Well-publicized worms Worm propagation curve Scanning strategies (uniform, permutation, hitlist, subnet) Three factors define worm spread:
|
|
|
- Hilary Wood
- 5 years ago
- Views:
Transcription
1 Wll-publczd worm Worm propagao curv Scag rag (uorm, prmuao, hl, ub) Thr acor d worm prad: o Sz o vulrabl populao Prvo pach vulrabl, cra hrogy o Ra o co (cag ad propagao ragy) Dploy rwall Drbu worm gaur o Lgh o cou prod Pach vulrabl ar h oubrak Th dpd o vral acor: o Raco m o Coam ragy addr blacklg ad co lrg o Dploym caro whr rpo dployd Evalua c o coam 4 hour ar h o Ir Quara: Rqurm or Coag Sl-Propagag Cod, Procdg o INFOCOM 3, D. Moor, C. Shao, G. Volkr, S. Savag Idalzd dploym: vryo dploy d ar gv prod Idalzd dploym: vryo dploy d ar gv prod
2 Raco m d o b wh mu, o cod W d o u co lrg W d o hav xv dploym o ky po h Ir Moor ougog coco amp o w ho Wh ra xcd 5 pr cod, pu h rmag rqu a quu Wh umbr o rqu a quu xcd op all commucao Implmg ad g a vru hrol, Procdg o Ux Scury Sympoum 3, J. Twycro, M. Wllamo Orgazao har alr ad worm gaur wh hr rd o Svry o alr crad a mor co amp ar dcd o Each ho ha a vry hrhold ar whch dploy rpo Alr prad ju lk worm do o Mu b ar o ovrak worm prad o Ar om m o o w co dco, alr wll b rmovd Cooprav Rpo Srag or Larg-Scal Aack Mgao, Procdg o DISCEX 3, D. Norjr, J. Row, K. Lv A umbr o rd cra, rpo ar Propagag al alarm a problm
3 Early dco would gv m o rac ul h co ha prad Th goal o h papr o dv chqu ha dc w worm a hy ju ar pradg Moorg: o Moor ad collc worm ca rac o Obrvao daa vry oy o w hav o lr w ca rom Old worm ca Por ca by hackg oolk C. C. Zou, W. Gog, D. Towly, ad L. Gao. "Th Moorg ad Early Dco o Ir Worm," IEEE/ACM Traaco o Nworkg. Dco: o Tradoal aomaly dco: hrhold-bad Chck rac bur (hor-rm or log-rm). Dcul: Fal alarm ra o Trd Dco Maur umbr o cd ho ad u o dc worm xpoal growh rd a h bgg Worm uormly ca h Ir o No hl bu ub cag allowd Addr pac cad IPv4 di Smpl pdmc modl: = β * I *( N I ) d Dc worm hr. Should hav xp. prad Provd comprhv obrvao daa o a worm acv or h arly dco o h worm Co o : o Malwar Warg Cr (MWC) o Drbud moor Igr ca moor moor comg rac gog o uud addr Egr ca moor moor ougog rac 3
4 Igr moor collc: o Numbr o ca rcvd a rval o IP addr o cd ho ha hav ca o h moor Egr moor collc: o Avrag worm ca ra Malwar Warg Cr (MWC) moor: o Worm avrag ca ra o Toal umbr o ca moord o Numbr o cd ho obrvd MWC collc ad aggrga rpor rom drbud moor I oal umbr o ca ovr a hrhold or vral cocuv rval, MWC acva h Kalma lr ad bg o h hypoh ha h umbr o cd ho ollow xpoal drbuo Populao: N=36,, Ico ra: α =.8/hour, Sca ra η = 358/m, Ially cd: I = Moord IP pac, Moorg rval: Δ = mu x 5 Icd I Obrvd Icd C c d a m E Ral valu o! Emad valu o! Populao: N=, Sca ra η = 4/c, Ially cd: I = o h Moord IP pac, Moorg rval: Δ = cod d c o # x # o cd ho I a r o c d a m E Ral valu o! Emad valu o! Tm (mu) 5 5 Tm (cod) Tm (cod) Icd ho α mao Icd ho α mao Worm prad vry a (mu, cod) o Nd auomac mgao I h a w worm, o gaur x o Mu apply bhavour-bad aomaly dco o Bu h ha a al-pov problm! W do wa o drop lgma coco! Dyamc quara o Aum guly ul prov oc o Forbd work acc o upcou ho or a hor m o Th gcaly low dow h worm prad C. C. Zou, W. Gog, ad D. Towly. "Worm Propagao Modlg ad Aaly udr Dyamc Quara D," ACM CCS Workhop o Rapd Malcod (WORM'3), Bhavor-bad aomaly dco ca po ou upcou ho o Nd a chqu ha low dow worm prad bu do hur lgma rac much o Aum guly ul prov oc chqu wll brly drop all ougog coco amp (or a pcc rvc) rom a upcou ho o Ar a whl ju aum ha ho halhy, v o prov o o Th hould low dow worm bu cau oly ra rrupo o lgma rac 4
