New Error Model of Entropy Encoding for Image Compression

Size: px
Start display at page:

Download "New Error Model of Entropy Encoding for Image Compression"

Transcription

1 Wb St: Eml: Volum, Issu, Mrch Aprl 03 ISSN Nw Error Modl of Etropy Ecodg for Img Comprsso Moht Mshr, Rjsh Tjw, Assstt Profssor Ary Collg of Egrg & IT, Jpur (RAJASTHAN Abstrct: Etropy codg provds th losslss comprsso of dt symbols d s crtcl compot sgl comprsso lgorthm. I our cs w hv dsgd w modl whch rducs th Itrpxl rdudcy d hv bttr rsults s comprd to othr modls lk losslss prdctv cod (LPC d Dffrtl puls cod modulto (DPCM. Our w proposd Schm for Huffm codg wll chv hghr comprsso s w hv lso rduc th stdrd dvto th rror mg trmdously s compr to LPC d DPCM. Kywords: Losslss comprsso, Lossy comprsso, Rdudcy, Losslss prdctv modl. INTRODUCTION Img comprsso s th pplcto of dt comprsso o dgtl mgs. I ffct, th objctv s to rduc rdudcy of th mg dt ordr to b bl to stor or trsmt dt ffct form. Img comprsso c b lossy or losslss. Losslss comprsso s somtms prfrrd for rtfcl mgs such s tchcl drwgs, cos d comcs. Ths s bcus lossy comprsso mthods. [] Espclly wh usd t low bt rts, troduc comprsso rtfcts. Losslss comprsso mthods my lso b prfrrd for hgh vlu cott, such s mdcl mgry or mg scs md for rchvl purposs. Lossy mthods r spclly sutbl for turl mgs such s photos pplctos whr mor loss of fdlty s ccptbl to chv substtl rducto bt rt.[], [3].Rdudcy d Irrlvcy Rdudcy grl trms, rfrs to th qulty or stt of bg rdudt, tht s xcdg wht s cssry or orml, or duplcto. Ths c hv gtv cootto, spclly rhtorc: suprfluous or rpttv, or postv mplcto, spclly grg: srvg s duplct for prvtg flur of tr systm. Dt comprsso s chvd wh o of th followg rdudcs r rducd or rmovd. A mportt xmpl of dt rrlvcy occurs th vsulzto of gryscl mgs of hgh dymc rg,.g. bt or mor. It s xprmtl fct tht for moochrom mgs 6 to 8 bts of dymc rg s th lmt of hum vsul sstvty; y xtr bt dos ot dd prcptul vlu d c b lmtd.[3]..typs of Rdudcs (A Codg Rdudcy A grt dl of formto bout th pprc of mg c b obtd from hstogrm of ts gry lvl. Lt us ssum tht dscrt rdom vrbl r k th trvl [0, ] rprsts th gry lvls of mg d r tht ch k occurs wth th probblty P r ( r k. Pr ( rk k k 0,,... l (. Whr L s th umbr of gry lvls, k s th umbr of tms th k th gry lvls ppr th mg. r If th umbr of bts usd to rprst ch vlu of k s L( r k th th vrg umbr of bts rqurd to rprst ch pxl s L vg L( rk. Pr ( rk (. Thus th totl umbr of bts rqurd cod M x N mg s (MNL vg (B Itr-pxl Rdudcy Th cods usd to rprst th gry lvls of mg hv othg to do wth th corrlto btw pxls. Ths corrltos rsult from th structurl or gomtrc rltoshps btw th objcts of th mgs. (C Psycho- vsul rdudcy Th brghtss of rgo, s prcvd by th y, dpds o th fctors othr th smply th lght rflctd by th rgo. For xmpl, tsty vrtos c b prcvd r of costt tsty. Such phom rsults from th fct tht th y dos ot rspod wth qul sstvty to ll vsul formto. Crt formto s sd to b psycho-vsully rdudt.[], [9]. Grl Img Comprsso Modl Trsform: Th m gol of trsform stg s to dcorrlt th orgl mg dt, so tht th orgl sgl (mg rgy s rdstrbutd mog smll st Volum, Issu Mrch Aprl 03 Pg 4

