2018 TIGP: Advanced Nanotechnology (A) Part 1: Photonic Crystals and Devices

Size: px
Start display at page:

Download "2018 TIGP: Advanced Nanotechnology (A) Part 1: Photonic Crystals and Devices"

Transcription

1 08 TIGP: Advaced Naotecholog A Part : Photoc Crstals ad Devces Lecture # M-Hsug Shh Research Ceter for Appled Sceces RCAS Academa Sca Tawa Mar. 4 08

2 Comparso of Quatum Mechacs ad lectrdamcs Table teded comparso betwee quatum mechacs a perodc potetal ad electromagetsm a perodc delectrc. What s the ma fucto that cotas all of the formato? How do we separate out the tme depedece of the fucto to ormal modes? What s the master equato that determes the ormal modes of the sstem? Are there a other codtos o the ma fucto? Where does the perodct QUANTUM MCHANICS IN A PRIODIC POTNTIAL CRYSTAL The scalar wave fucto rt. r t C r e t / h pad a set of eerg egestates r p V r r r m The Schrödger equato. Yes t must be ormalable. LCTROMAGNTISM IN A PRIODIC DILCTRIC PHOTONIC CRYSTAL The magetc vector feld H r t. t H r t C H r e pad a set of harmoc modes H r. H r H r r c The Mawell equatos. Yes the feld must be both ormalable ad trasverse: H 0 The potetal: The delectrc: r r R

3 of the sstem eter? Is there a teracto betwee ormal modes? What mportat propertes do the ormal modes have commo? O what fact about the master equato do the mportat propertes rel? What s the varatoal theorem we use to determe the ormal modes ad frequeces? What s the heurstc that goes alog wth the QUANTUM MCHANICS IN A PRIODIC POTNTIAL CRYSTAL V r V r R for all lattce vectors R. Yes there s a electroelectro repulsve teracto that* maes large-scale computato dffcult. gestates wth dfferet eerges are orthogoal the have real egevalues ad ca be foud wth a varatoal prcple. The Hamltoa H s a lear Hermta operator. var var H s mmed whe s a egestate of HHamltoa. The wave fucto cocetrates regos of low potetal whle remag orthogoal to 3 LCTROMAGNTISM IN A PRIODIC DILCTRIC PHOTONIC CRYSTAL for all lattce vectors R. I the lear regme lght modes ca pass rght through oe aother udsturbed ad ca be calculated depedetl. Modes wth dfferet frequeces are orthogoal the have real postve egevalues ad ca be foud wth a varatoal prcple. The Mawell operator s a lear Hermta operator. var H H H H var s mmed whe H Feld s a ormal mode of. The felds cocetrate ther electrcal eerg hgh- regos whle remag orthogoal to lower modes.

4 varatoal theorem? What s the phscal eerg of the sstem? Is there a atural legth scale to the sstem? What s the mathematcal statemet that sas: A s a smmetr of the sstem? How do we classf the ormal modes usg a sstem s smmetres? What does the dscrete traslatoal smmetr of a crstal allow us to do? What are the allowable values for the wave v vector? What do we mea b the term bad QUANTUM MCHANICS IN A PRIODIC POTNTIAL CRYSTAL lower states. The egevalue of the Hamltoa. Usuall because costats such as the Bohr radus set the legth scale. A commutes wth the Hamltoa: A H 0. Dstgush them b how the trasform uder a smmetr operato A. r u r e r Wrte the wave fucto Bloch form. The le the Brllou oe recprocal space. The fuctos whch tell us the eerges 4 LCTROMAGNTISM IN A PRIODIC DILCTRIC PHOTONIC CRYSTAL 8 dr D H The electromagetc eerg. No solutos are scalable to a legth scale. A commutes wth the Mawell operator: A 0. Dstgush them b how the trasform uder a smmetr operato A. H r u r e r Wrte the harmoc modes Bloch form. The le the Brllou oe recprocal space. The fuctos whch tell us the frequeces of the

