FILTER BANK MULTICARRIER WITH LAPPED TRANSFORMS

Size: px
Start display at page:

Download "FILTER BANK MULTICARRIER WITH LAPPED TRANSFORMS"

Transcription

1 FILTER BANK ULTICARRIER WITH LAPPED TRANSFORS aurc Bllagr, CNA Davd attra, aro Tada, Uv.Napol arch 5

2 Obctvs A multcarrr approach to mprov o OFD for futur wrlss systms - asychroous mult-usr accss - spctral sparato for coxstc - robustss to chal mparmts CFO p most of OFD faturs - spctral ffccy - mmum dlay - smplcty of cocpt - low computatoal complxty

3 Lappd Trasform: - dfto - mplmtato Outl Trasmsso systm prformac - sgal charactrstcs - chal qualzato Complx lappd trasform for FBC Op ssus - mplmtato - chal qualzato - carrr frqucy offst compsato

4 Lappd trasform Itroducd dcads ago to mprov th dscrmato of crtcal spctral compots sgal comprsso : tm doma ; : frqucy doma ; T, = h cos[ h =s[ + ] ] prfct dcomposto-rcostructo ovrlappg factor: K= ral procssg ovlty commucatos: frqucy doma qualzato

5 LT commucatos Ral lappd trasform QA modulato Lappd-OFD Complx lappd trasform PA modulato FBC-PA h Tc, ;, = +

6 Frqucy rspos Rspos of s fltr h: H L cos f f = f db Ampltud OFD Lappd-OFD ut=sub-carrr spacg Frqucy pars of symmtrcal carrrs stad of carrrs for th DFT

7 LT trasmsso ult-carrr trasmsso wth T, QA modulato ca b usd - dpdt ral procssg of ral ad magary parts of data FBC schm wth ovrlappg K= - dlay: qualzato th rcvr ca b prformd th frqucy doma o addtoal dlay frqucy doma rsdual CFO compsato mult-usr scaro

8 Implmtato Obctv: us a -DFT for frqucy doma qualzato Exprsso of th trasform ad -DFT + frqucy doma fltrg + phas shfts coffcts [ ] ] ][ [ 4, T = ]] [ ] [ [ 4, T =

9 Trascvr structur Trasmttr Rcvr data S / P + Q A Traspos Lappd Trasform Ovrlap / add + P/S chal S / P FFT E q u a l z a t o S f l t r Lappd Trasform P o s t p r o c s s. Q A d t c t + P/S data out mttd symbols of sampls ovrlap by sampls symbol rat: / qualzato at FFT output

10 Emttd spctrum Th lappd trasform dfs sub-carrrs - a sub-chal cossts of parts: ad - A f f -f -f / / -/ Spctrum: cotuous / fragmtd f.5 Ampltud.5 Ampltud Frqucy Frqucy

11 Trasmsso systm prformac

12 Evlop of mttd sgal Impact of tmg offst. a m p l tu d T O t m Tmg offst: to ; sgal-to-trfrc rato OFD: GT: guard tm SIR / L = to/ s to// SIR = / OFD togt /

13 Half rat schms Sgal-to-trfrc rato half rat SIR LHR = s / s 8 3 / to to + 6 to / Emttd sgal vlop. A m p ltud r a l m a g a ry. A m p ltud tm tm full rat half rat

14 SIR curvs Sgal-to-trfrc rato db SIR OFD-GT=6 /6 OFD-GT=3 /8 Lappd-OFD-half rat Lappd-OFD Tmg offst =56 ax. to = / SIR = 7.4 db BER =.5 4-QA

15 ultpath chal qualzato chal trasfr fucto C Z trfrc powr P = = c Z P P P P = = = c [ f f ] f = / s // SNR: multply trfrc+os by qualzr rspos

16 Bt rror rat =56 sub-chals Chal: ITU-R vh.b max.dlay:. < /4 Profl: dlay: ampl.: QA 64-QA

17 Asychroous accss OFD CP = 64 /4 O-tap FBC: OQA ; sgl tap qualzr ; K= FS-FBC: OQA ; frqucy doma qualzato OFD-lap: QA ; lappd trasform Chal ITU-R vh.b Eb/No= db 4-QA Symmtry

18 Pa-to-avrag powr rato PAPR Complmtary cumulatv dstrbuto fucto 5 db CCDF -5 - L-OFD ral/magary data L-OFD complx data full rat OFD L-OFD ral data ampltud

