CDMA Network Design. Robert Akl, D.Sc.

Size: px
Start display at page:

Download "CDMA Network Design. Robert Akl, D.Sc."

Transcription

1 CDMA Network Desgn Robert Akl, D.Sc.

2 Outlne CDMA overvew and nter-cell effects Network capacty Senstvty analyss Base staton locaton Plot-sgnal power Transmsson power of the mobles Numercal results

3 Problem Statement How to match cell desgn to user dstrbuton for a gven number of base statons? CDMA network capacty calculaton Reverse sgnal power and power control Plot-sgnal power Base staton locaton

4 CDMA Capacty Issues Depends on nter-cell nterference and ntra-cell nterference Complete frequency reuse Soft Handoff Power Control Sectorzaton Voce actvty detecton Graceful degradaton

5 Relatve Average Inter-Cell Interference I m s the path loss exponent. ζ s the decbel attenuaton E ω due to shadowng, and has zero mean and standard devaton χ 2 ζ m ζ r ( x,y) E m 2 r C ( x,y) /χ n Area( C ζ ) ω da( x,y) σ s.

6 Soft Handoff User s permtted to be n soft handoff to ts two nearest cells.

7 Soft Handoff (c) regon 2 (c) regon 2 (b) regon 2 (a) regon 2 ) ( E ) ( E ) ( E ) ( E x,y ωda r r χ r r I x,y ωda r r χ r r I x,y ωda r r χ r r I x,y ωda r r χ r r I k k k k k ζ m ζ m k ζ m m k k ζ m k ζ m ζ m m ζ m ζ m k ζ m m k k ζ m ζ m ζ m m

8 Inter-Cell Interference Factor κ n per user nter - cell nterference factor from cell to cell. users n cell producea relatve average nterference n cell equal to n κ.

9 Capacty Regon

10 Network Capacty Transmsson power of mobles Plot-sgnal power Base staton locaton

11 Power Compensaton Factor Fne tune the nomnal transmsson power of the mobles PCF defned for each cell PCF s a desgn tool to maxmze the capacty of the entre network

12 Power Compensaton Factor (PCF) Interference s lnear n PCF Fnd the senstvty of the network capacty w.r.t. the PCF

13 Senstvty w.r.t. plot-sgnal power Increasng the plot-sgnal power of one cell: Increases ntra-cell nterference and decreases nter-cell nterference n that cell Opposte effect takes place n adacent cells

14 Senstvty w.r.t. Locaton Movng a cell away from neghbor A and closer to neghbor B: Inter-cell nterference from neghbor A ncreases Inter-cell nterference from neghbor B decreases

15 Optmzaton usng PCF

16 Optmzaton usng Locaton

17 Optmzaton usng Plot-sgnal Power max T subect to M 1 n n, (network capacty) M 1 n β κ for 1,..., M. ( C β, L ) c ( ) eff 0,

18 Combned Optmzaton

19 Twenty-seven Cell CDMA Network Unform user dstrbuton profle. Network capacty equals 559 smultaneous users. Unform placement s optmal for unform user dstrbuton.

20 Three Hot Spot Clusters All three hot spots have a relatve user densty of 5 per grd pont. Network capacty decreases to 536. Capacty n cells 4, 15, and 19, decreases from 18 to 3, 17 to 1, and 17 to 9.

21 Optmzaton usng PCF Network capacty ncreases to 555. Capacty n cells 4, 15, and 19, ncreases from 3 to 12, 1 to 9, and 9 to 14. Smallest cellcapacty s 9.

22 Optmzaton usng Plot-sgnal Power Network capacty ncreases to 546. Capacty n cells 4, 15, and 19, ncreases from 3 to 11, 1 to 9, and 9 to 16. Smallest cellcapacty s 9.

23 Optmzaton usng Locaton Network capacty ncreases to 549. Capacty n cells 4, 15, and 19, ncreases from 3 to 14, 1 to 8, and 9 to 17. Smallest cellcapacty s 8.

24 Combned Optmzaton Network capacty ncreases to 565. Capacty n cells 4, 15, and 19, ncreases from 3 to 16, 1 to 13, and 9 to 16. Smallest cellcapacty s 13.

25

26

27 Combned Optmzaton (m.c.)

28

29

30 Combned Optmzaton (m.c.) Network capacty ncreases to 564. Capacty n cells 4, 15, and 19, ncreases from 3 to 17, 1 to 17, and 9 to 17. Smallest cellcapacty s 17.