5 structure? What s the phscal org of the bad structure? What happes sde a gap the bad structure? What do we call the bads mmedatel above ad below the gap? How do we troduce defects to the sstem? What s the result of troducg a defect? How do we classf dfferet tpes of defects? QUANTUM MCHANICS IN A PRIODIC POTNTIAL CRYSTAL of the allowed egestates. The electro wave scatters coheretl from the dfferet potetal regos. No propagatg electros that eerg rage are allowed to est regardless of wave vector. The bad above the gap s the coducto bad; the bad below the gap s the valece bad. B addg foreg atoms to the crstal whch breas the traslatoal smmetr of the atomc potetal. It mght create a allowed state a bad gap thereb permttg a localed electro state aroud the defect. Door atoms pull states from the coducto bad to the gap; acceptor atoms pull states from the valece 5 LCTROMAGNTISM IN A PRIODIC DILCTRIC PHOTONIC CRYSTAL allowed harmoc modes. The electromagetc felds scatter coheretl at the terfaces betwee dfferet delectrc regos. No eteded modes that frequec rage are allowed to est regardless of wave vector. The bad above the gap s the ar bad; the bad below the gap s the delectrc bad. B chagg the delectrc costat of certa regos whch breas the traslatoal smmetr of r. It mght create a allowed state a bad gap thereb permttg a localed mode aroud the defect. Delectrc defects pull states from the ar bad to the gap; ar defects pull states from the delectrc bad to the gap.

6 I short wh s the stud of the phscs of the sstem mportat? QUANTUM MCHANICS IN A PRIODIC POTNTIAL CRYSTAL bad to the gap. We ca talor the electroc propertes of materals to our eeds. LCTROMAGNTISM IN A PRIODIC DILCTRIC PHOTONIC CRYSTAL We ca talor the optcal propertes of materals to our eeds. 6

7 7

8 8

9 We had dscussed the bad structure for -D photoc crstals. Two popular tpes of lattces rectagular & tragular lattces had bee show the prevous otes. For most of applcato wth photoc crstals the bad gap the bad structure s oe of the most mportat parameters. A lot of applcatos use the optcal propertes sde or aroud the photoc bad gap so photoc crstal s oe tpe of photoc bad gap materals PBG wth the cotrol of the bad gap characterstcs. We ca egeer the optcal propertes of the optcal/photoc devces. It s qute smlar to the bad gap of semcoductor electrc materal. People ca egeer electroc propertes b cotrollg the bad gap of S GaAs or others. -D photoc crstals bad gap ca be decded b the lattce geometr ad the parameters to dscuss how those parameters tae some effect for the bad gap. r/a value of photoc crstal holes or rods Wthout chagg the materal or the lattce tpe we ca 9 a r. Now we are gog

10 ust var the hole or rod radus r to modf the bad gap frequec or wave legth ad wdth. Let s cosder a -D tragular lattce wth ar holes embedded the delectrc materal aga..0 a r Now we use =3.4 ad var r/a value of ar holes. The bad dagram of photoc lattces wth the dfferet r/a values are show below. We have the bad structure of T ad TM modes wth the r/a values. Apparetl the bads shft to hgher frequec as the r/a value creases. However the shfts mght be dfferet because of the dfferet polaratos..e. T TM. The bad gap whch s shaded the bad dagrams s ot alwas ested for all r/a values. I ths eample the T bad gap start to show up whe the r/a value s large tha 0.8. The bad gap s aroud 0

11 ormaled frequec 0.. We ca plot the photoc bad gap posto versus the r/a value whch s show the page 86. The hgher curve s the frequec posto of the top boudar of the bad gap whle the lower curve s frequec of bottom boudar of the bad gap. The rego surrouded b the two curves s the bad gap rego wth vared r/a values. Ths bad gap rego goes to hgher frequec area whe r/a value crease. The wdth of the photoc bad gap whch s the dfferece betwee two curves top-left fgure s plotted the bottom fgure. The bad gap wdth creases as r/a value crease ad t reaches the mamum 0.8 ormaled frequec at r/a value 0.4. After the mamum pot the gap wdth start to decrease as r/a value creases. For ths case we had realed The photoc bad gap ca be shfted b varg r/a value. It meas that the devce operated wave legth ca be cotrolled b the r/a value of photoc lattces.

12

13 3

14 4

15 The wdth of photoc bad gap ca be modfed wth the dfferet r/a value. Larger bad gap gves us more degree of freedom to desg the photoc crstal devces ad more defect modes or bads from the photoc crstal defect cavtes or wavegudes. 5

16 Refractve de of photoc crstal lattces I et eample we are gog to eame the effect due to the varato of the refractve de of the photoc crstal lattces. We stll tae the tragular lattces wth ar holes as a eample. Now we f the r/a=0.3 ad de of ar holes.0. The bacgroud de s vared from.5 to a r r 0.3 a hole.0 6

17 7

18 3 Complete bad gap for both polaratos I the prevous eamples some have the photoc bad gap for T modes some have bad gap ol for TM mode ad some eve have o bad gap for the both polaratos. Is t possble to have the photoc bad gap for both T ad TM polaratos smultaeousl? The aswer s Yes! Now let s tae a loo to a eample frst the we ll aale the reaso for T ad TM bad a r.0 gaps. Cosder a -D tragular photoc crstal lattces wth the ar holes aga. Ths tme we ll let =3.4 8

19 ad r/a value vared from 0.35 to The bad structures of T ad TM modes wth these r/a values are show below. 9

20 Accordg to the bad dagrams the prevous pages we ca plot the bad gap posto ormaled frequec versus r/a value le we dd the page 9. I the top fgure of page 9 0

21 the square-curves are the top & bottom boudares for T bad gap whle the crcle-curves are the bad gap boudares versus r/a value for TM mode. We also plot the wdth of bad gaps for T & TM modes versus r/a value of lattces the bottom fgure.

