A Deterministic Model for Channel Capacity with Utility

Size: px
Start display at page:

Download "A Deterministic Model for Channel Capacity with Utility"

Transcription

1 CAPTER 6 A Detestc Model fo Chel Cct wth tlt 6. todcto Chel cct s tl oeto ssocted wth elble cocto d defed s the hghest te t whch foto c be set ove the chel wth btl sll obblt of eo. Chel codg theoes d the coveses ove tht the cct C c be defed s fol tht deeds o the chels obbltsc desctos s gve below: C 6.. Sho 948 oved tht 6.. s the cct of eo less chels whee s the chel t s the chel ott d s the vege tl foto betwee d. The eqto 6.. eteded to the ltg eesso C l s 6..2 f t ests the chel cct of the cet chel wth eo s eql to the of vege tl foto whee t s the seqece of legth wth coesodg ott seqece. Dobsh 964 oved tht the chel cct gve b eqto 6..2 s fo foto stble chels whle fo foto stble chels the fol 6..2 does ot covese to lt. Eles of foto stble chels clde the stto egl decoosble chels the stto otct chels d veges eo less chels. Now the qesto ses whethe thee est colete geel fol fo chel cct whch does ot eqe ssto sch s eoless foto stblt sttot cslt etc.ved d 994 hs defed geel fol fo chel cct s follows: 3

2 C s deote s t ocess the whee 2 fo of seqece of fte- desol dstbto d 2... s the coesodg ott seqece of fte desol dstbtos d s the foto te o tl foto betwee W W P d. ee s dced b v the chel : A B whch s bt seqece of desol codtol ott dstbtos fo A to B whee A d B e the t d ott lhbets esectvel. Chel cct gve b 6.. ossesses the followg oetes: The chel cct s o egtve.e. C becse C. C log sce C log. C log sce C log. s cotos d cocve fcto of. ths chte we dscss the dscete eoless chels d the clssfcto secto 6.2. secto 6.3 we std dffeet tes of chel cct. secto 6.4 we dscss chel cct wth tlt. secto 6.5 we defe chel cct of dscete eoless chels wth tlt d ove two theoes o t. 4

3 6.2 Clssfcto of Dscete Meoless Chels A tssso chel c be secfed tes of the set of ts vlble t the t tel the set of ott vlble t ott tel d fo ech t the obblt ese o the ott evets codtol o tht t. ee we dscss the Dscete Meoless Chel D.M.C.. Ths s chel fo whch the t d ott e ech seqece of lettes fo fte lhbet d fo whch the ott lette t gve te deeds sttcll ol o the coesodg t lette. Cosde Dscete Meoless Chel DMC wth t lhbets ott lhbets d chel t[ A ] A f s do vble tg o the vles wth the obbltes esectvel the the ott lso becoes do vble. The ot dstbto of the t d dstbto of s gve b DMC c be clssfed dffeet ws s follows: 6.2. Lossless Chel f fo ll t dstbtos. othe wods lossless chel s chctezed b the fct tht the t s deteed b the ott d hece o tsto eos c occ. b Detestc Chel f o fo ll..e. f s detee b o eqvletl fo ll t dstbto A ele of detestc chel 5

4 s oe whose t s the dett of lg cd ced fo od 52-cd c d whose ott s the st of the cd. f cd s ced t do so tht ll vles of d hece of e eqll lel the the foto ocessed s log 4. c Setc Chel f ech ow of the chel t cots the se set of bes d ech col of cots the se set of bes q q2... q. Fo ele the t d eesets setc chels. The ows of chel t e detcl ecet fo etto d sll fo cols. t s edte coseqece of the defto of setc chel tht fo sch chel s deedet of the t dstbto d deeds ol o the chel obbltes. t be oted tht f the obbltes ssocted wth the ott lhbet e ece log Theefoe

5 log β β β β Fge 6.2.: B setc chel Fo t dstbto. The bove fge shows the ele of B Setc Chel. 6.3 Clssfcto of Chel Cct Chel cct gve b Sho eqto 6.. c be clssfed s follows: 6.3. ε Cct of Chel The ε cct of chel s the cct stsfg < ε < s the se of the tes the bts e chel sbol tht e stotcll chevble b seqece of chel bloc codes ech hvg bloc eo obblt ε. Fo the geel fte-lhbet chel odel Ved d 994 obted foto theoetc fol fo ε-cct whch s vld fo ll bt t ost cotble vles of ε. Defto 6.3. Let s Ved- chel whee s fte set of chel t lhbet s set of fte ott lhbet d s codtol obblt. The fo ech d ech ostve tege N. Let N be the lgest ostve tege N sch tht thee est bloc code fo the chel of sze N wth bloc eo obblt ε. The ε- cct of the chel s defed s follows: 7

6 C l t s log N 6.3. The ε cct fcto of the chel s the fcto defed o whch s ech ε ϵ the ε-cct. The cct fcto s o-decesg d theefoe t hs t ost cotble dscottes. The cl eslts bot ε-cct e gve below: b Fo cet chel t s ow tht the ε-cct cocdes wth chel cct fo eve < ε <. Sch chel cct s sd to osses stog cct. Fo ele dscete chels fte stte decoosble chels d dtve ose chels. Fo ddtve ose fte- lhbet chels wth stto oegodc do ose Pthsth 964 estblshed othe defto fo ε-cct whch s vld t eve cott ot of the ε-cct fcto. Actll Pthsth ε-cct c be obted fo Ved- 994 cct bt t s sle sce Pthsth s chels e secl cse of Ved Vble Rte Chel Cct the vble te chel codg the ecode dst ts te d/o othe esoces sch s owe d bdwdth to the ctl chel codto o stte. Whe the chel stte s ow t tstte the vege of the cctes chevble fo the dvdl chel sttes s ott fdetl lt. ee we dscss soe codg stteges b whch we obt vble te chel cct. Fed Bloc legth Chel Cct ths cse the be of tstted foto bts d the be of obseved chel sbols bloc legth e esecfed. Let s seqece of t lhbets d s seqece of ott lhbets d let f d g e ecode d decode esectvel sch tht 8

7 f g : {} : {} whose vege bloc eo obblt d te stsf l l f R esectvel. The we obt the oto of covetol Sho chel cct. b Vble -to Fed Chel Cct Whe the be of obseved chel sbols bloc legth s esecfed bt the be of elbl ecoveed foto bts deeds o chel codtos. Let. log 2 The fo ecode-decode c be defed s : f : : g : 2... wth essge. Let deote the chel esose to the t : f The be of cosectvel ecoveed bts L s defed s the lgest tege sch tht : g : : g L whee deote bts 2 g d s the do vble tht deeds o the ecode decode essge d chel elzto. A llstto of L s gve the followg fge