19 Complx lappd trasform for FBC

20 Complx trasform ad mplmtato Dfto Factorzato Implmtato Tc, Tc, = Phas shfts by multpls of / Frqucy doma fltrg, coffcts: [ ] ultply by tm shft: ½ Ivrs FFT of sz = s[ [ ] Ovrlap ad add ovrlappg factor K= + ] ;, 3

21 Trasmttr structur ultcarrr trasmttr d ral S / P P h a s s h f t s F l t r F F T o v r l a p + a d d P / S y chal PA modulato ultcarrr symbol lgth: Symbol rat:

22 Emttd spctrum =56 ; Numbr of usd sub-chals: 3x ; bary data ; 46 bts pr symbol ; rat: /.4 Ampltud Frqucy

23 Rcvr structur ultcarrr rcvr Frqucy doma qualzato Sub-chal fltrg aftr qualzato CFO compsato: trpolatd fltr coffcts p h a s s h f t s f l t r F F T d t c t o S / P y d p u t b u f f r q u a l z r P / S

24 Systm mpuls rspos frqucy tm Total magary trfrc powr: uty

25 SIR curvs Sgal-to-trfrc rato / tmg offst SIR 4 db OFD -GT=6 / OFD -GT=3 /8 5 FB C -P A Tmg offst =56 axmum tmg offst: / ; BER =.5 bary data asychroous accss

26 Bt rror rat =56 sub-chals Chal: ITU-R vh.b max.dlay:. < /4 Profl: dlay: ampl.: QA / -PA 64-QA / 8-PA

27 Carrr frqucy offst Compsato at sub-chal lvl mult-usr scaro CFO = δf ; Fltr output at tm for = m + +/ Rcvr fltr coffcts tm doma I th frqucy doma: trpolato of tal st [ ] f r r r x h y = = δ f r r f r x h y δ δ / / = + = h h f CFO ; / = δ

28 CFO compsato Compsato pr sub-chal or group of sub-chals Phas shft + trpolatd fltr coffcts db SIR trpolato:6 coffcts trpolato:4 coffcts 5 OFD 5 o fltr coffct trpolato CFO ut:sub-carrr spacg

29 BER vrsus CFO Prformac of OFD, lappd OFD, FBC-PA 4-QA/-PA Eb/No = 8dB Normalzd CFO C: full compsato C3: 3 coffcts C5: 5 coffcts C7: 7 coffcts

30 Op ssus Algorthmc aspcts Gralzato xtdd lappd trasform Othr systm optos ad paramtr slcto Optmzato of th structur Effct mplmtato mmal complxty Prformac aalyss multpl asychroous usrs Comparso wth hacd OFD tchqus fltrd OFD, uvrsal fltrd multcarrr, gralzd FD

31 Op ssus Ntworg aspcts Sgl carrr tchqus Prambl ad plots for burst trasmsso Duplxg: TDD, FDD, full duplx IO ad massv IO Compatblty wth OFD Capablty to mt 5G prformac obctvs µs tm budgt for PHY, 55 db ACLR, short bursts,

Multipath diversity of precoded OFDM with linear equalization

Multipath diversity of precoded OFDM with linear equalization Uvrsty of Wollogog Rsarch Ol Faculty of Iformatcs - aprs (Archv) Faculty of Egrg ad Iformato Sccs 8 ultpath dvrsty of prcodd OFD wth lar qualzato Xaojg uag Uvrsty of Wollogog, huag@uow.du.au ublcato Dtals

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

BER Analysis of Optical Wireless Signals through Lognormal Fading Channels with Perfect CSI

BER Analysis of Optical Wireless Signals through Lognormal Fading Channels with Perfect CSI 7th tratoal Cofrc o Tlcommucatos BER Aalyss of Optcal Wrlss Sgals through ogormal Fadg Chals wth rfct CS Hassa Morad, Maryam Falahpour, Hazm H. Rfa Elctrcal ad Computr Egrg Uvrsty of Olahoma Tulsa, OK,

More information

The Role of Branch-Correlation for an MC-CDMA System Combining with Coherent Diversity over Frequency Selective Channels

The Role of Branch-Correlation for an MC-CDMA System Combining with Coherent Diversity over Frequency Selective Channels WEA RAACIO o COMMUICAIO a-hg La, Joy Iog-Zog Ch, Chh W Lou, I. Ma Huag h Rol of Brach-Corrlato for a MC-CDMA yst Cog wth Cohrt Dvrsty ovr Frqucy lctv Chals a-hg La, *Joy Iog-Zog Ch, Chh W Lou, ad I Ma

More information

SER/BER in a Fading Channel

SER/BER in a Fading Channel SER/BER in a Fading Channl Major points for a fading channl: * SNR is a R.V. or R.P. * SER(BER) dpnds on th SNR conditional SER(BER). * Two prformanc masurs: outag probability and avrag SER(BER). * Ovrall,