22

23 What s mportat for the photoc crstal bad gap? We have doe several eamples for the dfferet photoc crstals ad plotted ther bad dagrams ad the photoc bad gaps. To appl photoc crstals the real devces ad egeer ther optcal propertes aroud the bad gap rego we should dscuss several e ssues for the photoc bad gap. Scalg propertes of photoc crstals I M there s o fudametal legth scale other tha the assumpto that the sstem s macroscopc. Therefore photoc crstals there s o fudametal costat wth the dmesos of legth. The relatoshp betwee M problems s the dfferece ol b a cotracto or epaso of all dstaces. Cosder the egevalue equato for Hr [ H r] H r r Now we mae a compresso or epaso for the structure ' r the r ' r s 3

24 where s s a scale factor. We ca also mae chages of the varables r sr ' s the the above equato becomes r' r' s ' [ s ' H ] H r' s s s r' r' ' [ ' H ] H ' r' s s s Ths s ust the same egevalue equato wth the mode r H r= H ad frequec =. Ths meas that we ca s s obta the ew mode ad ew frequec ust scale a factor s from the orgal solutos of the Mawell s equatos. To uderstad the scalg propertes of photoc crstals let s cosder the followed eample. 4

25 a.0 r r Now we fed & ad 0.3 a but we var the lattce costat a to stud the chages the bad structure & bad gaps. I page 97 we plot the bad dagrams for a =000 m ad a =000 m. We realed that bad structures loos same the ormale frequec ut a. It meas the frequec var learl wth the scale of the lattce cost..e. a The rato of dfferet delectrc costats. I the prevous dscusso for -D photoc crstal bad structures we had eamed the bads ad bad gaps var wth the dfferet delectrc costats. However s the absolutel value of a delectrc costat the e parameter for the bad gap? 5

26 Actuall what s reall mportat s the rato of the dfferet costats ot the actual values. Let s cosder ths eample. a r Now we fed a & r the lattces. We also fed the rato of two refractve dces 3 = to 6 =. but vared the actual values from The bad dagrams are plotted the page 98. 6

27 7

28 So as log as the rato fed we ca ust scale the delectrc costats to whatever we wat ad the optcal propertes wll be same. 8

29 Cocept of the Lght Coe/Le I -D photoc crstal we assumed the perodc structure - plae ad fte thcess -as. However for most of the cases we have the fte thcess the photoc crstal slab structure. It s mportat to cosder whether lght comg from a outsde homogeeous medum ca couple to the sde modes through the etrace boudar plae. Ths d of couplg mode called the lea mode because t s ot a egemode t s ot a egemode of the structure ad t s eerg lea to the outsde space through the couplg. No-lea mode lea mode 9

30 terface // Now cosder medums wth de &. I medum the sgal has wave vector ad frequec. I medum the sgal has. I couplg process the eerg coservato ad mometum coservato eerg to terface must be vald so = ad / = / / / I a homogeeous medum wth de the lght coe ca be descrbed b the relato c // c Whe =.e. ar the the lght le c ŷ ˆ 30

31 Now let s use the laer structure to descrbe the cocept of lght le coe. ŷ ˆ Cosder the above structure medum s embedded b medum ad the refractve de of medum s larger tha the refractve de of medum. Assume a mode medum wth the wave vector ad a propagato agle. After pass through the terface to the medum the mode has the wave vector ad the propagato agle. Note we have the Sell s law or s s // = // or s s 3

32 For a mode cofed the medum t meas that we eed have a mode total reflectve from the terface..e. c where s c s s or c so oce there s o wave propagate to the c medum from the terface. However ths does t dcate the lght feld vashes completel the medum. See the lea ad o-lea modes the fgure of page 99. 3

33 Photoc Crstal Slab Structure So far we had dscussed the photoc crstals whch the lattce s perodc -D ad ftel eteded the ẑ as. Now we are gog to cosder the photoc crstals wth a fte thcess. a b Addg the fte thcess for the -D photoc crstal lattces meas we have to deal wth the propagato vector the ẑ -as. ve for propagato -plae - plae the delectrc slab wll have guded modes whch there s ot ero. There are some effects from ths fte thcess of the slab 33