8 ENCODER CANNEL DECODER Fge 6.3.: Ele of L N the bove fg = = whle the ecoveed bt L =6. f R bts/chel s vble-to-fed chevble te d f thee ests seqece of code f : g : whose eected be of ecoveed bts stsfes R l f E L whee the eectto s ove the chel do tsfoto d f bts the vble-to-fed chevble te s the chel cct. c Fed to-vble Chel Cct f the be of tstted foto bts s esecfed the the be of chel obsevtos eqed ecoveg the deeds o chel codtos. Ths s the set of te less fot codes. Let decode d ecode e defed s follows: : g : d 2

9 : f : Fo defe the be N of chel sbols eqed to ecove the tstted bts s the sllest tege sch tht :... g f R bts/chel se s fed-to-vble chevble te d f thee s seqece : : of f g sch tht R l f EN E N = l s whee the eectto s ove the chel do tsfoto d f bts. The fed to-vble chevble te s the fed to-vble cct. 6.4 Chel Cct wth tlt cocto sste foto s tstted d ocessed vew of gol wth egd to whch essge st be effcet. The obectve of the sste s flflet of the gol d tht es thee est logcl bloc whch s to be ble to dscte the qlt of vos sgls ccodg the gve cteo. These cteos fo qlttve dffeetto of sgls e bsed o the elevce o the sgfcce o tlt of the foto whch s beg tstted b the. Let... be do vble. Let d = 2 be 2 tlt d obblt dstbtos esectvel whee > fo eve d. Bels d Gs968 todce the followg qtttve- qlttve ese of foto: = log 6.4. whee > =2 s clled tlt o otce ssocted to ll ossble evets of. The ese 6.4. hs lso bee clled sefl b Logo972. t c hve ve sll s well s ve lge vle deedg o. Ths the ge of the 2

10 ese soetes s geble whle stdg ts lcto. Ths ese does ot stsf ddtvt oet d does ot tt vle fo. To ovecoe these dffcltes Bh d ood 993 oosed d chctezed the followg two eses of sefl foto: d log log t be oted tht these eses hve vle whe fo ech d stsf ddtvt oet. d Coesodg to the codtol sefl foto ese c be defed s log / whee d e ot tlt d obblt dstbtos of d esectvel d s the codtol obblt dstbto. Let the we hve log O sl les we c defe etc. 22

11 23 Net we cosde log o log t les log log Sll we c ove tht Fo d togethe we hve o Let... 2 be set of t lhbet wth lettes d be set of ott lhbet wth lettes.let d d be the obblt dstbto fcto defed o d esectvel. Let be tlt coesodg wth fo ech. The vege sefl tl foto c be defed s

12 So the chel cct wth tltes o weghted chel cct of eoless chel s gve b C M A Dscete Meoless Chel d ts Chel Cct wth tlt A geel Method fo deteg the cct of dscete chel ws sggested b Mog 953. B lg ths ethod Te d Sh 995 coted the weghted cct of DMC de weghted efoce costts. ths secto we eset ew detestc odel fo weghted chel cct of dscete eoless chel tes of tl foto ese gve b A dscete eo less chel wth ts d otts sbols s descbed b stochstc t... : A :... A [ ] 2... d 2... whee d. Now we ove theoe fo vege tl foto of dscete eoless chels wth tlt. Theoe 6.5. The vege sefl tl foto ocessed b chel s cove fcto of the t obbltes. Poof: Let... 2 e o-egtve bes sch tht... defe t dstbto. Let s P P

13 25 the we shll ove tht the vege sefl tl foto coesodg to P stsfes whee s the vege sefl tl foto coesodg to t dstbto P. Let K the K ] [ we ow log Sce s st s of the chel obbltes thee foe we hve o K whee K log Eqto togethe wth edces to K. t les tht K log

14 26 log log o log log log log B the geelzed Sho s eqlt we hve h log log wth eqlt ol f fo ech t les log log Ths togethe wth gves. Sce s lws ostve.ece Theoe 6.5. s oved. Net we detee the chel cct wth tltes the followg theoe: Theoe Let stochstc t A whch descbes eo less chel be sqe d o sgl d q be the eleets th ow d th col of A - =2 the the chel cct wth tltes s gve b C q 6.5.7

15 27 ovde = costt = s. Poof: Mese c lso be wtte s We ze sbect to. Fo tht we sse tht the solto does ot volve d l Lgge s ethod of ltle. Let P N L Dffeettg L w.. t. d eqtg to zeo we hve. N L Sce theefoe 6.5. we ow log log K whee d K. Net dffeettg w..t. we hve ] log [ K 6.5.

16 Ths the eqto togethe wth 6.5. d 6.5. edces to [ log ] K KN. Sce theefoe c be wtte s { [ log ] KN} K we c wte t fo s follows: A t [ log ] NK 2 [ log : : [ log 2 ] NK ] NK K K : : K 2 Sce A s o- sgl t theefoe ts vese ests. Mltlg both sdes b t t A A we hve [ log ] NK K q Ag ltlg both sdes of b d sg ove we get N q Sce L s the s of cove d le fctos theefoe t s cove o the set of o-egtve bes. t les tht fo the gve N we c fd bsolte of the fcto L ove the do d efe to Ash 996.Ths the solto elds bsolte fo the foto ocessed. 28

17 29 f we ltl both sdes of b d sg ove we hve K KN log o N o N t les N M C fo d togethe we get q C whch s the eqed eslt. ece the oof of the Theoe s coleted. 6.6 Coclso ths chte we hve dscssed the chel cct d dffeet tes of chel cct. We hve lso dscssed the Dscete Meoless chel d ts clssfcto. We hve obted odel fo the chel cct of dscete eoless chel wth tlt d oved two theoes. theoe f we t oe o oe of eql to zeo we e essetll edcg the be of ossble otts. The edced chel t s o loge sqe so tht the eses of theoe do ot l. The geel oble of cotg chel cct s oble ecl lss best teted b cove ogg ethod.