More information

Estimation Theory. Chapter 4

Estimation Theory. Chapter 4 Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,

More information

Round-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform

Round-Off Noise of Multiplicative FIR Filters Implemented on an FPGA Platform Appl. Sc. 4, 4, 99-7; do:.339/app499 Artcl OPEN ACCESS appld sccs ISSN 76-347 www.mdp.com/joural/applsc Roud-Off Nos of Multplcatv FIR Fltrs Implmtd o a FPGA Platform Ja-Jacqus Vadbussch, *, Ptr L ad Joa

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

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

Beamforming towards regions of interest for multi-site mobile networks

Beamforming towards regions of interest for multi-site mobile networks Itratoal Zurch Smar o Commucatos (IZS) March Bamformg towards rgos of trst for mult-st mobl twors Paul Hurly Matthu Smo IBM Zurch sarch Laboratory CH-3 üschlo Swtzrlad Écol Polytchqu Fédéral d Lausa (EPFL)

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

Position Control of 2-Link SCARA Robot by using Internal Model Control

Position Control of 2-Link SCARA Robot by using Internal Model Control Mmors of th Faculty of Er, Okayama Uvrsty, Vol, pp 9-, Jauary 9 Posto Cotrol of -Lk SCARA Robot by us Itral Modl Cotrol Shya AKAMASU Dvso of Elctroc ad Iformato Systm Er Graduat School of Natural Scc ad

More information

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES AYMPTOTIC AD TOLERACE D-MODELLIG I ELATODYAMIC OF CERTAI THI-WALLED TRUCTURE B. MICHALAK Cz. WOŹIAK Dpartmt of tructural Mchacs Lodz Uvrsty of Tchology Al. Poltrchk 6 90-94 Łódź Polad Th objct of aalyss

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

A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 519.6(045)

A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 519.6(045) FACTA UNIVERSITATIS Srs: Mcacs Automatc Cotrol ad Rootcs Vol 4 N o 6 4 pp 33-39 A METHOD FOR NUMERICAL EVALUATING OF INVERSE Z-TRANSFORM UDC 59645 Prdrag M Raovć Momr S Staovć Slađaa D Marovć 3 Dpartmt

More information

DTFT Properties. Example - Determine the DTFT Y ( e ) of n. Let. We can therefore write. From Table 3.1, the DTFT of x[n] is given by 1

DTFT Properties. Example - Determine the DTFT Y ( e ) of n. Let. We can therefore write. From Table 3.1, the DTFT of x[n] is given by 1 DTFT Proprtis Exampl - Dtrmi th DTFT Y of y α µ, α < Lt x α µ, α < W ca thrfor writ y x x From Tabl 3., th DTFT of x is giv by ω X ω α ω Copyright, S. K. Mitra Copyright, S. K. Mitra DTFT Proprtis DTFT

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

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

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

Research on the Massive Data Classification Method in Large Scale Computer Information Management huangyun

Research on the Massive Data Classification Method in Large Scale Computer Information Management huangyun Itratoa Crc o Automato, Mchaca Cotro ad Computatoa Egrg (AMCCE 05) Rsarch o th Massv Data Cassfcato Mthod Larg Sca Computr Iformato Maagmt huagyu Chogqg ctroc grg Carr Acadmy, Chogqg 4733, Cha Kywords:

More information

Information Theoretic Upper Bound on the Capacity of Wireless Backhaul Networks

Information Theoretic Upper Bound on the Capacity of Wireless Backhaul Networks Iformato Thortc Uppr Boud o th Capacty of Wrlss Bachaul Ntwors Harprt S Dhllo ad Guspp Car Abstract W drv a formato thortc uppr boud o th capacty of a wrlss bachaul twor modld as a classcal radom xtdd

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

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 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

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

Audio-based Classification of Video Genre Using Multivariate Adaptive Regression Splines

Audio-based Classification of Video Genre Using Multivariate Adaptive Regression Splines Audo-basd Classfcato of Vdo Gr Usg ultvarat Adaptv Rgrsso Spls H E Latt, u War Abstract A larg umbr of rsarchrs ar attractd by vdo gr classfcato, vdo cotts rtrval ad smatcs rsarch vdo procssg ad aalyss

More information

Design of Functionally Graded Structures in Topology Optimization

Design of Functionally Graded Structures in Topology Optimization EgOpt 2008 - Itratoal Cofrc o Egrg Optmzato Ro d Jaro, Brazl, 0-05 Ju 2008. Dsg of Fuctoally Gradd Structurs Topology Optmzato Sylva R. M. d Almda, Glauco H. Paulo 2, Emlo C. N. Slva 3 Uvrsdad Fdral d

More information

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.