31

32

33

34 Conclusons Solved cell desgn problem: gven a user dstrbuton, found the optmal locaton and plot-sgnal power of the base statons and the reverse power of the mobles to maxmze network capacty. Unform network layout s optmal for unform user dstrbuton. Combned optmzaton ncreases network capacty sgnfcantly for non-unform user dstrbuton.

Flexible Allocation of Capacity in Multi-Cell CDMA Networks

Flexible Allocation of Capacity in Multi-Cell CDMA Networks Flexble Allocaton of Capacty n Mult-Cell CDMA Networs Robert Al, Manu Hegde, Mort Naragh-Pour*, Paul Mn Washngton Unversty, St. Lous, MO *Lousana State Unversty, Baton Rouge, LA Outlne Capacty and Probablty

More information

Cell Design to Maximize Capacity in CDMA Networks. Robert Akl, D.Sc.

Cell Design to Maximize Capacity in CDMA Networks. Robert Akl, D.Sc. Cell Design to Maximize Capacity in CDMA Networks Robert Akl, D.Sc. Outline CDMA inter-cell effects Capacity region Base station location Pilot-signal power Transmission power of the mobiles Maximize network

More information

Rethinking MIMO for Wireless Networks: Linear Throughput Increases with Multiple Receive Antennas

Rethinking MIMO for Wireless Networks: Linear Throughput Increases with Multiple Receive Antennas Retnng MIMO for Wreless etwors: Lnear Trougput Increases wt Multple Receve Antennas ar Jndal Unversty of Mnnesota Unverstat Pompeu Fabra Jont wor wt Jeff Andrews & Steven Weber MIMO n Pont-to-Pont Cannels

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

More information

MACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression

MACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression 11 MACHINE APPLIED MACHINE LEARNING LEARNING MACHINE LEARNING Gaussan Mture Regresson 22 MACHINE APPLIED MACHINE LEARNING LEARNING Bref summary of last week s lecture 33 MACHINE APPLIED MACHINE LEARNING

More information

Spatial Statistics and Analysis Methods (for GEOG 104 class).

Spatial Statistics and Analysis Methods (for GEOG 104 class). Spatal Statstcs and Analyss Methods (for GEOG 104 class). Provded by Dr. An L, San Dego State Unversty. 1 Ponts Types of spatal data Pont pattern analyss (PPA; such as nearest neghbor dstance, quadrat

More information

An Upper Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control

An Upper Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control An Upper Bound on SINR Threshold for Call Admsson Control n Multple-Class CDMA Systems wth Imperfect ower-control Mahmoud El-Sayes MacDonald, Dettwler and Assocates td. (MDA) Toronto, Canada melsayes@hotmal.com

More information

A Lower Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control

A Lower Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control A ower Bound on SIR Threshold for Call Admsson Control n Multple-Class CDMA Systems w Imperfect ower-control Mohamed H. Ahmed Faculty of Engneerng and Appled Scence Memoral Unversty of ewfoundland St.

More information

Linear Classification, SVMs and Nearest Neighbors

Linear Classification, SVMs and Nearest Neighbors 1 CSE 473 Lecture 25 (Chapter 18) Lnear Classfcaton, SVMs and Nearest Neghbors CSE AI faculty + Chrs Bshop, Dan Klen, Stuart Russell, Andrew Moore Motvaton: Face Detecton How do we buld a classfer to dstngush

More information

An Integrated Asset Allocation and Path Planning Method to to Search for a Moving Target in in a Dynamic Environment

An Integrated Asset Allocation and Path Planning Method to to Search for a Moving Target in in a Dynamic Environment An Integrated Asset Allocaton and Path Plannng Method to to Search for a Movng Target n n a Dynamc Envronment Woosun An Mansha Mshra Chulwoo Park Prof. Krshna R. Pattpat Dept. of Electrcal and Computer

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

TLCOM 612 Advanced Telecommunications Engineering II

TLCOM 612 Advanced Telecommunications Engineering II TLCOM 62 Advanced Telecommuncatons Engneerng II Wnter 2 Outlne Presentatons The moble rado sgnal envronment Combned fadng effects and nose Delay spread and Coherence bandwdth Doppler Shft Fast vs. Slow

More information

DUE: WEDS FEB 21ST 2018

DUE: WEDS FEB 21ST 2018 HOMEWORK # 1: FINITE DIFFERENCES IN ONE DIMENSION DUE: WEDS FEB 21ST 2018 1. Theory Beam bendng s a classcal engneerng analyss. The tradtonal soluton technque makes smplfyng assumptons such as a constant

More information

VQ widely used in coding speech, image, and video

VQ widely used in coding speech, image, and video at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng

More information

PES 1120 Spring 2014, Spendier Lecture 6/Page 1

PES 1120 Spring 2014, Spendier Lecture 6/Page 1 PES 110 Sprng 014, Spender Lecture 6/Page 1 Lecture today: Chapter 1) Electrc feld due to charge dstrbutons -> charged rod -> charged rng We ntroduced the electrc feld, E. I defned t as an nvsble aura

More information

The Concept of Beamforming

The Concept of Beamforming ELG513 Smart Antennas S.Loyka he Concept of Beamformng Generc representaton of the array output sgnal, 1 where w y N 1 * = 1 = w x = w x (4.1) complex weghts, control the array pattern; y and x - narrowband

More information

Cluster Validation Determining Number of Clusters. Umut ORHAN, PhD.

Cluster Validation Determining Number of Clusters. Umut ORHAN, PhD. Cluster Analyss Cluster Valdaton Determnng Number of Clusters 1 Cluster Valdaton The procedure of evaluatng the results of a clusterng algorthm s known under the term cluster valdty. How do we evaluate

More information

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M CIS56: achne Learnng Lecture 3 (Sept 6, 003) Preparaton help: Xaoyng Huang Lnear Regresson Lnear regresson can be represented by a functonal form: f(; θ) = θ 0 0 +θ + + θ = θ = 0 ote: 0 s a dummy attrbute

More information

Moo-rings. Marina Mooring Optimization. Group 8 Route 64 Brian Siefering Amber Mazooji Kevin McKenney Paul Mingardi Vikram Sahney Kaz Maruyama

Moo-rings. Marina Mooring Optimization. Group 8 Route 64 Brian Siefering Amber Mazooji Kevin McKenney Paul Mingardi Vikram Sahney Kaz Maruyama Moo-rngs Marna Moorng Optmzaton Group 8 Route 64 Bran Seferng Amber Mazooj Kevn McKenney Paul Mngard Vkram Sahney Kaz Maruyama Presentaton Overvew Introducton Problem Descrpton Assumptons Model Formulaton

More information

An Improved multiple fractal algorithm

An Improved multiple fractal algorithm Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton

More information

ELG4179: Wireless Communication Fundamentals S.Loyka. Frequency-Selective and Time-Varying Channels

ELG4179: Wireless Communication Fundamentals S.Loyka. Frequency-Selective and Time-Varying Channels Frequeny-Seletve and Tme-Varyng Channels Ampltude flutuatons are not the only effet. Wreless hannel an be frequeny seletve (.e. not flat) and tmevaryng. Frequeny flat/frequeny-seletve hannels Frequeny

More information

Visualization of the Economic Impact of Process Uncertainty in Multivariable Control Design

Visualization of the Economic Impact of Process Uncertainty in Multivariable Control Design Vsualzaton of the Economc Impact of Process Uncertanty n Multvarable Control Desgn Benjamn Omell & Donald J. Chmelewsk Department of Chemcal & Bologcal Engneerng Illnos Insttute of echnology Mass r r max

More information

En Route Traffic Optimization to Reduce Environmental Impact

En Route Traffic Optimization to Reduce Environmental Impact En Route Traffc Optmzaton to Reduce Envronmental Impact John-Paul Clarke Assocate Professor of Aerospace Engneerng Drector of the Ar Transportaton Laboratory Georga Insttute of Technology Outlne 1. Introducton

More information

An Optimization Model for Routing in Low Earth Orbit Satellite Constellations

An Optimization Model for Routing in Low Earth Orbit Satellite Constellations An Optmzaton Model for Routng n Low Earth Orbt Satellte Constellatons A. Ferrera J. Galter P. Mahey Inra Inra Inra Afonso.Ferrera@sopha.nra.fr Jerome.Galter@nra.fr Phlppe.Mahey@sma.fr G. Mateus A. Olvera

More information

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

As is less than , there is insufficient evidence to reject H 0 at the 5% level. The data may be modelled by Po(2).

As is less than , there is insufficient evidence to reject H 0 at the 5% level. The data may be modelled by Po(2). Ch-squared tests 6D 1 a H 0 : The data can be modelled by a Po() dstrbuton. H 1 : The data cannot be modelled by Po() dstrbuton. The observed and expected results are shown n the table. The last two columns

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

Classification as a Regression Problem

Classification as a Regression Problem Target varable y C C, C,, ; Classfcaton as a Regresson Problem { }, 3 L C K To treat classfcaton as a regresson problem we should transform the target y nto numercal values; The choce of numercal class

More information

Effective Power Optimization combining Placement, Sizing, and Multi-Vt techniques

