CODING & MODULATION Prof. Ing. Anton Čižmár, PhD. also from Digital Communications 4th Ed., J. G. Proakis, McGraw-Hill Int. Ed.

Size: px
Start display at page:

Download "CODING & MODULATION Prof. Ing. Anton Čižmár, PhD. also from Digital Communications 4th Ed., J. G. Proakis, McGraw-Hill Int. Ed."

Transcription

1 CODING & MODULATION Pof. Ig. Ato Čižá, PhD. alo fo Digital Couicatio 4th Ed., J. G. Poai, McGaw-Hill It. Ed.

2 5 Optiu Receive fo the AWGN Chael I the peviou chapte, we decied vaiou type of odulatio ethod that ay e ued to tait digital ifoatio though a couicatio chael. A we have oeved, the odulato at the taitte pefo the fuctio of appig the digital equece ito igal wavefo. Thi chapte deal with the deig ad pefoace chaacteitic of optiu eceive fo the vaiou odulatio ethod, whe the chael coupt the taitted igal y the additio of Gauia oie. 5. OPTIMUM RECEIVERS FOR SIGNALS CORRUPTED BY AWGN Let u egi y developig a atheatical odel fo the igal at the iput to the eceive. We aue that the taitte ed digital ifoatio y ue of M igal wavefo { t,,,,m.}. Each wavefo i taitted withi the yol igalig iteval of duatio T. To e pecific, we coide the taiio of ifoatio ove the iteval t T. The chael i aued to coupt the igal y the additio of white Gauia oie a illutated i Fig.5.-. Fig.5.- Model fo eceived igal paed though a AWGN chael Thu, the e eceived igal i the iteval t T ay e expeed a t t + t t T 5.- with powe pectal deity of t Φ f N W/Hz Baed o the oevatio of t ove the igal iteval, we wih to deig a eceive that i optiu i the ee that it iiize the poaility of aig a eo. It i coveiet to udivide the eceive ito two pat the igal deodulato ad the detecto Fig.5.-.

3 The fuctio of the igal deodulato i to covet the eceived wavefo t ito N- dieioal vecto [ N ], whee N i the dieio of the taitted igal wavefo. The fuctio of the detecto i to decide which of M poile igal wavefo wa taitted aed of the vecto. Fig. 5.- Receive cofiguatio Received igal t Sigal deodulato Detecto Output deciio Two ealizatio of the igal deodulato ae decied i the ext two ectio. Oe i aed o the ued of igal coelato. The ecod i aed o the ue of atched filte. The optiu detecto that follow the igal deodulato i deiged to iiize the poaility of eo. 5.. Coelatio Deodulato We decie a coelatio deodulato that decopoe the eceived igal ad oie ito N-dieioal vecto. The igal ad the oie ae expaded ito a eie of liealy weighted othooal ai fuctio {f t}. It i aued that the N ai fuctio {f t} pa the igal pace, o that evey oe of the poile taitted igal of the et { t}, ca e epeeted a a liea coiatio of {f t}. I the cae of the oie, the fuctio {f t} do ot pa the oie pace. Howeve, the oie te that fall outide the igal pace ae ielevat to the detectio of the igal. Suppoe the eceived igal t i paed though a paallel a of N co coelato which aically copute the pojectio of t oto the N ai fuctio {f t}, a illutated i Fig Thu we have

4 T t f t dt + T [ t + t] f t dt,,,n 5.- whee T T t f t dt t f t dt,,,n 5.-3 The igal i ow epeeted y the vecto with copoet,,,,n. Thei value deped o which of the M igal wa taitted. We ca expe the eceived igal i the iteval t T a t N N f t + f t + t N f t + t 5.-4 whee N t t f t 5.-5 i a zeo ea Gauia oie poce that epeet the diffeece etwee the oigial oie poce t ad the pat coepodig to the pojectio of t oto the ai fuctio {f t} ad i ielevat to the deciio a to which igal wa taitted. The deciio ay e aed etiely o the coelato output igal ad the oie copoet +,,,,N. The coelato output { } coditioed o the th igal eig taitted ae Gauia ado vaiale with ea E E ad equal vaiace σ σ N 5.-9 Sice, the oie copoet { } ae ucoelated Gauia ado vaiale, they ae alo tatitically idepedet. A a coequece, the coelato output { } coditioed

5 o the th igal eig taitted ae tatitically idepedet Gauia vaiale. Hece the coditioal poaility deity fuctio of the ado vaiale [ N ] ae iply N p p,,,m 5.- whee p πn [ exp N,,,N 5.- By utitutig 5.- ito 5.-, we otai the joit coditioal PDF N p exp N [,,,M 5.- N πn The coelato output ae ufficiet tatitic fo eachig a deciio o which of the M igal wa taitted. All the elevat ifoatio i cotaied i the coelato output { }. Hece t ay e igoed. Exaple 5.- Coide a M-ay aead PAM igal et i which the aic pule hape gt i ectagula i the peiod,t with the aplitude a Fig The additive oie i zeo ea white Gauia oie poce. Let u deteie the ai fuctio ft ad the output of the coelatio-type deodulato. The eegy i the ectagula pule i E g T g t dt a dt a T Figue 5--4 Sigal pule fo Exaple 5-- gt a [ T [ T t Sice the PAM igal et ha dieio N, thee i oly oe ai fuctio ft. Thi i give a f t { / T t T g t a T othewie The output of the coelatio-type deodulato i T t f t dt T T t dt

6 It i iteetig to ote that the coelato ecoe a iple itegato whe ft i ectagula. If we utitute fo t, we otai T t t dt T [ + ] + whee the oie te E ad σ N. The poaility deity fuctio fo the apled output i 5.. Matched-Filte Deodulato p πn [ ] exp N Itead of uig a a of N coelato to geeate the vaiale { }, we ay ue a a of N liea filte. Suppoe that the ipule epoe of the N filte ae h t f T t, t T 5.-4 whee {f t} ae the N ai fuctio ad h t outide of the iteval output of thee filte ae y t t t τ f τ h t τ dτ T t + τ dτ Now, if we aple the output of the filte at tt, we otai T y T τ f τ dτ { } t T. The,,,N 5.-5,,,N 5.-6 Hece, the apled output of the filte at tie t T ae exactly the et of value { } otaied fo the N liea coelato. A filte whoe ipule epoe ht T-t i called atched filte to the igal t. Figue 5--5 Sigal t ad filte atched to t t ht T-t A A T t T t a igal t Ipule epoe The epoe of ht T-t to the igal t i

7 t y t τ T t + τ dτ 5.-7 which i aically the tie-autocoelatio fuctio of the igal t. Fig.5.-6 illutate yt fo the tiagula igal pule how i Fig Note thet the autocoelatio fuctio yt i a eve fuctio of t, which attai a pea at tt. Figue 5--6 The atched filte output i autocoelatio fuctio of t t y tfig.5.-6 τ T t + τ dτ yt T T I the cae of the deodulato decied aove, the N atched filte ae atched to the ai fuctio {f t}. Fig.5.-7 illutate the atched filte deodulato that geeate the oeved vaiale { }. Popetie of the atched filte a If a igal t i coupted y AWGN, the filte with a ipule epoe atched to t axiize the output igal-to-oie atio SNR, which i a follow SNR N T E t dt N 5.-4 Note that the output SNR fo the atched filte deped o the eegy of the wavefo t ut ot o the detailed chaacteitic of t. Thi i aothe iteetig popety of the atched filte The Optiu Detecto We have deotated that, fo a igal taitted ove a AWGN chael, eithe a coelatio deodulato o a atched filte deodulato poduce the vecto [ N ], which cotai all the elevat ifoatio i the eceived igal wavefo. I thi ectio we decie the optiu deciio ule aed o the oevatio vecto. We aue that thee i o eoy i igal taitted i ucceive igal iteval.

8 We wih to deig a igal detecto that ae a deciio o the taitted igal i each igal iteval aed o the oevatio of the vecto i each iteval uch that the poaility of a coect deciio i axiized. We coide a deciio ule aed o the coputatio of the poteioi poailitie defied a Pigal wa taitted,,,m which we aeviate a P. The deciio citeio i aed o electig the igal coepodig to the axiu of the et of poteio poailitie {P } iiize the poaility of eo ad axiize the poaility of coect deciio. Thi deciio citeio i called the axiu a poteioi poaility MAP citeio. Uig Baye ule, the poteio poailitie ay e expeed a p P P p whee p i the coditioal PDF of the oeved vecto give ad P i the a pioi poaility of the th igal eig taitted. The deoiato of ay e expeed a M p p P Soe iplificatio occu i the MAP citeio whe the M igal ae equally poale apioi, i.e. P /M fo all M. Futheoe, we ote that the deoiato i i idepedet of which igal i taitted. Coequetly, the deciio ule aed o fidig the igal that axiize P i equivalet to fidig the igal that axiize p. The coditioal PDF p o ay ootoic fuctio of it i uually called the lielihood fuctio. The deciio citeio aed o axiu of p ove the M igal i called the axiu-lielihood ML citeio. We oeve that a detecto aed o the MAP citeio ad oe that i aed o the ML citeio ae the ae deciio a log a the a pioi poailitie P ae all equal. i.e., the igal { ae equipoale. I the cae of a AWGN chael, the lielihood fuctio p i give y Equatio 5.-. To iplify the coputatio, we ay wo with the atual logaith of p, which i a ootoic fuctio. Thu l p N l π N 5.-4 N N