More information

Data Modeling using Kernels and Information Theoretic Learning

Data Modeling using Kernels and Information Theoretic Learning Data Modlg usg Krls ad Iformato Thortc Larg Jos C. Prcp Computatoal uroegrg Laboratory Elctrcal ad Computr Egrg Dpartmt Uvrsty of Florda www.cl.ufl.du prcp@cl.ufl.du Ackowldgmts Dr. Joh Fshr Dr. Dog Xu

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

Optimal Design of Two-Channel Recursive Parallelogram Quadrature Mirror Filter Banks Ju-Hong Lee, Yi-Lin Shieh

Optimal Design of Two-Channel Recursive Parallelogram Quadrature Mirror Filter Banks Ju-Hong Lee, Yi-Lin Shieh Worl cay of cc Er a choloy Itratoal Joural of oputr a Iato Er Vol:8 o:7 4 Optal s of wo-hal Rcursv aralllora Quaratur rror Fltr Baks Ju-o L Y-L hh Itratoal cc Ix oputr a Iato Er Vol:8 o:7 4 wast.orublcato99989

More information

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Estimation of Population Variance Using a Generalized Double Sampling Estimator r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst

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

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

UNIVERSITY OF CINCINNATI. I, Joon-Hyun Lee, hereby submit this as part of the requirement for the degree of: Ph.D.

UNIVERSITY OF CINCINNATI. I, Joon-Hyun Lee, hereby submit this as part of the requirement for the degree of: Ph.D. UNIVESITY OF CINCINNATI March 8, I, Joo-Hyu, hrby submt ths as part of th rqurmt for th dgr of: Ph.D. : Mchacal Egrg It s ttld: DEVEOPMENT OF NEW TECHNIQUE FO DAMPING IDENTIFICATION AND SOUND TANSMISSION

More information

Problem Value Score Earned No/Wrong Rec -3 Total

Problem Value Score Earned No/Wrong Rec -3 Total GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING ECE6 Fall Quiz # Writt Eam Novmr, NAME: Solutio Kys GT Usram: LAST FIRST.g., gtiit Rcitatio Sctio: Circl t dat & tim w your Rcitatio

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

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

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing-

Outline. Why speech processing? Speech signal processing. Advanced Multimedia Signal Processing #5:Speech Signal Processing 2 -Processing- Outlin Advancd Multimdia Signal Procssing #5:Spch Signal Procssing -Procssing- Intllignt Elctronic Systms Group Dpt. of Elctronic Enginring, UEC Basis of Spch Procssing Nois Rmoval Spctral Subtraction

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

Linear-Quadratic-Gaussian Optimization of Urban Transportation Network with Application to Sofia Traffic Optimization

Linear-Quadratic-Gaussian Optimization of Urban Transportation Network with Application to Sofia Traffic Optimization BULGARIAN ACADEMY OF SCIENCES CYBERNEICS AND INFORMAION ECHNOLOGIES Volum 16 No 3 Sofa 216 Prt ISSN: 1311-972; Ol ISSN: 1314-481 DOI: 1.1515/cat-216-41 Lar-Quadratc-Gaussa Optmzato of Urba rasportato Ntwork

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

EE 232 Lightwave Devices. Photodiodes

EE 232 Lightwave Devices. Photodiodes EE 3 Lgwav Dvcs Lcur 8: oocoucors a p-- ooos Rag: Cuag, Cap. 4 Isrucor: Mg C. Wu Uvrsy of Calfora, Brkly Elcrcal Egrg a Compur Sccs Dp. EE3 Lcur 8-8. Uvrsy of Calfora oocoucors ω + - x Ara w L Euval Crcu

More information

Minimum and maximum Power Adaptation Methods using Haar Wavelet for Image Transmission using QPSK Modulation

Minimum and maximum Power Adaptation Methods using Haar Wavelet for Image Transmission using QPSK Modulation ISSN: 78-7798 Itratoal Joural of Scc, Egrg ad chology Rarch (IJSER) Volum, Iu, Augut 0 Mmum ad mamum Mthod ug Haar Wavlt for Imag ramo ug QPSK Modulato M. Padmaja, Dr. P. Satyaarayaa, K. Praua 3,G.Nav

More information

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia Part B: Trasform Mthods Chaptr 3: Discrt-Tim Fourir Trasform (DTFT) 3. Discrt Tim Fourir Trasform (DTFT) 3. Proprtis of DTFT 3.3 Discrt Fourir Trasform (DFT) 3.4 Paddig with Zros ad frqucy Rsolutio 3.5

More information

This is a repository copy of Estimation of generalised frequency response functions.