5 Aum w hav om aomaly dco program ha lag a ho a upcou o Quara h ho o Rla ar m T o Th ho may b quarad mulpl m h aomaly dco ra a alarm o Sc h do ac halhy ho oprao a lo w ca hav mor v aomaly dco chqu A cou ho quarad ar m u λ A ucpbl ho ally quarad ar m u λ Quara m T, ar ha w rla h ho A w w cagor: o Quarad cou R() o Quarad ucpbl Q() Ially 75, vulrabl ho Quara ra o cou ho λ =. pr cod o Icou ho wll b quarad ar 5 cod o avrag Quara ra o ucpbl ho pr cod λ =.35 o Thr ar al alarm pr ho pr day T= cod T=c T=3c 5
6 Clag I() Clag R() 6
Poisson Arrival Process
1 Poisso Arrival Procss Arrivals occur i) i a mmorylss mar ii) [ o arrival durig Δ ] = λδ + ( Δ ) P o [ o arrival durig Δ ] = 1 λδ + ( Δ ) P o P j arrivals durig Δ = o Δ for j = 2,3, ( ) o Δ whr lim =
Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables
Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of
8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system
8. Quug sysms Cos 8. Quug sysms Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs lc8. S-38.45 Iroduco o Tlraffc Thory Srg 5 8. Quug sysms 8.
Chap 2: Reliability and Availability Models
Chap : lably ad valably Modls lably = prob{s s fully fucog [,]} Suppos from [,] m prod, w masur ou of N compos, of whch N : # of compos oprag corrcly a m N f : # of compos whch hav fald a m rlably of h
Poisson Arrival Process
Poisso Arrival Procss Arrivals occur i) i a mmylss mar ii) [ o arrival durig Δ ] = λδ + ( Δ ) P o [ o arrival durig Δ ] = λδ + ( Δ ) P o P j arrivals durig Δ = o Δ f j = 2,3, o Δ whr lim =. Δ Δ C C 2 C
1973 AP Calculus BC: Section I
97 AP Calculus BC: Scio I 9 Mius No Calculaor No: I his amiaio, l dos h aural logarihm of (ha is, logarihm o h bas ).. If f ( ) =, h f ( ) = ( ). ( ) + d = 7 6. If f( ) = +, h h s of valus for which f
An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair
Mor ppl Novmbr 8 N-Compo r Rparabl m h Rparma Dog Ohr ork a ror Rpar Jag Yag E-mal: jag_ag7@6om Xau Mg a uo hg ollag arb Normal Uvr Yaq ua Taoao ag uppor b h Fouao or h aural o b prov o Cha 5 uppor b h
Final Exam : Solutions
Comp : Algorihm and Daa Srucur Final Exam : Soluion. Rcuriv Algorihm. (a) To bgin ind h mdian o {x, x,... x n }. Sinc vry numbr xcp on in h inrval [0, n] appar xacly onc in h li, w hav ha h mdian mu b
LM A F LABL Y H FRMA H P UBLCA B LV B ACCURA ALL R PC H WVR W C A AU M RP BLY FR AY C QUC RUL G F RM H U HR F H FRMA C A HR UBJC CHA G WHU C R V R W H
H R & C C M RX700-2 Bx C LM A F LABL Y H FRMA H P UBLCA B LV B ACCURA ALL R PC H WVR W C A AU M RP BLY FR AY C QUC RUL G F RM H U HR F H FRMA C A HR UBJC CHA G WHU C R V R W H PUBLCA M AY B U CRP RA UCH
Lecture 12: Introduction to nonlinear optics II.