9 The axiu of l p ove i equivalet to fidig the igal that iiize the Euclidea ditace iiu ditace detectio N D, 5.-4 We call D,,,,,M, the ditace etic. Hece, fo the AWGN chael, the deciio ule aed o the ML citeio educe to fidig the igal that i cloet i ditace to the eceived igal vecto. We hall efe to thi deciio ule a iiu ditace detectio. Aothe itepetatio of the optiu deciio ule aed o the ML citeio i otaied y expadig the ditace etic i Eq.5.-4 a N N + D,. +,,,M 5.-4 N The te i coo to all ditace etic, ad, hece, it ay e igoed i the coputatio of the etic. The eult i a et of odified ditace etic D ', Note that electig the igal that iiize D, i equivalet to electig the igal that axiize the etic C, -D,, i.e., C, The te. epeet the pojectio of the eceived igal vecto oto each of the M poile taitted igal vecto. The value of each of thee pojectio i a eaue of the coelatio etwee the eceived vecto ad the th igal. Fo thi eao, we call C,,,,M, the coelatio etic fo decidig which of the M igal wa taitted. Fially, the te E,,,M, ay e viewed a ia te that eve a copeatio fo igal et that have uequal eegie, uch a PAM. If all igal have the ae eegy, ay alo e igoed i the coputatio of the coelatio etic C, ad the ditace etic D, o D,. It i eay to how Pole 5.5 that the coelatio etic C, ca alo e expeed a T C, t t dt E,,,M Theefoe, thee etic ca e geeated y a deodulato that co-coelate the eceived igal t with each of the M poile taitted igal ad adjut each coelato output fo the ia i the cae of eequal igal eegie. Equivaletly, the

10 eceived igal ay e paed though a a of M filte atched to the poile taitted igal{ t} ad apled at t T, the ed of the yolic iteval. Coequetly, the optiu eceive deodulato ad detecto ca e ipleeted i the alteative cofiguatio illutated i Fig I uay, we have deotated that the optiu ML detecto copute a et of M ditace D, o D, ad elect the igal coepodig to the allet ditace etic. Equivaletly, th optiu ML detecto copute a et of M coelato etic C, ad elect the igal coepodig to the laget coelatio etic. The aove developet fo the optiu detecto teated the ipotat cae i which all igal ae equally poale. I thi cae, the MAP citeio i equivalet to the ML citeio. Howeve, whe the igal ae ot equally poale, the optiu MAP detecto ae it deciio o the poailitie P,,,M, give y Eq o, equivaletly, o the etic PM, p P The followig exaple illutate thi coputatio fo iay PAM igal. Exaple Coide the cae of iay PAM igal i which the two poile igal poit ae E, whee E i the eegy pe it. The pio poailitie ae P p ad P - p. Let u deteie the etic fo the optiu MAP detecto whe the taitted igal i coupted with AWGN. The eceived igal vecto oe-dieioal fo iay PAM i ± E y T

11 whee y T i a zeo-ea Gauia ado vaiale with vaiace N σ. Coequetly, the coditioal PDF p fo the two igal ae exp E p σ πσ exp E p σ πσ The the etic PM, ad PM, ae exp, E p pp PM σ πσ exp, E p pp PM σ πσ 5.-5 If PM, > PM,, we elect a the taitted igal; othewie, we elect. Thi deciio ule ay e expeed a,, PM PM < > 5.-5 But + exp,, E E p p PM PM σ 5.-5 So that Eq.5.-5 ay e expeed a p p E E < > + l σ o equivaletly, p p N p p E < > l 4 l σ 5.-54

12 Thi i the fial fo fo the optiu detecto. It copute the coelatio etic p C, E ad copae it with thehold N l. Fig 5.- illutate the 4 p two igal poit ad. The thehold, deoted y τ h, divide the eal lie ito two egio, ay R ad R, whee R coit of the et of poit that ae geate tha τ h, ad R coit of the et of poit that ae le tha τ h. If E > τ h, the deciio i ade that wa taitted, ad if E < τ h, the deciio i ade that wa taitted. The thehold τ h deped o N ad p. If p/, τ h. If p>/, the igal poit i oe poale, ad, hece, τ h <. I thi cae, the egio R i lage tha R, o that i oe liely to e elected tha. If p</, the oppoite i the cae. Thu, the aveage poaility of eo i iiized. Figue 5-- Sigal pace epeetatio illutatig the opeatio of the optiu detecto fo iay PAM odulatio S - E τ h S E Regio R Regio R It i iteetig to ote that i the cae of uequal pio poailitie, it i eceay to ow ot oly the value of the pio poailitie ut alo the value of the powe pectal deity N, o, equivaletly, the oie-to-igal atio N /E, i ode to copute the thehold. Whe p/, the thehold i zeo, ad the owledge of N i ot equied y the detecto The Maxiu-Lielihood Sequece Detecto Whe the igal ha o eoy, the yol-y-yol detecto decied i the pecedig ectio i optiu i the ee of iiizig the poaility of a yol eo. O the othe had, whe the taitted igal ha eoy, i.e., the igal taitted i ucceive yol iteval ae itedepedet, the optiu detecto i a detecto that ae it deciio o oevatio of a equece of eceived igal ove ucceive igal iteval. I thi ectio, we decie a axiu-lielihood equece detectio algoith that eache fo the iiu Euclidea ditace path though the telli that chaacteize the eoy i the taitted igal. Let u coide, a a exaple, the NRZI igal. It eoy i chaacteized y the telli how i Fig

13 The igal taitted i each igal iteval i iay PAM. Hece, thee ae two poile taitted igal coepodig to the igal poit E, whee E i the eegy pe it. The output of the atched-filte o coelatio deodulato fo iay PAM i the th igal iteval ay e expeed a E + ± whee i a zeo-ea Gauia ado vaiale with vaiace N σ. Coequetly, the coditioal PDF fo the two poile taitted igal ae exp E p σ πσ exp E p σ πσ Fo ay give taitted equece, the joit PDF of,,, K ay e expeed a a poduct of K agial PDF, i.e., K K K K K p p exp exp,...,, σ πσ σ πσ whee eithe E o E. The, give the eceived equece,,, K at the output of the atched filte o coelatio deodulato, the detecto deteie the

14 K that axiize the coditioal PDF p,,..., K. Such detecto i called the axiu-lielihood ML equece-detecto. By taig the logaith of Eq ad eglectig the te that ae idepedet of,,, K, we fid that a equivalet ML equece detecto elect the equece that iiize the Euclidea ditace etic equece {,,..., } K D, 5.-6 I eachig though the telli fo the equece that iiize the Euclidea ditace D,, it ay appea that we ut copute the ditace D, fo evey poile equece. Fo the NRZI exaple, which eploy ojay odulatio, the total ue of equece i K, whee K i the ue of output otaied fo the deodulato. Howeve, thi i ot the cae. We ay educe the ue of equece i the telli each y uáy the Vitei algoith to eliiace equece a ew data i eceived fo the deodulato. The Vitei algoith i a equetial telli each algoith fo pefoig ML equece detectio. We aue that the each poce egi iitially at tate S. The coepodig telli i how i Fig.5.-. At tie tt, we eceive + fo the deodulato, ad at tt, we eceive +. Sice the igal eoy i oe it, which we deote y L, we oeve that the telli eache it egula teady tate fo afte two taitio. Thu upo eceipt of at tt ad theeafte, we oeve that thee ae two igal path eteig each of the ode ad two igal path leavig each ode. The two path eteig ode S at tt coepod to the ifoatio it, ad, o, equivaletly, to the igal poit E, E ad E, E, epectively. The two path eteig ode S at

15 tt coepod to the ifoatio it, ad, o, equivaletly, to the igal poit E, E ad E, E, epectively. Fo the two path eteig ode S, we copute the two Euclidea ditace etic D +, + E + E D +, E + E 5.-6 y uig the output ad fo the deodulato. The Vitei algoith copae thee two etic ad dicad the path havig the lage geate-ditace etic. The othe path with the lowe etic i aved ad i called the uvivo at tt. Siilaly, fo the two path eteig ode S at tt, we copute the two Euclidea ditace etic D D, + E + E, E + E 5.-6 y uig the output ad fo the deodulato. The two etic ae copaed ad the igal path with the lage etic i eliiated. Thu, at tt, we ae left with two uvivo path, oe at ode S ad the othe at ode S, ad thei coepodig etic. The igal path at ode S ad S ae the exteded alog the two uvivo path. Upo eceipt of 3 at t3t, we copute the etic of the two path eteig tate S. Suppoe the uvivo at tt ae the path, at S ad, at S. The, the two etic fo the path eteig S at t3t ae D +,, D, + 3 E D +,, D, + 3 E Thee two etic ae copaed ad the path with the lage geate-ditace etic i eliiated. Siilaly, the etic fo the two path eteig S at t3t ae D D,, D, + 3 E,, D, + 3 E Thee two etic ae copaed ad the path with the lage geate-ditace etic i eliiated. Thi poce i cotiued a each ew igal aple i eceived fo the deodulato. Thu, the Vitei algoith copute two etic fo the two igal path eteig a

16 ode at each tage of the telli each ad eliiate oe of the two path at each ode. The two uvivo path ae the exteded fowad to the ext tate. Theefoe, the ue of path eached i the telli i educed y a facto of at each tage. 5. PERFORMANCE OF THE OPTIMUM RECEIVER FOR MEMORYLESS MODULATION 5.. Poaility of Eo fo Biay Modulatio PAM atipodal - whee the two igal wavefo ae tgt ad t-gt, ad gt i a aitay pule that i ozeo i the iteval t T ad zeo elewhee. The eegy i the pule gt i E g. Fig.5.- Let u aue that the two igal ae equally liely ad that igal t wa taitted. The the eceived igal fo the atched filte o coelatio deodulato i + E whee epeet the additive Gauia oie copoet, which ha zeo ea ad vaiace σ N. I thi cae, the deciio ule aed o the coelatio etic give y Eq copae with the thehold zeo. If >, the deciio i ade favo of t, ad if <, the deciio i ade that t wa taitted. The two coditioal PDF of ae E / N p e 5.- π N p + E / N e 5.-3 π N

