndr M. Pie Deprmen of Mhemi,

More information

Super-Gaussian, super-diffusive transport of multi-mode active matter

Super-Gaussian, super-diffusive transport of multi-mode active matter Super-Guin, uper-diffuive rnpor of muli-mode ive mer Seungoo Hhn,, Snggeun Song -3, De Hyun Kim -3, Gil-Suk Yng,3, Kng Tek Lee 4 * Jeyoung Sung -3 * Creive Reerh Iniiive Cener for Chemil Dynmi in Living

More information

1 The Network Flow Problem

1 The Network Flow Problem 5-5/65: Deign & Anlyi of Algorihm Ooer 5, 05 Leure #0: Nework Flow I l hnged: Ooer 5, 05 In hee nex wo leure we re going o lk ou n imporn lgorihmi prolem lled he Nework Flow Prolem. Nework flow i imporn

More information

Graduate Algorithms CS F-18 Flow Networks

Graduate Algorithms CS F-18 Flow Networks Grue Algorihm CS673-2016F-18 Flow Nework Dvi Glle Deprmen of Compuer Siene Univeriy of Sn Frnio 18-0: Flow Nework Diree Grph G Eh ege weigh i piy Amoun of wer/eon h n flow hrough pipe, for inne Single

More information

Solutions to assignment 3

Solutions to assignment 3 D Sruure n Algorihm FR 6. Informik Sner, Telikeplli WS 03/04 hp://www.mpi-.mpg.e/~ner/oure/lg03/inex.hml Soluion o ignmen 3 Exerie Arirge i he ue of irepnie in urreny exhnge re o rnform one uni of urreny

More information

ON DIFFERENTIATION OF A LEBESGUE INTEGRAL WITH RESPECT TO A PARAMETER

ON DIFFERENTIATION OF A LEBESGUE INTEGRAL WITH RESPECT TO A PARAMETER Mh. Appl. 1 (2012, 91 116 ON DIFFERENTIATION OF A LEBESGUE INTEGRAL WITH RESPECT TO A PARAMETER JIŘÍ ŠREMR Abr. The im of hi pper i o diu he bolue oninuiy of erin ompoie funion nd differeniion of Lebegue

More information

Maximum Flow. Flow Graph

Maximum Flow. Flow Graph Mximum Flow Chper 26 Flow Grph A ommon enrio i o ue grph o repreen flow nework nd ue i o nwer queion ou meril flow Flow i he re h meril move hrough he nework Eh direed edge i ondui for he meril wih ome

More information

Robust Network Coding for Bidirected Networks

Robust Network Coding for Bidirected Networks Rou Nework Coding for Bidireed Nework A. Sprinon, S. Y. El Rouyhe, nd C. N. Georghide Ar We onider he prolem of nding liner nework ode h gurnee n innneou reovery from edge filure in ommuniion nework. Wih

More information

Research Article The General Solution of Differential Equations with Caputo-Hadamard Fractional Derivatives and Noninstantaneous Impulses

Research Article The General Solution of Differential Equations with Caputo-Hadamard Fractional Derivatives and Noninstantaneous Impulses Hindwi Advnce in Mhemicl Phyic Volume 207, Aricle ID 309473, pge hp://doi.org/0.55/207/309473 Reerch Aricle The Generl Soluion of Differenil Equion wih Cpuo-Hdmrd Frcionl Derivive nd Noninnneou Impule

More information

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704 Chper IX The Inegrl Trnform Mehod IX. The plce Trnform November 4, 7 699 IX. THE APACE TRANSFORM IX.. The plce Trnform Definiion 7 IX.. Properie 7 IX..3 Emple 7 IX..4 Soluion of IVP for ODE 74 IX..5 Soluion

More information

Three Dimensional Coordinate Geometry

Three Dimensional Coordinate Geometry HKCWCC dvned evel Pure Mhs. / -D Co-Geomer Three Dimensionl Coordine Geomer. Coordine of Poin in Spe Z XOX, YOY nd ZOZ re he oordine-es. P,, is poin on he oordine plne nd is lled ordered riple. P,, X Y

More information

AN IMPROVED CREEP AND SHRINKAGE BASED MODEL FOR DEFLECTIONS OF COMPOSITE MEMBERS REINFORCED WITH CARBON FIBER REINFORCED BARS

AN IMPROVED CREEP AND SHRINKAGE BASED MODEL FOR DEFLECTIONS OF COMPOSITE MEMBERS REINFORCED WITH CARBON FIBER REINFORCED BARS N MPROVED CREEP ND SHRNKGE BSED MODEL FOR DEFLECTONS OF COMPOSTE MEMBERS RENFORCED WTH CRBON FBER RENFORCED BRS M.. Fruqi, S. Bhdr D. Sun, nd J. Si Deprmen o Civil nd rhieurl Engineering, Tex & M Univeriy,

