ECE 194C Acoustic Target Tracking in Sensor Networks Methods for acoustic target tracking.
|
|
- Avis Russell
- 5 years ago
- Views:
Transcription
1 ECE 94C Aout Target Trag Seor etwor Method for aout target trag. ear Feld Sgal-tregth rato. Cro-orrelato wth broadat aout gal Sum ro-orrelato o ror gal owledge Far-feld Mamum-lelhood gle oure MUSIC multle oure Alteratg mamzato multle oure.
2 Cell-Baed Loalzato Ref: D. L, K. Wog, Y. Hu ad A. Saeed Deteto, lafato IEEE Sg. Pro. Mag.
3 Loalzato of Aout Target Tme-of-arrval le-of-bearg obtaed va beamformg dreto of troget aout gature. Fgure, Ref: Wag ad Chadraaa, IEEE Sg. Pro. Mag. Jul. 3
4 Woodeer Loalzato Ref: H. Wag et. al. Aout Seor etwor for Woodeer Loalzato, SPIE Coferee Pro. 5, alo Platform for ollaboratve Aout Sgal Proeg. 4
5 ear-feld v. Far Feld Wavefrot urvature Plae-wave Aromato. 5
6 ear-feld Proagato Model, Soure, r T α, r T α + 6
7 Aout Trag ug Eerg Rato Problem: Soure ower S T uow Soluto: Ue reeved eerg rato at dfferet eor Ref: L, Wog, Hu Saeed IEEE Sg. Pro. Magaze ρ, r r α α T α / T α α ρ, α ρ α, 7
8 8 Eerg Rato Target Loalzato For eah ar of eor, addate target oto le o a rle [ ] ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ α α α α,,,, r r
9 Eamle 3 Seor Form rle,,3,3 9 - ρ, o meter o meter 9
10 Leat-Suare Soluto Model for meaured rato wth addtve oe α ρ, +, olear leat-uare oluto ehautve earh. arg m + ρ,
11 Eamle 4 Seor/Leat-Suare Soluto o meter o meter
12 Coheret Correlato Eamle aout gature tru
13 Autoorrelato ρ 3
14 Seor etwor Coheret Correlato Ref: Q. Wag, W. Che, R. Zheg, K. Lee ad L. Sha Aout target trag ug t wrele eor deve, ISP 3 Ue Referee Beao Shrozato RBS to ma eor lo to global tme. Cluterhead tramt beao to eor, eor rel wth ther dvdual lo readg. Cluterhead omute orreto fator. Clo Beao Cluterhead Clo Beao Clo 3 Beao 4
15 Coheret Correlato All eor reord rular buffer of oud. Cluterhead determe whe a gal-of-teret reet. Cluterhead tramt t reeved waveform to eor. Seor ro-orrelate ther reorded oud wth broadat aet. 5
16 6 Coheret Correlato [ ] v d d r + + ρ v d r + ρ d d f d / Dela etmate e.
17 Cro-Correlato Tme-of-Arrval Etmate ˆ ma arg r 7
18 8 Tragulato Seor tramt arrval tme etmate to luterhead. Cluterhead ue rage euato to olve for target oto ad tart tme. Rage euato eed of oud aro. 33 m/ / / / t t t Tme t of oure tramo uow, but we have three euato, three uow,,t.