34 structure. There s o loger a complete photoc bad gap. It s stll possble to have the bad gaps for the guded modes of the slab. Modes caot be classfed as pure T or TM modes. So let s dscuss the two ssues oe b oe. Frst thg s the dsappearace of a complete bad gap. Ths s due to the o-ero. ve two-dmesoal photoc crstals that are fte the thrd dmesoal Here s the ẑ -as the photoc bad gap dsappears whe we allow out-of-plae 0 propagato. Here we show the bad dagrams from -D photoc crstals. The are perodc - plae ad ftel the ẑ -as. 34

35 Fg. Bad dagrams for the photoc crstals from a Fg. a ad b Fg. b. The shaded rego dcates the frequeces of states troduced whe vertcal propagato.e. perpedcular to the plae of perodct s permtted. If we restrct the photoc lattce to a fte thcess the we epect that there wll be modes guded b the delectrc slab ad radato modes or lea modes. 35

36 The dsperso relato for a slab wavegude s followed radato modes c guded modes Lea modes c The bad structure for a slab photoc crstal lattce has the smlar stuato. Bads below the lght le are guded b the delectrc slab. Bads above the lght le are the lea waves. Ther eerg s 36

37 ot cofed sde the delectrc slab. There s also a cotuum of radato modes above the lght le. The lght le s gve as the case of the slab wavegude b the free propagato plae wave dsperso relato of the claddg. The pressure of the radato modes elmates the complete bad gap. It s stll possble however to obta gaps the guded spectrum. Lght the frequec rage correspodg to the gap the guded modes ca t propagate the plae of the slab. Accordg to the above argumets oce we have a fte thcess for the -D photoc crstals the bad dagrams of -D lattces the page 0 wll become the bad dagrams show the et page. 37

38 Fg.4. Proected bad dagrams correspodg to the two slabs Fg.3. Whether states are eve or odd wth respect to the horotal mrror plae of the slab s dcated b ope or flled crcles respectvel. The shaded regos above the lght coe are radato modes or lea modes for the -D photoc crstal slabs. Ol the bads below the lght coes are cosdered as the guded or 38

39 cofed modes the slab. We are most lel to use those modes for the real devces. Now let s tal about the polarato of slab photoc crstals. Sce the smmetr uder reflecto through - plae s o loger vald for all the slab modes ca ot be classfed as eve or odd modes for all. However t s possble to separate the slab modes to eve to odd at some pots. Cosder the two eamples I II.0.0 ˆ ẑ ample I s a suspeded membrae photoc crstal structure whch has ar claddg above ad bottom of the slab. I ths case we ll have reflecto smmetr at - plae of = 0.e. at ceter of the slab. We therefore have separated eve ad odd modes at = 0. I the - plae of the ceter of the slab the eve modes are T ad the odd modes are TM. However at all other the eve modes are ol T-le ad the odd modes are 39 ŷ.0

40 ol TM-le. The are ot rgorousl T or TM. I eample II we caot have a whch has the reflecto smmetr for the photoc crstal sstem. For ths case we ol have T-le or eve-le modes ad TM-le or odd-le modes. 40

41 Normaled frequec a/ Defects ad defect modes from the photoc crstals After the dscusso of o-defect photoc crstal lattces we are gog to stud the photoc crstals wth the defects or the defect les. For a tragular photoc crstal wth the defect we have the bad dagram for T mode. Defect modes Defect bads Complete Bad Gap 0. M M K I-plae Wavevector Now f we create a defect pot b removg a ar hole from the lattces to form a defect cavt. * Cofe photo test sde the defect 4

42 Removed hole The we ll smultaeousl have some defect modes the photoc bad gap regos. The modes descrbe the photo / wave behavor assocated wth the defect we created. Removed le If we remove a le from the lattce to form a wavegude we ll have the defect bads the gap rego to descrbe the character of the wave propagato sde or aroud the defect le area. There are several otes we should meto for the defect modes / bads of the photoc crstals The defect modes whch are formed b removg some 4

43 lattce pots are ol the solated modes the bad gaps. It meas that there s o allowed modes / states betwee those defect modes for the photoc crstal cavt. The defect bads whch are created b removg a le of lattces are composed b ma defect modes the gaps. Sce the defects the lattces ope oe-dmesoal degree of freedom a pot or state o the defect bads s the allowed state for the photoc crstal wavegude. 3 The defect modes ad bads are ot ol located sde the completel bad gap regos but also the gap rego outsde the complete bad gap. 43

44 Cavt The D 4 photoc crstal defect cavt o a SOI substrate. Wavegude The wth photoc crstal wavegude a GaAs suspeded membrae. 44

