Adaptive Voice Smoothing with Optimal Playback Delay Based on the ITU-T E-Model

Size: px
Start display at page:

Download "Adaptive Voice Smoothing with Optimal Playback Delay Based on the ITU-T E-Model"

Transcription

1 Adapve Voce Smoohng wh Opmal Playback Delay Baed on he ITU-T E-Model Shyh-Fang Huang, Ec Hao-Kuang Wu 2, and Pao-Ch Chang 3 Depamen of Eleconc Engneeng, Naonal Cenal Unvey, Tawan, hf@vaplab.ee.ncu.edu.w 2 Depamen of Compue Scence & Infomaon Engneeng, Naonal Cenal Unvey, Tawan, hao@ce.ncu.edu.w 3 Depamen of Communcaon Engneeng, Naonal Cenal Unvey, Tawan, pcchang@ce.ncu.edu.w Abac. Peceved voce qualy manly affeced by IP newok mpamen uch a delay, je and packe lo. Adapve moohng buffe a he ecevng end can compenae fo he effec of je baed on a adeoff beween delay and lo o achve a be voce qualy. Th wok fomulae an onlne lo model whch ncopoae buffe ze and apple he ITU-T E-model appoach o opmze he delay-lo poblem. Dnc fom he ohe opmal moohe, he popoed opmal moohe uable fo mo of codec cae he lowe complexy. Snce he adapve moohng cheme noduce vaable playback delay, he buffe e-ynchonzaon beween he capue and he playback become eenal. Th wok alo peen a buffe e-ynchonzaon algohm baed on lence kppng o peven unaccepable nceae n he buffe peloadng delay and even buffe oveflow. Smulaon expemen valdae ha he popoed adapve moohe achve gnfcan mpovemen n he voce qualy. Inoducon The apd poge of he developmen of IP-bae newok ha enabled numeou applcaon ha delve no only adonal daa bu alo mulmeda nfomaon n eal me. The nex geneaon newok, lke an ALL-IP newok, a fuue end o negae all heeogeneou wed and wele newok and povde eamle woldwde mobly. In an All-IP newok, one evoluon of he new geneaon Inene applcaon wll ealze VoIP evce ha people can alk feely aound hough he moble-phone, he dekop and VoIP elephone a any me and place. Unfounaely, he IP-baed newok do no guaanee he avalable bandwdh and aue he conan delay je (.e., he delay vaance) fo eal me applcaon. In ohe wod, ndvdual anmon delay fo a gven flow of packe n a newok may be connung o change caued by vayng affc load and dffeng oung pah due o congeon, o ha he packe newok delay fo a connuou ee of neval (.e.

2 alkpu) a he eceve may no be he ame (.e. conan) a he ende. In addon, a packe delay may noduce by he gnal hand-ou o he dffeence of bandwdh anpoaon n wele/fxed newok. Fo delay enve applcaon, a domnang poon of packe loe mgh be lkely due o delay conan. A lae packe ha ave afe a delay hehold deemned by playback me eaed a a lo packe. A gh delay hehold no only degade he qualy of playback bu alo educe he effecve bandwdh becaue a lage facon of delveed packe ae dopped. In fac, delay and lo ae nomally no ndependen of each ohe. In ode o educe he lo mpac, a numbe of applcaon ulze an adapve moohng echnque n whch buffe ae adoped o educe he qualy damage caued by lo packe. Howeve, a lage buffe wll noduce exceve end-o-end delay and deeoae he mulmeda qualy n neacve ealme applcaon. Theefoe, a adeoff equed beween nceaed packe lo and buffe delay o acheve afacoy eul fo playou buffe algohm. In he pa, he wok on he degadaon of he voce qualy conde he effec of packe lo, bu no ha of packe delay. Whn leaue on pedcng delay, he ue of Paeo dbuon n [] of compung he dbuon paamee and ebuldng he new dbuon o pedc he nex packe delay, and he ue of neual newok model o lean affc behavo [2]. The ue of Paeo dbuon o a neual newok model eque elavely hgh complexy o a long leanng peod. Theefoe, we conde he moohe [3]-[9] whch employ acal newok paamee elaed wh he voce chaacec,.e. lo, delay and alk-pu ha have gnfcan nfluence o he voce qualy. They deec delay pke n affc and quckly calculae he equed buffe ze o keep he qualy a good a poble. The E-model a compuaonal model, andadzed by ITU-T n G.07, G.09 and G.3 whch ue he vaou anmon paamee o pedc he ubjecve qualy of packezed voce. Unfounaely, he E-model complex o analyze n he opmzaon poce. An alenave udy o apply a mplfed E-model, f popoed by R. Cole and J. Roenbluh [0], baed upon obeved anpo meauemen n he VoIP gaeway and he anpo pah. Auho ndcaed he mplfed E-model mehod eque moe paen cae fo ace o enhance he valdaon. Azo and Lobna [], and L. Sun and E. Ifeacho [2] popoed o ulze a mplfed E-model ha conde he lo and delay ogehe o e a dje me, whch he opmal playou delay deved by a dynamc pogammng-baed oluon. Howeve, he uably and he accuacy of a mplfed E-model wll be lmed by nonypcal affc paen. Fo pecepual-baed buffe opmzaon cheme fo VoIP, voce qualy evaluaed a a key mec nce epeen ue peceved QoS. Howeve, eque an effcen and accuae objecve way o opmze peceved voce qualy. To conde he well-defned delay and lo mpamen of he E-model, we employ a complee E- model fo he qualy opmzaon o oban opmal peceved voce qualy. In a packe wchng newok, whou a eynchonzaon cheme, a playback clock wh a mno fequency eo wll evenually caue a buffe oveflow o an undeflow a he ecevng end. The oveflow packe ae uually dcaded due o he fne buffe ze and he eal-me equemen. Th dconnuy caued by dcaded packe mgh ceae an unpleaan effec o he playback qualy becaue he

3 lo packe could be he mpoan pa of he gnal. Th effec moe eou fo audo gnal han vdeo gnal becaue human ea ae moe enve o he connuy of ound han human eye. The conbuon of h pape ae hee-fold: () A new mehod opmzng voce qualy fo VoIP ealy appled o codec whch wee well-defned n he ITU-T E- model. () Dffeen fom he ohe opmal moohe, ou opmal moohe ha he lowe complexy wh O ( n). () A feable cheme noduced o olve he buffe e-ynchonzaon poblem. 2 Relaed Wok The Spke Deecon (SD) algohm ha been uded by many eeache [3]-[9]. A delay pke defned a a udden and gnfcan nceae of newok delay n a ho peod ofen le han one ound-p. Th algohm adju he moohng ze,.e. playback delay, a he begnnng of each alk-pu. The eul of h algohm ae heefoe compaed o he eul obaned heen. The SD Algohm n [3] emae he playou me p of he f packe n a alk-pu fom he mean newok delay d and he vaance v fo packe a p = + d + γ v () whee epeen he me a whch packe geneaed a he endng ho and γ a conan faco ued o e he playou me o be fa enough beyond he delay emae uch ha only a mall facon of he avng packe could be lo due o lae aval. The value of γ = 4 ued n mulaon [3]. The emae ae ecompued each me a packe ave, bu only appled when a new alk-pu naed. The mean newok delay d and vaance v ae calculaed baed on a lnea ecuve fle chaacezed by he faco α and β. If n > d If n d - - d = d v = v d = d v = v + ( )n + ( )d + ( )n + ( )d n n (SPIKE_MODE) whee n he oal delay noduced by he newok [3] and ypcal value of α and β ae and 0.75 [], epecvely. The decon o elec α and β baed on he cuen delay condon. The condon n > d epeen newok congeon (SPIKE_MODE) and he wegh β ued o emphaze he cuen newok delay. On he ohe hand, n d epeen newok affc able, and α ued o emphaze he long-em aveage. (2)