19 9 Leat-Suare Tragulato t t t v t / ˆ,, m arg ˆ, ˆ, ˆ ˆ / Aume tme-of-arrval meauremet error are..d. Gaua. Otmal oluto for oto/tramo tme olear leat-uare Soluto va modfed grd earh
20 Coheret Trag Searo
21 Trag a Movg Aout Soure
22 ooheret Aout Loalzato Correlato-baed method reure owledge of aout waveform. Alteratve aroah baed o ro-orrelato betwee eor deedet of waveform truture. Ref. Che, Hudo ad Yao Mamum-lelhood oure loalzato ad uow eor loato etmato IEEE Tra. Sg. Pro.. Coder ro-orrelato betwee two eor: arg ma
23 Sum Cro-Correlato Coder dela-dfferee tramo tme t ael. arg ma / + t / / + t / Loalze b mamzg um ro-orrelato Do ot eed to ow or tart tme t! J 3
24 4 FFT Imlemetato of Sum Cro- Correlato e X e X J / / π π Reall tme-hft orreod to lear hae hft FFT
25 5 FFT Imlemetato Cot d., * /, / / /, B B B X e X e e e X J π π π π
26 6 Beamformer Iterretato, S S J S e S e X e B e S X π π π π e S X / π
27 Eamle of FFT-Baed ooheret Loalzato 7
28 Beamformer Magtude ,396 J o meter o meter 7 J e π X + / f amle 8
29 Far-Feld Dreto-of-Arrval I the Wag ad Chadraaa earo IEEE Sg. Pro. Mag. olated luter of eor oerate the far feld. Ue le-of-bearg dreto-of-arrval to tragulate Smlfe ommuato ut tramt LOB to a etral roeor for tragulato. 9
30 3 Far-Feld Uform Lear Arra Model d / d / / / / d d d M / d d d
31 ULA Sgal LOB 3
32 ULA Sgal LOB 45 deg d π / 4 / 3d π / 4 / 3
33 33 Cro-Correlato for LOB Etmato ma arg ˆ / ma arg / / J J d d d
34 34 FFT-Baed Beamformg Ue aal for ooheret ear-feld ae, / e X e B J d S e S X π π π Cluterhead omute le-of-bearg ad FFT Ref. Wag ad Chadraaa
35 ULA Seor Searo 35
36 36 Beamformer Outut /,,,, amle f d X e B J π
37 Far-Feld Multle Soure Loalzato RF Reoe of a atea arra to gle RF oure, fre. f. Ae πf t Ae f t d / z t π + v t Ae πf t M d / d / d / 37
38 Mamum-Lelhood Soluto for RF Loalzato Drete-tme model after dowovero A Ae z π + v a + v M Ae π M Gaua oe ML oluto ehautve earh. ˆ ML m arg z a 38
39 39 Multle Soure Loalzato + + M A A A e e v a v z π π M Arra reoe to oure,..., m arg ˆ ML a z Cure of dmeoalt Comlet order Q for Q uatzato of agle.
40 ML Soluto Soure 4 log z π / 4, 3π /
41 Soluto for Multle Soure Loalzato Mamum-lelhood Eoetal omlet umber of oure Q Alteratg Mamzato Reure evaluato of roeto matre, multle terato. MUSIC Multle Soure Idetfato ad Clafato Poor erformae at low SR. Reure ree arra albrato. 4
42 4 MUSIC Algorthm I a a z z R ˆ v H l H z P l l σ + ] [ ] [..... H H z v M U U U U R Λ > > > + σ λ λ λ λ λ a U U a H H J J,...,, ma arg Comute amle orrelato matr Perform the SVD to obta Comute the MUSIC etrum MUSIC dreto-of-arrval etmate are
43 MUSIC Algorthm Soluto 6 Agle σ. 5 σ σ. Mu Setrum 4 3 π/4, 3π/8 σ
44
CS 2750 Machine Learning Lecture 5. Density estimation. Density estimation
CS 750 Mache Learg Lecture 5 esty estmato Mlos Hausrecht mlos@tt.edu 539 Seott Square esty estmato esty estmato: s a usuervsed learg roblem Goal: Lear a model that rereset the relatos amog attrbutes the
More informationTest Paper-II. 1. If sin θ + cos θ = m and sec θ + cosec θ = n, then (a) 2n = m (n 2 1) (b) 2m = n (m 2 1) (c) 2n = m (m 2 1) (d) none of these
Test Paer-II. If s θ + cos θ = m ad sec θ + cosec θ =, the = m ( ) m = (m ) = m (m ). If a ABC, cos A = s B, the t s C a osceles tragle a eulateral tragle a rght agled tragle. If cos B = cos ( A+ C), the
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationSimple Linear Regression Analysis
LINEAR REGREION ANALYSIS MODULE II Lecture - 5 Smple Lear Regreo Aaly Dr Shalabh Departmet of Mathematc Stattc Ida Ittute of Techology Kapur Jot cofdece rego for A jot cofdece rego for ca alo be foud Such
More informationUnsupervised Learning and Other Neural Networks
CSE 53 Soft Computg NOT PART OF THE FINAL Usupervsed Learg ad Other Neural Networs Itroducto Mture Destes ad Idetfablty ML Estmates Applcato to Normal Mtures Other Neural Networs Itroducto Prevously, all
More informationReaction Time VS. Drug Percentage Subject Amount of Drug Times % Reaction Time in Seconds 1 Mary John Carl Sara William 5 4
CHAPTER Smple Lear Regreo EXAMPLE A expermet volvg fve ubject coducted to determe the relatohp betwee the percetage of a certa drug the bloodtream ad the legth of tme t take the ubject to react to a tmulu.