17 Thee two coditioal PDF ae how i Fig.5.-. Figue 5.- Coditioal pdf of two igal p/ p/ E E Give that t wa taitted, the poaility of eo i iply the poaility that <, i.e., E P e p d Q 5.-4 N Whee Qx i the Q-fuctio. Siilaly, if we aue that t wa taitted, E E + ad the poaility that > i alo P e Q. N Sice the igal t ad t ae equally liely to e taitted, the aveage poaility of eo i E P P e + P e Q 5.-5 N We hould oeve two ipotat chaacteitic of thi pefoace eaue. Fit, the poaility of eo deped oly o the atio E /N ad ot o ay othe detailed chaacteitic of the igal ad the oie. Secodly, we ote that E /N i alo the output SNR fo the atched-filte ad coelatio deodulato. The atio E /N i uually called the igal-to-oie atio pe it. The poaility ay e alo expeed i te of the ditace etwee the two igal ad. Fo Fig.5.-, we oeve that the two igal ae epaated y the ditace d E. By utitutig E d ito Eq.5.-5, we otai 4 d P Q 5.-6 N Biay othogoal igal Sigal vecto ad ae two-dieioal, a how i Fig.5.-3, ad ay e expeed a [ [ E ] E ] 5.-7

18 Fig.5.-3 The aveage eo poaility fo iay othogoal igal i Q N E Q P γ 5.- whee, y defiitio, γ i the SNR pe it. The eo poaility veu loge /N fo thee two type of igal i how i Fig A oeved fo thi figue, at ay give eo poaility, the E /N equied fo othogoal igal i 3 db oe tha fo atipodal igal. 5.. Poaility of Eo fo M-ay Othogoal Sigal The poaility of a -it yol eo i dy N E y dx e P M y x / exp π π 5.-

19 The ae expeio fo the poaility of eo i otaied whe ay oe of the othe M- igal i taitted. Sice all the M igal ae equally liely, the expeio fo P M give i Eq.5.- i the aveage poaility of a yol eo. I copaig the pefoace of vaiou digital odulatio ethod, it i deiale to have the poaility of eo expeed i te of the SNR pe it, E /N, itead of the SNR pe yol, E /N. With M. each yol covey it of ifoatio, ad hece E E. Thu, Eq.5.- ay e expeed i te of E /N y utitutig fo E. Soetie, it i alo deiale to covet the poaility of a yol eo ito a equal poaility of a iay digit eo. Fo equipoale othogoal igal, all yol eo ae equipoale ad occu with poaility PM P M M 5.- Futheoe, thee ae way i which it out of ay e i eo. Hece, the aveage ue of it eo pe -it yol i PM P M 5.-3 ad the aveage it eo poaility i jut the eult i Eq.5.-3 divided y, the ue of it pe yol. Thu, P P P M M, >> 5.-4 The gaph of the poaility of a iay digit eo a a fuctio of the SNR pe it, E /N, ae how i Fig.5.-5 fo M,4,6,6,3, ad 64. Thi figue illutate that, y iceaig the ue M of wavefo, oe ca educe the SNR pe it equied to achieve a give poaility of a it eo. Fo exaple, to achieve a P -5, the equied SNR pe it i a little oe tha db fo M, ut if M i iceaed to 64 igal wavefo 6 it/yol, the equied SNR pe it i appoxiately 6 db avig of ove 6 db!!!. What i the iiu equied E /N to achieve a aitaily all poaility of eo a M? [E/N > l db]. Thi iiu SNR E /N i called the Shao liit fo a AWGN chael.

20 5..3 Poaility of Eo fo M-ay Biothogoal Sigal 5..4 Poaility of Eo fo Siplex Sigal The poaility of eo fo iplex igal i idetical to the poaility of eo fo othogoal igal, ut thi pefoace i achieved with a avig of M log ρ log db M i SNR. Fo M, the avig i 3 db. Howeve, a M i iceaed, the avig i SNR appoache db.

21 5..5 Poaility of Eo fo M-ay Biay-Coded Sigal e If di i the iiu Euclidea ditace of the M igal wavefo, the the poaility of a yol eo i uppe-ouded a e e d i < < d i PM M P M Q exp N 4N The value of the iiu Euclidea ditace will deped o the electio of the code wod, i.e., deig of the code Poaility of Eo fo M-ay PAM Poaility of a yol eo M 6log M Eav P M Q M M N

22 5..7 Poaility of Eo fo M-ay PSK π π PM Q γ i Q γ i M M Poaility of Eo fo QAM Recall, that QAM igal wavefo ay e expeed a t A g tcoπf t A g ti πf t 5.-7 c c c whee A c ad A ae the ifoatio-eaig igal aplitude of the quadatue caie ad gt i the igal pule. The vecto epeetatio of thee wavefo i [ ] Ac Eg A Eg To deteie the poaility of eo fo QAM, we ut pecify the igal poit cotellatio. We egi with QAM igal et that have M4 poit. Fig.5.-4 illutate two fou-poit igal et. The fit i a fou-phae odulated igal ad the ecod i a QAM igal with two aplitude level, laeled A ad A, ad fou phae. Becaue the poaility of eo i doiated y the iiu ditace etwee pai of igal poit, let u ipoe the coditio that d e i A fo oth igal cotellatio ad let u evaluate the aveage taitte powe, aed o the peie that all igal poit ae equally poale. Figue 5.-4 Two fou-poit igal cotellatio d + A A A da

23 Fo the fou-phae igal, we have P av A A Fo the two-aplitude, fou-phae QAM, we place the poit o cicle of adii A ad 3 A. Thu, d e A, ad i P av A A 4 [3 + ] A which i the ae aveage powe a the M4 phae igal cotellatio. Hece, fo all pactical pupoe, the eo ate pefoace of the two igal et i the ae. I othe wod, thee i o advatage of the two-aplitude QAM igal et ove M4-phae odulatio. Next, let u coide M8 QAM, a how i Fig d e i A. Auig that the igal poit ae equally poale, the aveage taitted igal powe i M M A P av Ac + A ac + a M M whee a c,a ae the coodiate of the igal poit, oalized y A. a c P av 6A P av 6.83A d P av 4.73A

24 Theefoe, the fouth igal et equie appoxiately db le powe tha the fit two ad.6 db le powe tha the thid to achieve the ae poaility of eo. Fo M 6, thee ae ay oe poiilitie fo electig the QAM igal poit i the two-dieioal pace. Fo exaple, we ay chooe a cicula ultiaplitude cotellatio fo M6. I thi cae, the igal poit at a give aplitude level ae phae-otated y π elative to the igal poit at adjacet aplitude level. Howeve, 4 the cicula 6-QAM i ot the et 6-poit QAM igal cotellatio fo the AWGN chael. Rectagula QAM igal cotellatio have the ditict advatage of eig eaily geeated a two PAM igal ipeed o phae-quadatue caie. I additio, they ae eaily deodulated. Although they ae ot the et M-ay QAM igal cotellatio fo M 6, the aveage taitted powe equied to achieve a give iiu ditace i oly lightly geate tha the aveage powe equied fo the et M-ay QAM igal cotellatio. Fo thee eao, ectagula M-ay QAM igal ae ot fequetly ued i pactice. If we eploy the optiu detecto that ae it deciio o the optiu ditace etic, it i elatively taightfowad to how that the yol eo poaility i tightly uppe-ouded a 3Eav P M 4Q 5.-8 M N fo ay, whee Eav/N i the aveage SNR pe it. The poaility of a yol eo i plotted i Fig.5.-6.

25 5.. Copaio of Digital Modulatio Method The digital odulatio ethod ca e copaed i a ue way. Fo exaple, oe ca copae the o the ai of the SNR equied to achieve a pecific poaility of eo. Howeve, uch a copaio would ot e vey eaigful, ule it wee ade o the ai of oe cotait, uch a a fixed data ate of taiio o, equivaletly, o the ai of a fixed adwidth. With thi goal i id, let u coide the adwidth equieet fo eveal odulatio ethod. Fo ultiphae igal the chael adwidth equied i iply the adwidth of the equivalet low-pa igal pule gt, which deped o it detailed chaacteitic. Fo ou pupoe, we aue that gt i a pule of duatio T ad that it adwidth W i appoxiately equal to the ecipocal of T. Thu, W/T ad, ice T/Rlog M/R, it follow that R W log M Theefoe, a M i iceaed, the chael adwidth equied, whe the it ate R i fixed, deceae. The adwidth efficiecy i eaued y the it ate to awidth atio, which i R W log M The adwidth-efficiet ethod fo taittig PAM i igle-idead. The, the chael adwidth equied to tait the igal i appoxiately equal to /T ad, ice T/Rlog M/R, it follow that R W log M Thi i a facto of ette tha PSK. I the cae of QAM, we have two othogoal caie, with each caie havig a PAM igal. Thu, we doule the ate elative to PAM. Howeve, the QAM igal ut e taitted via doule-idead. Coequetly, QAM ad PAM have the ae adwidth efficiecy whe the adwidth i efeeced to the ad-pa igal. Othogoal igal have totally diffeet adwidth equieet. If the M othogoal igal ae cotucted y ea of othogoal caie with iiu fequecy epaatio of /T fo othogoality, the adwidth equied fo taiio of log M ifoatio it i

26 W M M M R T / log M I thi cae, the adwidth iceae a M iceae. Siila elatiohip otai fo iplex ad iothogoal igal. I the cae of iothogoal igal, the equied adwidth i oe-half of that fo othogoal igal. I the cae of PAM, QAM ad PSK, iceaig M eult i a highe it ate-toadwidth atio R/W. The cot of achievig the highe data ate i a iceae i SNR pe it Thee odulatio ethod ae appopiate fo couicatio chael that ae adwidth-liited R/W> ad whee thee i ufficietly high SNR to uppot iceae i M telephoe chael, icowave adio chael M-ay othogoal igal yield a it ate-to-adwidth atio of R/W<. A M iceae, R/W deceae due to a iceae i the equied chael adwidth. SNR pe it equied to achieve a give eo poaility deceae a M iceae. Thee odulatio ae appopiate fo powe-liited chael that have ufficietly lage adwidth to accoodate a lage ue of igal

27 A M, the eo poaility ca e ade a all a deied, povided that E / N > db. Thi i the iiu SNR pe it equied to achieve eliale taiio i the liit a the chael adwidth W ad the coepodig it ate-yo-adwidth atio R / W. The atio C/W, whee CR i the capacity i it/, epeet the highet achievale it ate-to-adwidth atio o thi chael. Hece, it eve a the uppe oud o the adwidth efficiecy of ay type of odulatio.