This is a repository copy of Estimation of generalised frequency response functions. hs s a rpostory copy of Estmato of gralsd frqucy rspos fuctos. Wht Ros Rsarch Ol URL for ths papr: http://prts.whtros.ac.uk/74654/ Moograph: L, L.M. ad Bllgs, S.A. 9 Estmato of gralsd frqucy rspos fuctos.

More information

Channel Estimation Error Modeling for System Simulations

Channel Estimation Error Modeling for System Simulations IEEE C80.6m-07/08 Projct Ttl Dat Submtt Sour() R: Abtract Purpo Not Rla Patt Polc IEEE 80.6 Broaba Wrl Ac Workg Group Chal Etmato Molg for Stm Smulato 007--05 Krha Saaa, Jff Zhuag, K

More information

Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform

Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform. Discrete Fourier Transform Discrt Fourir Trasform Dfiitio - T simplst rlatio btw a lt- squc x dfid for ω ad its DTFT X ( ) is ω obtaid by uiformly sampli X ( ) o t ω-axis btw ω < at ω From t dfiitio of t DTFT w tus av X X( ω ) ω

More information

An Architecture for Integrating VLBI Digital Processing into the Next Generation IRAM PdBI Correlator

An Architecture for Integrating VLBI Digital Processing into the Next Generation IRAM PdBI Correlator An Archtctur for Intgratng VLBI Dgtal Procssng nto th Nxt Gnraton IRAM PdBI Corrlator Robrto G. García IRAM (Grnobl) Sptmbr 2010 Abstract Th nxt gnraton dgtal backnd for th Platau d Bur ntrfromtr wll b

More information

An integral approach to phase shifting interferometry using a super-resolution frequency estimation method

An integral approach to phase shifting interferometry using a super-resolution frequency estimation method A tgral approach to phas shftg trfromtry usg a supr-rsoluto frqucy stmato mtho Abht Patl, Rash Lagou, * a Pramo Rastog Appl Computg a Mchacs Laboratory, Swss Fral sttut of Tchology, 5- Lausa, Swtrla *

More information

Notation for Mixed Models for Finite Populations

Notation for Mixed Models for Finite Populations 30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p

More information

Suzan Mahmoud Mohammed Faculty of science, Helwan University

Suzan Mahmoud Mohammed Faculty of science, Helwan University Europa Joural of Statstcs ad Probablty Vol.3, No., pp.4-37, Ju 015 Publshd by Europa Ctr for Rsarch Trag ad Dvlopmt UK (www.ajourals.org ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN WEIBULL DISTRIBUTION

More information

LINEAR SYSTEMS THEORY

LINEAR SYSTEMS THEORY Fall Introduton to Mdal Engnrng INEAR SYSTEMS THEORY Ho Kung Km Ph.D. houng@puan.a.r Shool of Mhanal Engnrng Puan Natonal Unvrt Evn / odd / prod funton Thn about on & n funton! Evn f - = ; Odd f - = -;

More information

Systems in Transform Domain Frequency Response Transfer Function Introduction to Filters

Systems in Transform Domain Frequency Response Transfer Function Introduction to Filters LTI Discrt-Tim Systms i Trasform Domai Frqucy Rspos Trasfr Fuctio Itroductio to Filtrs Taia Stathai 811b t.stathai@imprial.ac.u Frqucy Rspos of a LTI Discrt-Tim Systm Th wll ow covolutio sum dscriptio

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

On the Optimal Number of Hops in Infrastructure-based Fixed Relay Networks

On the Optimal Number of Hops in Infrastructure-based Fixed Relay Networks Ths full txt ar was r rvw at th rcto of IEEE Commucatos Socty subjct mattr xrts for ublcato th IEEE GLOBECO 5 rocgs. O th Otmal Numbr of Hos Ifrastructur-bas Fx Rlay Ntworks Ara Flora a Halm Yakomroglu

More information

DFT: Discrete Fourier Transform

DFT: Discrete Fourier Transform : Discrt Fourir Trasform Cogruc (Itgr modulo m) I this sctio, all lttrs stad for itgrs. gcd m, = th gratst commo divisor of ad m Lt d = gcd(,m) All th liar combiatios r s m of ad m ar multils of d. a b

More information

Jordan Representation of Perfect Reconstruction Filter Banks using Nilpotent Matrices