45 Qualt factor Q ad photo lfetme Qualt factor Q s a mportat parameter for a cavt. It mal descrbes the photo lfe tme sde the cavt or the eerg stored the cavt. There are several deftos or descrptos for the qualt factor of a cavt. eerg stored the sstem at resoace Q eerg lost a ccle of oscllato or W T -dw dt W -dw dt 0 0 Wt W0 e 0t - Q W : Stored eerg : Resoace freq. We ca also have Q related to the photo lfetme τ p or p Q 0 Q 0 P 45

46 3 I epereces we ca obta the Q value from the spectrum Q 0 0 I photoc crstals we use the photoc crstal defect structure serve as the resoat cavt for the desged defect modes. The qualt factor Q wll represet a measure of how ma oscllatos tae place sde the cavt before the ected photo eerg dsspate out of the cavt. It meas that the photo eerg for arrow frequec bads be trapped the cavt for loger perod of tme f we have a hgh-q cavt. Now the mportat questo becomes how the defect cavt should be desged for troducg the hgh-q modes to the structure. There are several eamples of the hgh-q photoc crstal cavtes the followg pages. 46

47 Photoc Crstal D4 Lasers IGaAsP Membrae 47

48 Fte-dfferece tme-doma FDTD smulato method The fte-dfferece tme-doma FDTD method s wdel used to drectl solve tme-depedet Mawells equatos ow. FDTD was orgall proposed for M waves wth log wavelegths such as mcrowaves because the spatal dscrmato requremet s small ~ 0 0. I 966 Yee descrbed the bass of FDTD umercal techque for M wave the tme doma o a space grd. K.S. Yee I Trasacto of Ateas ad Propagato Recetl FDTD method s popular smulatg photoc bad gap structure ad other photoc devces. For 3-D smulato of the Mawell s equatos t become more complcated. The tme-depedet Mawell s equatos are H 0 t H t Ther { } compoet represetatos are 48

49 49 t H 3 t H 4 t H 5 H H t 6 H H t 7 H H t We assume that are spatal dscretato ad that t s a tme step the fucto F t s dscreted as 8 F t F t F

50 I the paper of 966 Yee orgated a set of fte-dfferece equatos for the tme-depedet Mawell s curl equatos for the lossless materals case 0 0. The above fgure shows Yee space lattce whch llustrates the Yee algorthm. Ths algorthm ceters ts ad H compoets 3-D space so that ever compoet s surrouded b four crculatg H compoets ad each H compoet s surrouded four crculatg compoet. 50

51 5 The Yee algorthm also ceters ts ad H compoets tme. All of the computato the modeled space are completed ad stored memor for partcular tme pot usg prevousl stored H data. The all of the H computatos the space are completed ad stored memor usg the data ust computed. The ccle begs aga wth the recomputato of the compoets based o the ew obtaed H. Ths process cotues utl tme-step fshed. Yee used cetered fte-dfferece or cetral-dfferece epressos for the space ad tme dervatves. Ths wa ca obta smpler programg ad secod-order accurac. For eample the frst spatal dervatve of the fucto F t F t F wll be ] [ O F F F

52 F F Ad the frst tme dervatve of s F F O[ t ] t t There are two otes we should uderstad that Yee chose the cetral-dfferece space ad tme.. Wth the cetral-dfferece space tme Yee s goal s the secod-order accurate cetral dfferecg O[ ] { O[ t ]} but the dfferece s ol stead of.. Yee chose he cetral-dfferece spacetme because we ca terleave ad H compoets the space lattce at tervals of t. Wth Yee algorthm we ca re-wrte 3-D Mawell s equatos. 5

53 53 quato become t H H equato 3 become t H H equato 4 become t H H

54 54 quato 5 become H H H H t equato 6 become H H H H t quato 7 become

55 55 H H H H t

56 56

57 57

58 The followg llustrato shows how we appl FDTD to real structure.. Frst we costruct the structure we wat to smulate. The materal propertes are assged to correspoded locatos. For eample refractve de r [.e. delectrc fucto [ r ]. After settg-up the r detectors the desged structure to collect the sgal we ca lauch the tal sources ad let them 58

59 propagatg followg the FDTD algorthm.. The detectors wll collect the sgal vared wth tme durg the FDTD calculato process. Fgure shows a eample of the collected test versus tme from a detector. The mpulse tal ectato s usuall appled the smulato. Ad the collected sgal goes stable or sa coverge after amout of smulato tmeor smulato steps. 3. After the smulato fshg the sgal s collected sa It b the detectors. The fourer trasform wll be appled to trasfer tme-doma spectrum to frequec-doma spectrum. Sce tme t ad frequec are the cougates the Fourer trasformato. Fgure 3 s a frequec-doma spectrum whch shows the domat frequeces durg the FDTD propagato the smulated structure. 4. Not ol frequec-doma formato wll be obtaed from the FDTD smulato we also ca have more detals about ths structure le mode profles 59