28 SELECTED PROBLEMS 5. A atched filte ha the fequecy epoe j e H f jπf a Deteie the ipule epoe ht coepodig to Hf. Deteie the igal wavefo to which the filte chaacteitic i atched. πft 5. Coide the igal A/ T t co πf ct t t T othewie a Deteie the ipule epoe of the atched filte fo the igal. Deteie the output of the atched filte at tt. c Suppoe the igal t i paed though a coelato that coelate the iput t with t. Deteie the value of the coelato output at tt. Copae you eult with tha i.

29 5.4 A iay digital couicatio yte eploy the igal t, ta, t T t T fo taittig the ifoatio. Thi i called o-off igalig. The deodulato cocoelate the eceived igal t with t ad aple the output of the coelato at tt. a Deteie the optiu detecto fo a AWGN chael ad the optiu thehold, auig that the igal ae equally poale. Deteie the poaility of eo a a fuctio of the SNR. How doe o-off igalig copae with atipodal igalig?

30

31 5.5 The coelatio etic give y Equatio ae N N C,,,,, M whee T t f T t f t dt t dt

32 Show that the coelatio etic ae equivalet to the etic T T dt t dt t t C, 5.6 Coide the equivalet low-pa coplex-valued igal l t, t T, with eegy T l dt t ε Suppoe that thi igal i coupted y AWGN, which i epeeted y it equivalet low-pa fo zt. Hece, the oeved igal i T t t z t t l l +, The eceived igal i paed a filte that ha a equivalet low-pa ipule epoe h l t. Deteie h l t o that the filte axiize the SNR at it output at tt.

33 5. I Sectio 4.3. it wa how that the iiu fequecy epaatio fo othogoality of iay FSK igal with coheet detectio i f/t. Howeve, a lowe eo poaility i poile with coheet detectio of FSK if f i iceaed eyod /T. Show that the optiu value of f i,75t ad deteie the poaility of eo fo thi value of f.

34 5.6 Coide that octal igal poit cotellatio i Figue P5.6 a The eaet-eigho igal poit i the 8-QAM igal cotellatio ae epaated i ditace y A uit. Deteie the adii a ad of the ie ad oute cicle. The adjacet igal poit i the 8-PSK ae epaated y a ditace of A uit. Deteie the adiu of the cicle. c Deteie the aveage taitte powe fo two igal cotellatio ad copae the two powe. What i the elative powe advatage of oe cotellatio ove the othe? Aue that all igal poit ae equally poale. Figue P5.6

35 a 45 8-PSK 8-QAM 5.7 Coide the 8-poit QAM igal cotellatio how i Figue P5.6 a I it poile to aig thee data it to each poit of the igal cotellatio uch that eaet adjacet poit diffe i oly oe it poitio?

36 Deteie the yol ate if the deied it ate i 9Mit/. 5.8 Suppoed that iay PSK i ued fo taittig ifoatio ove a AWGN with a powe pectal deity of N W/Hz. The taitted igal eegy i ε A T, whee T i the it iteval ad A i the igal aplitude. Deteie the igal aplitude equied to achieve a eo poaility of -6 whe the data ate i c it/. d it/ e Mit/ 5.9 Coide a igal detecto with a iput

37 ± A + whee +A ad A occu with equal poaility ad the oie vaiale i chaacteized y the Laplacia PDF how i Figue P5.9. a Deteie the poaility of eo a a fuctio of the paaete A ad σ. Deteie the SNR equied to achieve a eo poaility of -5. How doe the SNR copae with the eult fo a Gauia PDF? Figue P5.9 p e σ / σ

38 5. Coide the two 8-poit QAM igal cotellatio how i Figue P5.. The iiu ditace etwee adjacet poit i A. Deteie the aveage taitted powe fo each cotellatio, auig that the igal poit ae equally poale. Which cotellatio i oe powe-efficiet? Figue P5. a

39 5. Fo the QAM igal cotellatio how Figue P5., deteie the optiu deciio oudaie fo the detecto, auig that the SNR i ufficietly high o that eo oly occu etwee adjacet poit. Figue P

40 5.3 Two quadatue caie co πf c t ad i πf c t ae ued to tait digital ifoatio though a AWGN chael at two diffeet data ate, it/ ad it/. Deteie the elative aplitude of the igal fo the two caie o that the ε / N fo the two chael i idetical.

41 5.6 Coide a digital couicatio yte that tait ifoatio via QAM ove a voice-ad telephoe chael at a ate of 4 yol/. The additive oie i aued to e white ad Gauia. a Deteie the ε / N equied to achieve a eo poaility of -5 at 48 it/. Repeat a fo a ate of 96 it/ c Repeat a fo a ate of 9, it/ d What cocluio do you each fo thee eult?

42

43 5.7 Coide that fou-phae ad eight-phae igal cotellatio how i Figue P5.7. Deteie the adii ad of the cicle uch that the ditace etwee two adjacet poit i the two cotellatio i d. Fo thee eult, deteie the additioal taitted eegy equied i the i the 8-PSK igal to achieve the ae eo poaility a the fou-phae igal at the high SNR, whee the poaility of eo i deteied y eo i electig adjacet poit. Figue P5. d d

44 5.8 Digital ifoatio i to e taitted y caie odulatio though a additive Gauia oie chael with a adwidth of Hz ad N W/Hz. Deteie the axiu ate that ca e taitted though the chael fo fou-phae PSK, iay FSK, ad fou-fequecy othogoal FSK, which i detected ocoheetly.

45 5.36 A peech igal i apled at a ate of 8 Hz, logaithically copeed ad ecoded ito a PCM foat uig 8 it pe aple. The PCM data i taitted though a AWGN aead chael via M-level PAM. Deteie the adwidth equied fo taiio whe a M4. M8. c M6.

rad / sec min rev 60sec. 2* rad / sec s

rad / sec min rev 60sec. 2* rad / sec s EE 559, Exa 2, Spig 26, D. McCalley, 75 iute allowed. Cloed Book, Cloed Note, Calculato Peitted, No Couicatio Device. (6 pt) Coide a.5 MW, 69 v, 5 Hz, 75 p DFG wid eegy yt. he paaete o the geeato ae give

More information

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise Hi ue Hi ue -ay Deecio Pole Coide he ole of decidig which of hyohei i ue aed o oevig a ado vaiale (veco). he efoace cieia we coide i he aveage eo oailiy. ha i he oailiy of decidig ayhig ece hyohei H whe

More information

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r x k We assume uncorrelated noise v(n). LTH. September 2010

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r x k We assume uncorrelated noise v(n). LTH. September 2010 Optimal Sigal Poceig Leo 5 Chapte 7 Wiee Filte I thi chapte we will ue the model how below. The igal ito the eceive i ( ( iga. Nomally, thi igal i ditubed by additive white oie v(. The ifomatio i i (.

More information

Revision of Lecture Eight

Revision of Lecture Eight Revision of Lectue Eight Baseband equivalent system and equiements of optimal tansmit and eceive filteing: (1) achieve zeo ISI, and () maximise the eceive SNR Thee detection schemes: Theshold detection

More information

Math 7409 Homework 2 Fall from which we can calculate the cycle index of the action of S 5 on pairs of vertices as

Math 7409 Homework 2 Fall from which we can calculate the cycle index of the action of S 5 on pairs of vertices as Math 7409 Hoewok 2 Fall 2010 1. Eueate the equivalece classes of siple gaphs o 5 vetices by usig the patte ivetoy as a guide. The cycle idex of S 5 actig o 5 vetices is 1 x 5 120 1 10 x 3 1 x 2 15 x 1

More information

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r k s r x LTH. September Prediction Error Filter PEF (second order) from chapter 4

Lesson 5. Chapter 7. Wiener Filters. Bengt Mandersson. r k s r x LTH. September Prediction Error Filter PEF (second order) from chapter 4 Optimal Sigal oceig Leo 5 Capte 7 Wiee Filte I ti capte we will ue te model ow below. Te igal ito te eceie i ( ( iga. Nomally, ti igal i ditubed by additie wite oie (. Te ifomatio i i (. Alo, we ofte ued

More information

Lecture 24: Observability and Constructibility

Lecture 24: Observability and Constructibility ectue 24: Obsevability ad Costuctibility 7 Obsevability ad Costuctibility Motivatio: State feedback laws deped o a kowledge of the cuet state. I some systems, xt () ca be measued diectly, e.g., positio

More information

Progression. CATsyllabus.com. CATsyllabus.com. Sequence & Series. Arithmetic Progression (A.P.) n th term of an A.P.