Lcur : Iroduco o olar opcs II r Kužl ropagao of srog opc sgals propr olar ffcs Scod ordr ffcs! Thr-wav mxg has machg codo! Scod harmoc grao! Sum frqucy grao! aramrc grao Thrd ordr ffcs! Four-wav mxg! Opcal
Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables
Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug
Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution
raoal Joural of Sascs ad Ssms SSN 97-675 Volum, Numbr 7,. 575-58 sarch da Publcaos h://www.rublcao.com labl aalss of m - dd srss - srgh ssm wh h umbr of ccls follows bomal dsrbuo T.Sumah Umamahswar, N.Swah,
Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year
Gau Thors Elmary Parcl Physcs Sro Iraco Fomoloy o Bo cadmc yar - Gau Ivarac Gau Ivarac Whr do Laraas or Hamloas com from? How do w kow ha a cra raco should dscrb a acual hyscal sysm? Why s h lcromac raco
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
Fractal diffusion retrospective problems
Iraoa ora o App Mahac croc a Copr Avac Tchoo a Scc ISSN: 47-8847-6799 wwwaccor/iamc Ora Rarch Papr Fraca o rropcv prob O Yaro Rcv 6 h Ocobr 3 Accp 4 h aar 4 Abrac: I h arc w h rropcv vr prob Th rropcv
T h e C S E T I P r o j e c t
T h e P r o j e c t T H E P R O J E C T T A B L E O F C O N T E N T S A r t i c l e P a g e C o m p r e h e n s i v e A s s es s m e n t o f t h e U F O / E T I P h e n o m e n o n M a y 1 9 9 1 1 E T
(Reference: sections in Silberberg 5 th ed.)
ALE. Atomic Structur Nam HEM K. Marr Tam No. Sctio What is a atom? What is th structur of a atom? Th Modl th structur of a atom (Rfrc: sctios.4 -. i Silbrbrg 5 th d.) Th subatomic articls that chmists
Advanced Queueing Theory. M/G/1 Queueing Systems
Advand Quung Thory Ths slds ar rad by Dr. Yh Huang of Gorg Mason Unvrsy. Sudns rgsrd n Dr. Huang's ourss a GMU an ma a sngl mahn-radabl opy and prn a sngl opy of ah sld for hr own rfrn, so long as ah sld
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
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
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,
where: u: input y: output x: state vector A, B, C, D are const matrices
Sa pac modl: linar: y or in om : Sa q : f, u Oupu q : y h, u u Du F Gu y H Ju whr: u: inpu y: oupu : a vcor,,, D ar con maric Eampl " $ & ' " $ & 'u y " & * * * * [ ],, D H D I " $ " & $ ' " & $ ' " &
Overview. Introduction Building Classifiers (2) Introduction Building Classifiers. Introduction. Introduction to Pattern Recognition and Data Mining
Ovrv Iroduco o ar Rcogo ad Daa Mg Lcur 4: Lar Dcra Fuco Irucor: Dr. Gova Dpar of Copur Egrg aa Clara Uvry Iroduco Approach o uldg clafr Lar dcra fuco: dfo ad urfac Lar paral ca rcpro crra Ohr hod Lar Dcra
The Variance-Covariance Matrix
Th Varanc-Covaranc Marx Our bggs a so-ar has bn ng a lnar uncon o a s o daa by mnmzng h las squars drncs rom h o h daa wh mnsarch. Whn analyzng non-lnar daa you hav o us a program l Malab as many yps o
On the Existence and uniqueness for solution of system Fractional Differential Equations
OSR Jourl o Mhms OSR-JM SSN: 78-578. Volum 4 ssu 3 Nov. - D. PP -5 www.osrjourls.org O h Es d uquss or soluo o ssm rol Drl Equos Mh Ad Al-Wh Dprm o Appld S Uvrs o holog Bghdd- rq Asr: hs ppr w d horm o
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,
A L A BA M A L A W R E V IE W
A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N
k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)