More informationEVALUATION OF PERFORMANCE MEASURES OF FMS Bottleneck Model. Part mix Mix of the various part or product styles produced by the system
Natoal Ittute of Techology Calcut Deartmet of Mechacal Egeerg EVALUATION OF PERFORMANCE MEASURES OF FMS Bottleeck Model Provde tartg etmate of FMS deg arameter uch a roducto rate ad umber of worktato Bottleeck
More informationLecture 3 Naïve Bayes, Maximum Entropy and Text Classification COSI 134
Lecture 3 Naïve Baes, Mamum Etro ad Tet Classfcato COSI 34 Codtoal Parameterzato Two RVs: ItellgeceI ad SATS ValI = {Hgh,Low}, ValS={Hgh,Low} A ossble jot dstrbuto Ca descrbe usg cha rule as PI,S PIPS
More informationQuiz 1- Linear Regression Analysis (Based on Lectures 1-14)
Quz - Lear Regreo Aaly (Baed o Lecture -4). I the mple lear regreo model y = β + βx + ε, wth Tme: Hour Ε ε = Ε ε = ( ) 3, ( ), =,,...,, the ubaed drect leat quare etmator ˆβ ad ˆβ of β ad β repectvely,
More informationSimple Linear Regression. How To Study Relation Between Two Quantitative Variables? Scatter Plot. Pearson s Sample Correlation.
Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6. A Smple Regreo Problem I there relato betwee umber of power boat the area ad umber of maatee klled? Year NPB( )
More informationREVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION
REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I lear regreo, we coder the frequecy dtrbuto of oe varable (Y) at each of everal level of a ecod varable (X). Y kow a the depedet varable. The
More informationr y Simple Linear Regression How To Study Relation Between Two Quantitative Variables? Scatter Plot Pearson s Sample Correlation Correlation
Maatee Klled Correlato & Regreo How To Study Relato Betwee Two Quattatve Varable? Smple Lear Regreo 6.11 A Smple Regreo Problem 1 I there relato betwee umber of power boat the area ad umber of maatee klled?
More informationParametric Density Estimation: Bayesian Estimation. Naïve Bayes Classifier
arametrc Dest Estmato: Baesa Estmato. Naïve Baes Classfer Baesa arameter Estmato Suose we have some dea of the rage where arameters should be Should t we formalze such ror owledge hoes that t wll lead
More information8 The independence problem
Noparam Stat 46/55 Jame Kwo 8 The depedece problem 8.. Example (Tua qualty) ## Hollader & Wolfe (973), p. 87f. ## Aemet of tua qualty. We compare the Huter L meaure of ## lghte to the average of coumer
More informationSummarizing Bivariate Data. Correlation. Scatter Plot. Pearson s Sample Correlation. Summarizing Bivariate Data SBD - 1
Summarzg Bvarate Data Summarzg Bvarate Data - Eamg relato betwee two quattatve varable I there relato betwee umber of hadgu regtered the area ad umber of people klled? Ct NGR ) Nkll ) 447 3 4 3 48 4 4
More informationSTK3100 and STK4100 Autumn 2017
SK3 ad SK4 Autum 7 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Sectos 4..5, 4.3.5, 4.3.6, 4.4., 4.4., ad 4.4.3 Sectos 5.., 5.., ad 5.5. Ørulf Borga Deartmet of Mathematcs
More informationChapter 11 Systematic Sampling
Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of
More information2. Independence and Bernoulli Trials
. Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,
More informationPower Flow S + Buses with either or both Generator Load S G1 S G2 S G3 S D3 S D1 S D4 S D5. S Dk. Injection S G1
ower Flow uses wth ether or both Geerator Load G G G D D 4 5 D4 D5 ecto G Net Comple ower ecto - D D ecto s egatve sg at load bus = _ G D mlarl Curret ecto = G _ D At each bus coservato of comple power
More informationCS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x
CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear combato of put compoets f + + + K d d K k - parameters
More informationAnalyzing Control Structures
Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred
More informationChapter Newton-Raphson Method of Solving Simultaneous Nonlinear Equations
Chapter 7 Newto-Rapho Method o Solg Smltaeo Nolear Eqato Ater readg th chapter o hold be able to: dere the Newto-Rapho method ormla or mltaeo olear eqato deelop the algorthm o the Newto-Rapho method or
More informationA Mean- maximum Deviation Portfolio Optimization Model
A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study
More informationLinear Regression. Can height information be used to predict weight of an individual? How long should you wait till next eruption?