Effective Power Optimization combining Placement, Sizing, and Multi-Vt techniques Effectve Power Optmzaton combnng Placement, Szng, and Mult-Vt technques Tao Luo, Davd Newmark*, and Davd Z Pan Department of Electrcal and Computer Engneerng, Unversty of Texas at Austn *Advanced Mcro

More information

STUDY OF A THREE-AXIS PIEZORESISTIVE ACCELEROMETER WITH UNIFORM AXIAL SENSITIVITIES

STUDY OF A THREE-AXIS PIEZORESISTIVE ACCELEROMETER WITH UNIFORM AXIAL SENSITIVITIES STUDY OF A THREE-AXIS PIEZORESISTIVE ACCELEROMETER WITH UNIFORM AXIAL SENSITIVITIES Abdelkader Benchou, PhD Canddate Nasreddne Benmoussa, PhD Kherreddne Ghaffour, PhD Unversty of Tlemcen/Unt of Materals

More information

Cathy Walker March 5, 2010

Cathy Walker March 5, 2010 Cathy Walker March 5, 010 Part : Problem Set 1. What s the level of measurement for the followng varables? a) SAT scores b) Number of tests or quzzes n statstcal course c) Acres of land devoted to corn

More information

Use Subsurface Altitude and Groundwater Level Variations to Estimate Land Subsidence in Choushui River Alluvial Fan, Taiwan. Reporter : Ya-Yun Zheng

Use Subsurface Altitude and Groundwater Level Variations to Estimate Land Subsidence in Choushui River Alluvial Fan, Taiwan. Reporter : Ya-Yun Zheng Use Subsurface Alttude and Groundwater Level Varatons to Estmate Land Subsdence n Choushu Rver Alluval Fan, Tawan 1 Reporter : Ya-Yun Zheng Graduate School of Safety Health and Envronmental Engneerng,

More information

Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming 1

Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming 1 Average Decson hreshold of CA CFAR and excson CFAR Detectors n the Presence of Strong Pulse Jammng Ivan G. Garvanov and Chrsto A. Kabachev Insttute of Informaton echnologes Bulgaran Academy of Scences

More information

ˆ (0.10 m) E ( N m /C ) 36 ˆj ( j C m)

ˆ (0.10 m) E ( N m /C ) 36 ˆj ( j C m) 7.. = = 3 = 4 = 5. The electrc feld s constant everywhere between the plates. Ths s ndcated by the electrc feld vectors, whch are all the same length and n the same drecton. 7.5. Model: The dstances to

More information

Coarse-Grain MTCMOS Sleep

Coarse-Grain MTCMOS Sleep Coarse-Gran MTCMOS Sleep Transstor Szng Usng Delay Budgetng Ehsan Pakbazna and Massoud Pedram Unversty of Southern Calforna Dept. of Electrcal Engneerng DATE-08 Munch, Germany Leakage n CMOS Technology

More information

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018 MATH 5630: Dscrete Tme-Space Model Hung Phan, UMass Lowell March, 08 Newton s Law of Coolng Consder the coolng of a well strred coffee so that the temperature does not depend on space Newton s law of collng

More information

AS-Level Maths: Statistics 1 for Edexcel

AS-Level Maths: Statistics 1 for Edexcel 1 of 6 AS-Level Maths: Statstcs 1 for Edecel S1. Calculatng means and standard devatons Ths con ndcates the slde contans actvtes created n Flash. These actvtes are not edtable. For more detaled nstructons,

More information

Statistical Circuit Optimization Considering Device and Interconnect Process Variations

Statistical Circuit Optimization Considering Device and Interconnect Process Variations Statstcal Crcut Optmzaton Consderng Devce and Interconnect Process Varatons I-Jye Ln, Tsu-Yee Lng, and Yao-Wen Chang The Electronc Desgn Automaton Laboratory Department of Electrcal Engneerng Natonal Tawan

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Differentiating Gaussian Processes

Differentiating Gaussian Processes Dfferentatng Gaussan Processes Andrew McHutchon Aprl 17, 013 1 Frst Order Dervatve of the Posteror Mean The posteror mean of a GP s gven by, f = x, X KX, X 1 y x, X α 1 Only the x, X term depends on the

More information

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder.