TOTAL INTRNAL RFLTION Kmacs pops Sc h vcos a coplaa, l s cosd h cd pla cocds wh h X pla; hc 0. y y y osd h cas whch h lgh s cd fom h mdum of hgh dx of faco >. Fo cd agls ga ha h ccal agl s 1 ( /, h hooal
Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:
Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght
( A) ( B) ( C) ( D) ( E)
d Smsr Fial Exam Worksh x 5x.( NC)If f ( ) d + 7, h 4 f ( ) d is 9x + x 5 6 ( B) ( C) 0 7 ( E) divrg +. (NC) Th ifii sris ak has h parial sum S ( ) for. k Wha is h sum of h sris a? ( B) 0 ( C) ( E) divrgs
Chapter 13 Laplace Transform Analysis
Chapr aplac Tranorm naly Chapr : Ouln aplac ranorm aplac Tranorm -doman phaor analy: x X σ m co ω φ x X X m φ x aplac ranorm: [ o ] d o d < aplac Tranorm Thr condon Unlaral on-dd aplac ranorm: aplac ranorm
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
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
By Joonghoe Dho. The irradiance at P is given by
CH. 9 c CH. 9 c By Joogo Do 9 Gal Coao 9. Gal Coao L co wo po ouc, S & S, mg moocomac wav o am qucy. L paao a b muc ga a. Loca am qucy. L paao a b muc ga a. Loca po obvao P a oug away om ouc o a a P wavo
(7) CALAMINTHA NEPETA 'WHITE CLOUD' (15) RUDBECKIA FULGIDA VAR.SULLIVANTII 'GOLDSTURM' (2) SPIRAEA JAPONICA 'ANTHONY WATERER'
XD DC D D ; () C ' COUD' () UDCK FUGD VUV 'GODU' () JOC 'OY ' () CGO X CUFO 'K FO' C f d Dpm f ub K DVO C-Cu ud, u u K, J vd O x d, - OV DG D VC Gp f CO OJC: F K DVO D UY DCG CUO XG CC COCO QUD O DGD U
MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals
MECE 330 MECE 330 Masurms & Isrumao Sac ad Damc Characrscs of Sgals Dr. Isaac Chouapall Dparm of Mchacal Egrg Uvrs of Txas Pa Amrca MECE 330 Sgal Cocps A sgal s h phscal formao abou a masurd varabl bg
Part I- Wave Reflection and Transmission at Normal Incident. Part II- Wave Reflection and Transmission at Oblique Incident
Apl 6, 3 Uboudd Mda Gudd Mda Chap 7 Chap 8 3 mls 3 o 3 M F bad Lgh wavs md by h su Pa I- Wav Rlo ad Tasmsso a Nomal Id Pa II- Wav Rlo ad Tasmsso a Oblqu Id Pa III- Gal Rlao Bw ad Wavguds ad Cavy Rsoao
P a g e 5 1 of R e p o r t P B 4 / 0 9
P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e
Beechwood Music Department Staff
Beechwood Music Department Staff MRS SARAH KERSHAW - HEAD OF MUSIC S a ra h K e rs h a w t r a i n e d a t t h e R oy a l We ls h C o l le g e of M u s i c a n d D ra m a w h e re s h e ob t a i n e d
The Exile Began. Family Journal Page. God Called Jeremiah Jeremiah 1. Preschool. below. Tell. them too. Kids. Ke Passage: Ezekiel 37:27
Faily Jo Pag Th Exil Bg io hy u c prof b jo ou Shar ab ou job ab ar h o ay u Yo ra u ar u r a i A h ) ar par ( grp hav h y y b jo i crib blo Tll ri ir r a r gro up Allo big u r a i Rvi h b of ha u ha a
Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek
Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar
07 - SEQUENCES AND SERIES Page 1 ( Answers at he end of all questions ) b, z = n
07 - SEQUENCES AND SERIES Pag ( Aswrs at h d of all qustios ) ( ) If = a, y = b, z = c, whr a, b, c ar i A.P. ad = 0 = 0 = 0 l a l
TL 043 MODEL OF UNEXPECTED CREDIT RISK OF SUPPLY CHAIN BASED ON CATASTROPHE THEORY ZOU HUIXIA, SONG JIAO
L 43 MODL OF UXPCD CRDI RIK OF UPPLY CAI BAD O CAAROP ORY ZOU UIXIA, OG JIAO COOMIC AD MAAGM COOL, WUA UIVRIY, WUA, P.R. CIA 4372 Abrac h arcl r o apply caaroph hory o h rarch of uxpcd crd rk of h upply
Consider a system of 2 simultaneous first order linear equations
Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm
Jonathan Turner Exam 2-10/28/03
CS Algorihm n Progrm Prolm Exm Soluion S Soluion Jonhn Turnr Exm //. ( poin) In h Fioni hp ruur, u wn vrx u n i prn v u ing u v i v h lry lo hil in i l m hil o om ohr vrx. Suppo w hng hi, o h ing u i prorm
SUMMER 17 EXAMINATION
(ISO/IEC - 7-5 Crtifid) SUMMER 7 EXAMINATION Modl wr jct Cod: Important Instructions to aminrs: ) Th answrs should b amind by ky words and not as word-to-word as givn in th modl answr schm. ) Th modl answr
Wireless & Hybrid Fire Solutions
ic b 8 c b u i N5 b 4o 25 ii p f i b p r p ri u o iv p i o c v p c i b A i r v Hri F N R L L T L RK N R L L rr F F r P o F i c b T F c c A vri r of op oc F r P, u icoc b ric, i fxib r i i ribi c c A K
) and furthermore all X. The definition of the term stationary requires that the distribution fulfills the condition:
Assigm Thomas Aam, Spha Brumm, Haik Lor May 6 h, 3 8 h smsr, 357, 7544, 757 oblm For R b X a raom variabl havig ormal isribuio wih ma µ a variac σ (his is wri as ~ (,) X. by: R a. Is X ) a urhrmor all
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
Handout on. Crystal Symmetries and Energy Bands
dou o Csl s d g Bds I hs lu ou wll l: Th loshp bw ss d g bds h bs of sp-ob ouplg Th loshp bw ss d g bds h ps of sp-ob ouplg C 7 pg 9 Fh Coll Uvs d g Bds gll hs oh Th sl pol ss ddo o h l slo s: Fo pl h
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
Linear System Review. Linear System Review. Descriptions of Linear Systems: 2008 Spring ME854 - GGZ Page 1
8 Sprg ME854 - Z Pg r Sym Rvw r Sym Rvw r Sym Rvw crpo of r Sym: p m R y R R y FT : & U Y Trfr Fco : y or : & : d y d r Sym Rvw orollbly d Obrvbly: fo 3.: FT dymc ym or h pr d o b corollbl f y l > d fl
A Simple Representation of the Weighted Non-Central Chi-Square Distribution
SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch
Note: Torque is prop. to current Stationary voltage is prop. to speed
DC Mach Cotrol Mathmatcal modl. Armatr ad orq f m m a m m r a a a a a dt d ψ ψ ψ ω Not: orq prop. to crrt Statoary voltag prop. to pd Mathmatcal modl. Fld magtato f f f f d f dt a f ψ m m f f m fλ h torq
Conventional Hot-Wire Anemometer
Convnonal Ho-Wr Anmomr cro Ho Wr Avanag much mallr prob z mm o µm br paal roluon array o h nor hghr rquncy rpon lowr co prormanc/co abrcaon roc I µm lghly op p layr 8µm havly boron op ch op layr abrcaon
N C GM L P, u u y Du J W P: u,, uy u y j S, P k v, L C k u, u GM L O v L v y, u k y v v QV v u, v- v ju, v, u v u S L v: S u E x y v O, L O C u y y, k
Qu V vu P O B x 1361, Bu QLD 4575 C k N 96 N EWSLEE NOV/ DECE MBE 2015 E N u uu L O C 21 Nv, 2 QV v Py Cu Lv Su, 23 2 5 N v, 4 2015 u G M v y y : y quu u C, u k y Bu k v, u u vy v y y C k! u,, uu G M u
The Periodic Table. Periodic Properties. Can you explain this graph? Valence Electrons. Valence Electrons. Paramagnetism
Periodic Properties Atomic & Ionic Radius Energy Electron Affinity We want to understand the variations in these properties in terms of electron configurations. The Periodic Table Elements in a column
1- I. M. ALGHROUZ: A New Approach To Fractional Derivatives, J. AOU, V. 10, (2007), pp
Jourl o Al-Qus Op Uvrsy or Rsrch Sus - No.4 - Ocobr 8 Rrcs: - I. M. ALGHROUZ: A Nw Approch To Frcol Drvvs, J. AOU, V., 7, pp. 4-47 - K.S. Mllr: Drvvs o or orr: Mh M., V 68, 995 pp. 83-9. 3- I. PODLUBNY:
Solution to 1223 The Evil Warden.