Iter-erupto Tme Weght Correlato & Regreo 1 1 Lear Regreo 0 80 70 80 Heght 1 Ca heght formato be ued to predct weght of a dvdual? How log hould ou wat tll et erupto? Weght: Repoe varable (Outcome, Depedet)
More informationTransforms that are commonly used are separable
Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )
More informationCSE 5526: Introduction to Neural Networks Linear Regression
CSE 556: Itroducto to Neural Netorks Lear Regresso Part II 1 Problem statemet Part II Problem statemet Part II 3 Lear regresso th oe varable Gve a set of N pars of data , appromate d by a lear fucto
More informationLog1 Contest Round 2 Theta Complex Numbers. 4 points each. 5 points each
01 Log1 Cotest Roud Theta Complex Numbers 1 Wrte a b Wrte a b form: 1 5 form: 1 5 4 pots each Wrte a b form: 65 4 4 Evaluate: 65 5 Determe f the followg statemet s always, sometmes, or ever true (you may
More informationRegression and the LMS Algorithm
CSE 556: Itroducto to Neural Netorks Regresso ad the LMS Algorthm CSE 556: Regresso 1 Problem statemet CSE 556: Regresso Lear regresso th oe varable Gve a set of N pars of data {, d }, appromate d b a
More informationParameter Estimation
arameter Estmato robabltes Notatoal Coveto Mass dscrete fucto: catal letters Desty cotuous fucto: small letters Vector vs. scalar Scalar: la Vector: bold D: small Hgher dmeso: catal Notes a cotuous state
More informationSTA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1
STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal
More informationSampling Theory MODULE X LECTURE - 35 TWO STAGE SAMPLING (SUB SAMPLING)
Samplg Theory ODULE X LECTURE - 35 TWO STAGE SAPLIG (SUB SAPLIG) DR SHALABH DEPARTET OF ATHEATICS AD STATISTICS IDIA ISTITUTE OF TECHOLOG KAPUR Two stage samplg wth uequal frst stage uts: Cosder two stage
More informationSTK3100 and STK4100 Autumn 2018
SK3 ad SK4 Autum 8 Geeralzed lear models Part III Covers the followg materal from chaters 4 ad 5: Cofdece tervals by vertg tests Cosder a model wth a sgle arameter β We may obta a ( α% cofdece terval for
More informationENGI 3423 Simple Linear Regression Page 12-01
ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable
More informationROOT-LOCUS ANALYSIS. Lecture 11: Root Locus Plot. Consider a general feedback control system with a variable gain K. Y ( s ) ( ) K
ROOT-LOCUS ANALYSIS Coder a geeral feedback cotrol yte wth a varable ga. R( Y( G( + H( Root-Locu a plot of the loc of the pole of the cloed-loop trafer fucto whe oe of the yte paraeter ( vared. Root locu
More informationResearch on Efficient Turbo Frequency Domain Equalization in STBC-MIMO System
Research o Effcet urbo Freuecy Doma Eualzato SBC-MIMO System Wau uag Bejg echology ad Busess Uversty Bejg 00048.R. Cha Abstract. A effcet urbo Freuecy Doma Eualzato FDE based o symbol-wse mmum mea-suare
More informationLayered structures: transfer matrix formalism
Layered tructure: trafer matrx formalm Iterface betwee LI meda Trafer matrx formalm Petr Kužel Practcally oly oe formula to be kow order to calculate ay tructure Applcato: Atreflectve coatg Delectrc mrror,
More informationLecture Notes 2. The ability to manipulate matrices is critical in economics.
Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets
More informationCS 1675 Introduction to Machine Learning Lecture 12 Support vector machines
CS 675 Itroducto to Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mdterm eam October 9, 7 I-class eam Closed book Stud materal: Lecture otes Correspodg chapters
More informationTheory study about quarter-wave-stack dielectric mirrors
Theor tud about quarter-wave-tack delectrc rror Stratfed edu tratted reflected reflected Stratfed edu tratted cdet cdet T T Frt, coder a wave roagato a tratfed edu. A we kow, a arbtrarl olared lae wave
More informationK-Even Edge-Graceful Labeling of Some Cycle Related Graphs
Iteratoal Joural of Egeerg Scece Iveto ISSN (Ole): 9 7, ISSN (Prt): 9 7 www.jes.org Volume Issue 0ǁ October. 0 ǁ PP.0-7 K-Eve Edge-Graceful Labelg of Some Cycle Related Grahs Dr. B. Gayathr, S. Kousalya
More informationComputational Geometry
Problem efto omputatoal eometry hapter 6 Pot Locato Preprocess a plaar map S. ve a query pot p, report the face of S cotag p. oal: O()-sze data structure that eables O(log ) query tme. pplcato: Whch state
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More informationMONOPOLISTIC COMPETITION MODEL
MONOPOLISTIC COMPETITION MODEL Key gredets Cosumer utlty: log (/ ) log (taste for varety of dfferetated goods) Produto of dfferetated produts: y (/ b) max[ f, ] (reasg returs/fxed osts) Assume that good,
More informationIntroduction. Modeling Data. Approach. Quality of Fit. Likelihood. Probabilistic Approach
Introducton Modelng Data Gven a et of obervaton, we wh to ft a mathematcal model Model deend on adutable arameter traght lne: m + c n Polnomal: a + a + a + L+ a n Choce of model deend uon roblem Aroach
More informationLifetime Performance of an Energy Efficient Clustering Algorithm for Cluster-Based Wireless Sensor Networks
Lfetme Performace of a Eerg Effcet Cluterg Algorthm for Cluter-Baed Wrele Seor Networ Yug-Fa Huag, Wu-He Luo, Joh Sum, L-Huag Chag 3, Chh-We Chag, ad Rug-Chg Che 4 Graduate Ittute of Networg ad Commucato
More informationSupport vector machines II
CS 75 Mache Learg Lecture Support vector maches II Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Learl separable classes Learl separable classes: here s a hperplae that separates trag staces th o error
More informationLecture 3. Least Squares Fitting. Optimization Trinity 2014 P.H.S.Torr. Classic least squares. Total least squares.
Lecture 3 Optmzato Trt 04 P.H.S.Torr Least Squares Fttg Classc least squares Total least squares Robust Estmato Fttg: Cocepts ad recpes Least squares le fttg Data:,,,, Le equato: = m + b Fd m, b to mmze
More informationb. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.