Jordan Representation of Perfect Reconstruction Filter Banks using Nilpotent Matrices Procdgs of th 5th WSEAS Itratoa Cofrc o Sga Procssg, Istabu, ury, ay 7-9, 6 (pp-6) Jorda Rprstato of Prfct Rcostructo Ftr Bas usg Npott atrcs ASHA VIJAYAUAR *, G. ABHILASH Dpartmt of Ectrocs ad Commucato

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

The R Package PK for Basic Pharmacokinetics

The R Package PK for Basic Pharmacokinetics Wolfsggr, h R Pacag PK St 6 h R Pacag PK for Basc Pharmacotcs Mart J. Wolfsggr Dpartmt of Bostatstcs, Baxtr AG, Va, Austra Addrss of th author: Mart J. Wolfsggr Dpartmt of Bostatstcs Baxtr AG Wagramr Straß

More information

(looks like a time sequence) i function of ˆ ω (looks like a transform) 2. Interpretations of X ( e ) DFT View OLA implementation

(looks like a time sequence) i function of ˆ ω (looks like a transform) 2. Interpretations of X ( e ) DFT View OLA implementation viw of STFT Digital Spch Procssig Lctur Short-Tim Fourir Aalysis Mthods - Filtr Ba Dsig j j ˆ m ˆ. X x[ m] w[ ˆ m] ˆ i fuctio of ˆ loos li a tim squc i fuctio of ˆ loos li a trasform j ˆ X dfid for ˆ 3,,,...;

More information

Superbosonization meets Free Probability

Superbosonization meets Free Probability Suprbosoato mts Fr Probablty M Zrbaur jot wor wth S Madt Eulr Symposum St Ptrsburg Ju 3 009 Itroducto From momts to cumulats Larg- charactrstc fucto by fr probablty Suprbosoato Applcato to dsordrd scattrg

More information

Lens Design II. Lecture 6: Chromatical correction I Herbert Gross. Winter term

Lens Design II. Lecture 6: Chromatical correction I Herbert Gross. Winter term Ls Dsig II Lctur 6: Chromatical corrctio I 05--4 Hrbrt Gross Witr trm 05 www.iap.ui-a.d Prlimiary Schdul 0.0. Abrratios ad optimizatio Rptitio 7.0. Structural modificatios Zro oprads, ls splittig, ls additio,

More information

A Stochastic Approximation Iterative Least Squares Estimation Procedure

A Stochastic Approximation Iterative Least Squares Estimation Procedure Joural of Al Azhar Uvrst-Gaza Natural Sccs, 00, : 35-54 A Stochastc Appromato Itratv Last Squars Estmato Procdur Shahaz Ezald Abu- Qamar Dpartmt of Appld Statstcs Facult of Ecoomcs ad Admstrato Sccs Al-Azhar

More information

A LFM Interference Suppression Scheme Based on FRFT and Subspace Projection

A LFM Interference Suppression Scheme Based on FRFT and Subspace Projection teratoal Joural of Emergg Egeerg esearch ad Techology Volume 3, ssue 6, Jue 15, PP 157-16 SS 349-4395 (Prt) & SS 349-449 (Ole) A LF terferece Suppresso Scheme Based o FFT ad Subspace Projecto Xg ZOU 1

More information

A Study of Fundamental Law of Thermal Radiation and Thermal Equilibrium Process

A Study of Fundamental Law of Thermal Radiation and Thermal Equilibrium Process Itratoal Joural of Hgh Ergy Physcs 5; (3): 38-46 Publshd ol May 6, 5 (http://www.sccpublshggroup.com/j/jhp) do:.648/j.jhp.53. ISSN: 376-745 (Prt); ISSN: 376-7448 (Ol) A Study of Fudamtal Law of Thrmal

More information

From Fourier Series towards Fourier Transform

From Fourier Series towards Fourier Transform From Fourir Sris owards Fourir rasform D D d D, d wh lim Dparm of Elcrical ad Compur Eiri D, d wh lim L s Cosidr a fucio G d W ca xprss D i rms of Gw D G Dparm of Elcrical ad Compur Eiri D G G 3 Dparm

More information

Transforms that are commonly used are separable

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

More information

NON-SYMMETRY POWER IN THREE-PHASE SYSTEMS

NON-SYMMETRY POWER IN THREE-PHASE SYSTEMS O-YMMETRY OWER THREE-HAE YTEM Llana Marlna MATCA nvrsty of Orada, nvrstat str., no., 487, Orada; lmatca@uorada.ro Abstract. For thr-phas lctrcal systms, n non-symmtrcal stuaton, an analyz mthod costs on