2 Wb St: Eml: Volum, Issu, Mrch Aprl 03 ISSN of trsform coffcts. Th m of d-corrlto s to rmov th tr-pxl rdudcy; thrby provdg rprstto tht c b codd mor ffctly.[] Th zroth ordr tropy of th trsformd coffct s much lowr th tht of orgl mg. Ths trsforms should b rvrsbl tur so tht th orgl mg c b rcovrd by th vrs trsform; provdd o qutzto hs b prformd o forwrd trsform oprto. Qutzto / D-qutzto Stg: Th scod stg of qutzto / D-qutzto s th procss tht lds to th lossy comprsso. I th qutzto scto psycho-vsul rdudcy th mg s rducd by rmovg uwtd bts from th trsformd coffct. Ths stg lds to hgh comprsso rto d dstorto mg fdlty. Etropy Codg / Dcodg Stg: Th thrd stg of tropy codg, dtrms th umbr of bts rqurd to rprst prtculr mg t gv qutzto lvl. Th procss of tropy codg d dcodg s losslss. It mps th qutzd trsform coffcts to bt strm usg vrbl lgth cods. Ths stg xplots th codg rdudcy.[], [5] I optmum cod, symbols tht occurs mor frqutly (hv hghr probblts of occurrc wll hv shortr cod words th symbols tht occur lss frqutly. Th two symbols tht occurs lst frqutly wll hv th sm lgth.[0]. Etropy [8] Etropy codg whch s wy of losslss comprsso tht s do o mg ftr th qutzto stg. It bls to rprst mg mor ffct wy wth smllst mmory for storg or trsmsso. Th vrg formto pr sourc symbol s kow s tropy. [8] From sourc wth symbols, th Etropy of th sourc s th mmum thortcl of th vrg umbr of bt pr symbol H P log P P (. Whr s th probblty of th -symbol. Gv comprsso schm, ts ffccy c b msurd s: Effccy = H/(vrg codword lgth..3 Dffrtl tropy Th dffrtl tropy s gv by h ( x log (. Th dffrtl tropy of Guss rdom vrbl s drctly proportol to ts vrc. Fgur. MATHEMATICAL BACKGROUND Now w dscuss bout dffrt rror-fr comprsso pproch tht dos ot rqur dcomposto of mg to collcto of bts pls. Th pproch, commoly rfrrd to losslss prdctv codg, s bsd o lmtg th tr-pxl rdudcs of closly spcd pxls by xtrctg d codg oly th w formto ch pxl.[7].huffm Codg A commoly usd mthod for dt comprsso s Huffm codg. O of th most populr tchqus for rmovg codg rdudcy s du to Huffm. Wh codg th symbols of formto sourc dvdully, Huffm codg ylds th smllst possbl umbr of cod symbols pr sourc symbol. Th dffrtl tropy for th Guss dstrbuto hs th ddd dstcto tht t s lrgr th th dffrtl tropy of y othr cotuously dstrbutd rdom vrbl wth th sm vrc. Tht s, for y rdom vrbl X, wth vrc. [], [].3. Globl thrsholdg Th globl thrsholdg s bsd o th hstogrm of mg. I ths mthod w do prtto of th mg hstogrm usg sgl globl thrshold. Th succss of ths tchqu vry strogly dpds o how wll th hstogrm c b prttod.i most pplctos; thr s usully ough vrblty btw mgs tht, v f gobl thrsholdg s sutbl pproch d lgorthm cpbl of stmtg utomtclly th thrshold vlu for ch mg s rqurd.[9].4 Vrous Prdctv Codg Modls Now w hv dscussd bout som prdctv codg modls followg subsctos:..huffm codg lgorthm.4. Losslss prdctv modl [9] Wh w tlk bout rror-fr comprsso pproch th th pproch commoly rfrrd to losslss Volum, Issu Mrch Aprl 03 Pg 4

3 Wb St: Eml: Volum, Issu, Mrch Aprl 03 ISSN prdctv codg, s bsd o lmtg th tr-pxl rdudcs of closly spcd pxls by xtrctg d codg oly th w formto ch pxl. Th w formto of pxl s dfd s th dffrc btw th ctul d prdctv vlu of tht pxl.[9] Fgur. Losslss Prdctv Ecodr Fgur. Losslss Prdctv Dcodr.5 Dffrtl puls cod modulto Th bsc dffrtl codg systm s kow s th dffrtl puls cod modulto (DPCM systm. It s most populr s spch codg systm d s wdly usd tlpho commuctos. Th DPCM systm cossts of two mjor compots th prdctor d qutzr. [0] Prdcto Error Rcostructo s sˆ (.3. s sˆ (.3. Prdctv codg s bsclly usd to lmtg th tr-pxl rdudcs of closly spcd pxls. Th dstrbuto of put dt plys vry mportt rol bfor mg trsformto stg. If th put dt s o uform tur th th trsformd dt wll b mor uform typ d most of th coffcts wll hv sm vlu of rgy d to cod ths coffcts t wll rqur mor o of bts s th tropy wll b mor (As costrd gv by Sho s frst fudmtl thorm. Whrs cs oppost to ths f yhow th dt s uform bfor mg trsform stg th vry fw o of th trsformd coffcts wll hv most prt of th rgy d mor trsform coffcts wll hv vry lss frcto of rgy. So scod cs most of th coffcts c b lmtd wth rwrd of vry lss rgy loss d crsd comprsso prformc. Our w modl s bsd o th bov fct tht. I th proposd modl w hv trd to grt rror mg bfor mg trsform stg. Th motv bhd th grto of ths rror mg s to kp th vrg pxl vlu d th stdrd dvto t mmum so s to mk th dstrbuto mor uform to gt bttr comprsso prformc. Th codr s show followg fgur-.5 grts th pxl vlu wth th hlp of pst put. Frst t subtrct d dd th pxl vlu wth th prvous vlu d th t dvds th subtrctd d ddd wth th hlp of dvdr to grt Error Img(.Th dcodr just prform th rvrs oprto to rtrv th mg. Ecodr Rcostructo rror = qutzto rror s s q (.3.3 Th codr vlu s Fgur.4 Nw Modl Ecodr (.4. Dcodr Fgur.3: Dffrtl Puls Cod Modulto Modl Fgur.5 Nw Modl Dcodr.6 Nw Proposd Error Modl Th dcodd vlu s Volum, Issu Mrch Aprl 03 Pg 43

4 Wb St: Eml: Volum, Issu, Mrch Aprl 03 ISSN (.4. Vrtcl Dgol RESULTS W wll compr our Nw Proposd modl wth th LPC.W wll prov how our modl s bttr th LPC d Orgl mg. W hv lso show vrous rsults d tbls dpcto output of our work. 3. Dtld Alyss Proposd Modl LPC Modl Fgur 3.3: Orgl Bboo Img berror Img c Dcomposd Img t lvl hr Fgur 3.:Orgl Bboo Img berror Img c Dcomposd Img t lvl hr Fgur 4.4 : dtl horzotl hstogrm of bboo b dtl dgol hstogrm of bboo c dtl vrtcl hstogrm of bboo d Approxmto Hstogrm Orgl Img Hstogrm Tbl : Dtld lyss of bboo LPC mg Fgur 3.: dtl horzotl hstogrm of bboo b dtl dgol hstogrm of bboo c dtl vrtcl hstogrm of bboo d Approxmto Hstogrm Orgl Img Hstogrm Tbl Dtld lyss of bboo rror mg Nw modl M Stdr d Dvt o mx.vl u M.vl u trop y LPC modl Bboo rror mg Approxm to Horzotl Vrtcl M Stdrd Dvto Mx. vlu M.vlu tropy Bboo rror mg Dgol Approxmt o Horzotl Comprso of Rtd Ergy d Numbr of Zros for Dffrt Prdctv Modl Proposd Modl Volum, Issu Mrch Aprl 03 Pg 44