Difference Sets of Null Density Subsets of

Difference Sets of Null Density Subsets of dvces Pue Mthetcs 95-99 http://ddoog/436/p37 Pulshed Ole M (http://wwwscrpog/oul/p) Dffeece Sets of Null Dest Susets of Dwoud hd Dsted M Hosse Deptet of Mthetcs Uvest of Gul Rsht I El: hd@gulc h@googlelco

More information

Chapter Linear Regression

Chapter Linear Regression Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use

More information

A Dynamical Quasi-Boolean System

A Dynamical Quasi-Boolean System ULETNUL Uestăţ Petol Gze Ploeşt Vol LX No / - 9 Se Mtetă - otă - Fză l Qs-oole Sste Gel Mose Petole-Gs Uest o Ploest ots etet est 39 Ploest 68 o el: ose@-loesto stt Ths e oes the esto o ol theoetl oet:

More information

Chapter #2 EEE State Space Analysis and Controller Design

Chapter #2 EEE State Space Analysis and Controller Design Chpte EEE8- Chpte # EEE8- Stte Spce Al d Cotolle Deg Itodcto to tte pce Obevblt/Cotollblt Modle ede: D D Go - d.go@cl.c.k /4 Chpte EEE8-. Itodcto Ae tht we hve th ode te: f, ', '',.... Ve dffclt to td

More information

A PAIR OF HIGHER ORDER SYMMETRIC NONDIFFERENTIABLE MULTIOBJECTIVE MINI-MAXMIXED PROGRAMMING PROBLEMS

A PAIR OF HIGHER ORDER SYMMETRIC NONDIFFERENTIABLE MULTIOBJECTIVE MINI-MAXMIXED PROGRAMMING PROBLEMS Iteatoal Joal of Cote Scece ad Cocato Vol. 3, No., Jaa-Je 0,. 9-5 A PAIR OF HIGHER ORDER SYMMERIC NONDIFFERENIABLE MULIOBJECIVE MINI-MAXMIXED PROGRAMMING PROBLEMS Aa Ka ath ad Gaat Dev Deatet of Matheatcs,

More information

= y and Normed Linear Spaces

= y and Normed Linear Spaces 304-50 LINER SYSTEMS Lectue 8: Solutos to = ad Nomed Lea Spaces 73 Fdg N To fd N, we eed to chaacteze all solutos to = 0 Recall that ow opeatos peseve N, so that = 0 = 0 We ca solve = 0 ecusvel backwads

More information

On the Trivariate Polynomial Interpolation

On the Trivariate Polynomial Interpolation WSEAS RANSACIONS o MAHEMAICS Sle Sf O the vte Polol Itepolto SULEYMAN SAFAK Dvso of Mthetcs Fclt of Eee Do Elül Uvest 56 ıtepe c İ URKEY. sle.sf@de.ed.t Abstct: hs ppe s coceed wth the fole fo copt the

More information

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures.

are positive, and the pair A, B is controllable. The uncertainty in is introduced to model control failures. Lectue 4 8. MRAC Desg fo Affe--Cotol MIMO Systes I ths secto, we cosde MRAC desg fo a class of ult-ut-ult-outut (MIMO) olea systes, whose lat dyacs ae lealy aaetezed, the ucetates satsfy the so-called

More information

Fredholm Type Integral Equations with Aleph-Function. and General Polynomials

Fredholm Type Integral Equations with Aleph-Function. and General Polynomials Iteto Mthetc Fou Vo. 8 3 o. 989-999 HIKI Ltd.-h.co Fedho Te Iteg uto th eh-fucto d Gee Poo u J K.J. o Ittute o Mgeet tude & eech Mu Id u5@g.co Kt e K.J. o Ittute o Mgeet tude & eech Mu Id dehuh_3@hoo.co

More information

Chapter 17. Least Square Regression

Chapter 17. Least Square Regression The Islmc Uvest of Gz Fcult of Egeeg Cvl Egeeg Deptmet Numecl Alss ECIV 336 Chpte 7 Lest que Regesso Assocte Pof. Mze Abultef Cvl Egeeg Deptmet, The Islmc Uvest of Gz Pt 5 - CURVE FITTING Descbes techques

More information

Theory of Finsler spaces with ( λβ, ) Metric

Theory of Finsler spaces with ( λβ, ) Metric Theoy of Fsle sces wth ( λβ ) Metc Dhed Thu Kll Multle us Thuv Uvesty Kll DhdhNel E-l: dhedthuc@lco ABTRAT The of ths e s to toduce d study the cocet of ( ) theoes hve ee woout fo ( ) etc whee (x)y s oe

More information

SUBSEQUENCE CHARACTERIZAT ION OF UNIFORM STATISTICAL CONVERGENCE OF DOUBLE SEQUENCE

SUBSEQUENCE CHARACTERIZAT ION OF UNIFORM STATISTICAL CONVERGENCE OF DOUBLE SEQUENCE Reseach ad Coucatos atheatcs ad atheatcal ceces Vol 9 Issue 7 Pages 37-5 IN 39-6939 Publshed Ole o Novebe 9 7 7 Jyot cadec Pess htt//yotacadecessog UBEQUENCE CHRCTERIZT ION OF UNIFOR TTITIC CONVERGENCE

More information

Moments of Generalized Order Statistics from a General Class of Distributions

Moments of Generalized Order Statistics from a General Class of Distributions ISSN 684-843 Jol of Sttt Vole 5 28. 36-43 Moet of Geelzed Ode Sttt fo Geel l of Dtto Att Mhd Fz d Hee Ath Ode ttt eod le d eel othe odel of odeed do le e ewed el e of geelzed ode ttt go K 995. I th e exlt

More information

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN

European Journal of Mathematics and Computer Science Vol. 3 No. 1, 2016 ISSN ISSN Euroe Jour of Mthemtcs d omuter Scece Vo. No. 6 ISSN 59-995 ISSN 59-995 ON AN INVESTIGATION O THE MATRIX O THE SEOND PARTIA DERIVATIVE IN ONE EONOMI DYNAMIS MODE S. I. Hmdov Bu Stte Uverst ABSTRAT The

More information

ˆ SSE SSE q SST R SST R q R R q R R q

ˆ SSE SSE q SST R SST R q R R q R R q Bll Evas Spg 06 Sggested Aswes, Poblem Set 5 ECON 3033. a) The R meases the facto of the vaato Y eplaed by the model. I ths case, R =SSM/SST. Yo ae gve that SSM = 3.059 bt ot SST. Howeve, ote that SST=SSM+SSE

More information

ECONOMETRIC ANALYSIS ON EFFICIENCY OF ESTIMATOR ABSTRACT

ECONOMETRIC ANALYSIS ON EFFICIENCY OF ESTIMATOR ABSTRACT ECOOMETRIC LYSIS O EFFICIECY OF ESTIMTOR M. Khohev, Lectue, Gffth Uvet, School of ccoutg d Fce, utl F. K, tt Pofeo, Mchuett Ittute of Techolog, Deptet of Mechcl Egeeg, US; cuetl t Shf Uvet, I. Houl P.