Progression. CATsyllabus.com. CATsyllabus.com. Sequence & Series. Arithmetic Progression (A.P.) n th term of an A.P. Pogessio Sequece & Seies A set of umbes whose domai is a eal umbe is called a SEQUENCE ad sum of the sequece is called a SERIES. If a, a, a, a 4,., a, is a sequece, the the expessio a + a + a + a 4 + a

More information

Relation (12.1) states that if two points belong to the convex subset Ω then all the points on the connecting line also belong to Ω.

Relation (12.1) states that if two points belong to the convex subset Ω then all the points on the connecting line also belong to Ω. Lectue 6. Poectio Opeato Deiitio A.: Subset Ω R is cove i [ y Ω R ] λ + λ [ y = z Ω], λ,. Relatio. states that i two poits belog to the cove subset Ω the all the poits o the coectig lie also belog to Ω.

More information

By the end of this section you will be able to prove the Chinese Remainder Theorem apply this theorem to solve simultaneous linear congruences

By the end of this section you will be able to prove the Chinese Remainder Theorem apply this theorem to solve simultaneous linear congruences Chapte : Theoy of Modula Aithmetic 8 Sectio D Chiese Remaide Theoem By the ed of this sectio you will be able to pove the Chiese Remaide Theoem apply this theoem to solve simultaeous liea cogueces The

More information

Last time: Completed solution to the optimum linear filter in real-time operation

Last time: Completed solution to the optimum linear filter in real-time operation 6.3 tochatic Etimatio ad Cotrol, Fall 4 ecture at time: Completed olutio to the oimum liear filter i real-time operatio emi-free cofiguratio: t D( p) F( p) i( p) dte dp e π F( ) F( ) ( ) F( p) ( p) 4444443

More information

2.Decision Theory of Dependence

2.Decision Theory of Dependence .Deciio Theoy of Depedece Theoy :I et of vecto if thee i uet which i liely depedet the whole et i liely depedet too. Coolly :If the et i liely idepedet y oepty uet of it i liely idepedet. Theoy : Give

More information

Test 2 phy a) How is the velocity of a particle defined? b) What is an inertial reference frame? c) Describe friction.

Test 2 phy a) How is the velocity of a particle defined? b) What is an inertial reference frame? c) Describe friction. Tet phy 40 1. a) How i the velocity of a paticle defined? b) What i an inetial efeence fae? c) Decibe fiction. phyic dealt otly with falling bodie. d) Copae the acceleation of a paticle in efeence fae

More information

MATH Midterm Solutions

MATH Midterm Solutions MATH 2113 - Midtem Solutios Febuay 18 1. A bag of mables cotais 4 which ae ed, 4 which ae blue ad 4 which ae gee. a How may mables must be chose fom the bag to guaatee that thee ae the same colou? We ca

More information

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall Oct. Heat Equatio M aximum priciple I thi lecture we will dicu the maximum priciple ad uiquee of olutio for the heat equatio.. Maximum priciple. The heat equatio alo ejoy maximum priciple a the Laplace

More information

CHAPTER 5 : SERIES. 5.2 The Sum of a Series Sum of Power of n Positive Integers Sum of Series of Partial Fraction Difference Method

CHAPTER 5 : SERIES. 5.2 The Sum of a Series Sum of Power of n Positive Integers Sum of Series of Partial Fraction Difference Method CHAPTER 5 : SERIES 5.1 Seies 5. The Sum of a Seies 5..1 Sum of Powe of Positive Iteges 5.. Sum of Seies of Patial Factio 5..3 Diffeece Method 5.3 Test of covegece 5.3.1 Divegece Test 5.3. Itegal Test 5.3.3

More information

THE CONCEPT OF THE ROOT LOCUS. H(s) THE CONCEPT OF THE ROOT LOCUS

THE CONCEPT OF THE ROOT LOCUS. H(s) THE CONCEPT OF THE ROOT LOCUS So far i the tudie of cotrol yte the role of the characteritic equatio polyoial i deteriig the behavior of the yte ha bee highlighted. The root of that polyoial are the pole of the cotrol yte, ad their

More information

International Journal of Mathematical Archive-5(3), 2014, Available online through ISSN

International Journal of Mathematical Archive-5(3), 2014, Available online through   ISSN Iteatioal Joual of Mathematical Achive-5(3, 04, 7-75 Available olie though www.ijma.ifo ISSN 9 5046 ON THE OSCILLATOY BEHAVIO FO A CETAIN CLASS OF SECOND ODE DELAY DIFFEENCE EQUATIONS P. Mohakuma ad A.

More information

BINOMIAL THEOREM An expression consisting of two terms, connected by + or sign is called a

BINOMIAL THEOREM An expression consisting of two terms, connected by + or sign is called a BINOMIAL THEOREM hapte 8 8. Oveview: 8.. A epessio cosistig of two tems, coected by + o sig is called a biomial epessio. Fo eample, + a, y,,7 4 5y, etc., ae all biomial epessios. 8.. Biomial theoem If

More information

BINOMIAL THEOREM NCERT An expression consisting of two terms, connected by + or sign is called a

BINOMIAL THEOREM NCERT An expression consisting of two terms, connected by + or sign is called a 8. Oveview: 8.. A epessio cosistig of two tems, coected by + o sig is called a biomial epessio. Fo eample, + a, y,,7 4, etc., ae all biomial 5y epessios. 8.. Biomial theoem BINOMIAL THEOREM If a ad b ae

More information

The Discrete Fourier Transform

The Discrete Fourier Transform (7) The Discete Fouie Tasfom The Discete Fouie Tasfom hat is Discete Fouie Tasfom (DFT)? (ote: It s ot DTFT discete-time Fouie tasfom) A liea tasfomatio (mati) Samples of the Fouie tasfom (DTFT) of a apeiodic

More information

OPTIMAL ESTIMATORS FOR THE FINITE POPULATION PARAMETERS IN A SINGLE STAGE SAMPLING. Detailed Outline

OPTIMAL ESTIMATORS FOR THE FINITE POPULATION PARAMETERS IN A SINGLE STAGE SAMPLING. Detailed Outline OPTIMAL ESTIMATORS FOR THE FIITE POPULATIO PARAMETERS I A SIGLE STAGE SAMPLIG Detailed Outlie ITRODUCTIO Focu o implet poblem: We ae lookig fo a etimato fo the paamete of a fiite populatio i a igle adom

More information

2012 GCE A Level H2 Maths Solution Paper Let x,

2012 GCE A Level H2 Maths Solution Paper Let x, GCE A Level H Maths Solutio Pape. Let, y ad z be the cost of a ticet fo ude yeas, betwee ad 5 yeas, ad ove 5 yeas categoies espectively. 9 + y + 4z =. 7 + 5y + z = 8. + 4y + 5z = 58.5 Fo ude, ticet costs

More information

Advances in Mathematics and Statistical Sciences. On Positive Definite Solution of the Nonlinear Matrix Equation

Advances in Mathematics and Statistical Sciences. On Positive Definite Solution of the Nonlinear Matrix Equation Advace i Mathematic ad Statitical Sciece O Poitive Defiite Solutio of the Noliea * Matix Equatio A A I SANA'A A. ZAREA Mathematical Sciece Depatmet Pice Nouah Bit Abdul Rahma Uiveity B.O.Box 9Riyad 6 SAUDI

More information

Greatest term (numerically) in the expansion of (1 + x) Method 1 Let T

Greatest term (numerically) in the expansion of (1 + x) Method 1 Let T BINOMIAL THEOREM_SYNOPSIS Geatest tem (umeically) i the epasio of ( + ) Method Let T ( The th tem) be the geatest tem. Fid T, T, T fom the give epasio. Put T T T ad. Th will give a iequality fom whee value

More information

PRIMARY DECOMPOSITION, ASSOCIATED PRIME IDEALS AND GABRIEL TOPOLOGY

PRIMARY DECOMPOSITION, ASSOCIATED PRIME IDEALS AND GABRIEL TOPOLOGY Orietal J. ath., Volue 1, Nuber, 009, Page 101-108 009 Orietal Acadeic Publiher PRIARY DECOPOSITION, ASSOCIATED PRIE IDEALS AND GABRIEL TOPOLOGY. EL HAJOUI, A. IRI ad A. ZOGLAT Uiverité ohaed V aculté

More information

The Pigeonhole Principle 3.4 Binomial Coefficients

The Pigeonhole Principle 3.4 Binomial Coefficients Discete M athematic Chapte 3: Coutig 3. The Pigeohole Piciple 3.4 Biomial Coefficiets D Patic Cha School of Compute Sciece ad Egieeig South Chia Uivesity of Techology Ageda Ch 3. The Pigeohole Piciple

More information

Visiting from institute of Wave and Information, Xi an Jiaotong University, Xi an , China. B x and gmnpq( x ) are 2D functions.