More information

Optimal Waveform Design with Constant Modulus Constraint for Rank-One Target Detection

Optimal Waveform Design with Constant Modulus Constraint for Rank-One Target Detection Senor & Tranduer Vol. 63 Iue January 4 pp. 39-43 Senor & Tranduer 4 by IFSA Publihing S. L. p://www.enorporal.om Opimal Waveform Deign wih Conan odulu Conrain for Rank-One Targe Deeion Fengming XIN Bin

More information

two values, false and true used in mathematical logic, and to two voltage levels, LOW and HIGH used in switching circuits.

two values, false and true used in mathematical logic, and to two voltage levels, LOW and HIGH used in switching circuits. Digil Logi/Design. L. 3 Mrh 2, 26 3 Logi Ges nd Boolen Alger 3. CMOS Tehnology Digil devises re predominnly mnufured in he Complemenry-Mel-Oide-Semionduor (CMOS) ehnology. Two ypes of swihes, s disussed

More information

Bipartite Matching. Matching. Bipartite Matching. Maxflow Formulation

Bipartite Matching. Matching. Bipartite Matching. Maxflow Formulation Mching Inpu: undireced grph G = (V, E). Biprie Mching Inpu: undireced, biprie grph G = (, E).. Mching Ern Myr, Hrld äcke Biprie Mching Inpu: undireced, biprie grph G = (, E). Mflow Formulion Inpu: undireced,

More information

Contraction Mapping Principle Approach to Differential Equations

Contraction Mapping Principle Approach to Differential Equations epl Journl of Science echnology 0 (009) 49-53 Conrcion pping Principle pproch o Differenil Equions Bishnu P. Dhungn Deprmen of hemics, hendr Rn Cmpus ribhuvn Universiy, Khmu epl bsrc Using n eension of

More information

Chapter 2: Evaluative Feedback

Chapter 2: Evaluative Feedback Chper 2: Evluive Feedbck Evluing cions vs. insrucing by giving correc cions Pure evluive feedbck depends olly on he cion ken. Pure insrucive feedbck depends no ll on he cion ken. Supervised lerning is

More information

f t f a f x dx By Lin McMullin f x dx= f b f a. 2

f t f a f x dx By Lin McMullin f x dx= f b f a. 2 Accumulion: Thoughs On () By Lin McMullin f f f d = + The gols of he AP* Clculus progrm include he semen, Sudens should undersnd he definie inegrl s he ne ccumulion of chnge. 1 The Topicl Ouline includes

More information

Positive and negative solutions of a boundary value problem for a

Positive and negative solutions of a boundary value problem for a Invenion Journl of Reerch Technology in Engineering & Mngemen (IJRTEM) ISSN: 2455-3689 www.ijrem.com Volume 2 Iue 9 ǁ Sepemer 28 ǁ PP 73-83 Poiive nd negive oluion of oundry vlue prolem for frcionl, -difference

More information

Admin MAX FLOW APPLICATIONS. Flow graph/networks. Flow constraints 4/30/13. CS lunch today Grading. in-flow = out-flow for every vertex (except s, t)

Admin MAX FLOW APPLICATIONS. Flow graph/networks. Flow constraints 4/30/13. CS lunch today Grading. in-flow = out-flow for every vertex (except s, t) /0/ dmin lunch oday rading MX LOW PPLIION 0, pring avid Kauchak low graph/nework low nework direced, weighed graph (V, ) poiive edge weigh indicaing he capaciy (generally, aume ineger) conain a ingle ource

More information

Recent Enhancements to the MULTIFAN-CL Software

Recent Enhancements to the MULTIFAN-CL Software SCTB15 Working Pper MWG-2 Recen Enhncemen o he MULTIFAN-CL Sofwre John Hmpon 1 nd Dvid Fournier 2 1 Ocenic Fiherie Progrmme Secreri of he Pcific Communiy Noume, New Cledoni 2 Oer Reerch Ld. PO Box 2040

More information

4.8 Improper Integrals

4.8 Improper Integrals 4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls

More information

A Robust DOA Estimation Based on Sigmoid Transform in Alpha Stable Noise Environment

A Robust DOA Estimation Based on Sigmoid Transform in Alpha Stable Noise Environment MAEC Web of Conferences 73 0006 (08) hps://doi.org/0.05/mecconf/08730006 MIMA 08 A obus DOA Esimion Bsed on igmoid rnsform in Alph ble oise Environmen Li Li * Mingyn e Informion Engineering College Dlin

More information

Transformations. Ordered set of numbers: (1,2,3,4) Example: (x,y,z) coordinates of pt in space. Vectors