More information

CURVE FITTING LEAST SQUARES METHOD

CURVE FITTING LEAST SQUARES METHOD Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued

More information

χ be any function of X and Y then

χ be any function of X and Y then We have show that whe we ae gve Y g(), the [ ] [ g() ] g() f () Y o all g ()() f d fo dscete case Ths ca be eteded to clude fuctos of ay ube of ado vaables. Fo eaple, suppose ad Y ae.v. wth jot desty fucto,

More information

Available online through

Available online through Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo

More information

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle

Consumer theory. A. The preference ordering B. The feasible set C. The consumption decision. A. The preference ordering. Consumption bundle Thomas Soesso Mcoecoomcs Lecte Cosme theoy A. The efeece odeg B. The feasble set C. The cosmto decso A. The efeece odeg Cosmto bdle x ( 2 x, x,... x ) x Assmtos: Comleteess 2 Tastvty 3 Reflexvty 4 No-satato

More information

African Journal of Science and Technology (AJST) Science and Engineering Series Vol. 4, No. 2, pp GENERALISED DELETION DESIGNS

African Journal of Science and Technology (AJST) Science and Engineering Series Vol. 4, No. 2, pp GENERALISED DELETION DESIGNS Af Joul of See Tehology (AJST) See Egeeg See Vol. 4, No.,. 7-79 GENERALISED DELETION DESIGNS Mhel Ku Gh Joh Wylff Ohbo Dee of Mhe, Uvey of Nob, P. O. Bo 3097, Nob, Key ABSTRACT:- I h e yel gle ele fol

More information

Journal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi SOME PROPERTIES CONCERNING THE HYPERSURFACES OF A WEYL SPACE

Journal of Engineering and Natural Sciences Mühendislik ve Fen Bilimleri Dergisi SOME PROPERTIES CONCERNING THE HYPERSURFACES OF A WEYL SPACE Jou of Eee d Ntu Scece Mühed e Fe Be De S 5/4 SOME PROPERTIES CONCERNING THE HYPERSURFACES OF A EYL SPACE N KOFOĞLU M S Güze St Üete, Fe-Edeyt Füte, Mtet Böüü, Beştş-İSTANBUL Geş/Receed:..4 Ku/Accepted:

More information

A convex hull characterization

A convex hull characterization Pue d ppled Mthets Joul 4; (: 4-48 Pulshed ole My 4 (http://www.seepulshggoup.o//p do:.648/.p.4. ove hull htezto Fo Fesh Gov Qut Deptet DISG Uvesty of Se Itly El ddess: fesh@us.t (F. Fesh qut@us.t (G.

More information

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S

RECAPITULATION & CONDITIONAL PROBABILITY. Number of favourable events n E Total number of elementary events n S Fomulae Fo u Pobablty By OP Gupta [Ida Awad We, +91-9650 350 480] Impotat Tems, Deftos & Fomulae 01 Bascs Of Pobablty: Let S ad E be the sample space ad a evet a expemet espectvely Numbe of favouable evets

More information

«A first lesson on Mathematical Induction»

«A first lesson on Mathematical Induction» Bcgou ifotio: «A fist lesso o Mtheticl Iuctio» Mtheticl iuctio is topic i H level Mthetics It is useful i Mtheticl copetitios t ll levels It hs bee coo sight tht stuets c out the poof b theticl iuctio,

More information

NONDIFFERENTIABLE MATHEMATICAL PROGRAMS. OPTIMALITY AND HIGHER-ORDER DUALITY RESULTS

NONDIFFERENTIABLE MATHEMATICAL PROGRAMS. OPTIMALITY AND HIGHER-ORDER DUALITY RESULTS HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMANIAN ACADEMY, See A, OF HE ROMANIAN ACADEMY Volue 9, Nube 3/8,. NONDIFFERENIABLE MAHEMAICAL PROGRAMS. OPIMALIY AND HIGHER-ORDER DUALIY RESULS Vale PREDA Uvety

More information

SOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE

SOME REMARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOMIAL ASYMPTOTE D I D A C T I C S O F A T H E A T I C S No (4) 3 SOE REARKS ON HORIZONTAL, SLANT, PARABOLIC AND POLYNOIAL ASYPTOTE Tdeusz Jszk Abstct I the techg o clculus, we cosde hozotl d slt symptote I ths ppe the

More information

XII. Addition of many identical spins

XII. Addition of many identical spins XII. Addto of may detcal sps XII.. ymmetc goup ymmetc goup s the goup of all possble pemutatos of obects. I total! elemets cludg detty opeato. Each pemutato s a poduct of a ceta fte umbe of pawse taspostos.

More information

Stabilizing gain design for PFC (Predictive Functional Control) with estimated disturbance feed-forward

Stabilizing gain design for PFC (Predictive Functional Control) with estimated disturbance feed-forward Stblzg g sg o PFC Pctv Fctol Cotol wth stt stbc -ow. Zbt R. Hb. och Dtt o Pocss Egg Plt Dsg Lboto o Pocss Atoto Colog Uvst o Al Scc D-5679 öl Btzo St. -l: hl.zbt@sl.h-ol. {obt.hb l.och}@ h-ol. Abstct:

More information

Describes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates.

Describes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates. CURVE FITTING Descbes techques to ft cuves (cuve fttg) to dscete dt to obt temedte estmtes. Thee e two geel ppoches fo cuve fttg: Regesso: Dt ehbt sgfct degee of sctte. The stteg s to deve sgle cuve tht

More information

AN ALGEBRAIC APPROACH TO M-BAND WAVELETS CONSTRUCTION

AN ALGEBRAIC APPROACH TO M-BAND WAVELETS CONSTRUCTION AN ALGEBRAIC APPROACH TO -BAN WAELETS CONSTRUCTION Toy L Qy S Pewe Ho Ntol Lotoy o e Peeto Pe Uety Be 8 P. R. C Att T e eet le o to ott - otool welet e. A yte of ott eto ote fo - otool flte te olto e o

More information

Professor Wei Zhu. 1. Sampling from the Normal Population

Professor Wei Zhu. 1. Sampling from the Normal Population AMS570 Pofesso We Zhu. Samplg fom the Nomal Populato *Example: We wsh to estmate the dstbuto of heghts of adult US male. It s beleved that the heght of adult US male follows a omal dstbuto N(, ) Def. Smple