Solutio to 1 Th Evil Ward. This is o of thos vry rar PoWs (I caot thik of aothr cas) that o o solvd. About 10 of you submittd th basic approach, which givs a probability of 47%. I was shockd wh I foud
Physics 160 Lecture 3. R. Johnson April 6, 2015
Physics 6 Lcur 3 R. Johnson April 6, 5 RC Circui (Low-Pass Filr This is h sam RC circui w lookd a arlir h im doma, bu hr w ar rsd h frquncy rspons. So w pu a s wav sad of a sp funcion. whr R C RC Complx
Example: Two Stochastic Process u~u[0,1]
![Example: Two Stochastic Process u~u[0,1]](/thumbs/86/93797001.jpg "Example: Two Stochastic Process u~u[0,1]")
Co o Slo o Coco S Sh EE I Gholo h@h. ll Sochc Slo Dc Slo l h PLL c Mo o coco w h o c o Ic o Co B P o Go E A o o Po o Th h h o q o ol o oc o lco q ccc lco l Bc El: Uo Dbo Ucol Sl Ab bo col l G col G col
Frequency Response. Response of an LTI System to Eigenfunction
Frquncy Rsons Las m w Rvsd formal dfnons of lnary and m-nvaranc Found an gnfuncon for lnar m-nvaran sysms Found h frquncy rsons of a lnar sysm o gnfuncon nu Found h frquncy rsons for cascad, fdbac, dffrnc
AR(1) Process. The first-order autoregressive process, AR(1) is. where e t is WN(0, σ 2 )
AR() Procss Th firs-ordr auorgrssiv procss, AR() is whr is WN(0, σ ) Condiional Man and Varianc of AR() Condiional man: Condiional varianc: ) ( ) ( Ω Ω E E ) var( ) ) ( var( ) var( σ Ω Ω Ω Ω E Auocovarianc
Fourier Series: main points
BIOEN 3 Lcur 6 Fourir rasforms Novmbr 9, Fourir Sris: mai pois Ifii sum of sis, cosis, or boh + a a cos( + b si( All frqucis ar igr mulipls of a fudamal frqucy, o F.S. ca rprs ay priodic fucio ha w ca
Let's revisit conditional probability, where the event M is expressed in terms of the random variable. P Ax x x = =
L's rvs codol rol whr h v M s rssd rs o h rdo vrl. L { M } rrr v such h { M } Assu. { } { A M} { A { } } M < { } { } A u { } { } { A} { A} ( A) ( A) { A} A A { A } hs llows us o cosdr h cs wh M { } [ (
ECEN620: Network Theory Broadband Circuit Design Fall 2014
ECE60: work Thory Broadbad Circui Dig Fall 04 Lcur 6: PLL Trai Bhavior Sam Palrmo Aalog & Mixd-Sigal Cr Txa A&M Uivriy Aoucm, Agda, & Rfrc HW i du oday by 5PM PLL Trackig Rpo Pha Dcor Modl PLL Hold Rag
W I T H M A L I C E T O W ^ - R D I S T O N E AISTE) C H A R I T Y F O R A L L / SENT TO IONIA.
UB80B > R K? > V ( $ R AR 0 J ADVR RA DRA A L - R D A) A R Y R A L L / VL LLL K UY DBR 30 893 2 A - ( AL AL ; A (! -- DA R DG
XV Exponential and Logarithmic Functions
MATHEMATICS 0-0-RE Dirnial Calculus Marin Huard Winr 08 XV Eponnial and Logarihmic Funcions. Skch h graph o h givn uncions and sa h domain and rang. d) ) ) log. Whn Sarah was born, hr parns placd $000
Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23
BIO53 Bosascs Lcur 04: Cral Lm Thorm ad Thr Dsrbuos Drvd from h Normal Dsrbuo Dr. Juchao a Cr of Bophyscs ad Compuaoal Bology Fall 06 906 3 Iroduco I hs lcur w wll alk abou ma cocps as lsd blow, pcd valu
Homework #3. 1 x. dx. It therefore follows that a sum of the
Danil Cannon CS 62 / Luan March 5, 2009 Homwork # 1. Th natural logarithm is dfind by ln n = n 1 dx. It thrfor follows that a sum of th 1 x sam addnd ovr th sam intrval should b both asymptotically uppr-
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
Practice papers A and B, produced by Edexcel in 2009, with mark schemes. Practice Paper A. 5 cosh x 2 sinh x = 11,
Prai paprs A ad B, produd by Edl i 9, wih mark shms Prai Papr A. Fid h valus of for whih 5 osh sih =, givig your aswrs as aural logarihms. (Toal 6 marks) k. A = k, whr k is a ral osa. 9 (a) Fid valus of
Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983).