Transformations. Ordered set of numbers: (1,2,3,4) Example: (x,y,z) coordinates of pt in space. Vectors Trnformion Ordered e of number:,,,4 Emple:,,z coordine of p in pce. Vecor If, n i i, K, n, i uni ecor Vecor ddiion +w, +, +, + V+w w Sclr roduc,, Inner do roduc α w. w +,.,. The inner produc i SCLR!. w,.,

More information

3. Renewal Limit Theorems

3. Renewal Limit Theorems Virul Lborories > 14. Renewl Processes > 1 2 3 3. Renewl Limi Theorems In he inroducion o renewl processes, we noed h he rrivl ime process nd he couning process re inverses, in sens The rrivl ime process

More information

GEOMETRIC EFFECTS CONTRIBUTING TO ANTICIPATION OF THE BEVEL EDGE IN SPREADING RESISTANCE PROFILING

GEOMETRIC EFFECTS CONTRIBUTING TO ANTICIPATION OF THE BEVEL EDGE IN SPREADING RESISTANCE PROFILING GEOMETRIC EFFECTS CONTRIBUTING TO ANTICIPATION OF THE BEVEL EDGE IN SPREADING RESISTANCE PROFILING D H Dickey nd R M Brennn Solecon Lbororie, Inc Reno, Nevd 89521 When preding reince probing re mde prior

More information

September 20 Homework Solutions

September 20 Homework Solutions College of Engineering nd Compuer Science Mechnicl Engineering Deprmen Mechnicl Engineering A Seminr in Engineering Anlysis Fll 7 Number 66 Insrucor: Lrry Creo Sepember Homework Soluions Find he specrum

More information

The Forming Theory and Computer Simulation of the Rotary Cutting Tools with Helical Teeth and Complex Surfaces

The Forming Theory and Computer Simulation of the Rotary Cutting Tools with Helical Teeth and Complex Surfaces Compuer nd Informion Siene The Forming Theory nd Compuer Simulion of he Rory Cuing Tools wih Helil Teeh nd Comple Surfes Hurn Liu Deprmen of Mehnil Engineering Zhejing Universiy of Siene nd Tehnology Hngzhou

More information

Quadratic Fluency DA Functions as Non-uniform Sampling Functions for Interpolating Sampled-values

Quadratic Fluency DA Functions as Non-uniform Sampling Functions for Interpolating Sampled-values WSEAS TRANSACTIONS on CIRCUITS n SYSTEMS Kzui Kgihi Kenihi Ie Miueru Nur Kzuo Torihi Yuhiro Ohiy Hioi Muri Quri Flueny DA Funion Non-unifor Spling Funion for Inerpoling Sple-vlue KAZUKI KATAGISHI KENICHI

More information

0 for t < 0 1 for t > 0

0 for t < 0 1 for t > 0 8.0 Sep nd del funcions Auhor: Jeremy Orloff The uni Sep Funcion We define he uni sep funcion by u() = 0 for < 0 for > 0 I is clled he uni sep funcion becuse i kes uni sep = 0. I is someimes clled he Heviside

More information

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704

IX.1.1 The Laplace Transform Definition 700. IX.1.2 Properties 701. IX.1.3 Examples 702. IX.1.4 Solution of IVP for ODEs 704 Chper IX The Inegrl Trnform Mehod IX. The plce Trnform November 6, 8 699 IX. THE APACE TRANSFORM IX.. The plce Trnform Definiion 7 IX.. Properie 7 IX..3 Emple 7 IX..4 Soluion of IVP for ODE 74 IX..5 Soluion

More information

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY

SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO SOME PROBLEMS IN NUMBER THEORY VOL. 8, NO. 7, JULY 03 ISSN 89-6608 ARPN Jourl of Egieerig d Applied Sciece 006-03 Ai Reerch Publihig Nework (ARPN). All righ reerved. www.rpjourl.com SLOW INCREASING FUNCTIONS AND THEIR APPLICATIONS TO

More information

Searching for a theoretical relation between reverberation and the scattering coefficients of surfaces in a room

Searching for a theoretical relation between reverberation and the scattering coefficients of surfaces in a room Proeeing of he Aoui 212 Nne Conferene 23-27 April 212, Nne, Frne erhing for heoreil relion eeen revererion n he ering oeffiien of urfe in room J-J Emreh Univeriy of Liege - Aoui Lo, Cmpu u r-tilmn, B28,

More information

Fast Recursive Least Squares Algorithm for Acoustic Echo Cancellation Application

Fast Recursive Least Squares Algorithm for Acoustic Echo Cancellation Application SEI 7 4 h Inernaional Conferene: Siene of Eleroni, ehnologie of Informaion and eleommuniaion Marh 5-9, 7 UNISIA Fa Reurive Lea Square Algorihm for Aoui Eho Canellaion Appliaion Farid Ykhlef, A. Gueoum