More information

COMPLEX NUMBERS AND DE MOIVRE S THEOREM

COMPLEX NUMBERS AND DE MOIVRE S THEOREM COMPLEX NUMBERS AND DE MOIVRE S THEOREM OBJECTIVE PROBLEMS. s equl to b d. 9 9 b 9 9 d. The mgr prt of s 5 5 b 5. If m, the the lest tegrl vlue of m s b 8 5. The vlue of 5... s f s eve, f s odd b f s eve,

More information

6.6 Moments and Centers of Mass

6.6 Moments and Centers of Mass th 8 www.tetodre.co 6.6 oets d Ceters of ss Our ojectve here s to fd the pot P o whch th plte of gve shpe lces horzotll. Ths pot s clled the ceter of ss ( or ceter of grvt ) of the plte.. We frst cosder

More information

ON NILPOTENCY IN NONASSOCIATIVE ALGEBRAS

ON NILPOTENCY IN NONASSOCIATIVE ALGEBRAS Jourl of Algebr Nuber Theory: Advces d Applctos Volue 6 Nuber 6 ges 85- Avlble t http://scetfcdvces.co. DOI: http://dx.do.org/.864/t_779 ON NILOTENCY IN NONASSOCIATIVE ALGERAS C. J. A. ÉRÉ M. F. OUEDRAOGO

More information

Lecture 3 summary. C4 Lecture 3 - Jim Libby 1

Lecture 3 summary. C4 Lecture 3 - Jim Libby 1 Lecue su Fes of efeece Ivce ude sfoos oo of H wve fuco: d-fucos Eple: e e - µ µ - Agul oeu s oo geeo Eule gles Geec slos cosevo lws d Noehe s heoe C4 Lecue - Lbb Fes of efeece Cosde fe of efeece O whch

More information

Some Equivalent Forms of Bernoulli s Inequality: A Survey *

Some Equivalent Forms of Bernoulli s Inequality: A Survey * Ale Mthets 3 4 7-93 htt://oog/436/34746 Pulshe Ole Jul 3 (htt://wwwsog/joul/) Soe Euvlet Fos of Beoull s Ieult: A Suve * u-chu L Cheh-Chh eh 3 Detet of Ale Mthets Ntol Chug-Hsg Uvest Tw Detet of Mthets

More information

Stats & Summary

Stats & Summary Stts 443.3 & 85.3 Summr The Woodbur Theorem BCD B C D B D where the verses C C D B, d est. Block Mtrces Let the m mtr m q q m be rttoed to sub-mtrces,,,, Smlrl rtto the m k mtr B B B mk m B B l kl Product

More information

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS

3. REVIEW OF PROPERTIES OF EIGENVALUES AND EIGENVECTORS . REVIEW OF PROPERTIES OF EIGENVLUES ND EIGENVECTORS. EIGENVLUES ND EIGENVECTORS We hll ow revew ome bc fct from mtr theory. Let be mtr. clr clled egevlue of f there et ozero vector uch tht Emle: Let 9

More information

ANOTHER INTEGER NUMBER ALGORITHM TO SOLVE LINEAR EQUATIONS (USING CONGRUENCY)

ANOTHER INTEGER NUMBER ALGORITHM TO SOLVE LINEAR EQUATIONS (USING CONGRUENCY) ANOTHER INTEGER NUMBER ALGORITHM TO SOLVE LINEAR EQUATIONS (USING CONGRUENCY) Floet Smdche, Ph D Aocte Pofeo Ch of Deptmet of Mth & Scece Uvety of New Mexco 2 College Rod Gllup, NM 873, USA E-ml: md@um.edu

More information

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix. Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof

More information

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition

Overview. Review Superposition Solution. Review Superposition. Review x and y Swap. Review General Superposition ylcal aplace Soltos ebay 6 9 aplace Eqato Soltos ylcal Geoety ay aetto Mechacal Egeeg 5B Sea Egeeg Aalyss ebay 6 9 Ovevew evew last class Speposto soltos tocto to aal cooates Atoal soltos of aplace s eqato

More information

Generalisation on the Zeros of a Family of Complex Polynomials

Generalisation on the Zeros of a Family of Complex Polynomials Ieol Joul of hemcs esech. ISSN 976-584 Volume 6 Numbe 4. 93-97 Ieol esech Publco House h://www.house.com Geelso o he Zeos of Fmly of Comlex Polyomls Aee sgh Neh d S.K.Shu Deme of hemcs Lgys Uvesy Fdbd-

More information

Multidimensional fixed point results for two hybrid pairs in partially ordered metric space

Multidimensional fixed point results for two hybrid pairs in partially ordered metric space MAYFEB Jorl of Mtetcs - IN 7-69 Vol 7 - Pes 56-7 Mltesol fe ot reslts for two ybr rs rtlly orere etrc sce R A Rsw rr_rsw5@yooco I Mostf s6@yooco Dertet of Mtetcs Fclty of cece Asst Uversty Asst 756 Eyt

More information

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type J N Sce & Mh Res Vol 3 No (7) -7 Alble ole h://orlwlsogocd/deh/sr P-Coey Proery Msel-Orlcz Fco Sce o Boher ye Yl Rodsr Mhecs Edco Deree Fcly o Ss d echology Uerss sl Neger Wlsogo Cerl Jdoes Absrcs Corresodg

More information

I. Exponential Function

I. Exponential Function MATH & STAT Ch. Eoetil Fuctios JCCSS I. Eoetil Fuctio A. Defiitio f () =, whee ( > 0 ) d is the bse d the ideedet vible is the eoet. [ = 1 4 4 4L 4 ] ties (Resf () = is owe fuctio i which the bse is the

More information

Answer: First, I ll show how to find the terms analytically then I ll show how to use the TI to find them.

Answer: First, I ll show how to find the terms analytically then I ll show how to use the TI to find them. . CHAPTER 0 SEQUENCE, SERIES, d INDUCTION Secto 0. Seqece A lst of mers specfc order. E / Fd the frst terms : of the gve seqece: Aswer: Frst, I ll show how to fd the terms ltcll the I ll show how to se

More information

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx

On EPr Bimatrices II. ON EP BIMATRICES A1 A Hence x. is said to be EP if it satisfies the condition ABx Iteatoal Joual of Mathematcs ad Statstcs Iveto (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 www.jms.og Volume Issue 5 May. 4 PP-44-5 O EP matces.ramesh, N.baas ssocate Pofesso of Mathematcs, ovt. ts College(utoomous),Kumbakoam.