.46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationQuarter-Sweep Arithmetic Mean (QSAM) Iterative. Method for Second Kind Linear Fredholm Integral. Equations
Aled Mathematcal Sceces Vol. 4 o. 59 943-953 Quarter-Swee Arthmetc Mea () Iteratve Method for Secod d ear Fredholm Itegral Equatos M. S. Muthuvalu School of Scece ad Techology Uverst Malaysa Saah oced
More informationPerformance of Energy Efficient Relaying for Cluster-Based Wireless Sensor Networks
Commucatos of the IIMA Volume 7 Issue 3 Artcle 8 007 Performace of Eerg Effcet Relag for Cluster-Based Wreless Sesor Networs Yug-Fa Huag Graduate Isttute of Networg ad Commucato Egeerg Chaoag Uverst of
More informationSpring Ammar Abu-Hudrouss Islamic University Gaza
١ ١ Chapter Chapter 4 Cyl Blo Cyl Blo Codes Codes Ammar Abu-Hudrouss Islam Uversty Gaza Spr 9 Slde ٢ Chael Cod Theory Cyl Blo Codes A yl ode s haraterzed as a lear blo ode B( d wth the addtoal property
More informationModel Fitting, RANSAC. Jana Kosecka
Model Fttg, RANSAC Jaa Kosecka Fttg: Issues Prevous strateges Le detecto Hough trasform Smple parametrc model, two parameters m, b m + b Votg strateg Hard to geeralze to hgher dmesos a o + a + a 2 2 +
More informationObjectives of Multiple Regression
Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of
More informationRatio-Type Estimators in Stratified Random Sampling using Auxiliary Attribute
roceedg of te Iteratoal Multoferece of Egeer ad omuter cett 0 Vol I IME 0 Marc - 0 Hog Kog Rato-ye Etmator tratfed Radom amlg ug Auxlary Attrbute R V K g A Amed Member IAEG Abtract ome rato-tye etmator
More informationINTRODUCTION TO INERTIAL CONFINEMENT FUSION
INRODUCION O INERIAL CONFINEMEN FUSION R. Bett Lecture 1 Formula or hot pot temperature Reved dyamc model ad gto codto Etropy he ormula below wa derved Lecture 9. It repreet the maxmum value o the cetral
More informationPHYS Look over. examples 2, 3, 4, 6, 7, 8,9, 10 and 11. How To Make Physics Pay PHYS Look over. Examples: 1, 4, 5, 6, 7, 8, 9, 10,
PHYS Look over Chapter 9 Sectos - Eamples:, 4, 5, 6, 7, 8, 9, 0, PHYS Look over Chapter 7 Sectos -8 8, 0 eamples, 3, 4, 6, 7, 8,9, 0 ad How To ake Phscs Pa We wll ow look at a wa of calculatg where the
More informationBig Data Analytics. Data Fitting and Sampling. Acknowledgement: Notes by Profs. R. Szeliski, S. Seitz, S. Lazebnik, K. Chaturvedi, and S.
Bg Data Aaltcs Data Fttg ad Samplg Ackowledgemet: Notes b Profs. R. Szelsk, S. Setz, S. Lazebk, K. Chaturved, ad S. Shah Fttg: Cocepts ad recpes A bag of techques If we kow whch pots belog to the le, how
More informationCS473-Algorithms I. Lecture 12b. Dynamic Tables. CS 473 Lecture X 1
CS473-Algorthm I Lecture b Dyamc Table CS 473 Lecture X Why Dyamc Table? I ome applcato: We do't kow how may object wll be tored a table. We may allocate pace for a table But, later we may fd out that
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More informationIntroducing Sieve of Eratosthenes as a Theorem
ISSN(Ole 9-8 ISSN (Prt - Iteratoal Joural of Iovatve Research Scece Egeerg ad echolog (A Hgh Imact Factor & UGC Aroved Joural Webste wwwrsetcom Vol Issue 9 Setember Itroducg Seve of Eratosthees as a heorem
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Aalyss of Varace ad Desg of Exermets-I MODULE II LECTURE - GENERAL LINEAR HYPOTHESIS AND ANALYSIS OF VARIANCE Dr Shalabh Deartmet of Mathematcs ad Statstcs Ida Isttute of Techology Kaur Tukey s rocedure
More information( ) Thermal noise ktb (T is absolute temperature in kelvin, B is bandwidth, k is Boltzamann s constant) Shot noise
OISE Thermal oe ktb (T abolute temperature kelv, B badwdth, k Boltzama cotat) 3 k.38 0 joule / kelv ( joule /ecod watt) ( ) v ( freq) 4kTB Thermal oe refer to the ketc eergy of a body of partcle a a reult
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationATTITUDE INDEX GROUP DECISION-MAKING METHOD OF PERFORMANCE EVALUATION FOR COAL ENTERPRISES ENERGY CONSERVATION AND REDUCTION OF POLLUTANT EMISSIONS
ATTITUDE INDEX GROUP DECISION-MAKING METHOD OF PERFORMANCE EVALUATION FOR COAL ENTERPRISES ENERGY CONSERVATION AND REDUCTION OF POLLUTANT EMISSIONS GAO YAN LIU CHENCHEN Bussess admstrato ost dotor researh
More informationEigen analysis The correlation matrix plays a large role in statistical characterization and processing. It was previously shown that R is Hermetian
ge aalss he orrelato matr plas a large role statstal haraterzato ad proessg It as preosl sho that s ermeta We ll o frther aalze the orrelato matr throgh ege aalss - egeales ad etors - matr dagoalzato -
More information11.5 MAP Estimator MAP avoids this Computational Problem!