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder. PASSBAND DIGITAL MODULATION TECHNIQUES Consder the followng passband dgtal communcaton system model. cos( ω + φ ) c t message source m sgnal encoder s modulator s () t communcaton xt () channel t r a n

More information

SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko

SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko SGNose and AGDas - tools for processng of superconductng and absolute gravty data Vojtech Pálnkáš and Mloš Vaľko 1 Research Insttute of Geodesy, Topography and Cartography, Czech Republc SGNose Web tool

More information

Economics 130. Lecture 4 Simple Linear Regression Continued

Economics 130. Lecture 4 Simple Linear Regression Continued Economcs 130 Lecture 4 Contnued Readngs for Week 4 Text, Chapter and 3. We contnue wth addressng our second ssue + add n how we evaluate these relatonshps: Where do we get data to do ths analyss? How do

More information

Airflow and Contaminant Simulation with CONTAM

Airflow and Contaminant Simulation with CONTAM Arflow and Contamnant Smulaton wth CONTAM George Walton, NIST CHAMPS Developers Workshop Syracuse Unversty June 19, 2006 Network Analogy Electrc Ppe, Duct & Ar Wre Ppe, Duct, or Openng Juncton Juncton

More information

Calculation of time complexity (3%)

Calculation of time complexity (3%) Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add

More information

Interconnect Optimization for Deep-Submicron and Giga-Hertz ICs

Interconnect Optimization for Deep-Submicron and Giga-Hertz ICs Interconnect Optmzaton for Deep-Submcron and Gga-Hertz ICs Le He http://cadlab.cs.ucla.edu/~hele UCLA Computer Scence Department Los Angeles, CA 90095 Outlne Background and overvew LR-based STIS optmzaton

More information

Some modelling aspects for the Matlab implementation of MMA

Some modelling aspects for the Matlab implementation of MMA Some modellng aspects for the Matlab mplementaton of MMA Krster Svanberg krlle@math.kth.se Optmzaton and Systems Theory Department of Mathematcs KTH, SE 10044 Stockholm September 2004 1. Consdered optmzaton

More information

Statistical pattern recognition

Statistical pattern recognition Statstcal pattern recognton Bayes theorem Problem: decdng f a patent has a partcular condton based on a partcular test However, the test s mperfect Someone wth the condton may go undetected (false negatve

More information

Simultaneous BOP Selection and Controller Design for the FCC Process

Simultaneous BOP Selection and Controller Design for the FCC Process Smultaneous BOP Selecton and Controller Desgn for the FCC Process Benjamn Omell & Donald J. Chmelewsk Department of Chemcal & Bologcal Engneerng Outlne Motvatng Example Introducton to BOP Selecton and

More information

Mean Field / Variational Approximations

Mean Field / Variational Approximations Mean Feld / Varatonal Appromatons resented by Jose Nuñez 0/24/05 Outlne Introducton Mean Feld Appromaton Structured Mean Feld Weghted Mean Feld Varatonal Methods Introducton roblem: We have dstrbuton but

More information

Multi degree of freedom measurement of machine tool movements. W. Knapp, S.Weikert Institute for Machine Tools and Manufacturing (IWF), Swiss Federal

Multi degree of freedom measurement of machine tool movements. W. Knapp, S.Weikert Institute for Machine Tools and Manufacturing (IWF), Swiss Federal Mult degree of freedom measurement of machne tool movements W. Knapp, S.Wekert Insttute for Machne Tools and Manufacturng (IWF), Swss Federal \ ^r Az. ca, we z'tz rf @ z w/: Agpr. graz. ca Abstract The

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION do: 0.08/nature09 I. Resonant absorpton of XUV pulses n Kr + usng the reduced densty matrx approach The quantum beats nvestgated n ths paper are the result of nterference between two exctaton paths of

More information

Solutions to exam in SF1811 Optimization, Jan 14, 2015

Solutions to exam in SF1811 Optimization, Jan 14, 2015 Solutons to exam n SF8 Optmzaton, Jan 4, 25 3 3 O------O -4 \ / \ / The network: \/ where all lnks go from left to rght. /\ / \ / \ 6 O------O -5 2 4.(a) Let x = ( x 3, x 4, x 23, x 24 ) T, where the varable

More information

Let the Shape Speak - Discriminative Face Alignment using Conjugate Priors

Let the Shape Speak - Discriminative Face Alignment using Conjugate Priors Let the Shape Speak - Dscrmnatve Face Algnment usng Conjugate Prors Pedro Martns, Ru Casero, João F. Henrques, Jorge Batsta http://www.sr.uc.pt/~pedromartns Insttute of Systems and Robotcs Unversty of

More information

Computing Correlated Equilibria in Multi-Player Games

Computing Correlated Equilibria in Multi-Player Games Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,

More information

Jacobian mapping between vertical coordinate systems in data assimilation (ITSC-14 RTSP-WG action c)