More information

COMP 465: Data Mining More on PageRank

COMP 465: Data Mining More on PageRank COMP 465: Dt Mnng Moe on PgeRnk Sldes Adpted Fo: www.ds.og (Mnng Mssve Dtsets) Powe Iteton: Set = 1/ 1: = 2: = Goto 1 Exple: d 1/3 1/3 5/12 9/24 6/15 = 1/3 3/6 1/3 11/24 6/15 1/3 1/6 3/12 1/6 3/15 Iteton

More information

MTH 146 Class 7 Notes

MTH 146 Class 7 Notes 7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg

More information

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

More information

Maximum likelihood estimate of phylogeny. BIOL 495S/ CS 490B/ MATH 490B/ STAT 490B Introduction to Bioinformatics April 24, 2002

Maximum likelihood estimate of phylogeny. BIOL 495S/ CS 490B/ MATH 490B/ STAT 490B Introduction to Bioinformatics April 24, 2002 Mmm lkelhood eme of phylogey BIO 9S/ S 90B/ MH 90B/ S 90B Iodco o Bofomc pl 00 Ovevew of he pobblc ppoch o phylogey o k ee ccodg o he lkelhood d ee whee d e e of eqece d ee by ee wh leve fo he eqece. he

More information

Lecture 2: The Simple Regression Model

Lecture 2: The Simple Regression Model Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.

More information

On The Circulant K Fibonacci Matrices

On The Circulant K Fibonacci Matrices IOSR Jou of Mthetcs (IOSR-JM) e-issn: 78-578 p-issn: 39-765X. Voue 3 Issue Ve. II (M. - Ap. 07) PP 38-4 www.osous.og O he Ccut K bocc Mtces Sego co (Deptet of Mthetcs Uvesty of Ls Ps de G C Sp) Abstct:

More information

2. Elementary Linear Algebra Problems

2. Elementary Linear Algebra Problems . Eleety e lge Pole. BS: B e lge Suoute (Pog pge wth PCK) Su of veto opoet:. Coputto y f- poe: () () () (3) N 3 4 5 3 6 4 7 8 Full y tee Depth te tep log()n Veto updte the f- poe wth N : ) ( ) ( ) ( )

More information

Week 10: DTMC Applications Ranking Web Pages & Slotted ALOHA. Network Performance 10-1

Week 10: DTMC Applications Ranking Web Pages & Slotted ALOHA. Network Performance 10-1 Week : DTMC Alictions Rnking Web ges & Slotted ALOHA etwok efonce - Outline Aly the theoy of discete tie Mkov chins: Google s nking of web-ges Wht ge is the use ost likely seching fo? Foulte web-gh s Mkov

More information

Technical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005.

Technical Appendix for Inventory Management for an Assembly System with Product or Component Returns, DeCroix and Zipkin, Management Science 2005. Techc Appedx fo Iveoy geme fo Assemy Sysem wh Poduc o Compoe eus ecox d Zp geme Scece 2005 Lemm µ µ s c Poof If J d µ > µ he ˆ 0 µ µ µ µ µ µ µ µ Sm gumes essh he esu f µ ˆ > µ > µ > µ o K ˆ If J he so

More information

Mathematical Statistics

Mathematical Statistics 7 75 Ode Sttistics The ode sttistics e the items o the dom smple ed o odeed i mitude om the smllest to the lest Recetl the impotce o ode sttistics hs icesed owi to the moe equet use o opmetic ieeces d

More information

Coordinate Transformations

Coordinate Transformations Coll of E Copt Scc Mchcl E Dptt Nots o E lss Rvs pl 6, Istcto: L Ctto Coot Tsfotos Itocto W wt to c ot o lss lttv coot ssts. Most stts hv lt wth pol sphcl coot ssts. I ths ots, w wt to t ths oto of fft

More information

Certain Expansion Formulae Involving a Basic Analogue of Fox s H-Function

Certain Expansion Formulae Involving a Basic Analogue of Fox s H-Function vlle t htt:vu.edu l. l. Mth. ISSN: 93-9466 Vol. 3 Iue Jue 8. 8 36 Pevouly Vol. 3 No. lcto d led Mthetc: Itetol Joul M Cet Exo Foule Ivolvg c logue o Fox -Fucto S.. Puoht etet o c-scece Mthetc College o

More information

Numerical Methods for Eng [ENGR 391] [Lyes KADEM 2007] Direct Method; Newton s Divided Difference; Lagrangian Interpolation; Spline Interpolation.

Numerical Methods for Eng [ENGR 391] [Lyes KADEM 2007] Direct Method; Newton s Divided Difference; Lagrangian Interpolation; Spline Interpolation. Nuecl Methods o Eg [ENGR 39 [Les KADEM 7 CHAPTER V Itepolto d Regesso Topcs Itepolto Regesso Dect Method; Newto s Dvded Deece; Lgg Itepolto; ple Itepolto Le d o-le Wht s tepolto? A ucto s ote gve ol t

More information

Union, Intersection, Product and Direct Product of Prime Ideals

Union, Intersection, Product and Direct Product of Prime Ideals Globl Jourl of Pure d Appled Mthemtcs. ISSN 0973-1768 Volume 11, Number 3 (2015), pp. 1663-1667 Reserch Id Publctos http://www.rpublcto.com Uo, Itersecto, Product d Drect Product of Prme Idels Bdu.P (1),

More information

Kummer Beta -Weibull Geometric Distribution. A New Generalization of Beta -Weibull Geometric Distribution

Kummer Beta -Weibull Geometric Distribution. A New Generalization of Beta -Weibull Geometric Distribution ttol Jol of Ss: Bs Al Rsh JSBAR SSN 37-453 Pt & Ol htt://gss.og/.h?joljolofbsaal ---------------------------------------------------------------------------------------------------------------------------

More information

Closing the Gap of Multicast Capacity for Hybrid Wireless Networks

Closing the Gap of Multicast Capacity for Hybrid Wireless Networks Closg the Gp of Multcst Cpcty fo Hybd Weless Netwos Xg-Yg L, Xufe Mo d Shoje Tg Abstct We study the ultcst cpcty of do hybd weless etwo cosstg of weless tels d bse sttos. Assue tht weless tels odes e doly

More information

The z-transform. LTI System description. Prof. Siripong Potisuk

The z-transform. LTI System description. Prof. Siripong Potisuk The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put

More information

7.5-Determinants in Two Variables

7.5-Determinants in Two Variables 7.-eteminnts in Two Vibles efinition of eteminnt The deteminnt of sque mti is el numbe ssocited with the mti. Eve sque mti hs deteminnt. The deteminnt of mti is the single ent of the mti. The deteminnt