Ovrvw B r rh r: R-k r -3-4 r 00 Ig L Gør Amor Dm rogrmmg Nwork fow Srg mhg Srg g Comuo gomr Irouo o NP-om Rom gorhm B r rh r -3-4 r Aow,, or 3 k r o Prf Evr h from roo o f h m gh mr h E w E R E R rgr h
Bayesian Credibility for Excess of Loss Reinsurance Rating. By Mark Cockroft 1 Lane Clark & Peacock LLP
By Cly o c o Lo Rc Rg By M Coco L Cl & Pcoc LLP GIRO coc 4 Ac Th pp c how o v cly wgh w po- pc-v o c o lo c. Th po co o Poo-Po ol ch wh po G o. Kywo c o lo c g By cly Poo Po G po Acowlg cl I wol l o h
P a g e 3 6 of R e p o r t P B 4 / 0 9
P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J
Shortest Paths in Graphs. Paths in graphs. Shortest paths CS 445. Alon Efrat Slides courtesy of Erik Demaine and Carola Wenk
S 445 Shortst Paths n Graphs lon frat Sls courtsy of rk man an arola Wnk Paths n raphs onsr a raph G = (V, ) wth -wht functon w : R. Th wht of path p = v v v k s fn to xampl: k = w ( p) = w( v, v + ).
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 /
Probability & Statistics,
Probability & Statistics, BITS Pilai K K Birla Goa Campus Dr. Jajati Kshari Sahoo Dpartmt of Mathmatics BITS Pilai, K K Birla Goa Campus Poisso Distributio Poisso Distributio: A radom variabl X is said
Pupil / Class Record We can assume a word has been learned when it has been either tested or used correctly at least three times.
2 Pupi / Css Rr W ssum wr hs b r wh i hs b ihr s r us rry s hr ims. Nm: D Bu: fr i bus brhr u firs hf hp hm s uh i iv iv my my mr muh m w ih w Tik r pp push pu sh shu sisr s sm h h hir hr hs im k w vry
On a problem of J. de Graaf connected with algebras of unbounded operators de Bruijn, N.G.
O a problm of J. d Graaf coctd with algbras of uboudd oprators d Bruij, N.G. Publishd: 01/01/1984 Documt Vrsio Publishr s PDF, also kow as Vrsio of Rcord (icluds fial pag, issu ad volum umbrs) Plas chck
ELG3150 Assignment 3
ELG350 Aigmt 3 Aigmt 3: E5.7; P5.6; P5.6; P5.9; AP5.; DP5.4 E5.7 A cotrol ytm for poitioig th had of a floppy dik driv ha th clodloop trafr fuctio 0.33( + 0.8) T ( ) ( + 0.6)( + 4 + 5) Plot th pol ad zro
2016 FALL PARKS DIVISION AND WATER UTILITY LANDSCAPING
- - - - - - - - - - - - - - - - - DU F O K CD G K CUC G K D K FD K GY GOF COU O K G O K GD O K KGO-OYX K O CK K G K COD K K K (KG O) UY O G CK KY UY F D UY CY OF DO D OF UC OK U O: --; --; --; --; ---
Power Spectrum Estimation of Stochastic Stationary Signals
ag of 6 or Spctru stato of Stochastc Statoary Sgas Lt s cosr a obsrvato of a stochastc procss (). Ay obsrvato s a ft rcor of th ra procss. Thrfor, ca say:
Factors Success op Ten Critical T the exactly what wonder may you referenced, being questions different the all With success critical ten top the of l
Fr Su p T rl T xl r rr, bg r ll Wh u rl p l Fllg ll r lkg plr plr rl r kg: 1 k r r u v P 2 u l r P 3 ) r rl k 4 k rprl 5 6 k prbl lvg hkg rl 7 lxbl F 8 l S v 9 p rh L 0 1 k r T h r S pbl r u rl bv p p
THE STOCHASTIC SEASONAL BEHAVIOR OF THE NATURAL GAS PRICE
E SOCASIC SEASONAL BEAVIOR OF E NAURAL GAS PRICE. Irouco Aré García ra Fracco Javr Poblacó García Prlmary Draf Ju-5 Acamc a pracor hav b rcly payg ao o a facal grg u: h valuao a hgg of commoy cog clam.
Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek
Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd
Applications of semi-markov processes in reliability
rbk Alco o m-mrko roc rlbl - TA # 3-4 7 Dcmbr - Scl I rbk rczk Nl Ur d old Alco o m-mrko roc rlbl Kword m-mrko roc rlbl rdom lr r cold db m wh rr Abrc Th bc do d horm rom h m-mrko roc hor r dcd h r Th
IV. First Law of Thermodynamics. Cooler. IV. First Law of Thermodynamics
D. Applcatons to stady flow dvcs. Hat xchangrs - xampl: Clkr coolr for cmnt kln Scondary ar 50 C, 57,000 lbm/h Clkr? C, 5 ton/h Coolr Clkr 400 C, 5 ton/h Scondary ar 0 C, 57,000 lbm/h a. Assumptons. changs
Nuclear Chemistry -- ANSWERS
Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay
OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9
OH BOY! O h Boy!, was or igin a lly cr eat ed in F r en ch an d was a m a jor s u cc ess on t h e Fr en ch st a ge f or young au di enc es. It h a s b een s een by ap pr ox i ma t ely 175,000 sp ect at
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
NUCON NRNON CONRNC ON CURRN RN N CHNOOGY, 011 oo uul o w ul x ol volv y y oll. y ov,., - o lo ll vy ul o Mo l u v ul (G) v Gl vlu oll. u 3- [11]. 000
![NUCON NRNON CONRNC ON CURRN RN N CHNOOGY, 011 oo uul o w ul x ol volv y y oll. y ov,., - o lo ll vy ul o Mo l u v ul (G) v Gl vlu oll. u 3- [11]. 000](/thumbs/93/113547584.jpg "NUCON NRNON CONRNC ON CURRN RN N CHNOOGY, 011 oo uul o w ul x ol volv y y oll. y ov,., - o lo ll vy ul o Mo l u v ul (G) v Gl vlu oll. u 3- [11]. 000")
NU O HMB NRM UNVRY, HNOOGY, C 8 0 81, 8 3-1 01 CMBR, 0 1 1 l oll oll ov ll lvly lu ul uu oll ul. w o lo u uol u z. ul l u oll ul. quk, oll, vl l, lk lo, - ul o u v (G) v Gl o oll. ul l u vlu oll ul uj
Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule
Lcur : Numrical ngraion Th Trapzoidal and Simpson s Rul A problm Th probabiliy of a normally disribud (man µ and sandard dviaion σ ) vn occurring bwn h valus a and b is B A P( a x b) d () π whr a µ b -
Chapter 5 The Laplace Transform. x(t) input y(t) output Dynamic System
EE 422G No: Chapr 5 Inrucor: Chung Chapr 5 Th Laplac Tranform 5- Inroducion () Sym analyi inpu oupu Dynamic Sym Linar Dynamic ym: A procor which proc h inpu ignal o produc h oupu dy ( n) ( n dy ( n) +
48U 47U SENA = 46U 45U 44U 43U 42U 41U 40U 39U 38U 37U 36U 35U 34U 33U 32U 31U 30U 29U 28U 27U 26U 25U 24U 23U 22U 21U 20U 19U 18U 17U 16U 15U 14U
5 6 7 C"J A ("") X S +A QXkV SASCAL PAL X LiAAAAAJAAAAAJiiJ ffffff/ffffff//ffffffffij//j/ ffffiii/j/f/ff/jfff/f/fi/j/fffffj//f/f SS Sees :::::::: U65 SA U 7U 6U 5U U U U X,JL, OOR.LJS!"" oij,,..,, o o
The Periodic Table of Elements
The Periodic Table of Elements 8 Uuo Uus Uuh (9) Uup (88) Uuq (89) Uut (8) Uub (8) Rg () 0 Ds (9) 09 Mt (8) 08 Hs (9) 0 h () 0 Sg () 0 Db () 0 Rf () 0 Lr () 88 Ra () 8 Fr () 8 Rn () 8 At (0) 8 Po (09)
Response of LTI Systems to Complex Exponentials
3 Fourir sris coiuous-im Rspos of LI Sysms o Complx Expoials Ouli Cosidr a LI sysm wih h ui impuls rspos Suppos h ipu sigal is a complx xpoial s x s is a complx umbr, xz zis a complx umbr h or h h w will
1985 AP Calculus BC: Section I
985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b
Math 266, Practice Midterm Exam 2
Mh 66, Prcic Midrm Exm Nm: Ground Rul. Clculor i NOT llowd.. Show your work for vry problm unl ohrwi d (pril crdi r vilbl). 3. You my u on 4-by-6 indx crd, boh id. 4. Th bl of Lplc rnform i vilbl h l pg.
Poisson process Markov process
E2200 Quuing hory and lraffic 2nd lcur oion proc Markov proc Vikoria Fodor KTH Laboraory for Communicaion nwork, School of Elcrical Enginring 1 Cour oulin Sochaic proc bhind quuing hory L2-L3 oion proc