60 see attachmets spectrum wavelegth ad etc. for eample fgure 4 s a bad dagram or bad structure of the smulated structure from the frequec-doma spectrum ad the tal propagato of the sources. 60

61 FDTD for D4 cavt Slab de =3.67 Hole de=.0 *From Y.C. Yag. 6

62 Qualt factor Q ad photo lfetme Qualt factor Q s a mportat parameter for a cavt. It mal descrbes the photo lfe tme sde the cavt or the eerg stored the cavt. There are several deftos or descrptos for the qualt factor of a cavt. eerg stored the sstem at resoace Q eerg lost a ccle of oscllato or W T -dw dt W -dw dt 0 0 Wt W0 e 0t - Q W : Stored eerg : Resoace freq. We ca also have Q related to the photo lfetme τ p or p Q 0 Q 0 P 6

63 spectrum 3 I epereces we ca obta the Q value from the Q 0 0 I photoc crstals we use the photoc crstal defect structure serve as the resoat cavt for the desged defect modes. The qualt factor Q wll represet a measure of how ma oscllatos tae place sde the cavt before the ected photo eerg dsspate out of the cavt. It meas that the photo eerg for arrow frequec bads be trapped the cavt for loger perod of tme f we have a hgh-q cavt. Now the mportat questo becomes how the defect cavt should be desged for troducg the hgh-q modes to the structure. There are several eamples of the hgh-q photoc crstal cavtes the followg pages. 63

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I

MMJ 1113 FINITE ELEMENT METHOD Introduction to PART I MMJ FINITE EEMENT METHOD Cotut requremets Assume that the fuctos appearg uder the tegral the elemet equatos cota up to (r) th order To esure covergece N must satsf Compatblt requremet the fuctos must have

More information

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

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

More information

The E vs k diagrams are in general a function of the k -space direction in a crystal

The E vs k diagrams are in general a function of the k -space direction in a crystal vs dagram p m m he parameter s called the crystal mometum ad s a parameter that results from applyg Schrödger wave equato to a sgle-crystal lattce. lectros travelg dfferet drectos ecouter dfferet potetal

More information

Lecture 07: Poles and Zeros

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

More information

Evolution Operators and Boundary Conditions for Propagation and Reflection Methods

Evolution Operators and Boundary Conditions for Propagation and Reflection Methods voluto Operators ad for Propagato ad Reflecto Methods Davd Yevck Departmet of Physcs Uversty of Waterloo Physcs 5/3/9 Collaborators Frak Schmdt ZIB Tlma Frese ZIB Uversty of Waterloo] atem l-refae Nortel

More information

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

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

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

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

More information

L5 Polynomial / Spline Curves

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

More information

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

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

More information

Physics 114 Exam 2 Fall Name:

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

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

MOLECULAR VIBRATIONS

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

More information

Functions of Random Variables

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

More information

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

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

More information

Introduction to local (nonparametric) density estimation. methods

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

More information

Chapter 9 Jordan Block Matrices

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

More information

Unsupervised Learning and Other Neural Networks

Unsupervised Learning and Other Neural Networks CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all

More information

x y exp λ'. x exp λ 2. x exp 1.

x y exp λ'. x exp λ 2. x exp 1. egecosmcd Egevalue-egevector of the secod dervatve operator d /d hs leads to Fourer seres (se, cose, Legedre, Bessel, Chebyshev, etc hs s a eample of a systematc way of geeratg a set of mutually orthogoal

More information

Centroids & Moments of Inertia of Beam Sections

Centroids & Moments of Inertia of Beam Sections RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol

More information

EECE 301 Signals & Systems

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

More information

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

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

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

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

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

More information

Ellipsometry Overview

Ellipsometry Overview llpsometry Overvew ~ R Δ p ρ = ta( Ψ) e = ~ Rs ñ(λ) = (λ) + k(λ) ε = ñ 2 p-plae s-plae p-plae plae of cdece s-plae llpsometry buldg-blocks Lght ad Polarzato Materals / Optcal Costats Iteracto of Lght wth

More information

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the

More information

DKA method for single variable holomorphic functions

DKA method for single variable holomorphic functions DKA method for sgle varable holomorphc fuctos TOSHIAKI ITOH Itegrated Arts ad Natural Sceces The Uversty of Toushma -, Mamhosama, Toushma, 770-8502 JAPAN Abstract: - Durad-Kerer-Aberth (DKA method for

More information

Lecture Notes 2. The ability to manipulate matrices is critical in economics.