More information

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find

CBSE SAMPLE PAPER SOLUTIONS CLASS-XII MATHS SET-2 CBSE , ˆj. cos. SECTION A 1. Given that a 2iˆ ˆj. We need to find BSE SMLE ER SOLUTONS LSS-X MTHS SET- BSE SETON Gv tht d W d to fd 7 7 Hc, 7 7 7 Lt, W ow tht Thus, osd th vcto quto of th pl z - + z = - + z = Thus th ts quto of th pl s - + z = Lt d th dstc tw th pot,,

More information

A New Batch FHE Scheme over the Integers

A New Batch FHE Scheme over the Integers A New Bth FHE Shee oe the Iteges Kw W Sog K Cho U Shoo of Mthets K I Sg Uesty yogyg Deot eoe s e of Koe Astt The FHE (fy hoooh eyto) shees [7 3] sed o the odfed AGCD oe (osefee AGCD oe) e ee to t tts ese

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information

FRACTIONAL MELLIN INTEGRAL TRANSFORM IN (0, 1/a)

FRACTIONAL MELLIN INTEGRAL TRANSFORM IN (0, 1/a) Ieol Jol o Se Reeh Pblo Volme Ie 5 y ISSN 5-5 FRACTIONAL ELLIN INTEGRAL TRANSFOR IN / S.. Kh R..Pe* J.N.Slke** Deme o hem hh Aemy o Egeeg Al-45 Pe I oble No.: 98576F No.: -785759 Eml-mkh@gml.om Deme o

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt

More information

The formulae in this booklet have been arranged according to the unit in which they are first

The formulae in this booklet have been arranged according to the unit in which they are first Fomule Booklet Fomule Booklet The fomule ths ooklet hve ee ge og to the ut whh the e fst toue. Thus te sttg ut m e eque to use the fomule tht wee toue peeg ut e.g. tes sttg C mght e epete to use fomule

More information

Lecture 9-3/8/10-14 Spatial Description and Transformation

Lecture 9-3/8/10-14 Spatial Description and Transformation Letue 9-8- tl Deton nd nfomton Homewo No. Due 9. Fme ngement onl. Do not lulte...8..7.8 Otonl et edt hot oof tht = - Homewo No. egned due 9 tud eton.-.. olve oblem:.....7.8. ee lde 6 7. e Mtlb on. f oble.

More information

MATRIX AND VECTOR NORMS

MATRIX AND VECTOR NORMS Numercl lyss for Egeers Germ Jord Uversty MTRIX ND VECTOR NORMS vector orm s mesure of the mgtude of vector. Smlrly, mtr orm s mesure of the mgtude of mtr. For sgle comoet etty such s ordry umers, the

More information

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com

More information

Lower and upper bound for parametric Useful R-norm information measure

Lower and upper bound for parametric Useful R-norm information measure Iteratoal Joral of Statstcs ad Aled Mathematcs 206; (3): 6-20 ISS: 2456-452 Maths 206; (3): 6-20 206 Stats & Maths wwwmathsjoralcom eceved: 04-07-206 Acceted: 05-08-206 haesh Garg Satsh Kmar ower ad er

More information

Keywords: Heptic non-homogeneous equation, Pyramidal numbers, Pronic numbers, fourth dimensional figurate numbers.

Keywords: Heptic non-homogeneous equation, Pyramidal numbers, Pronic numbers, fourth dimensional figurate numbers. [Gol 5: M 0] ISSN: 77-9655 IJEST INTENTIONL JOUNL OF ENGINEEING SCIENCES & ESECH TECHNOLOGY O the Hetc No-Hoogeeous Euto th Four Ukos z 6 0 M..Gol * G.Suth S.Vdhlksh * Dertet of MthetcsShrt Idr Gdh CollegeTrch

More information

DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCE-CONSTRAINED PROGRAMMING APPROACH. 1. Introduction

DATA ENVELOPMENT ANALYSIS WITH FUZZY RANDOM INPUTS AND OUTPUTS: A CHANCE-CONSTRAINED PROGRAMMING APPROACH. 1. Introduction Iaa Joa of Fzz Sstes Vo. No. 5. -9 DAA ENVEOPMEN ANAYSIS WIH FUZZY ANDOM INPUS AND OUPUS: A CHANCE-CONSAINED POGAMMING APPOACH S. AMEZANZADEH M. MEMAIANI AND S. SAAI ABSAC. I ths ae we dea wth fzz ado

More information

Introduction to mathematical Statistics

Introduction to mathematical Statistics Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs

More information

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi

( m is the length of columns of A ) spanned by the columns of A : . Select those columns of B that contain a pivot; say those are Bi Assgmet /MATH 47/Wte Due: Thusday Jauay The poblems to solve ae umbeed [] to [] below Fst some explaatoy otes Fdg a bass of the colum-space of a max ad povg that the colum ak (dmeso of the colum space)

More information

A Unified Formula for The nth Derivative and The nth Anti-Derivative of the Bessel Function of Real Orders

A Unified Formula for The nth Derivative and The nth Anti-Derivative of the Bessel Function of Real Orders Aec Joul of Aled Mthetc d Stttc 5 Vol 3 No 3-4 Avlble ole t htt://ubceubco/j/3/3/3 Scece d Educto Publhg DOI:69/j-3-3-3 A Ufed Foul fo The th Devtve d The th At-Devtve of the eel Fucto of Rel Ode Mhe M

More information

Generalized Duality for a Nondifferentiable Control Problem

Generalized Duality for a Nondifferentiable Control Problem Aeca Joal of Appled Matheatcs ad Statstcs, 4, Vol., No. 4, 93- Avalable ole at http://pbs.scepb.co/aas//4/3 Scece ad Edcato Pblsh DO:.69/aas--4-3 Geealzed Dalty fo a Nodffeetable Cotol Poble. Hsa,*, Vkas

More information

Complete Classification of BKM Lie Superalgebras Possessing Strictly Imaginary Property

Complete Classification of BKM Lie Superalgebras Possessing Strictly Imaginary Property Appled Mthemtcs 4: -5 DOI: 59/m4 Complete Clssfcto of BKM Le Supelges Possessg Stctly Imgy Popety N Sthumoothy K Pydhs Rmu Isttute fo Advced study Mthemtcs Uvesty of Mds Che 6 5 Id Astct I ths ppe complete