.5 MAP timator ecall that the hit-or-mi cot function gave the MAP etimator it maimize the a oteriori PDF Q: Given that the MMS etimator i the mot natural one why would we conider the MAP etimator? A: If
More informationFormulas and Tables from Beginning Statistics
Fmula ad Table from Begg Stattc Chater Cla Mdot Relatve Frequecy Chater 3 Samle Mea Poulato Mea Weghted Mea Rage Lower Lmt Uer Lmt Cla Frequecy Samle Se µ ( w) w f Mamum Data Value - Mmum Data Value Poulato
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More informationFRST 531 Applied multivariate statistics
FRS 5 ppled multvarate tattc a varable a tool developed Decrptve motl, rather tha feretal a clafcato of method pe of varable:. Dcrete veru cotuou. Nomal -- ame Ordal -- have order Iterval relate to oe
More informationBinary classification: Support Vector Machines
CS 57 Itroducto to AI Lecture 6 Bar classfcato: Support Vector Maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Supervsed learg Data: D { D, D,.., D} a set of eamples D, (,,,,,
More informationSTATISTICS 13. Lecture 5 Apr 7, 2010
STATISTICS 13 Leture 5 Apr 7, 010 Revew Shape of the data -Bell shaped -Skewed -Bmodal Measures of eter Arthmet Mea Meda Mode Effets of outlers ad skewess Measures of Varablt A quattatve measure that desrbes
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationSingular Value Decomposition. Linear Algebra (3) Singular Value Decomposition. SVD and Eigenvectors. Solving LEs with SVD
Sgular Value Decomosto Lear Algera (3) m Cootes Ay m x matrx wth m ca e decomosed as follows Dagoal matrx A UWV m x x Orthogoal colums U U I w1 0 0 w W M M 0 0 x Orthoormal (Pure rotato) VV V V L 0 L 0
More informationKR20 & Coefficient Alpha Their equivalence for binary scored items
KR0 & Coeffcet Alpha Ther equvalece for bary cored tem Jue, 007 http://www.pbarrett.et/techpaper/r0.pdf f of 7 Iteral Cotecy Relablty for Dchotomou Item KR 0 & Alpha There apparet cofuo wth ome dvdual
More informationCarbonyl Groups. University of Chemical Technology, Beijing , PR China;
Electroc Supplemetary Materal (ESI) for Physcal Chemstry Chemcal Physcs Ths joural s The Ower Socetes 0 Supportg Iformato A Theoretcal Study of Structure-Solublty Correlatos of Carbo Doxde Polymers Cotag
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationELEC 6041 LECTURE NOTES WEEK 1 Dr. Amir G. Aghdam Concordia University
ELEC 604 LECTURE NOTES WEEK Dr mr G ghdam Cocorda Uverst Itroducto - Large-scale sstems are the mult-ut mult-outut (MIMO) sstems cosstg of geograhcall searated comoets - Eamles of large-scale sstems clude
More informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More informationLine Fitting and Regression
Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots
More informationAustralian Journal of Basic and Applied Sciences. Full-Sweep SOR Iterative Method to Solve Space-Fractional Diffusion Equations
Australa Joural of Basc ad Aled Sceces, 8(4) Secal 14, Paes: 153-158 AENSI Jourals Australa Joural of Basc ad Aled Sceces ISSN:1991-8178 Joural home ae: www.abasweb.com Full-Swee SOR Iteratve Method to
More informationON A NEUMANN EQUILIBRIUM STATES IN ONE MODEL OF ECONOMIC DYNAMICS
oral of re ad Appled Mathemats: Advaes ad Applatos Volme 8 Nmber 2 207 ages 87-95 Avalable at http://setfadvaes.o. DO: http://d.do.org/0.8642/pamaa_7002866 ON A NEUMANN EQULBRUM STATES N ONE MODEL OF ECONOMC
More informationBorn-Oppenheimer Approximation. Kaito Takahashi