4 In emang he delay and vaance, he SD Algohm ulze only wo value, α and β, whch ae mple bu may no be adequae, paculaly when he affc unable. Fo example, an unde-emaed poblem when a newok become pked, bu he delay n ju below he d, he SD Algohm wll evaluae he newok o be able and wll no ene he SIPKE_MODE. 3 Adapve Smoohe wh Opmal Delay-Lo Tade off The popoed opmal moohe deved ung he E-model o ade off he delay and lo. Th mehod nvolve, f, buldng he affc delay model and he lo model. Second, he delay and lo mpamen of he E-model ae calculaed accodng o he delay and he lo model. Thd, he E-model ank R maxmzed and hu he delay and lo opmzed oluon obaned. 3. E-model Decpon In he E-model, a ang faco R epeen voce qualy and conde elevan anmon paamee fo he condeed connecon. I defned n [3] a: R = Ro I Id Ie _eff + A (3) Ro denoe he bac gnal-o-noe ao, whch deved fom he um of dffeen noe ouce whch conan ccu noe and oom noe, end and eceve loudne ang. I denoe he um of all mpamen aocaed wh he voce gnal, whch deved fom he ncoec loudne level, non-opmum deone and quanzng doon. Id epeen he mpamen due o delay of voce gnal, ha he um of Talke Echo delay (Ide), Lene Echo delay (Idle) and end-o-end delay (Idd). Ie _ eff denoe he equpmen mpamen, dependng on he low b ae codec (Ie, Bpl) and packe lo (Ppl) level. Fnally, he advanage faco A no elaon o all ohe anmon paamee. The ue of faco A n a pecfc applcaon lef o he degne decon. 3.2 The Delay and Lo Model n E-model Fo peceved buffe degn, ccal o undeand he delay dbuon modelng a decly elaed o buffe lo. The chaacec of packe anmon delay ove Inene can be epeened by acal model whch follow Exponenal dbuon fo Inene packe (fo an UDP affc) ha been hown o conen wh an Exponenal dbuon [4]. In ode o deve an onlne lo model, he packe endo-end delay aumed a an exponenal dbuon wh paamee µ a he

5 ecevng end fo low complexy and eay mplemenaon. The CDF of he delay dbuon F ( ) can alo be epeened by [5] F( ) and he PDF of he delay dbuon f ( ) f ( ) = df( ) d u = e (4) µ = µ e whee µ defned a he nvee of he aveage mean delay. In a eal-me applcaon, a packe lo ha olely caued by exa delay can be deved fom he delay model f ( ). The value of b epeen he moohng me fo a moohe. When a packe delay exceed b, a packe lo wll occu. The lo funcon l(b ) can be deved a (5) λ µ b µ b ( ) f ( ) d = ( e ) = e + e = e l b = b b () 3.3 Opmzaon on E-model The delay and lo faco ove anmon have geae mpac o he voce qualy han he envonmen o equpmen. To mplfy he opmzaon complexy, and nvegae on delay and lo mpamen, we make hee aumpon n a communcaon connecon a he followng: (). The ccu noe, oom noe and emnae gnal wll no change. ( Ro and I ae fxed). (). An echo delay n he Sende/Receve wll no change. (Ide and Idle ae fxed). (). A codec wll no change (Ie fxed). In [3], R ewen a Eq. (7) whee Idd appoxmaed by ( Ro I Ide Idle + A) Idd Ie _ eff R = (7) ( + X ) Idd = , 3 X ln 00 X =, ln ( 2) (8) and when T > 00 m and Idd =0 when T 00, a a

6 Ie _ eff = Ie + 95 ( Ie) Ppl Ppl + Bpl (9) Faco Ie and Bpl ae defned n [] and T a one-way abolue delay fo echofee connecon. Due o he hee aumpon above, he opmzaon poce can be concenaed on he paamee of Idd and Ie_eff. Eq. (7) deved o yeld Eq. (0) R = Con an X ( X ) ( Ie) Ppl Ppl + Bpl, (0) The dffeenal equaon when > 00 m dr d agned o zeo o maxmze R o yeld he be qualy. Accodng o Eq. (), he lo pobably Ppl = e µ b, o we can ge R µ 5 ' X X ' µ e Bpl 25 ( ) + X X X + X µ µ Bpl + e + 2 Bpl e ' = = () log, X = 00, X log 2 ' = log 2 cuen The oluon fo ae dffcul o ge decly fom Eq. () nce conan he complex polynomal and exponenal funcon, Theefoe, we wll y o olve he be moohng me wh a numecal appoach. We noce he followng hee condon. (). In Eq. (8), when he moohng me 00 m, Idd zeo (no delay mpamen). I mple a moohe hould e he mnmum moohng delay o 00 m o peven he mo packe lo. (). The maxmum end-o-end delay of 250m accepable fo mo ue applcaon o peven eou voce qualy deucon. (). Fo a common low b ae codec, lke G.723. and G.729, he fame ae 30 m and 0 m, epecvely, o he gcd(0,30) 0 m. Baed on he above condon, we can udy he ffeen cae, = 0 m, 2 = 20 m,, 5 = 250 m, o calculae he coepondence, µ, µ 2,, µ 5, by he numecal analy n Eq. () and an eo le han Table how he moohng me coepondng o µ. We can obeve ha a µ nceae, he moohe wll enlage he moohng me o mooh he lae packe. Accodng o Table, he popoed moohe wll calculae he cuen µ ( µ ) a he begnnng of each alk-pu and each fo a mnmum n whch afe µ n µ cuen. The opmal moohng me wll be 00 + n 0 m o keep he opmal voce qualy.

7 Table. The elaon of moohng me and aval ae moohng me µ (/ec) moohng me µ (/ec) = 0 m µ = = 90 m µ =.95 9 µ µ 8.52 µ 2 9. µ µ µ = 20 m µ 2 = = 200 m = 3 = 30 m µ 3 =.49 = 20 m = 4 = 40 m µ 4 = = 220 m = 5 = 50 m µ 5 = = 230 m = = 0 m µ = = 240 m = 7 = 70 m µ = = 250 m = 7 µ = 80 m = 4 Buffe Re-ynchonzaon A neceay condon ha a moohe can wok coecly he ynchonzaon beween he capue and he playback. Th econ popoe a buffe e-ynchonzaon machne (BRM) o help ynchonzaon and he clock df analy of eynchonzaon o valdae he effecvene. 4. Buffe Re-ynchonzaon Machne Th wok popoe a ynchonzaon cheme ha egmen audo gnal by deecng lence. The mmach beween he capue and he playback clock olved by kppng lence a he ecevng end. The duaon of he len peod may be hoened neglgbly degadng he qualy of playback. An acve packe conan vocecompeed daa, wheea a len packe doe no. Skppng ome len packe wll no gnfcanly degade he qualy of he voce, bu can effcenly peven he buffe fom oveflowng. Noably, k (could be adjued) connuou len packe could be ulzed o epaae dffeen alkpu. Fgue depc he buffe e-ynchonzaon algohm. In-ae, Smooh-ae, Play-ae and Skp-ae ae ued o epeen he voce confeence nalng, he buffe moohng, he buffe playng ou, and he len packe kppng, epecvely, and A and S epeen an acve packe and a len packe, epecvely. In he In-ae he buffe wa fo he f avng packe o nalze a voce confeence. If In-ae eceve an S, ay n In-ae; ohewe when an A eceved, he Smooh-ae acvaed o mooh he packe. In he Smooh-ae, he moohng me b compued by applyng he opmal adapve moohe algohm dynamcally. When he buffe moohng me ove b, he Play-ae acvaed; ohewe ay n Smooh-ae fo moohng. In he Play-Sae he packe feched fom he buffe and played ou. In fechng poce, when encoune hee conecuve S packe, mplyng ha he alk-pu can be ended, he buffe e-