More information

University of Pavia, Pavia, Italy. North Andover MA 01845, USA

University of Pavia, Pavia, Italy. North Andover MA 01845, USA Iteatoal Joual of Optmzato: heoy, Method ad Applcato 27-5565(Pt) 27-6839(Ole) wwwgph/otma 29 Global Ifomato Publhe (HK) Co, Ltd 29, Vol, No 2, 55-59 η -Peudoleaty ad Effcecy Gogo Gog, Noma G Rueda 2 *

More information

Sequences and series Mixed exercise 3

Sequences and series Mixed exercise 3 eqees seies Mixe exeise Let = fist tem = ommo tio. tem = 7 = 7 () 6th tem = 8 = 8 () Eqtio () Eqtio (): 8 7 8 7 8 7 m to te tems 0 o 0 0 60.7 60.7 79.089 Diffeee betwee 0 = 8. 79.089 =.6 ( s.f.) 0 The

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

TiCC TR November, Gauss Sums, Partitions and Constant-Value Codes. A.J. van Zanten. TiCC, Tilburg University Tilburg, The Netherlands

TiCC TR November, Gauss Sums, Partitions and Constant-Value Codes. A.J. van Zanten. TiCC, Tilburg University Tilburg, The Netherlands Tlburg ceter for Cogto d Coucto P.O. Box 953 Tlburg Uversty 5 LE Tlburg, The Netherlds htt://www.tlburguversty.edu/reserch/sttutes-d-reserch-grous/tcc/cc/techcl-reorts/ El: tcc@uvt.l Coyrght A.J. v Zte,

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

ICS141: Discrete Mathematics for Computer Science I

ICS141: Discrete Mathematics for Computer Science I Uversty o Hw ICS: Dscrete Mthemtcs or Computer Scece I Dept. Iormto & Computer Sc., Uversty o Hw J Stelovsy bsed o sldes by Dr. Be d Dr. Stll Orgls by Dr. M. P. Fr d Dr. J.L. Gross Provded by McGrw-Hll

More information

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ] Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles

More information

Language Processors F29LP2, Lecture 5

Language Processors F29LP2, Lecture 5 Lnguge Pocessos F29LP2, Lectue 5 Jmie Gy Feuy 2, 2014 1 / 1 Nondeteministic Finite Automt (NFA) NFA genelise deteministic finite utomt (DFA). They llow sevel (0, 1, o moe thn 1) outgoing tnsitions with

More information

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES Avlble ole t http://sc.org J. Mth. Comput. Sc. 4 (04) No. 05-7 ISSN: 97-507 SUM PROPERTIES OR THE K-UCAS NUMBERS WITH ARITHMETIC INDEXES BIJENDRA SINGH POOJA BHADOURIA AND OMPRAKASH SIKHWA * School of

More information

PROGRESSION AND SERIES

PROGRESSION AND SERIES INTRODUCTION PROGRESSION AND SERIES A gemet of umbes {,,,,, } ccodig to some well defied ule o set of ules is clled sequece Moe pecisely, we my defie sequece s fuctio whose domi is some subset of set of

More information

--- Deceased Information. A1ry't (Ay't olll n5. F\ease turn page ) lslamic Community Center of Tempe. Please print all information clearly.

--- Deceased Information. A1ry't (Ay't olll n5. F\ease turn page ) lslamic Community Center of Tempe. Please print all information clearly. A1y't (Ay't lll l uty ete ee lese t ll t lely. Deese t st e Mle e s t\e Lee De Bth Age Dte Seultv t\ube h :;;"' :;...".::.."'t ' ' ue uetttte ube use Deth Heste Als ( y); he t vle hve lll be use t etg

More information

Nonlocal Boundary Value Problem for Nonlinear Impulsive q k Symmetric Integrodifference Equation

Nonlocal Boundary Value Problem for Nonlinear Impulsive q k Symmetric Integrodifference Equation OSR ol o Mec OSR-M e-ssn: 78-578 -SSN: 9-765X Vole e Ve M - A 7 PP 95- wwwojolog Nolocl Bo Vle Poble o Nole lve - Sec egoeece Eo Log Ceg Ceg Ho * Yeg He ee o Mec Yb Uve Yj PR C Abc: A oe ole lve egoeece

More information

Integration by Parts for D K

Integration by Parts for D K Itertol OPEN ACCESS Jourl Of Moder Egeerg Reserc IJMER Itegrto y Prts for D K Itegrl T K Gr, S Ry 2 Deprtmet of Mtemtcs, Rgutpur College, Rgutpur-72333, Purul, West Begl, Id 2 Deprtmet of Mtemtcs, Ss Bv,

More information

CURVE FITTING ON EMPIRICAL DATA WHEN BOTH VARIABLES ARE LOADED BY ERRORS

CURVE FITTING ON EMPIRICAL DATA WHEN BOTH VARIABLES ARE LOADED BY ERRORS TOME VI (ye 8) FASCICULE (ISSN 584 665) CURVE FITTING ON EMPIRICAL ATA WHEN BOTH VARIABLES ARE LOAE BY ERRORS ANRÁS NYĺRI LÁSZLÓ ÖNÖZSY Pofesso emets etmet of Fd d Het Egeeg Uvesty of Msoc H-55 Msoc-Egyetemváos

More information

Camera calibration & radiometry

Camera calibration & radiometry Caera calbrato & radoetr Readg: Chapter 2, ad secto 5.4, Forsth & oce Chapter, Hor Optoal readg: Chapter 4, Forsth & oce Sept. 2, 22 MI 6.8/6.866 rofs. Freea ad Darrell Req: F 2, 5.4, H Opt: F 4 Req: F

More information

Spectral Continuity: (p, r) - Α P And (p, k) - Q

Spectral Continuity: (p, r) - Α P And (p, k) - Q IOSR Joul of Mthemtcs (IOSR-JM) e-issn: 2278-5728, p-issn: 2319-765X Volume 11, Issue 1 Ve 1 (J - Feb 215), PP 13-18 wwwosjoulsog Spectl Cotuty: (p, ) - Α P Ad (p, k) - Q D Sethl Kum 1 d P Mhesw Nk 2 1

More information

Fibonacci and Lucas Numbers as Tridiagonal Matrix Determinants

Fibonacci and Lucas Numbers as Tridiagonal Matrix Determinants Rochester Isttute of echology RI Scholr Wors Artcles 8-00 bocc d ucs Nubers s rdgol trx Deterts Nth D. Chll Est Kod Copy Drre Nry Rochester Isttute of echology ollow ths d ddtol wors t: http://scholrwors.rt.edu/rtcle

More information