Lecture Notes 2. The ability to manipulate matrices is critical in economics. Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets

More information

ECE606: Solid State Devices Lecture 13 Solutions of the Continuity Eqs. Analytical & Numerical

ECE606: Solid State Devices Lecture 13 Solutions of the Continuity Eqs. Analytical & Numerical ECE66: Sold State Devces Lecture 13 Solutos of the Cotuty Eqs. Aalytcal & Numercal Gerhard Klmeck gekco@purdue.edu Outle Aalytcal Solutos to the Cotuty Equatos 1) Example problems ) Summary Numercal Solutos

More information

HOOKE'S LAW. THE RATE OR SPRING CONSTANT k.

HOOKE'S LAW. THE RATE OR SPRING CONSTANT k. Practces Group Sesso Date Phscs Departmet Mechacs Laborator Studets who made the practce Stamp cotrol Deadle Date HOOKE'S LAW. THE RATE OR SPRING CONSTANT k. IMPORTANT: Iclude uts ad errors all measuremets

More information

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model

( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

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

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line HUR Techcal Report 000--9 verso.05 / Frak Borg (borgbros@ett.f) A Study of the Reproducblty of Measuremets wth HUR Leg Eteso/Curl Research Le A mportat property of measuremets s that the results should

More information

1 Lyapunov Stability Theory

1 Lyapunov Stability Theory Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10,

PHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10, PHYS Look over Chapter 9 Sectos - Eamples:, 4, 5, 6, 7, 8, 9, 0, PHYS Look over Chapter 7 Sectos -8 8, 0 eamples, 3, 4, 6, 7, 8,9, 0 ad How To ake Phscs Pa We wll ow look at a wa of calculatg where the

More information

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

Descriptive Statistics

Descriptive Statistics Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

Binary plasmonic waveguide arrays. Modulational instability and gap solitons.

Binary plasmonic waveguide arrays. Modulational instability and gap solitons. Bary plasmoc wavegude arrays. Modulatoal stablty ad gap soltos. Aldo Audtore Matteo Cofort Costato De Agels Dpartmeto d Igegera dell Iformaoe Uverstà degl Stud d Bresca Alejadro B. Aceves Souther Methodst

More information

QR Factorization and Singular Value Decomposition COS 323

QR Factorization and Singular Value Decomposition COS 323 QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?

More information

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58 Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488

More information

Kernel-based Methods and Support Vector Machines

Kernel-based Methods and Support Vector Machines Kerel-based Methods ad Support Vector Maches Larr Holder CptS 570 Mache Learg School of Electrcal Egeerg ad Computer Scece Washgto State Uverst Refereces Muller et al. A Itroducto to Kerel-Based Learg

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon

Periodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon EE105 - Srg 007 Mcroelectroc Devces ad Crcuts Perodc Table of Elemets Lecture Semcoductor Bascs Electroc Proertes of Slco Slco s Grou IV (atomc umber 14) Atom electroc structure: 1s s 6 3s 3 Crystal electroc

More information

Investigating Cellular Automata

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

More information

Objectives of Multiple Regression

Objectives of Multiple Regression Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of

More information

5.1 Finite Difference Time Domain Technique (FDTD)

5.1 Finite Difference Time Domain Technique (FDTD) Cotets 5.1 Fte Dfferece Tme Doma Techque (FDTD) 5. Theoretcal aalss of cross patch atea 5.3 Theoretcal aalss of cross patch atea wth X-slot 5.4 Theoretcal aalss of Frequec recofgurable polarato dverst

More information

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013 ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Stationary states of atoms and molecules

Stationary states of atoms and molecules Statoary states of atos ad olecules I followg wees the geeral aspects of the eergy level structure of atos ad olecules that are essetal for the terpretato ad the aalyss of spectral postos the rotatoal

More information

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations. III- G. Bref evew of Grad Orthogoalty Theorem ad mpact o epresetatos ( ) GOT: h [ () m ] [ () m ] δδ δmm ll GOT puts great restrcto o form of rreducble represetato also o umber: l h umber of rreducble

More information

Chapter 11 Systematic Sampling

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

More information

Laboratory I.10 It All Adds Up

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

More information

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

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

More information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

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

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

More information

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

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

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

More information

Transforms that are commonly used are separable

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

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

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

More information

Lecture 5: Interpolation. Polynomial interpolation Rational approximation

Lecture 5: Interpolation. Polynomial interpolation Rational approximation Lecture 5: Iterpolato olyomal terpolato Ratoal appromato Coeffcets of the polyomal Iterpolato: Sometme we kow the values of a fucto f for a fte set of pots. Yet we wat to evaluate f for other values perhaps