More information

THE EXTENDED TANH METHOD FOR SOLVING THE -DIMENSION NONLINEAR DISPERSIVE LONG WAVE EQUATION

THE EXTENDED TANH METHOD FOR SOLVING THE -DIMENSION NONLINEAR DISPERSIVE LONG WAVE EQUATION Jornl of Mhemil Sienes: Adnes nd Appliions Volme Nmer 8 Pes 99- THE EXTENDED TANH METHOD FOR SOLVING THE ( ) -DIMENSION NONLINEAR DISPERSIVE LONG WAVE EQUATION SHENGQIANG TANG KELEI ZHANG nd JIHONG RONG

More information

Solutions for Nonlinear Partial Differential Equations By Tan-Cot Method

Solutions for Nonlinear Partial Differential Equations By Tan-Cot Method IOSR Journl of Mhemics (IOSR-JM) e-issn: 78-578. Volume 5, Issue 3 (Jn. - Feb. 13), PP 6-11 Soluions for Nonliner Pril Differenil Equions By Tn-Co Mehod Mhmood Jwd Abdul Rsool Abu Al-Sheer Al -Rfidin Universiy

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

B Signals and Systems I Solutions to Midterm Test 2. xt ()

B Signals and Systems I Solutions to Midterm Test 2. xt () 34-33B Signals and Sysems I Soluions o Miderm es 34-33B Signals and Sysems I Soluions o Miderm es ednesday Marh 7, 7:PM-9:PM Examiner: Prof. Benoi Boule Deparmen of Elerial and Compuer Engineering MGill

More information

Flow Networks Alon Efrat Slides courtesy of Charles Leiserson with small changes by Carola Wenk. Flow networks. Flow networks CS 445

Flow Networks Alon Efrat Slides courtesy of Charles Leiserson with small changes by Carola Wenk. Flow networks. Flow networks CS 445 CS 445 Flow Nework lon Efr Slide corey of Chrle Leieron wih mll chnge by Crol Wenk Flow nework Definiion. flow nework i direced grph G = (V, E) wih wo diingihed erice: orce nd ink. Ech edge (, ) E h nonnegie

More information

Direct Sequence Spread Spectrum II

Direct Sequence Spread Spectrum II DS-SS II 7. Dire Sequene Spread Speru II ER One igh hink ha DS-SS would have he following drawak. Sine he RF andwidh i ie ha needed for a narrowand PSK ignal a he ae daa rae R, here will e ie a uh noie

More information

Physics 2A HW #3 Solutions

Physics 2A HW #3 Solutions Chper 3 Focus on Conceps: 3, 4, 6, 9 Problems: 9, 9, 3, 41, 66, 7, 75, 77 Phsics A HW #3 Soluions Focus On Conceps 3-3 (c) The ccelerion due o grvi is he sme for boh blls, despie he fc h he hve differen

More information

A LOG IS AN EXPONENT.

A LOG IS AN EXPONENT. Ojeives: n nlze nd inerpre he ehvior of rihmi funions, inluding end ehvior nd smpoes. n solve rihmi equions nlill nd grphill. n grph rihmi funions. n deermine he domin nd rnge of rihmi funions. n deermine

More information

Physic 231 Lecture 4. Mi it ftd l t. Main points of today s lecture: Example: addition of velocities Trajectories of objects in 2 = =

Physic 231 Lecture 4. Mi it ftd l t. Main points of today s lecture: Example: addition of velocities Trajectories of objects in 2 = = Mi i fd l Phsic 3 Lecure 4 Min poins of od s lecure: Emple: ddiion of elociies Trjecories of objecs in dimensions: dimensions: g 9.8m/s downwrds ( ) g o g g Emple: A foobll pler runs he pern gien in he

More information

Probability, Estimators, and Stationarity

Probability, Estimators, and Stationarity Chper Probbiliy, Esimors, nd Sionriy Consider signl genered by dynmicl process, R, R. Considering s funcion of ime, we re opering in he ime domin. A fundmenl wy o chrcerize he dynmics using he ime domin

More information

Let. x y. denote a bivariate time series with zero mean.

Let. x y. denote a bivariate time series with zero mean. Linear Filer Le x y : T denoe a bivariae ime erie wih zero mean. Suppoe ha he ime erie {y : T} i conruced a follow: y a x The ime erie {y : T} i aid o be conruced from {x : T} by mean of a Linear Filer.