5 Wb St: Eml: Volum, Issu, Mrch Aprl 03 ISSN Fgur 4.5 Alyzto of bboo rror Img t Lvl wth Hr Tbl 3 Dffrt st of for bboo rror Img S.o Globl Thrshold Rtd Ergy (% No of Zros (% LPC Modl Fgur 4.5 Alyzto of bboo LPC Img t Lvl Wth Hr Tbl 3. Dffrt st of for bboo Lpc Img S.o Globl Thrshold Rtd Ergy No of Zros (% (% CONCLUSION I our proposd modl th rsults gv us w st of pxl whch forms th rror mg or rsdul mg. Th rsdul dstrbuto s typclly zro m d much mor compct th th dstrbuto of orgl mg. By dcrsg th m vlu, w hv lso dcrsd th vrg codword lgth. Th w ppld stdrd wvlt trsform (Hr t lvl d clcultd th rtd rgy d th umbrs of zros d foud tht th rtd rgy d umbr of zros for our w modl hv th bttr rsults wh thrshold vlu s tk vry low. Sc th pxl r uformly dstrbutd spc dom, th th wll b o-uformly dstrbutd frqucy dom d ll th rgy wll b cotd by oly fw frqucy coffcts d t c b glctd th frqucs hvg vry low of rgy whch wll hlpd us to us lss bts of dt durg codg. So w hv cocludd tht mor compct dstrbuto (uform rsults lowr tropy whch dtrms th mmum vrg cod word lgth tht s ttbl wthout formto loss, hc chv hghr comprsso rto wthout formto loss. REFERENCES []. R.C.Gozls & R.E.Wood, Dgtl Img Procssg, d Low Prc Ed., Prtc Hll, 007. []. K Syood, Dt Comprsso, d Ed., Morg Kufm, 005. [3]. D Slomo, Dt comprsso Th Complt Rfrc, 3rd Ed., Sprgr, 004. [4]. Myug-S Sog Etropy codg Wvlt Img comprsso, 006. [5]. G.E.Bllloch Itroducto to dt comprsso, 00. [6]. W-W Lu & M.P.Gough A Fst dptv Huffm codg lgorthm IEEE Trs.Commu. vol.4, 993. [7]. K Syood, Loslss Comprsso Hdbook, d Ed., Acdmc Prss,003. [8]. D.A.Huffm, A mthod for th costructo of mmum-rdudcy cods, Proc.IRE, vol.40, pp.098-0, 95. [9]. R.C.Gozls & R.E.Wood, Dgtl Img Procssg Usg MATLAB, st Low Prc Ed., Prtc Hll, 008. [0]. Thoms Wgd, Dgtl Img Commucto, 000. AUTHOR Moht Mshr rcvd th B.Tch. Elctrocs d Commucto Eg from Jyp Isttut d Egrg d Tchology 009. Currtly h s workg Ary Collg of Eg & IT s Assstt profssor. Now h s workg o th r of mg procssg Rjsh Tjw rcvd th B.E. Computr Scc from Govrmt Eg collg Ajmr 008. Currtly h s workg Ary Collg of Eg & IT s Assstt profssor. Now h s workg o th r of mg procssg. Volum, Issu Mrch Aprl 03 Pg 45

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

CHAPTER 4. FREQUENCY ESTIMATION AND TRACKING

CHAPTER 4. FREQUENCY ESTIMATION AND TRACKING CHPTER 4. FREQUENCY ESTITION ND TRCKING 4.. Itroducto Estmtg mult-frquc susodl sgls burd os hs b th focus of rsrch for qut som tm [68] [58] [46] [64]. ost of th publshd rsrch usd costrd ft mpuls rspos

More information

Linear Prediction Analysis of Speech Sounds

Linear Prediction Analysis of Speech Sounds Lr Prdcto Alyss of Sch Souds Brl Ch 4 frcs: X Hug t l So Lgug Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus sch rcogto rocdgs of th I Stbr 993 5-47 Lr Prdctv

More information

Linear Prediction Analysis of

Linear Prediction Analysis of Lr Prdcto Alyss of Sch Souds Brl Ch Drtt of Coutr Scc & Iforto grg Ntol Tw Norl Uvrsty frcs: X Hug t l So Lgug g Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus

More information

SYSTEMS OF LINEAR EQUATIONS

SYSTEMS OF LINEAR EQUATIONS SYSES OF INER EQUIONS Itroducto Emto thods Dcomposto thods tr Ivrs d Dtrmt Errors, Rsdus d Codto Numr Itrto thods Icompt d Rdudt Systms Chptr Systms of r Equtos /. Itroducto h systm of r qutos s formd

More information

More Statistics tutorial at 1. Introduction to mathematical Statistics

More Statistics tutorial at   1. Introduction to mathematical Statistics Mor Sttstcs tutorl t wwwdumblttldoctorcom Itroducto to mthmtcl Sttstcs Fl Soluto A Gllup survy portrys US trprurs s " th mvrcks, drmrs, d lors whos rough dgs d ucompromsg d to do t thr ow wy st thm shrp

More information

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black

1. Stefan-Boltzmann law states that the power emitted per unit area of the surface of a black Stf-Boltzm lw stts tht th powr mttd pr ut r of th surfc of blck body s proportol to th fourth powr of th bsolut tmprtur: 4 S T whr T s th bsolut tmprtur d th Stf-Boltzm costt= 5 4 k B 3 5c h ( Clcult 5

More information

Linear Algebra Existence of the determinant. Expansion according to a row.