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

Note on the Computation of Sample Size for Ratio Sampling

Note on the Computation of Sample Size for Ratio Sampling Not o th Computato of Sampl Sz for ato Samplg alr LMa, Ph.D., PF Forst sourcs Maagmt Uvrst of B.C. acouvr, BC, CANADA Sptmbr, 999 Backgroud ato samplg s commol usd to rduc cofdc trvals for a varabl of

More information

Phase Rotation for the 80 MHz ac Mixed Mode Packet

Phase Rotation for the 80 MHz ac Mixed Mode Packet Phas Rotation for th 80 MHz 802.11ac Mixd Mod Packt Dat: 2010-07-12 Authors: Nam Affiliations Addrss Phon mail Lonardo Lanant Jr. Kyushu Inst. of Tchnology Kawazu 680-, Iizuka, JAPAN Yuhi Nagao Kyushu

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

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

Problem Set #2 Due: Friday April 20, 2018 at 5 PM.

Problem Set #2 Due: Friday April 20, 2018 at 5 PM. 1 EE102B Spring 2018 Signal Procssing and Linar Systms II Goldsmith Problm St #2 Du: Friday April 20, 2018 at 5 PM. 1. Non-idal sampling and rcovry of idal sampls by discrt-tim filtring 30 pts) Considr

More information

Attitude Determination GPS/INS Integration System Design Using Triple Difference Technique

Attitude Determination GPS/INS Integration System Design Using Triple Difference Technique Joural of Elctrcal Egrg & Tchology Vol. 7, No., pp. 1~, 1 1 http://dx.do.org/1.37/jeet.1.7..1 Atttud Dtrmato GPS/INS Itgrato Systm Dsg Usg Trpl Dffrc Tchqu Sag Ho Oh*, Dog-Hwa Hwag, Chask Park** ad Sag

More information

Group Consensus of Second-Order Multi-agent Networks with Multiple Time Delays

Group Consensus of Second-Order Multi-agent Networks with Multiple Time Delays Itratoal Cofrc o Appld Mathmatcs, Smulato ad Modllg (AMSM 6) Group Cossus of Scod-Ordr Mult-agt Ntworks wth Multpl Tm Dlays Laghao J* ad Xyu Zhao Chogqg Ky Laboratory of Computatoal Itllgc, Chogqg Uvrsty

More information

ECE Department Univ. of Maryland, College Park

ECE Department Univ. of Maryland, College Park EEE63 Part- Tr-basd Filtr Banks and Multirsolution Analysis ECE Dpartmnt Univ. of Maryland, Collg Park Updatd / by Prof. Min Wu. bb.ng.umd.du d slct EEE63); minwu@ng.umd.du md d M. Wu: EEE63 Advancd Signal

More information

Communication Technologies

Communication Technologies Communication Tchnologis. Principls of Digital Transmission. Structur of Data Transmission.2 Spctrum of a Data Signal 2. Digital Modulation 2. Linar Modulation Mthods 2.2 Nonlinar Modulations (CPM-Signals)

More information

15/03/1439. Lectures on Signals & systems Engineering

15/03/1439. Lectures on Signals & systems Engineering Lcturs o Sigals & syms Egirig Dsigd ad Prd by Dr. Ayma Elshawy Elsfy Dpt. of Syms & Computr Eg. Al-Azhar Uivrsity Email : aymalshawy@yahoo.com A sigal ca b rprd as a liar combiatio of basic sigals. Th

More information

Using Nonlinear Filter for Adaptive Blind Channel Equalization

Using Nonlinear Filter for Adaptive Blind Channel Equalization HAMDRZA BAKHSH Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Us oar Ftr or Adaptv Bd Cha quazato MOHAMMAD POOYA Dpt. o ctrca ad Coputr r Shahd Uvrsty Qo Hhway, Thra, RA Abstract: trsybo trrc

More information

A New Fast Acquisition Algorithm for GPS Receivers

A New Fast Acquisition Algorithm for GPS Receivers A Nw Fast Acuston Algorthm for GS cvrs Hung Sok So *, Chansk ark **, and Sang Jong L *** * pt. of Elctroncs Engnrng, Chungnam Natonal Unvrsty, ajon 35-764 Kora (l : 8-4-85-399; Fax : 8-4-83-4494 ; E-mal:

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

Linear-Phase FIR Transfer Functions. Functions. Functions. Functions. Functions. Functions. Let

Linear-Phase FIR Transfer Functions. Functions. Functions. Functions. Functions. Functions. Let It is impossibl to dsign an IIR transfr function with an xact linar-phas It is always possibl to dsign an FIR transfr function with an xact linar-phas rspons W now dvlop th forms of th linarphas FIR transfr

More information

Numbering Systems Basic Building Blocks Scaling and Round-off Noise. Number Representation. Floating vs. Fixed point. DSP Design.