Visiting from institute of Wave and Information, Xi an Jiaotong University, Xi an , China. B x and gmnpq( x ) are 2D functions. Efficiet 3D illuiatio aalyi uig local expoetial fae Jia Mao* Ru-Sha Wu Modelig ad Iagig Laboatoy, IGPP, Uiveity of Califoia, Sata Cuz Viitig fo ititute of Wave ad Ifoatio, Xi a Jiaotog Uiveity, Xi a 749,

More information

Strong Result for Level Crossings of Random Polynomials

Strong Result for Level Crossings of Random Polynomials IOSR Joual of haacy ad Biological Scieces (IOSR-JBS) e-issn:78-8, p-issn:19-7676 Volue 11, Issue Ve III (ay - Ju16), 1-18 wwwiosjoualsog Stog Result fo Level Cossigs of Rado olyoials 1 DKisha, AK asigh

More information

Strong Result for Level Crossings of Random Polynomials. Dipty Rani Dhal, Dr. P. K. Mishra. Department of Mathematics, CET, BPUT, BBSR, ODISHA, INDIA

Strong Result for Level Crossings of Random Polynomials. Dipty Rani Dhal, Dr. P. K. Mishra. Department of Mathematics, CET, BPUT, BBSR, ODISHA, INDIA Iteatioal Joual of Reseach i Egieeig ad aageet Techology (IJRET) olue Issue July 5 Available at http://wwwijetco/ Stog Result fo Level Cossigs of Rado olyoials Dipty Rai Dhal D K isha Depatet of atheatics

More information

Math 166 Week-in-Review - S. Nite 11/10/2012 Page 1 of 5 WIR #9 = 1+ r eff. , where r. is the effective interest rate, r is the annual

Math 166 Week-in-Review - S. Nite 11/10/2012 Page 1 of 5 WIR #9 = 1+ r eff. , where r. is the effective interest rate, r is the annual Math 66 Week-i-Review - S. Nite // Page of Week i Review #9 (F-F.4, 4.-4.4,.-.) Simple Iteest I = Pt, whee I is the iteest, P is the picipal, is the iteest ate, ad t is the time i yeas. P( + t), whee A

More information

( ) 1 Comparison Functions. α is strictly increasing since ( r) ( r ) α = for any positive real number c. = 0. It is said to belong to

( ) 1 Comparison Functions. α is strictly increasing since ( r) ( r ) α = for any positive real number c. = 0. It is said to belong to Compaiso Fuctios I this lesso, we study stability popeties of the oautoomous system = f t, x The difficulty is that ay solutio of this system statig at x( t ) depeds o both t ad t = x Thee ae thee special

More information

LECTURE 14. m 1 m 2 b) Based on the second law of Newton Figure 1 similarly F21 m2 c) Based on the third law of Newton F 12

LECTURE 14. m 1 m 2 b) Based on the second law of Newton Figure 1 similarly F21 m2 c) Based on the third law of Newton F 12 CTU 4 ] NWTON W O GVITY -The gavity law i foulated fo two point paticle with ae and at a ditance between the. Hee ae the fou tep that bing to univeal law of gavitation dicoveed by NWTON. a Baed on expeiental

More information

% $ ( 3 2)R T >> T Fermi

% $ ( 3 2)R T >> T Fermi 6 he gad caoical eemble theoy fo a ytem i equilibium with a heat/paticle eevoi Hiohi Matuoka I thi chapte we will dicu the thid appoach to calculate themal popetie of a micocopic model the caoical eemble

More information

Technical Report: Bessel Filter Analysis

Technical Report: Bessel Filter Analysis Sasa Mahmoodi 1 Techical Repot: Bessel Filte Aalysis 1 School of Electoics ad Compute Sciece, Buildig 1, Southampto Uivesity, Southampto, S17 1BJ, UK, Email: sm3@ecs.soto.ac.uk I this techical epot, we

More information

Module II: Part A. Optical Fibers

Module II: Part A. Optical Fibers Module II: Pat A Optical Fibes Optical Fibes as Tasissio Mediu Mai Liitatio: Atteuatio Although fibes have bee kow sice the 8 s as ediu fo light tasissio, thei pactical use becae evidet whe losses whee

More information

Isolated Word Recogniser

Isolated Word Recogniser Lecture 5 Iolated Word Recogitio Hidde Markov Model of peech State traitio ad aligmet probabilitie Searchig all poible aligmet Dyamic Programmig Viterbi Aligmet Iolated Word Recogitio 8. Iolated Word Recogier

More information

Recursion. Algorithm : Design & Analysis [3]

Recursion. Algorithm : Design & Analysis [3] Recusio Algoithm : Desig & Aalysis [] I the last class Asymptotic gowth ate he Sets Ο, Ω ad Θ Complexity Class A Example: Maximum Susequece Sum Impovemet of Algoithm Compaiso of Asymptotic Behavio Aothe

More information

a) The average (mean) of the two fractions is halfway between them: b) The answer is yes. Assume without loss of generality that p < r.

a) The average (mean) of the two fractions is halfway between them: b) The answer is yes. Assume without loss of generality that p < r. Solutios to MAML Olympiad Level 00. Factioated a) The aveage (mea) of the two factios is halfway betwee them: p ps+ q ps+ q + q s qs qs b) The aswe is yes. Assume without loss of geeality that p

More information

Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics Chapte : Decptve Stattc Peequte: Chapte. Revew of Uvaate Stattc The cetal teecy of a oe o le yetc tbuto of a et of teval, o hghe, cale coe, ofte uaze by the athetc ea, whch efe a We ca ue the ea to ceate

More information

Mapping Radius of Regular Function and Center of Convex Region. Duan Wenxi

Mapping Radius of Regular Function and Center of Convex Region. Duan Wenxi d Iteatioal Cofeece o Electical Compute Egieeig ad Electoics (ICECEE 5 Mappig adius of egula Fuctio ad Cete of Covex egio Dua Wexi School of Applied Mathematics Beijig Nomal Uivesity Zhuhai Chia 363463@qqcom

More information

Ch 3.4 Binomial Coefficients. Pascal's Identit y and Triangle. Chapter 3.2 & 3.4. South China University of Technology

Ch 3.4 Binomial Coefficients. Pascal's Identit y and Triangle. Chapter 3.2 & 3.4. South China University of Technology Disc ete Mathem atic Chapte 3: Coutig 3. The Pigeohole Piciple 3.4 Biomial Coefficiets D Patic Cha School of Compute Sciece ad Egieeig South Chia Uivesity of Techology Pigeohole Piciple Suppose that a

More information

SVD ( ) Linear Algebra for. A bit of repetition. Lecture: 8. Let s try the factorization. Is there a generalization? = Q2Λ2Q (spectral theorem!

SVD ( ) Linear Algebra for. A bit of repetition. Lecture: 8. Let s try the factorization. Is there a generalization? = Q2Λ2Q (spectral theorem! Liea Algeba fo Wieless Commuicatios Lectue: 8 Sigula Value Decompositio SVD Ove Edfos Depatmet of Electical ad Ifomatio echology Lud Uivesity it 00-04-06 Ove Edfos A bit of epetitio A vey useful matix

More information

Heat Equation: Maximum Principles

Heat Equation: Maximum Principles Heat Equatio: Maximum Priciple Nov. 9, 0 I thi lecture we will dicu the maximum priciple ad uiquee of olutio for the heat equatio.. Maximum priciple. The heat equatio alo ejoy maximum priciple a the Laplace

More information

rad rev 60sec p sec 2 rad min 2 2

rad rev 60sec p sec 2 rad min 2 2 NAME: EE 459/559, Exa 1, Fall 2016, D. McCalley, 75 inute allowed (unle othewie diected) Cloed Book, Cloed Note, Calculato Peitted, No Counication Device. The following infoation ay o ay not be ueful fo

More information

SOLUTION: The 95% confidence interval for the population mean µ is x ± t 0.025; 49

SOLUTION: The 95% confidence interval for the population mean µ is x ± t 0.025; 49 C22.0103 Sprig 2011 Homework 7 olutio 1. Baed o a ample of 50 x-value havig mea 35.36 ad tadard deviatio 4.26, fid a 95% cofidece iterval for the populatio mea. SOLUTION: The 95% cofidece iterval for the

More information

Complementary Dual Subfield Linear Codes Over Finite Fields

Complementary Dual Subfield Linear Codes Over Finite Fields 1 Complemetay Dual Subfield Liea Codes Ove Fiite Fields Kiagai Booiyoma ad Somphog Jitma,1 Depatmet of Mathematics, Faculty of Sciece, Silpao Uivesity, Naho Pathom 73000, hailad e-mail : ai_b_555@hotmail.com

More information

Einstein Classes, Unit No. 102, 103, Vardhman Ring Road Plaza, Vikas Puri Extn., Outer Ring Road New Delhi , Ph. : ,

Einstein Classes, Unit No. 102, 103, Vardhman Ring Road Plaza, Vikas Puri Extn., Outer Ring Road New Delhi , Ph. : , MB BINOMIAL THEOREM Biomial Epessio : A algebaic epessio which cotais two dissimila tems is called biomial epessio Fo eample :,,, etc / ( ) Statemet of Biomial theoem : If, R ad N, the : ( + ) = a b +

More information

Lecture 10: Bounded Linear Operators and Orthogonality in Hilbert Spaces

Lecture 10: Bounded Linear Operators and Orthogonality in Hilbert Spaces Lecture : Bouded Liear Operators ad Orthogoality i Hilbert Spaces 34 Bouded Liear Operator Let ( X, ), ( Y, ) i i be ored liear vector spaces ad { } X Y The, T is said to be bouded if a real uber c such

More information

Precision Spectrophotometry

Precision Spectrophotometry Peciion Spectophotomety Pupoe The pinciple of peciion pectophotomety ae illutated in thi expeiment by the detemination of chomium (III). ppaatu Spectophotomete (B&L Spec 20 D) Cuvette (minimum 2) Pipet:

More information

TRAVELING WAVES. Chapter Simple Wave Motion. Waves in which the disturbance is parallel to the direction of propagation are called the

TRAVELING WAVES. Chapter Simple Wave Motion. Waves in which the disturbance is parallel to the direction of propagation are called the Chapte 15 RAVELING WAVES 15.1 Simple Wave Motion Wave in which the ditubance i pependicula to the diection of popagation ae called the tanvee wave. Wave in which the ditubance i paallel to the diection

More information

Multivector Functions

Multivector Functions I: J. Math. Aal. ad Appl., ol. 24, No. 3, c Academic Pess (968) 467 473. Multivecto Fuctios David Hestees I a pevious pape [], the fudametals of diffeetial ad itegal calculus o Euclidea -space wee expessed

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 30 Sigal & Sytem Prof. Mark Fowler Note Set #8 C-T Sytem: Laplace Traform Solvig Differetial Equatio Readig Aigmet: Sectio 6.4 of Kame ad Heck / Coure Flow Diagram The arrow here how coceptual flow

More information

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing Commet o Dicuio Sheet 18 ad Workheet 18 ( 9.5-9.7) A Itroductio to Hypothei Tetig Dicuio Sheet 18 A Itroductio to Hypothei Tetig We have tudied cofidece iterval for a while ow. Thee are method that allow

More information

( ) ( ) ( ) ( ) Solved Examples. JEE Main/Boards = The total number of terms in the expansion are 8.