o-oppehee ppoato Kato Takahah toc Ut Fo quatu yte uch a ecto ad olecule t eae to ue ut that ft the=tomc UNT Ue a of ecto (ot kg) Ue chage of ecto (ot coulob) Ue hba fo agula oetu (ot kg - ) Ue 4pe 0 fo
More informationPhysics 114 Exam 2 Fall Name:
Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout
More informationHigh-performance flat-panel solar thermoelectric generators with high thermal concentration
SUPPLEMENTARY INFORMATION do: 0.038/mat303 Hgh-erformae flat-ael solar thermoeletr geerators wth hgh thermal oetrato Dael Kraemer, Bed Poudel, Hse-Pg Feg, J. Chrstoher Caylor, Bo Yu, Xao Ya, Y Ma, Xaowe
More information1. Linear second-order circuits
ear eco-orer crcut Sere R crcut Dyamc crcut cotag two capactor or two uctor or oe uctor a oe capactor are calle the eco orer crcut At frt we coer a pecal cla of the eco-orer crcut, amely a ere coecto of
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationLinear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab
Lear Regresso Lear Regresso th Shrkage Some sldes are due to Tomm Jaakkola, MIT AI Lab Itroducto The goal of regresso s to make quattatve real valued predctos o the bass of a vector of features or attrbutes.
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationLINEARLY CONSTRAINED MINIMIZATION BY USING NEWTON S METHOD
Jural Karya Asl Loreka Ahl Matematk Vol 8 o 205 Page 084-088 Jural Karya Asl Loreka Ahl Matematk LIEARLY COSTRAIED MIIMIZATIO BY USIG EWTO S METHOD Yosza B Dasrl, a Ismal B Moh 2 Faculty Electrocs a Computer
More informationA conic cutting surface method for linear-quadraticsemidefinite
A coc cuttg surface method for lear-quadratcsemdefte programmg Mohammad R. Osoorouch Calfora State Uversty Sa Marcos Sa Marcos, CA Jot wor wth Joh E. Mtchell RPI July 3, 2008 Outle: Secod-order coe: defto
More informationMTH 212 Formulas page 1 out of 7. Sample variance: s = Sample standard deviation: s = s
MTH Formula age out of 7 DESCRIPTIVE TOOLS Poulatio ize = N Samle ize = x x+ x +... + x x Poulatio mea: µ = Samle mea: x = = N ( µ ) ( x x) Poulatio variace: = Samle variace: = N Poulatio tadard deviatio:
More informationChap.4 Ray Theory. The Ray theory equations. Plane wave of homogeneous medium
The Ra theor equatio Plae wave of homogeeou medium Chap.4 Ra Theor A plae wave ha the dititive propert that it tregth ad diretio of propagatio do ot var a it propagate through a homogeeou medium p vae
More informationPredicting the eruption time after observed an eruption of 4 minutes in duration.
Lear Regreo ad Correlato 00 Predctg the erupto tme after oberved a erupto of 4 mute durato. 90 80 70 Iter-erupto Tme.5.0.5 3.0 3.5 4.0 4.5 5.0 5.5 Durato A ample of tererupto tme wa take durg Augut -8,
More informationGeneral Method for Calculating Chemical Equilibrium Composition
AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt
More informationDTS5322-SC01: SC01: Control Systems
DTS53-SC0: SC0: Cotrol Sytem Be M. Che Profeor Deartmet of Electrcal & Comuter Egeerg Natoal Uverty of Sgaore Phoe: 656-89 Offce: E4-06 06-0808 Emal: bmche@u.edu.g ~ Webte: htt://www.bmche.et Lat Udated:
More informationFourth Order Four-Stage Diagonally Implicit Runge-Kutta Method for Linear Ordinary Differential Equations ABSTRACT INTRODUCTION
Malasa Joural of Mathematcal Sceces (): 95-05 (00) Fourth Order Four-Stage Dagoall Implct Ruge-Kutta Method for Lear Ordar Dfferetal Equatos Nur Izzat Che Jawas, Fudzah Ismal, Mohamed Sulema, 3 Azm Jaafar
More informationCorrelation and Regression Analysis
Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the
More information