8 ynchonzaon pocedue hen wche o he Skp-ae. In he Skp-ae, f A feched fom buffe, mean he new alk-pu ha begun, and hen kp emaned len packe n he buffe, and wche o he Smooh-ae o mooh he nex alkpu. Ohewe, f S feched fom buffe, mple cuen alk-pu no ended and wll be decoded o play ou a he ame ae. Wh he above fou-ae machne, he moohe can mooh he packe a he begnnng of he alkpu o avod buffe undeflow n he Smooh-ae and kp he len packe a he end of he alkpu o peven he oveflow n he Skp-ae. S Buffe<b In A Smooh Buffe>=b A A o Numbe of S <= k Play Numbe of S > k Skp S Fg.. Buffe Re-ynchonzaon Machne 4.2 Effecvene of Re-ynchonzaon To demonae he effecvene of e-ynchonzaon machne fo buffe oveflow, we analyze he clock nconence conan a he followng. C and C epeen he ende clock (fame/ec) and he eceve clock, epecvely, and M a and M denoe he mean acve packe and mean len packe n a alkpu, epecvely. The buffe oveflow caued by he clock nconence (dffeence) wll occu when C lage han C condon. C C, he dffeence value by ubacng he eceve clock fom he ende clock, epeen he pove clock df beween he C C M + M fame _me epeen ende and he eceve. Theefoe, ( ) (( ) ) he mean exa buffe ze caued by he pove clock df fo a mean alkpu me. In ode o dnguh he conecuve alkpu, a leae k len packe ae ulzed. Theefoe, he moohe ha M k len packe o be kpped and eynchonze wh he followng alkpu. When he e-ynchonzaon machne afe ( C C ) ( ( M + M ) fame _ me) ( M k) a a (2), he buffe oveflow caued by he pove clock df wll no occu.

9 5 Smulaon 5. Smulaon Confguaon A e of mulaon expemen ae pefomed o evaluae he effecvene of he popoed adapve moohng cheme. The OPNET mulaon ool ae adoped o ace he voce affc anpoed beween wo dffeen LAN fo a VoIP envonmen. Nney peonal compue wh G.729 affc ae deployed n each LAN. The duaon and fequency of he connecon me of he peonal compue follow Exponenal dbuon. Ten fve-mnue mulaon wee un o pobe he backbone newok delay paen, whch wee ued o ace he adapve moohe and compae he effec of he ognal wh he adaped voce qualy lae. *90 Roue T Roue *90 Fg. 2. The mulaon envonmen of VoIP delay (m) 400 Vaance Packe Numbe Talk Spu (a) The delay of affc Fg. 3. VoIP affc paen (b) The vaance of affc Fg. 2 how he ypcal newok opology n whch a T (.544 Mbp) backbone connec wo LAN, and 00 Mbp lne ae conneced whn each LAN. The popagaon delay of all lnk aumed o be a conan value and wll be gnoed (he devave value wll be zeo) n he opmzaon poce. The buffe ze of he bolenecked oue aumed o be nfne nce he pefomance compaon of adapve moohe wll be affeced by ovedue packe lo (ove he deadlne) and no affeced by he packe lo n oue buffe. The newok end-o-end delay of a G.729 packe wh daa fame ze (0 bye) and RTP/UDP/IP heade (40 bye) meaued fo en fve-mnue mulaon by employng he OPNET mulaon newok. Fgue 3(a) and 4(b) l one of he end-o-end affc delay paen and he coepondng delay vaance fo VoIP affc obeved a a gven eceve.

10 5.2 Voce Qualy n moohe The e equence ampled a 8 khz, econd long, and nclude Englh and Mandan enence poken by male and female. Fg. 4 how he E-model coe R of he voce qualy. I how ha he opmal mehod ha he gnfcan mpovemen n he voce qualy ove SD moohe, becaue ou popoed opmal moohe uly opmze wh he delay and lo mpamen n a anmon plannng of he E- model Rank of E-model (coe) Smoohe SD Opmal Numbe of Talkpu Fg. 4. The qualy coe of moohe 5.3 Re-ynchonzaon Effecvene fo he Pove Clock Df A lenng evaluaon expemen wa pefomed o analyze he equed pope numbe of len packe o egmen he conecuve alk-pu well. I wa found n ou expemen ha a lea hee len packe (e.q. 0 m pe packe n G.729) ae equed o epaae alkpu. We analyze he G.729 voce ouce ued n ou expemen and fnd he pecenage of he mean acve and mean len egmen lengh n a alkpu ae 0.5 and 0.49 epecvely, and he maxmum alkpu lengh 257 packe. p =3 adoped o egmen he conecuve alkpu. Fom he Eq. (2), we can calculae he effecve clock df beween he ende and he eceve C C hould be le han o equal o ( ) ( 257) = (fame/ec). Nomally, he clock df wll no be ove 47.8 (fame/ec) when a ende of G.729 anm 00 (fame/ec) o he newok. Conequenly, he moohe can avod he buffe oveflow well n ou cae. Concluon Th acle popoe an adapve moohng algohm ha ulze he complee E- model o opmze he moohng ze o oban he be voce qualy. The buffe eynchonzaon algohm alo popoed o peven buffe oveflow by kppng

11 ome len packe of he al of alk-pu. I can effcenly olve he mmach beween he capue and he playback clock. Numecal eul have hown ha ou popoed mehod can ge gnfcan mpovemen n he voce qualy whch balance he age delay and lo. Refeence []. Bazauka V., Seflng R.: Robu and effcen emaon of he al ndex of a onepaamee paeo dbuon. Noh Amecan Acuaal Jounal avalable a hp:// (2000) [2]. Ten P. L., Yuang M. C.: Inellgen voce moohe fo lence-uppeed voce ove nene. IEEE JSAC, Vol. 7, No.. (999) 29-4 [3]. Ramjee R., Kue J., Towley D., Schulznne H.: Adapve playou mechanm fo packezed audo applcaon n wde-aea newok. Poc. IEEE INFOCOM. (994) 80-8 [4]. Jeke D. R., Maag W., Samad B.: Adapve play-ou algohm fo voce packe. Poc. IEEE Conf. on Commun., Vol. 3. (200) [5]. Pno J., Chenen K. J.: An algohm fo playou of packe voce baed on adapve adjumen of alkpu lence peod. Poc. IEEE Conf. on Local Compue Newok. (999) []. Lang Y. J., Fabe N., God B.,: Adapve playou chedulng ung me-cale modfcaon n packe voce communcaon. Poc. IEEE Conf. on Acouc, Speech, and Sgnal Poceng, Vol. 3. (200) [7]. Kanal A., Kaandka A.: Adapve delay emaon fo low je audo ove Inene. IEEE GLOBECOM, Vol. 4. (200) [8]. Anandakuma A. K., McCee A., Pakoy E.: An adapve voce playou mehod fo VOP applcaon. IEEE GLOBECOM, Vol. 3. (200) [9]. DeLeon P., Seenan C. J.: An Adapve pedco fo meda playou buffeng. Poc. IEEE Conf. on Acouc, Speech, and Sgnal Poceng, Vol.. (999) [0]. Cole R., Roenbluh J.: Voce ove IP pefomance monong, Jounal on Compue Commun. Revew, Vol. 3. (200) []. Azo L., Lobna M.: Speech playou buffeng baed on a mplfed veon of he ITU- T E-model. IEEE Sgnal Poceng Lee, Vol., I 3. (2004) [2]. Sun L., Ifeacho E.: New model fo peceved voce qualy pedcon and he applcaon n playou buffe opmzaon fo VoIP newok. Poc. ICC. (2004) [3]. ITU-T Recommendaon G.07,: The E-model, a Compuaonal Model fo ue n Tanmon Plannng. (2003) [4]. Bolo J. C.: Chaacezng end-o-end packe delay and lo n he nene. Jounal of Hgh-Speed Newok, Vol. 2. (993) [5]. Fujmoo K., Aa S., Muaa M.: Sacal Analy of Packe Delay n he Inene and Applcaon o Playou Conol fo Seamng Applcaon. IEICE Tan. Commun., Vol. E84-B, No.. (200) []. ITU-T SG2 D.0: Emae of Ie and Bpl fo a ange of Codec. (2003)