( ) ( ) ( ) ( ) Solved Examples. JEE Main/Boards = The total number of terms in the expansion are 8. Mathematics. Solved Eamples JEE Mai/Boads Eample : Fid the coefficiet of y i c y y Sol: By usig fomula of fidig geeal tem we ca easily get coefficiet of y. I the biomial epasio, ( ) th tem is c T ( y )

More information

Some characterizations for Legendre curves in the 3-Dimensional Sasakian space

Some characterizations for Legendre curves in the 3-Dimensional Sasakian space IJST (05) 9A4: 5-54 Iaia Joual of Sciece & Techology http://ijthiazuaci Some chaacteizatio fo Legede cuve i the -Dimeioal Saakia pace H Kocayigit* ad M Ode Depatmet of Mathematic, Faculty of At ad Sciece,

More information

State space systems analysis

State space systems analysis State pace ytem aalyi Repreetatio of a ytem i tate-pace (tate-pace model of a ytem To itroduce the tate pace formalim let u tart with a eample i which the ytem i dicuio i a imple electrical circuit with

More information

12.6 Sequential LMMSE Estimation

12.6 Sequential LMMSE Estimation 12.6 Sequetial LMMSE Estimatio Same kid if settig as fo Sequetial LS Fied umbe of paametes (but hee they ae modeled as adom) Iceasig umbe of data samples Data Model: [ H[ θ + w[ (+1) 1 p 1 [ [[0] [] ukow

More information

Using Difference Equations to Generalize Results for Periodic Nested Radicals

Using Difference Equations to Generalize Results for Periodic Nested Radicals Usig Diffeece Equatios to Geealize Results fo Peiodic Nested Radicals Chis Lyd Uivesity of Rhode Islad, Depatmet of Mathematics South Kigsto, Rhode Islad 2 2 2 2 2 2 2 π = + + +... Vieta (593) 2 2 2 =

More information

Conditional Convergence of Infinite Products

Conditional Convergence of Infinite Products Coditioal Covegece of Ifiite Poducts William F. Tech Ameica Mathematical Mothly 106 1999), 646-651 I this aticle we evisit the classical subject of ifiite poducts. Fo stadad defiitios ad theoems o this

More information

International Journal of Mathematics Trends and Technology (IJMTT) Volume 47 Number 1 July 2017

International Journal of Mathematics Trends and Technology (IJMTT) Volume 47 Number 1 July 2017 Iteatioal Joual of Matheatics Teds ad Techology (IJMTT) Volue 47 Nube July 07 Coe Metic Saces, Coe Rectagula Metic Saces ad Coo Fixed Poit Theoes M. Sivastava; S.C. Ghosh Deatet of Matheatics, D.A.V. College

More information

Lecture 3 : Concentration and Correlation

Lecture 3 : Concentration and Correlation Lectue 3 : Cocetatio ad Coelatio 1. Talagad s iequality 2. Covegece i distibutio 3. Coelatio iequalities 1. Talagad s iequality Cetifiable fuctios Let g : R N be a fuctio. The a fuctio f : 1 2 Ω Ω L Ω

More information

On Almost Increasing Sequences For Generalized Absolute Summability

On Almost Increasing Sequences For Generalized Absolute Summability Joul of Applied Mthetic & Bioifotic, ol., o., 0, 43-50 ISSN: 79-660 (pit), 79-6939 (olie) Itetiol Scietific Pe, 0 O Alot Iceig Sequece Fo Geelized Abolute Subility W.. Suli Abtct A geel eult coceig bolute

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

= 5! 3! 2! = 5! 3! (5 3)!. In general, the number of different groups of r items out of n items (when the order is ignored) is given by n!

= 5! 3! 2! = 5! 3! (5 3)!. In general, the number of different groups of r items out of n items (when the order is ignored) is given by n! 0 Combiatoial Aalysis Copyight by Deiz Kalı 4 Combiatios Questio 4 What is the diffeece betwee the followig questio i How may 3-lette wods ca you wite usig the lettes A, B, C, D, E ii How may 3-elemet

More information

Asymptotic Expansions of Legendre Wavelet

Asymptotic Expansions of Legendre Wavelet Asptotic Expasios of Legede Wavelet C.P. Pade M.M. Dixit * Depatet of Matheatics NERIST Nijuli Itaaga Idia. Depatet of Matheatics NERIST Nijuli Itaaga Idia. Astact A e costuctio of avelet o the ouded iteval

More information

7-Speech Quality Assessment

7-Speech Quality Assessment 7-Speech Quality Aeet Quality Level Subjective Tet Objective Tet Itelligibility Naturale Quality Level Sythetic Quality Uder 4.8 kbp Couicatio Quality 4.8 to 3 kbp Toll Quality 3 to 64 kbp Broadcat Quality

More information

Crosscorrelation of m-sequences, Exponential sums and Dickson

Crosscorrelation of m-sequences, Exponential sums and Dickson Cosscoelatio o m-equeces, Epoetial sums ad Dicso polyomials To Helleseth Uiesity o Bege NORWAY Joit wo with Aia Johase ad Aleade Kholosha Itoductio Outlie m-sequeces Coelatio o sequeces Popeties o m-sequeces

More information

Auchmuty High School Mathematics Department Sequences & Series Notes Teacher Version

Auchmuty High School Mathematics Department Sequences & Series Notes Teacher Version equeces ad eies Auchmuty High chool Mathematics Depatmet equeces & eies Notes Teache Vesio A sequece takes the fom,,7,0,, while 7 0 is a seies. Thee ae two types of sequece/seies aithmetic ad geometic.

More information

DANIEL YAQUBI, MADJID MIRZAVAZIRI AND YASIN SAEEDNEZHAD

DANIEL YAQUBI, MADJID MIRZAVAZIRI AND YASIN SAEEDNEZHAD MIXED -STIRLING NUMERS OF THE SEOND KIND DANIEL YAQUI, MADJID MIRZAVAZIRI AND YASIN SAEEDNEZHAD Abstact The Stilig umbe of the secod id { } couts the umbe of ways to patitio a set of labeled balls ito

More information

Last time: Ground rules for filtering and control system design

Last time: Ground rules for filtering and control system design 6.3 Stochatic Etimatio ad Cotrol, Fall 004 Lecture 7 Lat time: Groud rule for filterig ad cotrol ytem deig Gral ytem Sytem parameter are cotaied i w( t ad w ( t. Deired output i grated by takig the igal

More information

Negative Exponent a n = 1 a n, where a 0. Power of a Power Property ( a m ) n = a mn. Rational Exponents =

Negative Exponent a n = 1 a n, where a 0. Power of a Power Property ( a m ) n = a mn. Rational Exponents = Refeece Popetie Popetie of Expoet Let a ad b be eal umbe ad let m ad be atioal umbe. Zeo Expoet a 0 = 1, wee a 0 Quotiet of Powe Popety a m a = am, wee a 0 Powe of a Quotiet Popety ( a b m, wee b 0 b)

More information

KEY. Math 334 Midterm II Fall 2007 section 004 Instructor: Scott Glasgow

KEY. Math 334 Midterm II Fall 2007 section 004 Instructor: Scott Glasgow KEY Math 334 Midtem II Fall 7 sectio 4 Istucto: Scott Glasgow Please do NOT wite o this exam. No cedit will be give fo such wok. Rathe wite i a blue book, o o you ow pape, pefeably egieeig pape. Wite you

More information

Announcements: The Rydberg formula describes. A Hydrogen-like ion is an ion that

Announcements: The Rydberg formula describes. A Hydrogen-like ion is an ion that Q: A Hydogelike io is a io that The Boh odel A) is cheically vey siila to Hydoge ios B) has the sae optical spectu as Hydoge C) has the sae ube of potos as Hydoge ) has the sae ube of electos as a Hydoge

More information

THE GRAVITATIONAL POTENTIAL OF A MULTIDIMENSIONAL SHELL

THE GRAVITATIONAL POTENTIAL OF A MULTIDIMENSIONAL SHELL THE GRAVITATIONAL POTENTIAL OF A MULTIDIMENSIONAL SHELL BY MUGUR B. RĂUŢ Abstact. This pape is a attept to geealize the well-kow expessio of the gavitatioal potetial fo oe tha thee diesios. We used the