Linear Algebra Existence of the determinant. Expansion according to a row. Lir Algbr 2270 1 Existc of th dtrmit. Expsio ccordig to row. W dfi th dtrmit for 1 1 mtrics s dt([]) = (1) It is sy chck tht it stisfis D1)-D3). For y othr w dfi th dtrmit s follows. Assumig th dtrmit

More information

Section 5.1/5.2: Areas and Distances the Definite Integral

Section 5.1/5.2: Areas and Distances the Definite Integral Scto./.: Ars d Dstcs th Dt Itgrl Sgm Notto Prctc HW rom Stwrt Ttook ot to hd p. #,, 9 p. 6 #,, 9- odd, - odd Th sum o trms,,, s wrtt s, whr th d o summto Empl : Fd th sum. Soluto: Th Dt Itgrl Suppos w

More information

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of Do Bgyoko () FOURIER SERIES I. INTRODUCTION Srs psos r ubqutous too o scc d grg. Th kds o pso to utz dpd o () th proprts o th uctos to b studd d (b) th proprts or chrctrstcs o th systm udr vstgto. Powr

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

Accuracy of ADC dynamic parameters measurement. Jiri Brossmann, Petr Cesak, Jaroslav Roztocil

Accuracy of ADC dynamic parameters measurement. Jiri Brossmann, Petr Cesak, Jaroslav Roztocil ccurcy o dymc prmtrs msurmt Jr Brossm Ptr Csk Jroslv Roztocl Czch Tchcl Uvrsty Prgu Fculty o Elctrcl Egrg Tchck CZ-667 Prgu 6 Czch Rpublc Pho: 40-4 35 86 Fx: 40-33 339 9 E-ml: jr.brossm@gml.com cskp@l.cvut.cz

More information

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder Nolr Rgrsso Mjor: All Egrg Mjors Auhors: Aur Kw, Luk Sydr hp://urclhodsgusfdu Trsforg Nurcl Mhods Educo for STEM Udrgrdus 3/9/5 hp://urclhodsgusfdu Nolr Rgrsso hp://urclhodsgusfdu Nolr Rgrsso So populr

More information

Quantum Mechanics & Spectroscopy Prof. Jason Goodpaster. Problem Set #2 ANSWER KEY (5 questions, 10 points)

Quantum Mechanics & Spectroscopy Prof. Jason Goodpaster. Problem Set #2 ANSWER KEY (5 questions, 10 points) Chm 5 Problm St # ANSWER KEY 5 qustios, poits Qutum Mchics & Spctroscopy Prof. Jso Goodpstr Du ridy, b. 6 S th lst pgs for possibly usful costts, qutios d itgrls. Ths will lso b icludd o our futur ms..

More information

Quantum Circuits. School on Quantum Day 1, Lesson 5 16:00-17:00, March 22, 2005 Eisuke Abe

Quantum Circuits. School on Quantum Day 1, Lesson 5 16:00-17:00, March 22, 2005 Eisuke Abe Qutum Crcuts School o Qutum Computg @Ygm D, Lsso 5 6:-7:, Mrch, 5 Esuk Ab Dprtmt of Appl Phscs Phsco-Iformtcs, CEST-JST, Ko vrst Outl Bloch sphr rprstto otto gts vrslt proof A rbtrr cotroll- gt c b mplmt

More information

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto {t-asano,

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto   {t-asano, School of Iformato Scc Chal Capacty 009 - Cours - Iformato Thory - Ttsuo Asao ad Tad matsumoto Emal: {t-asao matumoto}@jast.ac.jp Japa Advacd Isttut of Scc ad Tchology Asahda - Nom Ishkawa 93-9 Japa http://www.jast.ac.jp

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

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

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem

Extension Formulas of Lauricella s Functions by Applications of Dixon s Summation Theorem Avll t http:pvu.u Appl. Appl. Mth. ISSN: 9-9466 Vol. 0 Issu Dr 05 pp. 007-08 Appltos Appl Mthts: A Itrtol Jourl AAM Etso oruls of Lurll s utos Appltos of Do s Suto Thor Ah Al Atsh Dprtt of Mthts A Uvrst

More information

CHAPTER 7. X and 2 = X

CHAPTER 7. X and 2 = X CHATR 7 Sco 7-7-. d r usd smors o. Th vrcs r d ; comr h S vrc hs cs / / S S Θ Θ Sc oh smors r usd mo o h vrcs would coclud h s h r smor wh h smllr vrc. 7-. [ ] Θ 7 7 7 7 7 7 [ ] Θ ] [ 7 6 Boh d r usd sms

More information

Introduction to Laplace Transforms October 25, 2017

Introduction to Laplace Transforms October 25, 2017 Iroduco o Lplc Trform Ocobr 5, 7 Iroduco o Lplc Trform Lrr ro Mchcl Egrg 5 Smr Egrg l Ocobr 5, 7 Oul Rvw l cl Wh Lplc rform fo of Lplc rform Gg rform b gro Fdg rform d vr rform from bl d horm pplco o dffrl

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Errata for Second Edition, First Printing

Errata for Second Edition, First Printing Errt for Scond Edition, First Printing pg 68, lin 1: z=.67 should b z=.44 pg 1: Eqution (.63) should rd B( R) = x= R = θ ( x R) p( x) R 1 x= [1 G( x)] = θp( R) + ( θ R)[1 G( R)] pg 15, problm 6: dmnd of

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures

Chapter 2 Reciprocal Lattice. An important concept for analyzing periodic structures Chpt Rcpocl Lttc A mpott cocpt o lyzg podc stuctus Rsos o toducg cpocl lttc Thoy o cystl dcto o x-ys, utos, d lctos. Wh th dcto mxmum? Wht s th tsty? Abstct study o uctos wth th podcty o Bvs lttc Fou tsomto.