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs)

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs) USOD 005 uecal Sudy of Lage-aea An-Reonan Reflecng Opcal Wavegude (ARROW Vecal-Cavy Seconduco Opcal Aplfe (VCSOA anhu Chen Su Fung Yu School of Eleccal and Eleconc Engneeng Conen Inoducon Vecal Cavy Seconduco

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Mau Lkelhood aon Beln Chen Depaen of Copue Scence & Infoaon ngneeng aonal Tawan oal Unvey Refeence:. he Alpaydn, Inoducon o Machne Leanng, Chape 4, MIT Pe, 4 Saple Sac and Populaon Paaee A Scheac Depcon

More information

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing Peenaon fo Theoecal Condened Mae Phyc n TU Beln Geen-Funcon and GW appoxmaon Xnzheng L Theoy Depamen FHI May.8h 2005 Elecon n old Oulne Toal enegy---well olved Sngle pacle excaon---unde developng The Geen

More information

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( ) 5-1. We apply Newon s second law (specfcally, Eq. 5-). (a) We fnd he componen of he foce s ( ) ( ) F = ma = ma cos 0.0 = 1.00kg.00m/s cos 0.0 = 1.88N. (b) The y componen of he foce s ( ) ( ) F = ma = ma

More information

ESS 265 Spring Quarter 2005 Kinetic Simulations

ESS 265 Spring Quarter 2005 Kinetic Simulations SS 65 Spng Quae 5 Knec Sulaon Lecue une 9 5 An aple of an lecoagnec Pacle Code A an eaple of a knec ulaon we wll ue a one denonal elecoagnec ulaon code called KMPO deeloped b Yohhau Oua and Hoh Mauoo.

More information

Name of the Student:

Name of the Student: Engneeng Mahemacs 05 SUBJEC NAME : Pobably & Random Pocess SUBJEC CODE : MA645 MAERIAL NAME : Fomula Maeal MAERIAL CODE : JM08AM007 REGULAION : R03 UPDAED ON : Febuay 05 (Scan he above QR code fo he dec

More information

Multiple Batch Sizing through Batch Size Smoothing

Multiple Batch Sizing through Batch Size Smoothing Jounal of Indual Engneeng (9)-7 Mulple Bach Szng hough Bach Sze Smoohng M Bahadoghol Ayanezhad a, Mehd Kam-Naab a,*, Sudabeh Bakhh a a Depamen of Indual Engneeng, Ian Unvey of Scence and Technology, Tehan,

More information

An Approach to the Representation of Gradual Uncertainty Resolution in Stochastic Multiperiod Planning

An Approach to the Representation of Gradual Uncertainty Resolution in Stochastic Multiperiod Planning 9 h Euopean mpoum on Compue Aded oce Engneeng ECAE9 J. Jeow and J. hulle (Edo 009 Eleve B.V./Ld. All gh eeved. An Appoach o he epeenaon of Gadual Uncean eoluon n ochac ulpeod lannng Vcene co-amez a gnaco

More information

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr.

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr. Moden Enegy Funconal fo Nucle and Nuclea Mae By: lbeo noosa Teas &M Unvesy REU Cycloon 008 Meno: D. Shalom Shlomo Oulne. Inoducon.. The many-body poblem and he aee-fock mehod. 3. Skyme neacon. 4. aee-fock

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION HAPER : LINEAR DISRIMINAION Dscmnan-based lassfcaon 3 In classfcaon h K classes ( k ) We defned dsmnan funcon g () = K hen gven an es eample e chose (pedced) s class label as f g () as he mamum among g

More information

Handling Fuzzy Constraints in Flow Shop Problem

Handling Fuzzy Constraints in Flow Shop Problem Handlng Fuzzy Consans n Flow Shop Poblem Xueyan Song and Sanja Peovc School of Compue Scence & IT, Unvesy of Nongham, UK E-mal: {s sp}@cs.no.ac.uk Absac In hs pape, we pesen an appoach o deal wh fuzzy

More information

Rotor Power Feedback Control of Wind Turbine System with Doubly-Fed Induction Generator

Rotor Power Feedback Control of Wind Turbine System with Doubly-Fed Induction Generator Poceedn of he 6h WSEAS Inenaonal Confeence on Smulaon Modelln and Opmzaon Lbon Poual Sepembe -4 6 48 Roo Powe Feedback Conol of Wnd Tubne Syem wh Doubly-Fed Inducon Geneao J. Smajo Faculy of Eleccal Enneen

More information

1 Constant Real Rate C 1

1 Constant Real Rate C 1 Consan Real Rae. Real Rae of Inees Suppose you ae equally happy wh uns of he consumpon good oday o 5 uns of he consumpon good n peod s me. C 5 Tha means you ll be pepaed o gve up uns oday n eun fo 5 uns

More information

Recursive segmentation procedure based on the Akaike information criterion test

Recursive segmentation procedure based on the Akaike information criterion test ecuve egmenaon pocedue baed on he Aae nfomaon ceon e A-Ho SAO Depamen of Appled Mahemac and Phyc Gaduae School of Infomac Kyoo Unvey a@.yoo-u.ac.jp JAPAN Oulne Bacgound and Movaon Segmenaon pocedue baed

More information

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1 ps 57 Machne Leann School of EES Washnon Sae Unves ps 57 - Machne Leann Assume nsances of classes ae lneal sepaable Esmae paamees of lnea dscmnan If ( - -) > hen + Else - ps 57 - Machne Leann lassfcaon

More information

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova The XIII Inenaonal Confeence Appled Sochasc Models and Daa Analyss (ASMDA-009) Jne 30-Jly 3, 009, Vlns, LITHUANIA ISBN 978-9955-8-463-5 L Sakalaskas, C Skadas and E K Zavadskas (Eds): ASMDA-009 Seleced

More information

Copula Effect on Scenario Tree

Copula Effect on Scenario Tree IAENG Inenaonal Jounal of Appled Mahemac 37: IJAM_37 8 Copula Effec on Scenao Tee K. Suene and H. Panevcu Abac Mulage ochac pogam ae effecve fo olvng long-em plannng poblem unde unceany. Such pogam ae

More information

A hybrid method to find cumulative distribution function of completion time of GERT networks

A hybrid method to find cumulative distribution function of completion time of GERT networks Jounal of Indusal Engneeng Inenaonal Sepembe 2005, Vol., No., - 9 Islamc Azad Uvesy, Tehan Souh Banch A hybd mehod o fnd cumulave dsbuon funcon of compleon me of GERT newos S. S. Hashemn * Depamen of Indusal

More information

CHAPTER 3 DETECTION TECHNIQUES FOR MIMO SYSTEMS

CHAPTER 3 DETECTION TECHNIQUES FOR MIMO SYSTEMS 4 CAPTER 3 DETECTION TECNIQUES FOR MIMO SYSTEMS 3. INTRODUCTION The man challenge n he paccal ealzaon of MIMO weless sysems les n he effcen mplemenaon of he deeco whch needs o sepaae he spaally mulplexed