More information

EE Control Systems LECTURE 2

EE Control Systems LECTURE 2 Copyrigh F.L. Lewi 999 All righ reerved EE 434 - Conrol Syem LECTURE REVIEW OF LAPLACE TRANSFORM LAPLACE TRANSFORM The Laplace ranform i very ueful in analyi and deign for yem ha are linear and ime-invarian

More information

Magamp application and limitation for multiwinding flyback converter

Magamp application and limitation for multiwinding flyback converter Mgmp ppliion nd limiion for muliwinding flyk onverer C.-C. Wen nd C.-L. Chen Asr: A new mgmp ehnique for muliwinding flyk onverers is proposed. Idel opering priniple nd nlysis re presened. he pril irui

More information

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2)

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2) Laplace Tranform Maoud Malek The Laplace ranform i an inegral ranform named in honor of mahemaician and aronomer Pierre-Simon Laplace, who ued he ranform in hi work on probabiliy heory. I i a powerful

More information

Solutions to Problems from Chapter 2

Solutions to Problems from Chapter 2 Soluions o Problems rom Chper Problem. The signls u() :5sgn(), u () :5sgn(), nd u h () :5sgn() re ploed respecively in Figures.,b,c. Noe h u h () :5sgn() :5; 8 including, bu u () :5sgn() is undeined..5

More information

REAL ANALYSIS I HOMEWORK 3. Chapter 1

REAL ANALYSIS I HOMEWORK 3. Chapter 1 REAL ANALYSIS I HOMEWORK 3 CİHAN BAHRAN The quesions re from Sein nd Shkrchi s e. Chper 1 18. Prove he following sserion: Every mesurble funcion is he limi.e. of sequence of coninuous funcions. We firs

More information

A Kalman filtering simulation

A Kalman filtering simulation A Klmn filering simulion The performnce of Klmn filering hs been esed on he bsis of wo differen dynmicl models, ssuming eiher moion wih consn elociy or wih consn ccelerion. The former is epeced o beer

More information

The Ginzburg-Landau approximation for pattern forming systems. Guido Schneider

The Ginzburg-Landau approximation for pattern forming systems. Guido Schneider Gzburg-u pproimion pern mg yem Guido Shneider We re ereed pern mg yem loe fir biliy wih bounded erizion round homogeneou ground e wr olved by wih, k, n N, y f n (k, ye ik e n (k, ble ground e unble ground

More information

ON NEW INEQUALITIES OF SIMPSON S TYPE FOR FUNCTIONS WHOSE SECOND DERIVATIVES ABSOLUTE VALUES ARE CONVEX

ON NEW INEQUALITIES OF SIMPSON S TYPE FOR FUNCTIONS WHOSE SECOND DERIVATIVES ABSOLUTE VALUES ARE CONVEX Journl of Applied Mhemics, Sisics nd Informics JAMSI), 9 ), No. ON NEW INEQUALITIES OF SIMPSON S TYPE FOR FUNCTIONS WHOSE SECOND DERIVATIVES ABSOLUTE VALUES ARE CONVEX MEHMET ZEKI SARIKAYA, ERHAN. SET

More information

Amplitude modulation

Amplitude modulation Uni. Inroduion pliude odulion odulion i proe o vrying one o he hrerii o high requeny inuoidl he rrier in ordne wih he innneou vlue o he oduling he inorion ignl. The high requeny rrier ignl i heilly repreened

More information

e t dt e t dt = lim e t dt T (1 e T ) = 1

e t dt e t dt = lim e t dt T (1 e T ) = 1 Improper Inegrls There re wo ypes of improper inegrls - hose wih infinie limis of inegrion, nd hose wih inegrnds h pproch some poin wihin he limis of inegrion. Firs we will consider inegrls wih infinie

More information

Temperature Rise of the Earth

Temperature Rise of the Earth Avilble online www.sciencedirec.com ScienceDirec Procedi - Socil nd Behviorl Scien ce s 88 ( 2013 ) 220 224 Socil nd Behviorl Sciences Symposium, 4 h Inernionl Science, Socil Science, Engineering nd Energy

More information

8.1. a) For step response, M input is u ( t) Taking inverse Laplace transform. as α 0. Ideal response, K c. = Kc Mτ D + For ramp response, 8-1

8.1. a) For step response, M input is u ( t) Taking inverse Laplace transform. as α 0. Ideal response, K c. = Kc Mτ D + For ramp response, 8-1 8. a For ep repone, inpu i u, U Y a U α α Y a α α Taking invere Laplae ranform a α e e / α / α A α 0 a δ 0 e / α a δ deal repone, α d Y i Gi U i δ Hene a α 0 a i For ramp repone, inpu i u, U Soluion anual

More information

is the cut off frequency in rads.

is the cut off frequency in rads. 0 ELETRIAL IRUITS 9. HIGH RDER ATIVE FILTERS (With Tle) Introdution Thi development explin how to deign Butterworth (Mximlly Flt) or heyhev (Equl Ripple) Low P, High P or Bnd P tive filter. Thi tretment

More information

Convergence of Singular Integral Operators in Weighted Lebesgue Spaces

Convergence of Singular Integral Operators in Weighted Lebesgue Spaces EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 10, No. 2, 2017, 335-347 ISSN 1307-5543 www.ejpm.com Published by New York Business Globl Convergence of Singulr Inegrl Operors in Weighed Lebesgue

More information

A new model for solving fuzzy linear fractional programming problem with ranking function

A new model for solving fuzzy linear fractional programming problem with ranking function J. ppl. Res. Ind. Eng. Vol. 4 No. 07 89 96 Journl of pplied Reserch on Indusril Engineering www.journl-prie.com new model for solving fuzzy liner frcionl progrmming prolem wih rning funcion Spn Kumr Ds

More information

Designing A Fanlike Structure

Designing A Fanlike Structure Designing A Fnlike Sruure To proeed wih his lesson, lik on he Nex buon here or he op of ny pge. When you re done wih his lesson, lik on he Conens buon here or he op of ny pge o reurn o he lis of lessons.