More information

Preview. Graph. Graph. Graph. Graph Representation. Graph Representation 12/3/2018. Graph Graph Representation Graph Search Algorithms

Preview. Graph. Graph. Graph. Graph Representation. Graph Representation 12/3/2018. Graph Graph Representation Graph Search Algorithms /3/0 Prvw Grph Grph Rprsntton Grph Srch Algorthms Brdth Frst Srch Corrctnss of BFS Dpth Frst Srch Mnmum Spnnng Tr Kruskl s lgorthm Grph Drctd grph (or dgrph) G = (V, E) V: St of vrt (nod) E: St of dgs

More information

Inner Product Spaces INNER PRODUCTS

Inner Product Spaces INNER PRODUCTS MA4Hcdoc Ir Product Spcs INNER PRODCS Dto A r product o vctor spc V s ucto tht ssgs ubr spc V such wy tht th ollowg xos holds: P : w s rl ubr P : P : P 4 : P 5 : v, w = w, v v + w, u = u + w, u rv, w =

More information

National Quali cations

National Quali cations PRINT COPY OF BRAILLE Ntiol Quli ctios AH08 X747/77/ Mthmtics THURSDAY, MAY INSTRUCTIONS TO CANDIDATES Cdidts should tr thir surm, form(s), dt of birth, Scottish cdidt umbr d th m d Lvl of th subjct t

More information

Errata for Second Edition, First Printing

Errata for Second Edition, First Printing Errt for Scond Edition, First Printing pg 68, lin 1: z=.67 should b z=.44 pg 71: Eqution (.3) should rd B( R) = θ R 1 x= [1 G( x)] pg 1: Eqution (.63) should rd B( R) = x= R = θ ( x R) p( x) R 1 x= [1

More information

Entropy Equation for a Control Volume

Entropy Equation for a Control Volume Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

COLLECTION OF SUPPLEMENTARY PROBLEMS CALCULUS II

COLLECTION OF SUPPLEMENTARY PROBLEMS CALCULUS II COLLECTION OF SUPPLEMENTARY PROBLEMS I. CHAPTER 6 --- Trscdtl Fuctios CALCULUS II A. FROM CALCULUS BY J. STEWART:. ( How is th umbr dfid? ( Wht is pproimt vlu for? (c ) Sktch th grph of th turl potil fuctios.

More information

Introduction to Error Correcting codes in Quantum Computers

Introduction to Error Correcting codes in Quantum Computers Itroducto to rror Corrctg cods Qutum Computrs Pdro J. Sls-Prlt Dprtmto d Tcologís spcls Aplcds l Tlcomuccó Uvrsdd Poltécc d Mdrd Cudd Uvrstr s/, 8040 Mdrd -ml: psls@tst.upm.s PACS Numbrs: 0367-, 0367Lx

More information

Outline. Outline. Outline. Questions 2010/9/30. Introduction The Multivariate Normal Density and Its Properties

Outline. Outline. Outline. Questions 2010/9/30. Introduction The Multivariate Normal Density and Its Properties 9 Multvrt orml Dstruto Shyh-Kg Jg Drtmt of Eltrl Egrg Grdut Isttut of Commuto Grdut Isttut of tworkg d Multmd Outl Itroduto Th Multvrt orml Dsty d Its Prorts Smlg from Multvrt orml Dstruto d Mmum Lklhood

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Spectral Characteristics of Digitally Modulated Signals

Spectral Characteristics of Digitally Modulated Signals Strl Chrtrt of Dgtlly odultd Sgl 6:33:56 Wrl Couto holog Srg 5 Ltur7&8 Drtt of Eltrl Egrg Rutgr Uvrty Ptwy J 89 ught y Dr. ry dy ry@wl.rutgr.du Doutd y Bozh Yu ozh@d.rutgr.du trt: h ltur frt trodu th tdrd

More information

SOLVED EXAMPLES. Ex.1 If f(x) = , then. is equal to- Ex.5. f(x) equals - (A) 2 (B) 1/2 (C) 0 (D) 1 (A) 1 (B) 2. (D) Does not exist = [2(1 h)+1]= 3

SOLVED EXAMPLES. Ex.1 If f(x) = , then. is equal to- Ex.5. f(x) equals - (A) 2 (B) 1/2 (C) 0 (D) 1 (A) 1 (B) 2. (D) Does not exist = [2(1 h)+1]= 3 SOLVED EXAMPLES E. If f() E.,,, th f() f() h h LHL RHL, so / / Lim f() quls - (D) Dos ot ist [( h)+] [(+h) + ] f(). LHL E. RHL h h h / h / h / h / h / h / h As.[C] (D) Dos ot ist LHL RHL, so giv it dos

More information

Outline. Outline. Outline. Questions. Multivariate Normal Distribution. Multivariate Normal Distribution

Outline. Outline. Outline. Questions. Multivariate Normal Distribution. Multivariate Normal Distribution Multvrt orml Dstruto hyh-kg Jg Drtmt of Eltrl Egrg Grdut sttut of Commuto Grdut sttut of tworg d Multmd Outl troduto Th Multvrt orml Dsty d ts Prorts mlg from Multvrt orml Dstruto d Mmum Llhood Estmto

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

D. Bertsekas and R. Gallager, "Data networks." Q: What are the labels for the x-axis and y-axis of Fig. 4.2?

D. Bertsekas and R. Gallager, Data networks. Q: What are the labels for the x-axis and y-axis of Fig. 4.2? pd by J. Succ ECE 543 Octob 22 2002 Outl Slottd Aloh Dft Stblzd Slottd Aloh Uslottd Aloh Splttg Algoths Rfc D. Btsks d R. llg "Dt twoks." Rvw (Slottd Aloh): : Wht th lbls fo th x-xs d y-xs of Fg. 4.2?