Jacobian mapping between vertical coordinate systems in data assimilation (ITSC-14 RTSP-WG action c) www.ec.gc.ca Jacoban mappng between vertcal coordnate systems n data assmlaton (ITSC-14 RTSP-WG acton 2.1.1-c) Atmospherc Scence and Technology Drectorate Yves J. Rochon, Lous Garand, D.S. Turner, and

More information

ECE559VV Project Report

ECE559VV Project Report ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate

More information

AERODYNAMICS I LECTURE 6 AERODYNAMICS OF A WING FUNDAMENTALS OF THE LIFTING-LINE THEORY

AERODYNAMICS I LECTURE 6 AERODYNAMICS OF A WING FUNDAMENTALS OF THE LIFTING-LINE THEORY LECTURE 6 AERODYNAMICS OF A WING FUNDAMENTALS OF THE LIFTING-LINE THEORY The Bot-Savart Law The velocty nduced by the sngular vortex lne wth the crculaton can be determned by means of the Bot- Savart formula

More information

Chapter 3 Describing Data Using Numerical Measures

Chapter 3 Describing Data Using Numerical Measures Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The

More information

Motion Perception Under Uncertainty. Hongjing Lu Department of Psychology University of Hong Kong

Motion Perception Under Uncertainty. Hongjing Lu Department of Psychology University of Hong Kong Moton Percepton Under Uncertanty Hongjng Lu Department of Psychology Unversty of Hong Kong Outlne Uncertanty n moton stmulus Correspondence problem Qualtatve fttng usng deal observer models Based on sgnal

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

AGC Introduction

AGC Introduction . Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero

More information

Scatter Plot x

Scatter Plot x Construct a scatter plot usng excel for the gven data. Determne whether there s a postve lnear correlaton, negatve lnear correlaton, or no lnear correlaton. Complete the table and fnd the correlaton coeffcent

More information

Rigid body simulation

Rigid body simulation Rgd bod smulaton Rgd bod smulaton Once we consder an object wth spacal etent, partcle sstem smulaton s no longer suffcent Problems Problems Unconstraned sstem rotatonal moton torques and angular momentum

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Physics 181. Particle Systems

Physics 181. Particle Systems Physcs 181 Partcle Systems Overvew In these notes we dscuss the varables approprate to the descrpton of systems of partcles, ther defntons, ther relatons, and ther conservatons laws. We consder a system

More information

DISTRIBUTED OPTIMIZATION FOR COOPERATIVE MISSIONS IN UNCERTAIN ENVIRONMENTS

DISTRIBUTED OPTIMIZATION FOR COOPERATIVE MISSIONS IN UNCERTAIN ENVIRONMENTS DISTRIBUTED OPTIMIZATION FOR COOPERATIVE MISSIONS IN UNCERTAIN ENVIRONMENTS C. G. Cassandras Center for Informaton and Systems Engneerng Boston Unversty OUTLINE COOPERATIVE MISSION SETTING REWARD MAXIMIZATION

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Handout 6 Luca Trevisan September 12, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Handout 6 Luca Trevisan September 12, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 6 Luca Trevsan September, 07 Scrbed by Theo McKenze Lecture 6 In whch we study the spectrum of random graphs. Overvew When attemptng to fnd n polynomal

More information

Quantum and Classical Information Theory with Disentropy

Quantum and Classical Information Theory with Disentropy Quantum and Classcal Informaton Theory wth Dsentropy R V Ramos rubensramos@ufcbr Lab of Quantum Informaton Technology, Department of Telenformatc Engneerng Federal Unversty of Ceara - DETI/UFC, CP 6007

More information

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1] DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm

More information

STATISTICAL MECHANICS

STATISTICAL MECHANICS STATISTICAL MECHANICS Thermal Energy Recall that KE can always be separated nto 2 terms: KE system = 1 2 M 2 total v CM KE nternal Rgd-body rotaton and elastc / sound waves Use smplfyng assumptons KE of

More information

Variability-Driven Module Selection with Joint Design Time Optimization and Post-Silicon Tuning

Variability-Driven Module Selection with Joint Design Time Optimization and Post-Silicon Tuning Asa and South Pacfc Desgn Automaton Conference 2008 Varablty-Drven Module Selecton wth Jont Desgn Tme Optmzaton and Post-Slcon Tunng Feng Wang, Xaoxa Wu, Yuan Xe The Pennsylvana State Unversty Department

More information

Week 11: Chapter 11. The Vector Product. The Vector Product Defined. The Vector Product and Torque. More About the Vector Product

Week 11: Chapter 11. The Vector Product. The Vector Product Defined. The Vector Product and Torque. More About the Vector Product The Vector Product Week 11: Chapter 11 Angular Momentum There are nstances where the product of two vectors s another vector Earler we saw where the product of two vectors was a scalar Ths was called the