More information

Procedia Computer Science

Procedia Computer Science Procedi Compuer Science 00 (0) 000 000 Procedi Compuer Science www.elsevier.com/loce/procedi The Third Informion Sysems Inernionl Conference The Exisence of Polynomil Soluion of he Nonliner Dynmicl Sysems

More information

FURTHER GENERALIZATIONS. QI Feng. The value of the integral of f(x) over [a; b] can be estimated in a variety ofways. b a. 2(M m)

FURTHER GENERALIZATIONS. QI Feng. The value of the integral of f(x) over [a; b] can be estimated in a variety ofways. b a. 2(M m) Univ. Beogrd. Pul. Elekroehn. Fk. Ser. M. 8 (997), 79{83 FUTHE GENEALIZATIONS OF INEQUALITIES FO AN INTEGAL QI Feng Using he Tylor's formul we prove wo inegrl inequliies, h generlize K. S. K. Iyengr's

More information

Interpolation and Pulse Shaping

Interpolation and Pulse Shaping EE345S Real-Time Digial Signal Proceing Lab Spring 2006 Inerpolaion and Pule Shaping Prof. Brian L. Evan Dep. of Elecrical and Compuer Engineering The Univeriy of Texa a Auin Lecure 7 Dicree-o-Coninuou

More information

Generalized Projective Synchronization Using Nonlinear Control Method

Generalized Projective Synchronization Using Nonlinear Control Method ISSN 79-3889 (prin), 79-3897 (online) Inernionl Journl of Nonliner Siene Vol.8(9) No.,pp.79-85 Generlized Projeive Synhronizion Using Nonliner Conrol Mehod Xin Li Deprmen of Mhemis, Chngshu Insiue of Tehnology

More information

Jonathan Turner Exam 2-10/28/03

Jonathan Turner Exam 2-10/28/03 CS Algorihm n Progrm Prolm Exm Soluion S Soluion Jonhn Turnr Exm //. ( poin) In h Fioni hp ruur, u wn vrx u n i prn v u ing u v i v h lry lo hil in i l m hil o om ohr vrx. Suppo w hng hi, o h ing u i prorm

More information

INTEGRALS. Exercise 1. Let f : [a, b] R be bounded, and let P and Q be partitions of [a, b]. Prove that if P Q then U(P ) U(Q) and L(P ) L(Q).

INTEGRALS. Exercise 1. Let f : [a, b] R be bounded, and let P and Q be partitions of [a, b]. Prove that if P Q then U(P ) U(Q) and L(P ) L(Q). INTEGRALS JOHN QUIGG Eercise. Le f : [, b] R be bounded, nd le P nd Q be priions of [, b]. Prove h if P Q hen U(P ) U(Q) nd L(P ) L(Q). Soluion: Le P = {,..., n }. Since Q is obined from P by dding finiely

More information

A Theoretical Model of a Voltage Controlled Oscillator

A Theoretical Model of a Voltage Controlled Oscillator A Theoreical Model of a Volage Conrolled Ocillaor Yenming Chen Advior: Dr. Rober Scholz Communicaion Science Iniue Univeriy of Souhern California UWB Workhop, April 11-1, 6 Inroducion Moivaion The volage

More information

6.8 Laplace Transform: General Formulas

6.8 Laplace Transform: General Formulas 48 HAP. 6 Laplace Tranform 6.8 Laplace Tranform: General Formula Formula Name, ommen Sec. F() l{ f ()} e f () d f () l {F()} Definiion of Tranform Invere Tranform 6. l{af () bg()} al{f ()} bl{g()} Lineariy

More information

15/03/1439. Lecture 4: Linear Time Invariant (LTI) systems

15/03/1439. Lecture 4: Linear Time Invariant (LTI) systems Lecre 4: Liner Time Invrin LTI sysems 2. Liner sysems, Convolion 3 lecres: Implse response, inp signls s coninm of implses. Convolion, discree-ime nd coninos-ime. LTI sysems nd convolion Specific objecives

More information

Chapter 9 - The Laplace Transform

Chapter 9 - The Laplace Transform Chaper 9 - The Laplace Tranform Selece Soluion. Skech he pole-zero plo an region of convergence (if i exi) for hee ignal. ω [] () 8 (a) x e u = 8 ROC σ ( ) 3 (b) x e co π u ω [] ( ) () (c) x e u e u ROC

More information

Motion. Part 2: Constant Acceleration. Acceleration. October Lab Physics. Ms. Levine 1. Acceleration. Acceleration. Units for Acceleration.