More information

Simulation of Non-normal Autocorrelated Variables

Simulation of Non-normal Autocorrelated Variables Jounal of Moden Appled Sascal Mehods Volume 5 Issue Acle 5 --005 Smulaon of Non-nomal Auocoelaed Vaables HT Holgesson Jönöpng Inenaonal Busness School Sweden homasholgesson@bshse Follow hs and addonal

More information

Solution of Non-homogeneous bulk arrival Two-node Tandem Queuing Model using Intervention Poisson distribution

Solution of Non-homogeneous bulk arrival Two-node Tandem Queuing Model using Intervention Poisson distribution Volume-03 Issue-09 Sepembe-08 ISSN: 455-3085 (Onlne) RESEARCH REVIEW Inenaonal Jounal of Muldscplnay www.jounals.com [UGC Lsed Jounal] Soluon of Non-homogeneous bulk aval Two-node Tandem Queung Model usng

More information

Chapter 3: Vectors and Two-Dimensional Motion

Chapter 3: Vectors and Two-Dimensional Motion Chape 3: Vecos and Two-Dmensonal Moon Vecos: magnude and decon Negae o a eco: eese s decon Mulplng o ddng a eco b a scala Vecos n he same decon (eaed lke numbes) Geneal Veco Addon: Tangle mehod o addon

More information

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED)

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED) FIRMS IN THE TWO-ERIO FRAMEWORK (CONTINUE) OCTOBER 26, 2 Model Sucue BASICS Tmelne of evens Sa of economc plannng hozon End of economc plannng hozon Noaon : capal used fo poducon n peod (decded upon n

More information

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8 Molecula Evoluion and hylogeny Baed on: Dubin e al Chape 8. hylogeneic Tee umpion banch inenal node leaf Topology T : bifucaing Leave - N Inenal node N+ N- Lengh { i } fo each banch hylogeneic ee Topology

More information

A. Inventory model. Why are we interested in it? What do we really study in such cases.

A. Inventory model. Why are we interested in it? What do we really study in such cases. Some general yem model.. Inenory model. Why are we nereed n? Wha do we really udy n uch cae. General raegy of machng wo dmlar procee, ay, machng a fa proce wh a low one. We need an nenory or a buffer or

More information

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions:

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions: esng he Random Walk Hypohess If changes n a sees P ae uncoelaed, hen he followng escons hold: va + va ( cov, 0 k 0 whee P P. k hese escons n un mply a coespondng se of sample momen condons: g µ + µ (,,

More information

Memorandum COSOR 97-??, 1997, Eindhoven University of Technology

Memorandum COSOR 97-??, 1997, Eindhoven University of Technology Meoandu COSOR 97-??, 1997, Endhoven Unvey of Technology The pobably geneang funcon of he Feund-Ana-Badley ac M.A. van de Wel 1 Depaen of Maheac and Copung Scence, Endhoven Unvey of Technology, Endhoven,

More information

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,

More information

Chapter Finite Difference Method for Ordinary Differential Equations

Chapter Finite Difference Method for Ordinary Differential Equations Chape 8.7 Fne Dffeence Mehod fo Odnay Dffeenal Eqaons Afe eadng hs chape, yo shold be able o. Undesand wha he fne dffeence mehod s and how o se o solve poblems. Wha s he fne dffeence mehod? The fne dffeence

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

Real-coded Quantum Evolutionary Algorithm for Global Numerical Optimization with Continuous Variables

Real-coded Quantum Evolutionary Algorithm for Global Numerical Optimization with Continuous Variables Chnese Jounal of Eleconcs Vol.20, No.3, July 2011 Real-coded Quanum Evoluonay Algohm fo Global Numecal Opmzaon wh Connuous Vaables GAO Hu 1 and ZHANG Ru 2 (1.School of Taffc and Tanspoaon, Souhwes Jaoong

More information

A Demand System for Input Factors when there are Technological Changes in Production

A Demand System for Input Factors when there are Technological Changes in Production A Demand Syem for Inpu Facor when here are Technologcal Change n Producon Movaon Due o (e.g.) echnologcal change here mgh no be a aonary relaonhp for he co hare of each npu facor. When emang demand yem

More information

Lecture 5. Plane Wave Reflection and Transmission

Lecture 5. Plane Wave Reflection and Transmission Lecue 5 Plane Wave Reflecon and Tansmsson Incden wave: 1z E ( z) xˆ E (0) e 1 H ( z) yˆ E (0) e 1 Nomal Incdence (Revew) z 1 (,, ) E H S y (,, ) 1 1 1 Refleced wave: 1z E ( z) xˆ E E (0) e S H 1 1z H (

More information

Lecture 11: Stereo and Surface Estimation

Lecture 11: Stereo and Surface Estimation Lecure : Sereo and Surface Emaon When camera poon have been deermned, ung rucure from moon, we would lke o compue a dene urface model of he cene. In h lecure we wll udy he o called Sereo Problem, where

More information

Multiple Failures. Diverse Routing for Maximizing Survivability. Maximum Survivability Models. Minimum-Color (SRLG) Diverse Routing

Multiple Failures. Diverse Routing for Maximizing Survivability. Maximum Survivability Models. Minimum-Color (SRLG) Diverse Routing Mulple Falure Dvere Roung for Maxmzng Survvably One-falure aumpon n prevou work Mulple falure Hard o provde 100% proecon Maxmum urvvably Maxmum Survvably Model Mnmum-Color (SRLG) Dvere Roung Each lnk ha

More information

N 1. Time points are determined by the

N 1. Time points are determined by the upplemena Mehods Geneaon of scan sgnals In hs secon we descbe n deal how scan sgnals fo 3D scannng wee geneaed. can geneaon was done n hee seps: Fs, he dve sgnal fo he peo-focusng elemen was geneaed o

More information

Summary of Experimental Uncertainty Assessment Methodology With Example

Summary of Experimental Uncertainty Assessment Methodology With Example Summa of Epemenal ncean Aemen Mehodolog Wh Eample F. Sen, M. Mue, M-L. M enna,, and W.E. Echnge 5//00 1 Table of Conen A hlooph Temnolog ncean opagaon Equaon A fo Sngle Te A fo Mulple Te Eample Recommendaon

More information

Variability Aware Network Utility Maximization

Variability Aware Network Utility Maximization aably Awae Newok ly Maxmzaon nay Joseph and Gusavo de ecana Depamen of Eleccal and Compue Engneeng, he nvesy of exas a Ausn axv:378v3 [cssy] 3 Ap 0 Absac Newok ly Maxmzaon NM povdes he key concepual famewok

More information

) from i = 0, instead of i = 1, we have =

) from i = 0, instead of i = 1, we have = Chape 3: Adjusmen Coss n he abou Make I Movaonal Quesons and Execses: Execse 3 (p 6): Illusae he devaon of equaon (35) of he exbook Soluon: The neempoal magnal poduc of labou s epesened by (3) = = E λ

More information

Field due to a collection of N discrete point charges: r is in the direction from

Field due to a collection of N discrete point charges: r is in the direction from Physcs 46 Fomula Shee Exam Coulomb s Law qq Felec = k ˆ (Fo example, f F s he elecc foce ha q exes on q, hen ˆ s a un veco n he decon fom q o q.) Elecc Feld elaed o he elecc foce by: Felec = qe (elecc

More information

to Assess Climate Change Mitigation International Energy Workshop, Paris, June 2013

to Assess Climate Change Mitigation International Energy Workshop, Paris, June 2013 Decomposng he Global TIAM-Maco Maco Model o Assess Clmae Change Mgaon Inenaonal Enegy Wokshop Pas June 2013 Socaes Kypeos (PSI) & An Lehla (VTT) 2 Pesenaon Oulne The global ETSAP-TIAM PE model and he Maco