More information

page 11 equation (1.2-10c), break the bar over the right side in the middle

page 11 equation (1.2-10c), break the bar over the right side in the middle I. Corrctios Lst Updtd: Ju 00 Complx Vrils with Applictios, 3 rd ditio, A. Dvid Wusch First Pritig. A ook ought for My 007 will proly first pritig With Thks to Christi Hos of Swd pg qutio (.-0c), rk th

More information

Chapter 3 Fourier Series Representation of Periodic Signals

Chapter 3 Fourier Series Representation of Periodic Signals Chptr Fourir Sris Rprsttio of Priodic Sigls If ritrry sigl x(t or x[] is xprssd s lir comitio of som sic sigls th rspos of LI systm coms th sum of th idividul rsposs of thos sic sigls Such sic sigl must:

More information

Chapter Discrete Fourier Transform

Chapter Discrete Fourier Transform haptr.4 Dscrt Fourr Trasform Itroducto Rcad th xpota form of Fourr srs s Equatos 8 ad from haptr., wt f t 8, h.. T w t f t dt T Wh th abov tgra ca b usd to comput, h.., t s mor prfrab to hav a dscrtzd

More information

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt

More information

The Theory of Small Reflections

The Theory of Small Reflections Jim Stils Th Univ. of Knss Dt. of EECS 4//9 Th Thory of Smll Rflctions /9 Th Thory of Smll Rflctions Rcll tht w nlyzd qurtr-wv trnsformr usg th multil rflction viw ot. V ( z) = + β ( z + ) V ( z) = = R

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

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

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1)

Convergence Theorems for Two Iterative Methods. A stationary iterative method for solving the linear system: (1.1) Conrgnc Thors for Two Itrt Mthods A sttonry trt thod for solng th lnr syst: Ax = b (.) ploys n trton trx B nd constnt ctor c so tht for gn strtng stt x of x for = 2... x Bx c + = +. (.2) For such n trton

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

TOTAL LEAST SQUARES ALGORITHMS FOR FITTING 3D STRAIGHT LINES

TOTAL LEAST SQUARES ALGORITHMS FOR FITTING 3D STRAIGHT LINES IJMML 6: (07) 35-44 Mrch 07 ISSN: 394-58 vll t http://sctfcdvcsco DOI: http://ddoorg/0864/jmml_70088 OL LES SQURES LGORIHMS FOR FIING 3D SRIGH LINES Cupg Guo Juhu Pg d Chuto L School of Scc Ch Uvrst of

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS VSRT MEMO #05 MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS 01886 Fbrury 3, 009 Tlphon: 781-981-507 Fx: 781-981-0590 To: VSRT Group From: Aln E.E. Rogrs Subjct: Simplifid

More information

Stability Analysis of an Electric Parking Brake (EPB) System with a Nonlinear Proportional Controller

Stability Analysis of an Electric Parking Brake (EPB) System with a Nonlinear Proportional Controller Procdgs of th 17th World Cogrss h Itrtol Fdrto of utomtc Cotrol Stblty lyss of Elctrc Prkg Brk (EPB) Systm wth Nolr Proportol Cotrollr Youg O. L *, Choog W. L *, Chug C. Chug **, Yougsup So ***, Pljoo

More information

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture:

Lecture 11 Waves in Periodic Potentials Today: Questions you should be able to address after today s lecture: Lctur 11 Wvs in Priodic Potntils Tody: 1. Invrs lttic dfinition in 1D.. rphicl rprsnttion of priodic nd -priodic functions using th -xis nd invrs lttic vctors. 3. Sris solutions to th priodic potntil Hmiltonin

More information

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands

Handout 7. Properties of Bloch States and Electron Statistics in Energy Bands Hdout 7 Popts of Bloch Stts d Elcto Sttstcs Eg Bds I ths lctu ou wll l: Popts of Bloch fuctos Podc boud codtos fo Bloch fuctos Dst of stts -spc Elcto occupto sttstcs g bds ECE 407 Spg 009 Fh R Coll Uvst

More information

Instructions for Section 1

Instructions for Section 1 Instructions for Sction 1 Choos th rspons tht is corrct for th qustion. A corrct nswr scors 1, n incorrct nswr scors 0. Mrks will not b dductd for incorrct nswrs. You should ttmpt vry qustion. No mrks

More information

Integration by Guessing

Integration by Guessing Itgrtio y Gussig Th computtios i two stdrd itgrtio tchiqus, Sustitutio d Itgrtio y Prts, c strmlid y th Itgrtio y Gussig pproch. This mthod cosists of thr stps: Guss, Diffrtit to chck th guss, d th Adjust

More information

minimize c'x subject to subject to subject to

minimize c'x subject to subject to subject to z ' sut to ' M ' M N uostrd N z ' sut to ' z ' sut to ' sl vrls vtor of : vrls surplus vtor of : uostrd s s s s s s z sut to whr : ut ost of :out of : out of ( ' gr of h food ( utrt : rqurt for h utrt