More information

Optimum Design of Steel Frames Considering Uncertainty of Parameters

Optimum Design of Steel Frames Considering Uncertainty of Parameters 9 th World Congress on Structural and Multdscplnary Optmzaton June 13-17, 211, Shzuoka, Japan Optmum Desgn of Steel Frames Consderng ncertanty of Parameters Masahko Katsura 1, Makoto Ohsak 2 1 Hroshma

More information

Credit Card Pricing and Impact of Adverse Selection

Credit Card Pricing and Impact of Adverse Selection Credt Card Prcng and Impact of Adverse Selecton Bo Huang and Lyn C. Thomas Unversty of Southampton Contents Background Aucton model of credt card solctaton - Errors n probablty of beng Good - Errors n

More information

Week 9 Chapter 10 Section 1-5

Week 9 Chapter 10 Section 1-5 Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,

More information

Three-dimensional eddy current analysis by the boundary element method using vector potential

Three-dimensional eddy current analysis by the boundary element method using vector potential Physcs Electrcty & Magnetsm felds Okayama Unversty Year 1990 Three-dmensonal eddy current analyss by the boundary element method usng vector potental H. Tsubo M. Tanaka Okayama Unversty Okayama Unversty

More information

What would be a reasonable choice of the quantization step Δ?

What would be a reasonable choice of the quantization step Δ? CE 108 HOMEWORK 4 EXERCISE 1. Suppose you are samplng the output of a sensor at 10 KHz and quantze t wth a unform quantzer at 10 ts per sample. Assume that the margnal pdf of the sgnal s Gaussan wth mean

More information

Exact Evaluation of Outage Probability in Correlated Lognormal Shadowing Environment

Exact Evaluation of Outage Probability in Correlated Lognormal Shadowing Environment The 5 4th Internatonal Workshop on Physcs-Inspred Paradgms n Wreless Communcatons and etworks Exact Evaluaton o Outage Probablty n Correlated Lognormal Shadowng Envronment Marwane en Hcne¹, Rdha ouallegue²

More information

Interconnect Modeling

Interconnect Modeling Interconnect Modelng Modelng of Interconnects Interconnect R, C and computaton Interconnect models umped RC model Dstrbuted crcut models Hgher-order waveform n dstrbuted RC trees Accuracy and fdelty Prepared

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS

A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS Shervn Haamn A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS INTRODUCTION Increasng computatons n applcatons has led to faster processng. o Use more cores n a chp

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

NON LINEAR ANALYSIS OF STRUCTURES ACCORDING TO NEW EUROPEAN DESIGN CODE

NON LINEAR ANALYSIS OF STRUCTURES ACCORDING TO NEW EUROPEAN DESIGN CODE October 1-17, 008, Bejng, Chna NON LINEAR ANALYSIS OF SRUCURES ACCORDING O NEW EUROPEAN DESIGN CODE D. Mestrovc 1, D. Czmar and M. Pende 3 1 Professor, Dept. of Structural Engneerng, Faculty of Cvl Engneerng,

More information

Energy configuration optimization of submerged propeller in oxidation ditch based on CFD

Energy configuration optimization of submerged propeller in oxidation ditch based on CFD IOP Conference Seres: Earth and Envronmental Scence Energy confguraton optmzaton of submerged propeller n oxdaton dtch based on CFD To cte ths artcle: S Y Wu et al 01 IOP Conf. Ser.: Earth Envron. Sc.

More information

A correction model for zenith dry delay of GPS signals using regional meteorological sites. GPS-based determination of atmospheric water vapour

A correction model for zenith dry delay of GPS signals using regional meteorological sites. GPS-based determination of atmospheric water vapour Geodetc Week 00 October 05-07, Cologne S4: Appled Geodesy and GNSS A correcton model for zenth dry delay of GPS sgnals usng regonal meteorologcal stes Xaoguang Luo Geodetc Insttute, Department of Cvl Engneerng,

More information

CS 2750 Machine Learning. Lecture 5. Density estimation. CS 2750 Machine Learning. Announcements

CS 2750 Machine Learning. Lecture 5. Density estimation. CS 2750 Machine Learning. Announcements CS 750 Machne Learnng Lecture 5 Densty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square CS 750 Machne Learnng Announcements Homework Due on Wednesday before the class Reports: hand n before