Motion. Part 2: Constant Acceleration. Acceleration. October Lab Physics. Ms. Levine 1. Acceleration. Acceleration. Units for Acceleration. Moion Accelerion Pr : Consn Accelerion Accelerion Accelerion Accelerion is he re of chnge of velociy. = v - vo = Δv Δ ccelerion = = v - vo chnge of velociy elpsed ime Accelerion is vecor, lhough in one-dimensionl

More information

Fault-Tolerant Guaranteed Cost Control of Uncertain Networked Control Systems with Time-varying Delay

Fault-Tolerant Guaranteed Cost Control of Uncertain Networked Control Systems with Time-varying Delay IJCSNS Inernionl Journl of Compuer Science nd Nework Securiy VOL.9 No.6 June 9 3 Ful-olern Gurneed Cos Conrol of Uncerin Neworked Conrol Sysems wih ime-vrying Dely Guo Yi-nn Zhng Qin-ying Chin Universiy

More information

Math Week 12 continue ; also cover parts of , EP 7.6 Mon Nov 14

Math Week 12 continue ; also cover parts of , EP 7.6 Mon Nov 14 Mh 225-4 Week 2 coninue.-.3; lo cover pr of.4-.5, EP 7.6 Mon Nov 4.-.3 Lplce rnform, nd pplicion o DE IVP, epecilly hoe in Chper 5. Tody we'll coninue (from l Wednedy) o fill in he Lplce rnform ble (on

More information

A 1.3 m 2.5 m 2.8 m. x = m m = 8400 m. y = 4900 m 3200 m = 1700 m

A 1.3 m 2.5 m 2.8 m. x = m m = 8400 m. y = 4900 m 3200 m = 1700 m PHYS : Soluions o Chper 3 Home Work. SSM REASONING The displcemen is ecor drwn from he iniil posiion o he finl posiion. The mgniude of he displcemen is he shores disnce beween he posiions. Noe h i is onl

More information

Using hypothesis one, energy of gravitational waves is directly proportional to its frequency,

Using hypothesis one, energy of gravitational waves is directly proportional to its frequency, ushl nd Grviy Prshn Shool of Siene nd ngineering, Universiy of Glsgow, Glsgow-G18QQ, Unied ingdo. * orresponding uhor: : Prshn. Shool of Siene nd ngineering, Universiy of Glsgow, Glsgow-G18QQ, Unied ingdo,

More information

Chapter 7: Inverse-Response Systems

Chapter 7: Inverse-Response Systems Chaper 7: Invere-Repone Syem Normal Syem Invere-Repone Syem Baic Sar ou in he wrong direcion End up in he original eady-ae gain value Two or more yem wih differen magniude and cale in parallel Main yem

More information

A continuous-time approach to constraint satisfaction: Optimization hardness as transient chaos

A continuous-time approach to constraint satisfaction: Optimization hardness as transient chaos A coninuou-ime pproch o conrin ifcion: Opimizion hrdne rnien cho PN-II-RU-TE--- Finl Sineic Repor Generl im nd objecive of he projec Conrin ifcion problem (uch Boolen ifibiliy) coniue one of he hrde cle

More information

Consider a Binary antipodal system which produces data of δ (t)

Consider a Binary antipodal system which produces data of δ (t) Modulaion Polem PSK: (inay Phae-hi keying) Conide a inay anipodal yem whih podue daa o δ ( o + δ ( o inay and epeively. Thi daa i paed o pule haping ile and he oupu o he pule haping ile i muliplied y o(

More information

CS3510 Design & Analysis of Algorithms Fall 2017 Section A. Test 3 Solutions. Instructor: Richard Peng In class, Wednesday, Nov 15, 2017

CS3510 Design & Analysis of Algorithms Fall 2017 Section A. Test 3 Solutions. Instructor: Richard Peng In class, Wednesday, Nov 15, 2017 Uer ID (NOT he 9 igi numer): gurell4 CS351 Deign & Anlyi of Algorihm Fll 17 Seion A Te 3 Soluion Inruor: Rihr Peng In l, Weney, Nov 15, 17 Do no open hi quiz ookle unil you re iree o o o. Re ll he inruion

More information

Tutorial 2 Euler Lagrange ( ) ( ) In one sentence: d dx

Tutorial 2 Euler Lagrange ( ) ( ) In one sentence: d dx Tutoril 2 Euler Lgrnge In one entene: d Fy = F d Importnt ft: ) The olution of EL eqution i lled eterml. 2) Minmum / Mimum of the "Mot Simple prolem" i lo n eterml. 3) It i eier to olve EL nd hek if we