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

Gilbert the Green Tree Frog

Gilbert the Green Tree Frog Gbrt th Gr Tr Frog A org Kpr Kd book Wrtt by Stph Jk Iutrtd by T Eor ONCE UPON A TIME thr w frog md Gbrt. Gbrt w Gr Tr Frog. H fmy w gr. H hom w gr. Ad omtm v h food w gr. Gbrt w ck d trd of bg o gr th

More information

IFYFM002 Further Maths Appendix C Formula Booklet

IFYFM002 Further Maths Appendix C Formula Booklet Ittol Foudto Y (IFY) IFYFM00 Futh Mths Appd C Fomul Booklt Rltd Documts: IFY Futh Mthmtcs Syllbus 07/8 Cotts Mthmtcs Fomul L Equtos d Mtcs... Qudtc Equtos d Rmd Thom... Boml Epsos, Squcs d Ss... Idcs,

More information

U1. Transient circuits response

U1. Transient circuits response U. Tr crcu rpo rcu ly, Grdo Irí d omucco uro 6-7 Phlp Sm phlp.m@uh. Dprmo d Torí d l Sñl y omucco Idx Rcll Gol d movo r dffrl quo Rcll Th homoou oluo d d ordr lr dffrl quo Exmpl of d ordr crcu Il codo

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

(A) the function is an eigenfunction with eigenvalue Physical Chemistry (I) First Quiz

(A) the function is an eigenfunction with eigenvalue Physical Chemistry (I) First Quiz 96- Physcl Chmstry (I) Frst Quz lctron rst mss m 9.9 - klogrm, Plnck constnt h 6.66-4 oul scon Sp of lght c. 8 m/s, lctron volt V.6-9 oul. Th functon F() C[cos()+sn()] s n gnfuncton of /. Th gnvlu s (A)

More information

Multi-Section Coupled Line Couplers

Multi-Section Coupled Line Couplers /0/009 MultiSction Coupld Lin Couplrs /8 Multi-Sction Coupld Lin Couplrs W cn dd multipl coupld lins in sris to incrs couplr ndwidth. Figur 7.5 (p. 6) An N-sction coupld lin l W typiclly dsign th couplr

More information

A Monotone Process Replacement Model for a Two Unit Cold Standby Repairable System

A Monotone Process Replacement Model for a Two Unit Cold Standby Repairable System Itrtol Jorl of Egrg Rsrch d Dlopmt -ISS: 78-67 p-iss: 78-8 www.jrd.com Volm 7 Iss 8 J 3 PP. 4-49 A Mooto Procss Rplcmt Modl for Two Ut Cold Std Rprl Sstm Dr.B.Vt Rmd Prof.A. Mllrj Rdd M. Bhg Lshm 3 Assstt

More information

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since 56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

Second Handout: The Measurement of Income Inequality: Basic Concepts Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart

More information

Practice Final Exam. 3.) What is the 61st term of the sequence 7, 11, 15, 19,...?

Practice Final Exam. 3.) What is the 61st term of the sequence 7, 11, 15, 19,...? Discrt mth Prctic Fl Em.) Fd 4 (i ) i=.) Fd i= 6 i.) Wht is th 6st trm th squnc 7,, 5, 9,...? 4.) Wht s th 57th trm, 6,, 4,...? 5.) Wht s th sum th first 60 trms th squnc, 5, 7, 9,...? 6.) Suppos st A

More information

III Z-Plane Analysis

III Z-Plane Analysis III Z-Pl Aly opc to covrd. Itroducto. Ipul plg d dt hold 3. Otg th Z trfor y covoluto 4. Sgl rcotructo 5. h pul trfr fucto 6. Dgtl cotrollr d fltr III. Itroducto h dvtg of th trfor thod tht t l th gr to

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

Ekpenyong Emmanuel John and Gideon Sunday N. x (2.1) International Journal of Statistics and Applied Mathematics 2018; 3(4): 60-64

Ekpenyong Emmanuel John and Gideon Sunday N. x (2.1) International Journal of Statistics and Applied Mathematics 2018; 3(4): 60-64 Itrtol Jourl of Sttstcs d Appld Mtmtcs 8; 34 6-64 ISSN 456-45 Mts 8; 34 6-64 8 Stts & Mts www.mtsjourl.com Rcvd 8-5-8 Accptd 9-6-8 Ekpyo Emmul Jo Dprtmt of Sttstcs Mcl Okpr Uvrsty of Arcultur Umudk Nr

More information

ASSERTION AND REASON

ASSERTION AND REASON ASSERTION AND REASON Som qustios (Assrtio Rso typ) r giv low. Ech qustio cotis Sttmt (Assrtio) d Sttmt (Rso). Ech qustio hs choics (A), (B), (C) d (D) out of which ONLY ONE is corrct. So slct th corrct

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

[ ] Review. For a discrete-time periodic signal xn with period N, the Fourier series representation is

[ ] Review. For a discrete-time periodic signal xn with period N, the Fourier series representation is Discrt-tim ourir Trsform Rviw or discrt-tim priodic sigl x with priod, th ourir sris rprsttio is x + < > < > x, Rviw or discrt-tim LTI systm with priodic iput sigl, y H ( ) < > < > x H r rfrrd to s th

More information

Ch 1.2: Solutions of Some Differential Equations

Ch 1.2: Solutions of Some Differential Equations Ch 1.2: Solutions of Som Diffrntil Equtions Rcll th fr fll nd owl/mic diffrntil qutions: v 9.8.2v, p.5 p 45 Ths qutions hv th gnrl form y' = y - b W cn us mthods of clculus to solv diffrntil qutions of

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

INTEGRALS. Chapter 7. d dx. 7.1 Overview Let d dx F (x) = f (x). Then, we write f ( x)

INTEGRALS. Chapter 7. d dx. 7.1 Overview Let d dx F (x) = f (x). Then, we write f ( x) Chptr 7 INTEGRALS 7. Ovrviw 7.. Lt d d F () f (). Thn, w writ f ( ) d F () + C. Ths intgrls r clld indfinit intgrls or gnrl intgrls, C is clld constnt of intgrtion. All ths intgrls diffr y constnt. 7..