Numbering Systems Basic Building Blocks Scaling and Round-off Noise. Number Representation. Floating vs. Fixed point. DSP Design. Numbring Systms Basic Building Blocks Scaling and Round-off Nois Numbr Rprsntation Viktor Öwall viktor.owall@it.lth.s Floating vs. Fixd point In floating point a valu is rprsntd by mantissa dtrmining th

More information

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL

COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Sebasta Starz COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Abstract The am of the work s to preset a method of rakg a fte set of dscrete radom varables. The proposed method s based o two approaches:

More information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information

Basics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information : Maru Jutt Overvew he propertes of adlmted Gaussa chaels are further studed, parallel Gaussa chaels ad Gaussa chaels wth feedac are solved. Source he materal s maly ased o Sectos.4.6 of the course oo

More information

A Novel Symmetrical Heuristic Coefficient for Urban Microcellular Environments

A Novel Symmetrical Heuristic Coefficient for Urban Microcellular Environments A Novl Symmtrcal Hurstc Coffct for Urba crocllular Evromts Puspraj Sg Caua, mbr, IACSIT ad Sajay So Abstract A ovl urstc dffracto coffct s prstd wc s prfctly rcprocal ad symmtrcal. T prdcto obtad usg proposd

More information

Tolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data

Tolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data Itratoal Rfrd Joural of Egrg ad Scc (IRJES) ISSN (Ol) 319-183X, (Prt) 319-181 Volum, Issu 10 (Octobr 013), PP. 6-30 Tolrac Itrval for Expotatd Expotal Dstrbuto Basd o Groupd Data C. S. Kaad 1, D. T. Shr

More information

STATISTICAL PROPERTIES ANALYSIS OF Er 3+ -DOPED Ti:LiNbO 3 M -MODE STRAIGHT WAVEGUIDE AMPLIFIERS

STATISTICAL PROPERTIES ANALYSIS OF Er 3+ -DOPED Ti:LiNbO 3 M -MODE STRAIGHT WAVEGUIDE AMPLIFIERS Journal of Optolctronics and Advancd Matrials Vol. 6 No. March 4 p. 63-69 STATISTICAL PROPERTIES ANALYSIS OF Er 3+ -DOPED Ti:LiNbO 3 M -MODE STRAIGHT WAVEGUIDE AMPLIFIERS N. N. Puscas * Physics Dpartmnt

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

Frequency Correction

Frequency Correction Chaptr 4 Frquncy Corrction Dariush Divsalar Ovr th yars, much ffort has bn spnt in th sarch for optimum synchronizion schms th ar robust and simpl to implmnt [1,2]. Ths schms wr drivd basd on maximum-liklihood

More information

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

Discrete Fourier Transform (DFT)

Discrete Fourier Transform (DFT) Discrt Fourir Trasorm DFT Major: All Egirig Majors Authors: Duc guy http://umricalmthods.g.us.du umrical Mthods or STEM udrgraduats 8/3/29 http://umricalmthods.g.us.du Discrt Fourir Trasorm Rcalld th xpotial

More information

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time. Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss

More information

Exercises for lectures 7 Steady state, tracking and disturbance rejection

Exercises for lectures 7 Steady state, tracking and disturbance rejection Exrc for lctur 7 Stady tat, tracng and dturbanc rjcton Martn Hromčí Automatc control 06-3-7 Frquncy rpon drvaton Automatcé řízní - Kybrnta a robota W lad a nuodal nput gnal to th nput of th ytm, gvn by

More information

Signal,autocorrelation -0.6

Signal,autocorrelation -0.6 Sgal,autocorrelato Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato Phase ose p/.5..7.3 -. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.9.3.7. -.5 5 5 5 Tme Sgal,autocorrelato. Phase ose p/.8..6.

More information

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches.

22/ Breakdown of the Born-Oppenheimer approximation. Selection rules for rotational-vibrational transitions. P, R branches. Subjct Chmistry Papr No and Titl Modul No and Titl Modul Tag 8/ Physical Spctroscopy / Brakdown of th Born-Oppnhimr approximation. Slction ruls for rotational-vibrational transitions. P, R branchs. CHE_P8_M

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

Wireless Link Properties

Wireless Link Properties Opportustc Ecrypto for Robust Wreless Securty R. Chadramoul ( Moul ) moul@steves.edu Multmeda System, Networkg, ad Commucatos (MSyNC) Laboratory, Departmet of Electrcal ad Computer Egeerg, Steves Isttute

More information