More information

Meshless Surfaces. presented by Niloy J. Mitra. An Nguyen

Meshless Surfaces. presented by Niloy J. Mitra. An Nguyen Meshless Surfaces presented by Nloy J. Mtra An Nguyen Outlne Mesh-Independent Surface Interpolaton D. Levn Outlne Mesh-Independent Surface Interpolaton D. Levn Pont Set Surfaces M. Alexa, J. Behr, D. Cohen-Or,

More information

10/9/2003 PHY Lecture 11 1

10/9/2003 PHY Lecture 11 1 Announcements 1. Physc Colloquum today --The Physcs and Analyss of Non-nvasve Optcal Imagng. Today s lecture Bref revew of momentum & collsons Example HW problems Introducton to rotatons Defnton of angular

More information

Dimension Reduction and Visualization of the Histogram Data

Dimension Reduction and Visualization of the Histogram Data The 4th Workshop n Symbolc Data Analyss (SDA 214): Tutoral Dmenson Reducton and Vsualzaton of the Hstogram Data Han-Mng Wu ( 吳漢銘 ) Department of Mathematcs Tamkang Unversty Tamsu 25137, Tawan http://www.hmwu.dv.tw

More information

Assuming that the transmission delay is negligible, we have

Assuming that the transmission delay is negligible, we have Baseband Transmsson of Bnary Sgnals Let g(t), =,, be a sgnal transmtted over an AWG channel. Consder the followng recever g (t) + + Σ x(t) LTI flter h(t) y(t) t = nt y(nt) threshold comparator Decson ˆ

More information

Supplementary Information for Observation of Parity-Time Symmetry in. Optically Induced Atomic Lattices

Supplementary Information for Observation of Parity-Time Symmetry in. Optically Induced Atomic Lattices Supplementary Informaton for Observaton of Party-Tme Symmetry n Optcally Induced Atomc attces Zhaoyang Zhang 1,, Yq Zhang, Jteng Sheng 3, u Yang 1, 4, Mohammad-Al Mr 5, Demetros N. Chrstodouldes 5, Bng

More information

Support Vector Machines. Vibhav Gogate The University of Texas at dallas

Support Vector Machines. Vibhav Gogate The University of Texas at dallas Support Vector Machnes Vbhav Gogate he Unversty of exas at dallas What We have Learned So Far? 1. Decson rees. Naïve Bayes 3. Lnear Regresson 4. Logstc Regresson 5. Perceptron 6. Neural networks 7. K-Nearest

More information

Pulse Coded Modulation

Pulse Coded Modulation Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal

More information

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11)

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11) Gravtatonal Acceleraton: A case of constant acceleraton (approx. hr.) (6/7/11) Introducton The gravtatonal force s one of the fundamental forces of nature. Under the nfluence of ths force all objects havng

More information

Systematic Error Illustration of Bias. Sources of Systematic Errors. Effects of Systematic Errors 9/23/2009. Instrument Errors Method Errors Personal

Systematic Error Illustration of Bias. Sources of Systematic Errors. Effects of Systematic Errors 9/23/2009. Instrument Errors Method Errors Personal 9/3/009 Sstematc Error Illustraton of Bas Sources of Sstematc Errors Instrument Errors Method Errors Personal Prejudce Preconceved noton of true value umber bas Prefer 0/5 Small over large Even over odd

More information

Statistical machine learning and its application to neonatal seizure detection

Statistical machine learning and its application to neonatal seizure detection 19/Oct/2009 Statstcal machne learnng and ts applcaton to neonatal sezure detecton Presented by Andry Temko Department of Electrcal and Electronc Engneerng Page 2 of 42 A. Temko, Statstcal Machne Learnng

More information

Joint Scheduling and Power-Allocation for Interference Management in Wireless Networks

Joint Scheduling and Power-Allocation for Interference Management in Wireless Networks Jont Schedulng and Power-Allocaton for Interference Management n Wreless Networks Xn Lu *, Edwn K. P. Chong, and Ness B. Shroff * * School of Electrcal and Computer Engneerng Purdue Unversty West Lafayette,

More information

Instance-Based Learning (a.k.a. memory-based learning) Part I: Nearest Neighbor Classification

Instance-Based Learning (a.k.a. memory-based learning) Part I: Nearest Neighbor Classification Instance-Based earnng (a.k.a. memory-based learnng) Part I: Nearest Neghbor Classfcaton Note to other teachers and users of these sldes. Andrew would be delghted f you found ths source materal useful n

More information

Chapter 6. Supplemental Text Material

Chapter 6. Supplemental Text Material Chapter 6. Supplemental Text Materal S6-. actor Effect Estmates are Least Squares Estmates We have gven heurstc or ntutve explanatons of how the estmates of the factor effects are obtaned n the textboo.

More information