More information

Minimum Spanning Trees

Minimum Spanning Trees Mnmum Spnnng Trs Spnnng Tr A tr (.., connctd, cyclc grph) whch contns ll th vrtcs of th grph Mnmum Spnnng Tr Spnnng tr wth th mnmum sum of wghts 1 1 Spnnng forst If grph s not connctd, thn thr s spnnng

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

Different types of Domination in Intuitionistic Fuzzy Graph

Different types of Domination in Intuitionistic Fuzzy Graph Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,

More information

Washington State University

Washington State University he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us

More information

How much air is required by the people in this lecture theatre during this lecture?

How much air is required by the people in this lecture theatre during this lecture? 3 NTEGRATON tgrtio is us to swr qustios rltig to Ar Volum Totl qutity such s: Wht is th wig r of Boig 747? How much will this yr projct cost? How much wtr os this rsrvoir hol? How much ir is rquir y th

More information

THE TRANSMUTED GENERALIZED PARETO DISTRIBUTION. STATISTICAL INFERENCE AND SIMULATION RESULTS

THE TRANSMUTED GENERALIZED PARETO DISTRIBUTION. STATISTICAL INFERENCE AND SIMULATION RESULTS Mr l Btr vl Admy Stf Bullt Volum VIII 5 Issu Pulshd y Mr l Btr vl Admy Prss Costt Rom // Th jourl s dd : PROQUST STh Jourls PROQUST grg Jourls PROQUST Illustrt: Thology PROQUST Thology Jourls PROQUST Mltry

More information

National Quali cations

National Quali cations Ntiol Quli ctios AH07 X77/77/ Mthmtics FRIDAY, 5 MAY 9:00 AM :00 NOON Totl mrks 00 Attmpt ALL qustios. You my us clcultor. Full crdit will b giv oly to solutios which coti pproprit workig. Stt th uits

More information

Special Curves of 4D Galilean Space

Special Curves of 4D Galilean Space Irol Jourl of Mhml Egrg d S ISSN : 77-698 Volum Issu Mrh hp://www.jms.om/ hps://ss.googl.om/s/jmsjourl/ Spl Curvs of D ll Sp Mhm Bkş Mhmu Ergü Alpr Osm Öğrmş Fır Uvrsy Fuly of S Dprm of Mhms 9 Elzığ Türky

More information

TOPIC 5: INTEGRATION

TOPIC 5: INTEGRATION TOPIC 5: INTEGRATION. Th indfinit intgrl In mny rspcts, th oprtion of intgrtion tht w r studying hr is th invrs oprtion of drivtion. Dfinition.. Th function F is n ntidrivtiv (or primitiv) of th function

More information

, between the vertical lines x a and x b. Given a demand curve, having price as a function of quantity, p f (x) at height k is the curve f ( x,

, between the vertical lines x a and x b. Given a demand curve, having price as a function of quantity, p f (x) at height k is the curve f ( x, Clculus for Businss nd Socil Scincs - Prof D Yun Finl Em Rviw vrsion 5/9/7 Chck wbsit for ny postd typos nd updts Pls rport ny typos This rviw sht contins summris of nw topics only (This rviw sht dos hv

More information

Ordinary Least Squares at advanced level

Ordinary Least Squares at advanced level Ordary Last Squars at advacd lvl. Rvw of th two-varat cas wth algbra OLS s th fudamtal tchqu for lar rgrssos. You should by ow b awar of th two-varat cas ad th usual drvatos. I ths txt w ar gog to rvw

More information

# 1 ' 10 ' 100. Decimal point = 4 hundred. = 6 tens (or sixty) = 5 ones (or five) = 2 tenths. = 7 hundredths.

# 1 ' 10 ' 100. Decimal point = 4 hundred. = 6 tens (or sixty) = 5 ones (or five) = 2 tenths. = 7 hundredths. How os it work? Pl vlu o imls rprsnt prts o whol numr or ojt # 0 000 Tns o thousns # 000 # 00 Thousns Hunrs Tns Ons # 0 Diml point st iml pl: ' 0 # 0 on tnth n iml pl: ' 0 # 00 on hunrth r iml pl: ' 0

More information

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University Advced Algorthmc Prolem Solvg Le Arthmetc Fredrk Hetz Dept of Computer d Iformto Scece Lköpg Uversty Overvew Arthmetc Iteger multplcto Krtsu s lgorthm Multplcto of polyomls Fst Fourer Trsform Systems of

More information

A Class of Harmonic Meromorphic Functions of Complex Order

A Class of Harmonic Meromorphic Functions of Complex Order Borg Irol Jourl o D Mg Vol 2 No 2 Ju 22 22 A Clss o rmoc Mromorpc Fucos o Complx Ordr R Elrs KG Surm d TV Sudrs Asrc--- T sml work o Clu d Sl-Smll [3] o rmoc mppgs gv rs o suds o suclsss o complx-vlud

More information

FILTER BANK MULTICARRIER WITH LAPPED TRANSFORMS

FILTER BANK MULTICARRIER WITH LAPPED TRANSFORMS FILTER BANK ULTICARRIER WITH LAPPED TRANSFORS aurc Bllagr, CNA Davd attra, aro Tada, Uv.Napol arch 5 Obctvs A multcarrr approach to mprov o OFD for futur wrlss systms - asychroous mult-usr accss - spctral

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

APPENDIX: STATISTICAL TOOLS

APPENDIX: STATISTICAL TOOLS I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.

More information