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

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

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

More information

Analysis of Variance with Weibull Data

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

More information

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

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

More information

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model 1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed

More information

Chapter Two. An Introduction to Regression ( )

Chapter Two. An Introduction to Regression ( ) ubject: A Itroducto to Regresso Frst tage Chapter Two A Itroducto to Regresso (018-019) 1 pg. ubject: A Itroducto to Regresso Frst tage A Itroducto to Regresso Regresso aalss s a statstcal tool for the

More information

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov

MULTIDIMENSIONAL HETEROGENEOUS VARIABLE PREDICTION BASED ON EXPERTS STATEMENTS. Gennadiy Lbov, Maxim Gerasimov Iteratoal Boo Seres "Iformato Scece ad Computg" 97 MULTIIMNSIONAL HTROGNOUS VARIABL PRICTION BAS ON PRTS STATMNTS Geady Lbov Maxm Gerasmov Abstract: I the wors [ ] we proposed a approach of formg a cosesus

More information

Solid State Device Fundamentals

Solid State Device Fundamentals Sold State Devce Fudametals 9 polar jucto trasstor Sold State Devce Fudametals 9. polar Jucto Trasstor NS 345 Lecture ourse by Alexader M. Zatsev alexader.zatsev@cs.cuy.edu Tel: 718 98 81 4N101b Departmet

More information

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines CS 675 Itroducto to Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mdterm eam October 9, 7 I-class eam Closed book Stud materal: Lecture otes Correspodg chapters

More information

Correlation and Simple Linear Regression

Correlation and Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uverst Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i

THE COMPLETE ENUMERATION OF FINITE GROUPS OF THE FORM R 2 i ={R i R j ) k -i=i ENUMERATON OF FNTE GROUPS OF THE FORM R ( 2 = (RfR^'u =1. 21 THE COMPLETE ENUMERATON OF FNTE GROUPS OF THE FORM R 2 ={R R j ) k -= H. S. M. COXETER*. ths paper, we vestgate the abstract group defed by

More information

Evaluating Polynomials

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

More information

Beam Warming Second-Order Upwind Method

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

More information

Lecture 9: Tolerant Testing

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

More information

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

We have already referred to a certain reaction, which takes place at high temperature after rich combustion. ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a

More information

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

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

More information

Simple Linear Regression

Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uversty Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

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

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

More information

UNIVERSITY OF CALIFORNIA, BERKELEY DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES. Midterm I

UNIVERSITY OF CALIFORNIA, BERKELEY DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES. Midterm I UNIVERSITY OF CALIFORNIA, BERKELEY EPARTMENT OF ELECTRICAL ENGINEERING AN COMPUTER SCIENCES EECS 130 Professor Chemg Hu Fall 009 Mdterm I Name: Closed book. Oe sheet of otes s allowed. There are 8 pages

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING

0/1 INTEGER PROGRAMMING AND SEMIDEFINTE PROGRAMMING CONVEX OPIMIZAION AND INERIOR POIN MEHODS FINAL PROJEC / INEGER PROGRAMMING AND SEMIDEFINE PROGRAMMING b Luca Buch ad Natala Vktorova CONENS:.Itroducto.Formulato.Applcato to Kapsack Problem 4.Cuttg Plaes

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

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

More information

UNIT 7 RANK CORRELATION

UNIT 7 RANK CORRELATION UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7

More information

Analysis of Lagrange Interpolation Formula

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

More information

A New Family of Transformations for Lifetime Data

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

More information

STA302/1001-Fall 2008 Midterm Test October 21, 2008

STA302/1001-Fall 2008 Midterm Test October 21, 2008 STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS

DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS DIFFERENTIAL GEOMETRIC APPROACH TO HAMILTONIAN MECHANICS Course Project: Classcal Mechacs (PHY 40) Suja Dabholkar (Y430) Sul Yeshwath (Y444). Itroducto Hamltoa mechacs s geometry phase space. It deals

More information

PROJECTION PROBLEM FOR REGULAR POLYGONS

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

More information

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan M E M O R A N D U M DATE: 1 September, 1999 TO: Jm Russell FROM: Peter Tkack RE: Aalyss of wde ply tube wdg as compared to Kova Kore CC: Larry McMlla The goal of ths report s to aalyze the spral tube wdg

More information

6.4.5 MOS capacitance-voltage analysis

6.4.5 MOS capacitance-voltage analysis 6.4.5 MOS capactace-voltage aalyss arous parameters of a MOS devce ca be determed from the - characterstcs.. Type of substrate dopg. Isulator capactace = /d sulator thckess d 3. The mmum depleto capactace

More information

Module 7: Probability and Statistics

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

More information