More information

Fast Calibration for Robot Welding System with Laser Vision

Fast Calibration for Robot Welding System with Laser Vision Fas Calbaon fo Robo Weldng Ssem h Lase Vson Lu Su Mechancal & Eleccal Engneeng Nanchang Unves Nanchang, Chna Wang Guoong Mechancal Engneeng Souh Chna Unves of echnolog Guanghou, Chna Absac Camea calbaon

More information

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example C 188: Aificial Inelligence Fall 2007 epesening Knowledge ecue 17: ayes Nes III 10/25/2007 an Klein UC ekeley Popeies of Ns Independence? ayes nes: pecify complex join disibuions using simple local condiional

More information

Detection and Estimation Theory

Detection and Estimation Theory ESE 54 Detecton and Etmaton Theoy Joeph A. O Sullvan Samuel C. Sach Pofeo Electonc Sytem and Sgnal Reeach Laboatoy Electcal and Sytem Engneeng Wahngton Unvety 411 Jolley Hall 314-935-4173 (Lnda anwe) jao@wutl.edu

More information

Integer Programming Models for Decision Making of. Order Entry Stage in Make to Order Companies 1. INTRODUCTION

Integer Programming Models for Decision Making of. Order Entry Stage in Make to Order Companies 1. INTRODUCTION Inege Pogammng Models fo Decson Makng of Ode Eny Sage n Make o Ode Companes Mahendawah ER, Rully Soelaman and Rzal Safan Depamen of Infomaon Sysems Depamen of Infomacs Engneeng Insu eknolog Sepuluh Nopembe,

More information

Hierarchical Production Planning in Make to Order System Based on Work Load Control Method

Hierarchical Production Planning in Make to Order System Based on Work Load Control Method Unvesal Jounal of Indusal and Busness Managemen 3(): -20, 205 DOI: 0.389/ujbm.205.0300 hp://www.hpub.og Heachcal Poducon Plannng n Make o Ode Sysem Based on Wok Load Conol Mehod Ehsan Faah,*, Maha Khodadad

More information

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY Unvesy of Calfona, Davs Dae: June xx, 009 Depamen of Economcs Tme: 5 hous Mcoeconomcs Readng Tme: 0 mnues PRELIMIARY EXAMIATIO FOR THE Ph.D. DEGREE Pa I ASWER KEY Ia) Thee ae goods. Good s lesue, measued

More information

Calculus 241, section 12.2 Limits/Continuity & 12.3 Derivatives/Integrals notes by Tim Pilachowski r r r =, with a domain of real ( )

Calculus 241, section 12.2 Limits/Continuity & 12.3 Derivatives/Integrals notes by Tim Pilachowski r r r =, with a domain of real ( ) Clculu 4, econ Lm/Connuy & Devve/Inel noe y Tm Plchow, wh domn o el Wh we hve o : veco-vlued uncon, ( ) ( ) ( ) j ( ) nume nd ne o veco The uncon, nd A w done wh eul uncon ( x) nd connuy e he componen

More information

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic *

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic * Avalable onlne a wwwscencedeccom ScenceDec oceda Engneeng 69 4 85 86 4h DAAAM Inenaonal Smposum on Inellgen Manufacung and Auomaon Behavo of Inegal Cuves of he uaslnea Second Ode Dffeenal Equaons Alma

More information

Reflection and Refraction

Reflection and Refraction Chape 1 Reflecon and Refacon We ae now neesed n eplong wha happens when a plane wave avelng n one medum encounes an neface (bounday) wh anohe medum. Undesandng hs phenomenon allows us o undesand hngs lke:

More information

Modeling Background from Compressed Video

Modeling Background from Compressed Video Modelng acgound fom Compessed Vdeo Weqang Wang Daong Chen Wen Gao Je Yang School of Compue Scence Canege Mellon Unvesy Psbugh 53 USA Insue of Compung echnology &Gaduae School Chnese Academy of Scences

More information

New recipes for estimating default intensities

New recipes for estimating default intensities SFB 649 Dcuon Pape 9-4 New ecpe o emang deaul nene Alexande Baanov* Caen von Lee* Andé Wlch* * WeLB AG, Düeldo, Gemany SFB 6 4 9 E C O N O M I C R I S K B E R L I N h eeach wa uppoed by he Deuche Fochunggemencha

More information

Reinforcement learning

Reinforcement learning Lecue 3 Reinfocemen leaning Milos Hauskech milos@cs.pi.edu 539 Senno Squae Reinfocemen leaning We wan o lean he conol policy: : X A We see examples of x (bu oupus a ae no given) Insead of a we ge a feedback

More information

SCIENCE CHINA Technological Sciences

SCIENCE CHINA Technological Sciences SIENE HINA Technologcal Scences Acle Apl 4 Vol.57 No.4: 84 8 do:.7/s43-3-5448- The andom walkng mehod fo he seady lnea convecondffuson equaon wh axsymmec dsc bounday HEN Ka, SONG MengXuan & ZHANG Xng *

More information

Then the number of elements of S of weight n is exactly the number of compositions of n into k parts.

Then the number of elements of S of weight n is exactly the number of compositions of n into k parts. Geneating Function In a geneal combinatoial poblem, we have a univee S of object, and we want to count the numbe of object with a cetain popety. Fo example, if S i the et of all gaph, we might want to

More information

Extension of Wind Power Effects on Markets and Costs of Integration

Extension of Wind Power Effects on Markets and Costs of Integration Exenon of Wnd owe Effec on Make and Co of negaon Heke BRAND # Rüdge BARTH Choph WEBER ee MEBOM Dek Jan SWDER nue of Enegy Economc and he Raonal Ue of Enegy (ER Unvey of Suga Hebuehl. 49a 70565 Suga Gemany

More information

ajanuary't I11 F or,'.

ajanuary't I11 F or,'. ',f,". ; q - c. ^. L.+T,..LJ.\ ; - ~,.,.,.,,,E k }."...,'s Y l.+ : '. " = /.. :4.,Y., _.,,. "-.. - '// ' 7< s k," ;< - " fn 07 265.-.-,... - ma/ \/ e 3 p~~f v-acecu ean d a e.eng nee ng sn ~yoo y namcs

More information

Chapter 6 Plane Motion of Rigid Bodies

Chapter 6 Plane Motion of Rigid Bodies Chpe 6 Pne oon of Rd ode 6. Equon of oon fo Rd bod. 6., 6., 6.3 Conde d bod ced upon b ee een foce,, 3,. We cn ume h he bod mde of e numbe n of pce of m Δm (,,, n). Conden f he moon of he m cene of he

More information

Valuation and Risk Assessment of a Portfolio of Variable Annuities: A Vector Autoregression Approach

Valuation and Risk Assessment of a Portfolio of Variable Annuities: A Vector Autoregression Approach Jounal of Mahemacal Fnance, 8, 8, 49-7 hp://www.cp.og/jounal/jmf ISSN Onlne: 6-44 ISSN Pn: 6-44 Valuaon and Rk Aemen of a Pofolo of Vaable Annue: A Veco Auoegeon Appoach Albna Olando, Gay Pake Iuo pe le

More information

SUPERSONIC INVISCID FLOWS WITH THREE-DIMENSIONAL INTERACTION OF SHOCK WAVES IN CORNERS FORMED BY INTERSECTING WEDGES Y.P.