More information

M5. LTI Systems Described by Linear Constant Coefficient Difference Equations

M5. LTI Systems Described by Linear Constant Coefficient Difference Equations 5. LTI Systes Descied y Lie Costt Coefficiet Diffeece Equtios Redig teil: p.34-4, 245-253 3/22/2 I. Discete-Tie Sigls d Systes Up til ow we itoduced the Fouie d -tsfos d thei popeties with oly ief peview

More information

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University ELEC 37 LECTURE NOTES, WEE 4 Dr Amir G Aghdam Cocordia Uiverity Part of thee ote are adapted from the material i the followig referece: Moder Cotrol Sytem by Richard C Dorf ad Robert H Bihop, Pretice Hall

More information

STA 4032 Final Exam Formula Sheet

STA 4032 Final Exam Formula Sheet Chapter 2. Probability STA 4032 Fial Eam Formula Sheet Some Baic Probability Formula: (1) P (A B) = P (A) + P (B) P (A B). (2) P (A ) = 1 P (A) ( A i the complemet of A). (3) If S i a fiite ample pace

More information

Simulation of Spatially Correlated Large-Scale Parameters and Obtaining Model Parameters from Measurements

Simulation of Spatially Correlated Large-Scale Parameters and Obtaining Model Parameters from Measurements Simulation of Spatially Coelated Lage-Scale Paamete and Obtaining Model Paamete fom PER ZETTERBERG Stockholm Septembe 8 TRITA EE 8:49 Simulation of Spatially Coelated Lage-Scale Paamete and Obtaining Model

More information

BINOMIAL THEOREM & ITS SIMPLE APPLICATION

BINOMIAL THEOREM & ITS SIMPLE APPLICATION Etei lasses, Uit No. 0, 0, Vadhma Rig Road Plaza, Vikas Pui Et., Oute Rig Road New Delhi 0 08, Ph. : 9690, 87 MB Sllabus : BINOMIAL THEOREM & ITS SIMPLE APPLIATION Biomia Theoem fo a positive itegal ide;

More information

Multistep Runge-Kutta Methods for solving DAEs

Multistep Runge-Kutta Methods for solving DAEs Multitep Ruge-Kutta Method for olvig DAE Heru Suhartato Faculty of Coputer Sciece, Uiverita Idoeia Kapu UI, Depok 6424, Idoeia Phoe: +62-2-786 349 E-ail: heru@c.ui.ac.id Kevi Burrage Advaced Coputatioal

More information

( ) Physics 1401 Homework Solutions - Walker, Chapter 9

( ) Physics 1401 Homework Solutions - Walker, Chapter 9 Phyic 40 Conceptual Quetion CQ No Fo exaple, ey likely thee will be oe peanent deoation o the ca In thi cae, oe o the kinetic enegy that the two ca had beoe the colliion goe into wok that each ca doe on

More information

Chapter 2 Sampling distribution

Chapter 2 Sampling distribution [ 05 STAT] Chapte Samplig distibutio. The Paamete ad the Statistic Whe we have collected the data, we have a whole set of umbes o desciptios witte dow o a pape o stoed o a compute file. We ty to summaize

More information

Consider unordered sample of size r. This sample can be used to make r! Ordered samples (r! permutations). unordered sample

Consider unordered sample of size r. This sample can be used to make r! Ordered samples (r! permutations). unordered sample Uodeed Samples without Replacemet oside populatio of elemets a a... a. y uodeed aagemet of elemets is called a uodeed sample of size. Two uodeed samples ae diffeet oly if oe cotais a elemet ot cotaied

More information

EDEXCEL NATIONAL CERTIFICATE UNIT 28 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME 2- ALGEBRAIC TECHNIQUES TUTORIAL 1 - PROGRESSIONS

EDEXCEL NATIONAL CERTIFICATE UNIT 28 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME 2- ALGEBRAIC TECHNIQUES TUTORIAL 1 - PROGRESSIONS EDEXCEL NATIONAL CERTIFICATE UNIT 8 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME - ALGEBRAIC TECHNIQUES TUTORIAL - PROGRESSIONS CONTENTS Be able to apply algebaic techiques Aithmetic pogessio (AP): fist

More information

Quantum Mechanics Lecture Notes 10 April 2007 Meg Noah

Quantum Mechanics Lecture Notes 10 April 2007 Meg Noah The -Patice syste: ˆ H V This is difficut to sove. y V 1 ˆ H V 1 1 1 1 ˆ = ad with 1 1 Hˆ Cete of Mass ˆ fo Patice i fee space He Reative Haitoia eative coodiate of the tota oetu Pˆ the tota oetu tota

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I liear regreio, we coider the frequecy ditributio of oe variable (Y) at each of everal level of a ecod variable (X). Y i kow a the depedet variable.

More information

Perhaps the greatest success of his theory of gravity was to successfully explain the motion of the heavens planets, moons, &tc.

Perhaps the greatest success of his theory of gravity was to successfully explain the motion of the heavens planets, moons, &tc. AP Phyic Gavity Si Iaac Newton i cedited with the dicovey of gavity. Now, of coue we know that he didn t eally dicove the thing let face it, people knew about gavity fo a long a thee have been people.

More information

Revenue Efficiency Measurement With Undesirable Data in Fuzzy DEA

Revenue Efficiency Measurement With Undesirable Data in Fuzzy DEA 06 7th teatioal Cofeece o telliet Stem, Modelli ad Simulatio Reveue Efficiec Meauemet With deiale Data i Fuzz DEA Nazila Ahai Depatmet of Mathematic Adail Bach, lamic Azad iveit Adail, a. E-mail: azila.ahai@mail.com

More information

Discussion 02 Solutions

Discussion 02 Solutions STAT 400 Discussio 0 Solutios Spig 08. ~.5 ~.6 At the begiig of a cetai study of a goup of pesos, 5% wee classified as heavy smoes, 30% as light smoes, ad 55% as osmoes. I the fiveyea study, it was detemied

More information

Inference for A One Way Factorial Experiment. By Ed Stanek and Elaine Puleo

Inference for A One Way Factorial Experiment. By Ed Stanek and Elaine Puleo Infeence fo A One Way Factoial Expeiment By Ed Stanek and Elaine Puleo. Intoduction We develop etimating equation fo Facto Level mean in a completely andomized one way factoial expeiment. Thi development

More information

( ) ( x) SEG/San Antonio 2007 Annual Meeting. x x. x x. = is the local wavenumber. We will

( ) ( x) SEG/San Antonio 2007 Annual Meeting. x x. x x. = is the local wavenumber. We will Illuiatio aalyi ui local expoetial bealet Jia Mao* Ru-Sha Wu Modeli ad Iai aboatoy, IGPP, Uiveity of Califoia, Sata Cuz Viiti fo ititute of Wave ad Ifoatio, Xi a Jiaoto Uiveity, Xi a 70049, Chia Suay I

More information

MATH /19: problems for supervision in week 08 SOLUTIONS

MATH /19: problems for supervision in week 08 SOLUTIONS MATH10101 2018/19: poblems fo supevisio i week 08 Q1. Let A be a set. SOLUTIONS (i Pove that the fuctio c: P(A P(A, defied by c(x A \ X, is bijective. (ii Let ow A be fiite, A. Use (i to show that fo each

More information

Supplementary materials. Suzuki reaction: mechanistic multiplicity versus exclusive homogeneous or exclusive heterogeneous catalysis

Supplementary materials. Suzuki reaction: mechanistic multiplicity versus exclusive homogeneous or exclusive heterogeneous catalysis Geeal Pape ARKIVOC 009 (xi 85-03 Supplemetay mateials Suzui eactio: mechaistic multiplicity vesus exclusive homogeeous o exclusive heteogeeous catalysis Aa A. Kuohtia, Alexade F. Schmidt* Depatmet of Chemisty

More information

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes The Maximum-Lielihood Decodig Performace of Error-Correctig Codes Hery D. Pfister ECE Departmet Texas A&M Uiversity August 27th, 2007 (rev. 0) November 2st, 203 (rev. ) Performace of Codes. Notatio X,

More information

Hidden Markov Model Parameters

Hidden Markov Model Parameters .PPT 5/04/00 Lecture 6 HMM Traiig Traiig Hidde Markov Model Iitial model etimate Viterbi traiig Baum-Welch traiig 8.7.PPT 5/04/00 8.8 Hidde Markov Model Parameter c c c 3 a a a 3 t t t 3 c a t A Hidde

More information

The Hypergeometric Coupon Collection Problem and its Dual

The Hypergeometric Coupon Collection Problem and its Dual Joural of Idustrial ad Systes Egieerig Vol., o., pp -7 Sprig 7 The Hypergeoetric Coupo Collectio Proble ad its Dual Sheldo M. Ross Epstei Departet of Idustrial ad Systes Egieerig, Uiversity of Souther

More information

13.4 Scalar Kalman Filter

13.4 Scalar Kalman Filter 13.4 Scalar Kalma Filter Data Model o derive the Kalma filter we eed the data model: a 1 + u < State quatio > + w < Obervatio quatio > Aumptio 1. u i zero mea Gauia, White, u } σ. w i zero mea Gauia, White,

More information

Ch 11 Particulate suspensions

Ch 11 Particulate suspensions Ch 11 Paticulate upenion Iue Stability (dipeion) edientation igation wall lip Had phee Only igid epulion peent when paticle coe into contact Zeo hea vicoity ( 1+. φ) 5 1+.5φ + 6.φ d.5 ( φ) dφ exp( 5φ /

More information

Counting Functions and Subsets

Counting Functions and Subsets CHAPTER 1 Coutig Fuctios ad Subsets This chapte of the otes is based o Chapte 12 of PJE See PJE p144 Hee ad below, the efeeces to the PJEccles book ae give as PJE The goal of this shot chapte is to itoduce

More information