More information

SOME USEFUL MATHEMATICS

SOME USEFUL MATHEMATICS SOME USEFU MAHEMAICS SOME USEFU MAHEMAICS I is esy o mesure n preic he behvior of n elecricl circui h conins only c volges n currens. However, mos useful elecricl signls h crry informion vry wih ime. Since

More information

HORIZONTAL POSITION OPTIMAL SOLUTION DETERMINATION FOR THE SATELLITE LASER RANGING SLOPE MODEL

HORIZONTAL POSITION OPTIMAL SOLUTION DETERMINATION FOR THE SATELLITE LASER RANGING SLOPE MODEL HOIZONAL POSIION OPIMAL SOLUION DEEMINAION FO HE SAELLIE LASE ANGING SLOPE MODEL Yu Wng,* Yu Ai b Yu Hu b enli Wng b Xi n Surveying nd Mpping Insiue, No. 1 Middle Yn od, Xi n, Chin, 710054-640677@qq.com

More information

A Simple Method to Solve Quartic Equations. Key words: Polynomials, Quartics, Equations of the Fourth Degree INTRODUCTION

A Simple Method to Solve Quartic Equations. Key words: Polynomials, Quartics, Equations of the Fourth Degree INTRODUCTION Ausrlin Journl of Bsic nd Applied Sciences, 6(6): -6, 0 ISSN 99-878 A Simple Mehod o Solve Quric Equions Amir Fhi, Poo Mobdersn, Rhim Fhi Deprmen of Elecricl Engineering, Urmi brnch, Islmic Ad Universi,

More information

Properties of Logarithms. Solving Exponential and Logarithmic Equations. Properties of Logarithms. Properties of Logarithms. ( x)

Properties of Logarithms. Solving Exponential and Logarithmic Equations. Properties of Logarithms. Properties of Logarithms. ( x) Properies of Logrihms Solving Eponenil nd Logrihmic Equions Properies of Logrihms Produc Rule ( ) log mn = log m + log n ( ) log = log + log Properies of Logrihms Quoien Rule log m = logm logn n log7 =

More information

Hadamard-Type Inequalities for s-convex Functions

Hadamard-Type Inequalities for s-convex Functions Interntionl Mthemtil Forum, 3, 008, no. 40, 965-975 Hdmrd-Type Inequlitie or -Convex Funtion Mohmmd Alomri nd Mlin Dru Shool o Mthemtil Siene Fulty o Siene nd Tehnology Univeriti Kebngn Mlyi Bngi 43600

More information

Systems Variables and Structural Controllability: An Inverted Pendulum Case

Systems Variables and Structural Controllability: An Inverted Pendulum Case Reserch Journl of Applied Sciences, Engineering nd echnology 6(: 46-4, 3 ISSN: 4-7459; e-issn: 4-7467 Mxwell Scienific Orgniion, 3 Submied: Jnury 5, 3 Acceped: Mrch 7, 3 Published: November, 3 Sysems Vribles

More information

Exponential Decay for Nonlinear Damped Equation of Suspended String

Exponential Decay for Nonlinear Damped Equation of Suspended String 9 Inernionl Symoium on Comuing, Communicion, nd Conrol (ISCCC 9) Proc of CSIT vol () () IACSIT Pre, Singore Eonenil Decy for Nonliner Dmed Equion of Suended Sring Jiong Kemuwn Dermen of Mhemic, Fculy of

More information

A Point Estimate of Natural Mortality Coefficient of Southern Minke Whales Using JARPA data

A Point Estimate of Natural Mortality Coefficient of Southern Minke Whales Using JARPA data JA/J05/J6 A Poin Eime of url Morliy Coefficien of Souhern Minke Whle ing JAPA d EIJI TAAKA YOKO ZEITAI nd YOSHIHIO FJISE Tokyo niveriy of Mrine Science nd Technology Fculy of Mrine Science Konn 4-5-7 Mino-ku

More information

Location is relative. Coordinate Systems. Which of the following can be described with vectors??

Location is relative. Coordinate Systems. Which of the following can be described with vectors?? Locion is relive Coordine Sysems The posiion o hing is sed relive o noher hing (rel or virul) review o he physicl sis h governs mhemicl represenions Reerence oec mus e deined Disnce mus e nown Direcion

More information

12. Nyquist Sampling, Pulse-Amplitude Modulation, and Time- Division Multiplexing

12. Nyquist Sampling, Pulse-Amplitude Modulation, and Time- Division Multiplexing Nyqui Sapling, Pule-Apliude Modulaion, and Tie Diviion Muliplexing on Mac 2. Nyqui Sapling, Pule-Apliude Modulaion, and Tie- Diviion Muliplexing Many analogue counicaion ye are ill in wide ue oday. Thee

More information