SUPERSONIC INVISCID FLOWS WITH THREE-DIMENSIONAL INTERACTION OF SHOCK WAVES IN CORNERS FORMED BY INTERSECTING WEDGES Y.P. SUPERSONIC INVISCID FLOWS WITH THREE-DIMENSIONAL INTERACTION OF SHOCK WAVES IN CORNERS FORMED BY INTERSECTING WEDGES Y.P. Goonko, A.N. Kudyavev, and R.D. Rakhmov Inue of Theoecal and Appled Mechanc SB

More information

Flow Decomposition and Large Deviations

Flow Decomposition and Large Deviations ounal of funconal analy 14 2367 (1995) acle no. 97 Flow Decompoon and Lage Devaon Ge ad Ben Aou and Fabenne Caell Laboaoe de Mode laon ochaque e aque Unvee Pa-Sud (Ba^. 425) 91-45 Oay Cedex Fance Receved

More information

CFAR BI DETECTOR IN BINOMIAL DISTRIBUTION PULSE JAMMING 1. I. Garvanov. (Submitted by Academician Ivan Popchev on June 23, 2003)

CFAR BI DETECTOR IN BINOMIAL DISTRIBUTION PULSE JAMMING 1. I. Garvanov. (Submitted by Academician Ivan Popchev on June 23, 2003) FA BI EEO I BIOMIAL ISIBUIO PULSE JAMMIG I. Gavanov (Submtted by Academcan Ivan Popchev on June 3, 3) Abtact: In many pactcal tuaton, howeve, the envonment peence of tong pule ammng (PJ) wth hgh ntenty;

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

When to Treat Prostate Cancer Patients Based on their PSA Dynamics

When to Treat Prostate Cancer Patients Based on their PSA Dynamics When o Tea Posae Cance Paens Based on he PSA Dynamcs CLARA day on opeaons eseach n cance eamen & opeaons managemen Novembe 7 00 Mael S. Lave PhD Man L. Pueman PhD Sco Tyldesley M.D. Wllam J. Mos M.D CIHR

More information

Efficient Bayesian Network Learning for System Optimization in Reliability Engineering

Efficient Bayesian Network Learning for System Optimization in Reliability Engineering Qualy Technology & Quanave Managemen Vol. 9, No., pp. 97-, 202 QTQM ICAQM 202 Effcen Bayesan Newok Leanng fo Sysem Opmzaon n Relably Engneeng A. Gube and I. Ben-Gal Depamen of Indusal Engneeng, Faculy

More information

Robust Centralized Fusion Kalman Filters with Uncertain Noise Variances

Robust Centralized Fusion Kalman Filters with Uncertain Noise Variances ELKOMNIKA Indonean Jounal of Eleal Engneeng Vol., No.6, June 04, pp. 4705 ~ 476 DOI: 0.59/elkomnka.v6.5490 4705 Robu Cenalzed Fuon Kalman Fle wh Unean Noe Vaane Wen-juan Q, Peng Zhang, Z-l Deng* Depamen

More information

Backcalculation Analysis of Pavement-layer Moduli Using Pattern Search Algorithms

Backcalculation Analysis of Pavement-layer Moduli Using Pattern Search Algorithms Bakallaon Analyss of Pavemen-laye Modl Usng Paen Seah Algohms Poje Repo fo ENCE 74 Feqan Lo May 7 005 Bakallaon Analyss of Pavemen-laye Modl Usng Paen Seah Algohms. Inodon. Ovevew of he Poje 3. Objeve

More information

Optimal control of Goursat-Darboux systems in domains with curvilinear boundaries

Optimal control of Goursat-Darboux systems in domains with curvilinear boundaries Opmal conol of Goua-Daboux yem n doman wh cuvlnea boundae S. A. Belba Mahemac Depamen Unvey of Alabama Tucalooa, AL. 35487-0350. USA. e-mal: SBELBAS@G.AS.UA.EDU Abac. We deve neceay condon fo opmaly n

More information

Physics 201 Lecture 15

Physics 201 Lecture 15 Phscs 0 Lecue 5 l Goals Lecue 5 v Elo consevaon of oenu n D & D v Inouce oenu an Iulse Coens on oenu Consevaon l oe geneal han consevaon of echancal eneg l oenu Consevaon occus n sses wh no ne eenal foces

More information

2 shear strain / L for small angle

2 shear strain / L for small angle Sac quaons F F M al Sess omal sess foce coss-seconal aea eage Shea Sess shea sess shea foce coss-seconal aea llowable Sess Faco of Safe F. S San falue Shea San falue san change n lengh ognal lengh Hooke

More information

Support Vector Machines

Support Vector Machines Suppo Veco Machine CSL 3 ARIFICIAL INELLIGENCE SPRING 4 Suppo Veco Machine O, Kenel Machine Diciminan-baed mehod olean cla boundaie Suppo veco coni of eample cloe o bounday Kenel compue imilaiy beeen eample

More information

Low-complexity Algorithms for MIMO Multiplexing Systems

Low-complexity Algorithms for MIMO Multiplexing Systems Low-complexiy Algoihms fo MIMO Muliplexing Sysems Ouline Inoducion QRD-M M algoihm Algoihm I: : o educe he numbe of suviving pahs. Algoihm II: : o educe he numbe of candidaes fo each ansmied signal. :

More information

CHAPTER II AC POWER CALCULATIONS

CHAPTER II AC POWER CALCULATIONS CHAE AC OWE CACUAON Conens nroducon nsananeous and Aerage ower Effece or M alue Apparen ower Coplex ower Conseraon of AC ower ower Facor and ower Facor Correcon Maxu Aerage ower ransfer Applcaons 3 nroducon

More information

An axisymmetric incompressible lattice BGK model for simulation of the pulsatile ow in a circular pipe

An axisymmetric incompressible lattice BGK model for simulation of the pulsatile ow in a circular pipe INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS In. J. Nume. Meh. Fluds 005; 49:99 116 Publshed onlne 3 June 005 n Wley IneScence www.nescence.wley.com). DOI: 10.100/d.997 An axsymmec ncompessble

More information

Cooling of a hot metal forging. , dt dt

Cooling of a hot metal forging. , dt dt Tranen Conducon Uneady Analy - Lumped Thermal Capacy Model Performed when; Hea ranfer whn a yem produced a unform emperaure drbuon n he yem (mall emperaure graden). The emperaure change whn he yem condered

More information

ASTR 3740 Relativity & Cosmology Spring Answers to Problem Set 4.

ASTR 3740 Relativity & Cosmology Spring Answers to Problem Set 4. ASTR 3740 Relativity & Comology Sping 019. Anwe to Poblem Set 4. 1. Tajectoie of paticle in the Schwazchild geomety The equation of motion fo a maive paticle feely falling in the Schwazchild geomety ae

More information

A. Thicknesses and Densities

A. Thicknesses and Densities 10 Lab0 The Eath s Shells A. Thcknesses and Denstes Any theoy of the nteo of the Eath must be consstent wth the fact that ts aggegate densty s 5.5 g/cm (ecall we calculated ths densty last tme). In othe

More information

A Novel Fast Otsu Digital Image Segmentation Method

A Novel Fast Otsu Digital Image Segmentation Method The Inenaonal Aab Jounal of Infomaon Technology, Vol. 3, No. 4, July 06 47 A Novel Fas Osu Dgal Image Segmenaon Mehod Duaa AlSaeed,, Ahmed oudane,, and Al El-Zaa 3 Depamen of Compue Scence and Dgal Technologes,

More information

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have:

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have: Poblem Se #3 Soluons Couse 4.454 Maco IV TA: Todd Gomley, gomley@m.edu sbued: Novembe 23, 2004 Ths poblem se does no need o be uned n Queson #: Sock Pces, vdends and Bubbles Assume you ae n an economy

More information

A multi-band approach to arterial traffic signal optimization. Nathan H. Gartner Susan F. Assmann Fernando Lasaga Dennin L. Hou

A multi-band approach to arterial traffic signal optimization. Nathan H. Gartner Susan F. Assmann Fernando Lasaga Dennin L. Hou A mul-an appoach o aeal affc sgnal opmzaon Nahan H. Gane Susan F. Assmann Fenano Lasaga Dennn L. Hou MILP- The asc, symmec, unfom-h anh maxmzaon polem MILP- Exens he asc polem o nclue asymmec anhs n opposng

More information

Vehicle Suspension Inspection by Stewart Robot

Vehicle Suspension Inspection by Stewart Robot Vehcle Supenon Inpecon by Sewa Robo.Kazem 1,* and. Joohan 2 Downloaded fom www.u.ac. a 4:7 IRST on Wedneday Januay 23d 219 1 an Pofeo,Depamen of Eleccal Engneeng,Shahed Unvey, Tehan, Ian.2 c Suden Eleccal

More information

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions

Combinatorial Approach to M/M/1 Queues. Using Hypergeometric Functions Inenaional Mahemaical Foum, Vol 8, 03, no 0, 463-47 HIKARI Ld, wwwm-hikaicom Combinaoial Appoach o M/M/ Queues Using Hypegeomeic Funcions Jagdish Saan and Kamal Nain Depamen of Saisics, Univesiy of Delhi,

More information

Introduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms

Introduction ( Week 1-2) Course introduction A brief introduction to molecular biology A brief introduction to sequence comparison Part I: Algorithms Course organzaon Inroducon Wee -2) Course nroducon A bref nroducon o molecular bology A bref nroducon o sequence comparson Par I: Algorhms for Sequence Analyss Wee 3-8) Chaper -3, Models and heores» Probably

More information

Randomized Perfect Bipartite Matching

Randomized Perfect Bipartite Matching Inenive Algorihm Lecure 24 Randomized Perfec Biparie Maching Lecurer: Daniel A. Spielman April 9, 208 24. Inroducion We explain a randomized algorihm by Ahih Goel, Michael Kapralov and Sanjeev Khanna for

More information

The Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland

The Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland The Unque Soluon of Sochasc Dffeenal Equaons Dech Rye RyeDM@gawne.ch Mdaweg 3 CH-4500 Solohun Swzeland Phone +4132 621 13 07 Tme evesal n sysems whou an exenal df sngles ou he an-iô negal. Key wods: Sochasc

More information

The Backpropagation Algorithm

The Backpropagation Algorithm The Backpopagaton Algothm Achtectue of Feedfowad Netwok Sgmodal Thehold Functon Contuctng an Obectve Functon Tanng a one-laye netwok by teepet decent Tanng a two-laye netwok by teepet decent Copyght Robet

More information

Monetary policy and models

Monetary policy and models Moneay polcy and odels Kes Næss and Kes Haae Moka Noges Bank Moneay Polcy Unvesy of Copenhagen, 8 May 8 Consue pces and oney supply Annual pecenage gowh. -yea ovng aveage Gowh n oney supply Inflaon - 9

More information

Multiple Regressions and Correlation Analysis

Multiple Regressions and Correlation Analysis Mulple Regreon and Correlaon Analy Chaper 4 McGraw-Hll/Irwn Copyrgh 2 y The McGraw-Hll Compane, Inc. All rgh reerved. GOALS. Decre he relaonhp eween everal ndependen varale and a dependen varale ung mulple

More information

Chebyshev Polynomial Solution of Nonlinear Fredholm-Volterra Integro- Differential Equations

Chebyshev Polynomial Solution of Nonlinear Fredholm-Volterra Integro- Differential Equations Çny Ünvee Fen-Edeby Füle Jounl of A nd Scence Sy : 5 y 6 Chebyhev Polynol Soluon of onlne Fedhol-Vole Inego- Dffeenl Equon Hndn ÇERDİK-YASA nd Ayşegül AKYÜZ-DAŞCIOĞU Abc In h ppe Chebyhev collocon ehod

More information

An Efficient IP Based Approach for Multicast Routing Optimisation in Multi-homing Environments

An Efficient IP Based Approach for Multicast Routing Optimisation in Multi-homing Environments An Effcen IP Based Appoach fo Mulcas Roung Opmsaon n Mul-homng Envonmens N. Wang, G. Pavlou Unvesy of uey Guldfod, uey, U.K. {N.Wang, G.Pavlou}@suey.ac.uk Absac- In hs pape we addess he opmsaon poblem

More information

Physics 120 Spring 2007 Exam #1 April 20, Name

Physics 120 Spring 2007 Exam #1 April 20, Name Phc 0 Spng 007 E # pl 0, 007 Ne P Mulple Choce / 0 Poble # / 0 Poble # / 0 Poble # / 0 ol / 00 In eepng wh he Unon College polc on cdec hone, ued h ou wll nehe ccep no pode unuhozed nce n he copleon o

More information

Information Fusion Kalman Smoother for Time-Varying Systems

Information Fusion Kalman Smoother for Time-Varying Systems Infoaon Fuon alan oohe fo Te-Vayng ye Xao-Jun un Z- Deng Abac-- Fo he lnea dcee e-ayng ochac conol ye wh uleno coloed eaueen noe hee dbued opal fuon alan oohe ae peened baed on he opal nfoaon fuon ule

More information

MATRIX COMPUTATIONS ON PROJECTIVE MODULES USING NONCOMMUTATIVE GRÖBNER BASES

MATRIX COMPUTATIONS ON PROJECTIVE MODULES USING NONCOMMUTATIVE GRÖBNER BASES Jounal of lgeba Numbe heo: dance and pplcaon Volume 5 Numbe 6 Page -9 alable a hp://cenfcadance.co.n DOI: hp://d.do.og/.86/janaa_7686 MRIX COMPUIONS ON PROJCIV MODULS USING NONCOMMUIV GRÖBNR BSS CLUDI

More information

Lecture 11 SVM cont

Lecture 11 SVM cont Lecure SVM con. 0 008 Wha we have done so far We have esalshed ha we wan o fnd a lnear decson oundary whose margn s he larges We know how o measure he margn of a lnear decson oundary Tha s: he mnmum geomerc

More information

Component Score Weighting for GMM based Text-Independent Speaker Verification

Component Score Weighting for GMM based Text-Independent Speaker Verification Comonen Scoe Weghng fo G bae e-ineenen Seae Vefcaon Lang Lu 2, Yuan Dong, 2, Xanyu Zhao, Hao Yang 2, Jan Zhao 2, Hala Wang Fance elecom R&D Cene (Bejng, Bejng, 8, P. R. Chna {yuan.ong, anyu.zhao, hala.wang}@oange-fgou.com

More information

A Methodology for Detecting the Change of Customer Behavior based on Association Rule Mining

A Methodology for Detecting the Change of Customer Behavior based on Association Rule Mining A Mehodology fo Deecng he Change of Cusome Behavo based on Assocaon Rule Mnng Hee Seo Song, Soung He Km KAIST Gaduae School of Managemen Jae Kyeong Km KyungHee Unvesy Absac Undesandng and adapng o changes

More information

Hidden Markov Models

Hidden Markov Models Hdden Mkov Model Ronld J. Wllm CSG220 Spng 2007 Conn evel lde dped fom n Andew Mooe uol on h opc nd few fgue fom Ruell & ovg AIMA e nd Alpydn Inoducon o Mchne Lenng e. A Smple Mkov Chn /2 /3 /3 /3 3 2

More information

s = rθ Chapter 10: Rotation 10.1: What is physics?

s = rθ Chapter 10: Rotation 10.1: What is physics? Chape : oaon Angula poson, velocy, acceleaon Consan angula acceleaon Angula and lnea quanes oaonal knec enegy oaonal nea Toque Newon s nd law o oaon Wok and oaonal knec enegy.: Wha s physcs? In pevous

More information

Matrix reconstruction with the local max norm

Matrix reconstruction with the local max norm Marx reconrucon wh he local max norm Rna oygel Deparmen of Sac Sanford Unvery rnafb@anfordedu Nahan Srebro Toyoa Technologcal Inue a Chcago na@cedu Rulan Salakhudnov Dep of Sac and Dep of Compuer Scence

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information