Secure Chaotic Spread Spectrum Systems

Size: px
Start display at page:

Download "Secure Chaotic Spread Spectrum Systems"

Transcription

1 Seue Chaoi Sea Seum Sysems Ji Yu WSEAB ECE Deame Seves siue of Tehology Hoboke J 73

2 Oulie ouio Chaoi SS sigals Seuiy/ efomae ee eeives Biay oelaig eeio Mismah oblem aile-fileig base aoah Dual-aea aoah umeial esuls Colusios

3 ouio /D-Seue/ove ommuiaios Sea-seum sysems Die sequees biay sequees Chaoi sequees Fequey hoig Time hoig UWB eeos likelihoo-aio es Eegy eeo

4 Chaoi Sigals Geeae haoi seaig sequees Disee Chaoi Ma Exoeial Ma Tiagula Ma. Fo Examle: logisi ma x α x x x α 4 Biola sigalig a x DF of {a } f a a π a

5 oeies of Chaoi Sequees.8 Chaoi sequee logisi ma: α 4 x..5 f of a x.6.4 fa a o-biay a o-eioi Raom-like behavios Goo auo- a oss-oelaio age umbe of available seaig sequees fo mulile-aess aliaios

6 Sysem Moel Reeive Sigals a os ω H φ T H whee a a T τt The hi eoh τt is moele by.v. τ uifomly isibue i [.

7 Biay Coelaig Meho ikelihoo aio es Oimum ee Reeives Syhoous ohee Syhoous oohee Asyhoous ohee Asyhoous ohee Gaussia aoximaio Λ κ E ε φ ex q T ε φ bq os ω T

8 Syhoous Cohee Case Usig Gaussia aoximaio we obai Aea ow oise Amlifie T - Mahe File EX os φ ω / a Biay Syhoous Cohee Deeo 4 D C C FA D.5.5 λ λ k D C T k C T m δ σ δ ] [ ] [ ] [ a E a Va D a E a E C

9 Syhoous oohee Case Aea os ω Mahe File / ow oise Amlifie - si ω T EX COMB FTER The mea a vaiae of λ is m λ σ λ T C δ k T C.5D δ k D FA C C.5D

10 Asyhoous Cases Assume hi eoh is U[ T Cohee ase oohee ase 4 4 / τ τ τ τ τ τ λ C C FA C C D FA D / τ τ τ τ τ τ λ C C FA C C D FA D

11 efomae Comaiso Chaoi vs. Biay Sy Egegy Cohee-biay oohee-biay Cohee-haoi oohee-haoi FA. a eeio SRhi

12 aile-fileig Base Deeo Ueaiies i Chaoi Sigals Amliue ueaiy mismah oblems Fo all eeio seaios wih haoi sigals hase ueaiy oohee eeios Delay ueaiy Asyhoous eeios

13 aile-fileig Base Deeo Desig aile ses aoximae he ukow aom vaiables sele he mos likely aile saisially omba he ima ue o ueaiies Reue omuaioal omlexiy Uae ailes fo eah ieaio Fixe ailes fo eah ieaio

14 aile-fileig Base Deeo RT fuio wih aile fileig oie: obabiliy esiy fuios is use o sele he ailes a i j a φ i whih ae mosly lose o he aual amliue a hase. Cohee eeio oohee eeio Λ Λ a i j i i j a H H a Λ Λ a i j i i i i j a H H a φ

15 Aea ow oise Amlifie os ω φ TAZATO: ailes a j j j T jt C C aile-fileig Base Deeo Syhoous ohee eeives wih FA. a 5 a CACUATE: j a j H j H j H j H RESAMG a j j j Deeio obabiliy - Biay seq.: Biay Deeio ogisi seq.: Biay Deeio [6] Tiagula seq.: Biay Deeio [6] ogisi seq.: aile File Tiagula seq.: aile File YES j <? O - / DECSO SR hi

16 Asyhoous Deeio: Mulile samlig Obai mulile obsevaios by mulile samlig a τ omba elay ueaiy M O M M R M O M M R M O M M M O M M

17 aallel Deeio Algoihm RT fuio oie: The ow havig he miimum elay is auomaially selee by obabiliy esiy fuios o ee he esee of aio sigals. Λ H H max K Λ H H max K

18 umeial Resuls Asyhoous eeos wih vaious FA. a aallel eeio algoihm. Deeio obabiliy - ASY-CO: F a ASY-CO: F a ASY-CO: F a ASY-CO: F a ASY-C: F a ASY-C: F a ASY-C: Chao. Seq - Bi. Deeio SR hi

19 Dual-Aea Aoah: Syhoous ohee ase Sigal Moel Aea Deisio ow oise Amlifie ie Wave os φ ω os ψ φ ω θ T T / os / os λ θ π ψ θ FA D a a ψ φ ω φ ω 4 os os Deeio obabiliy

20 Dual-Aea Aoah: Syhoous oohee ase Aea Deisio ow oise Amlifie ie Wave os ω si ω θ T T T T Ω FA D θ θ

21 Dual-Aea Aoah: Asyhoous ase ie Wave θ Aea ow oise Amlifie T Deisio θ θ FA Ω D Ω θ osπ osθ / λ

22 umeial Resuls efomae of haoi DS SS sigals wih a FA. fo syhoous ohee eeos.

23 umeial Resuls efomae of haoi DS SS sigals wih vaious a hi SR -5 B. MC: Muual oulig.

24 Colusios The mismah bewee haoi sequees a biay eeio esuls i he efomae imoveme; aile-fileig base aoah a be use o omba he ueaiies a he imove he eeio efomae; Dual aea aoah a also suess he ueaiies; howeve i is subje o muual oulig.

25 Thak You! Ay uesios?

Adaptive Multiplexing Order Selection For Single-carrier MIMO Transmission

Adaptive Multiplexing Order Selection For Single-carrier MIMO Transmission Adaive Mulilexig Ode Seleio Fo Sigle-aie MIMO Tamiio Ryo AGAOKA Shiya KUMAGAI Teuya YAMAMOTO ad Fumiyui AACI e. of Commuiaio Egieeig Gaduae Shool of Egieeig Tohou Uiveiy 6-6-5 Aza-Aoba Aamai Aoba-u Sedai

More information

FBD of SDOF Base Excitation. 2.4 Base Excitation. Particular Solution (sine term) SDOF Base Excitation (cont) F=-(-)-(-)= 2ζω ωf

FBD of SDOF Base Excitation. 2.4 Base Excitation. Particular Solution (sine term) SDOF Base Excitation (cont) F=-(-)-(-)= 2ζω ωf .4 Base Exiaio Ipoa lass of vibaio aalysis Peveig exiaios fo passig fo a vibaig base hough is ou io a suue Vibaio isolaio Vibaios i you a Saellie opeaio Dis dives, e. FBD of SDOF Base Exiaio x() y() Syse

More information

Available online at J. Math. Comput. Sci. 2 (2012), No. 4, ISSN:

Available online at   J. Math. Comput. Sci. 2 (2012), No. 4, ISSN: Available olie a h://scik.og J. Mah. Comu. Sci. 2 (22), No. 4, 83-835 ISSN: 927-537 UNBIASED ESTIMATION IN BURR DISTRIBUTION YASHBIR SINGH * Deame of Saisics, School of Mahemaics, Saisics ad Comuaioal

More information

/ % ( + 9::1 9; / <;= % < 8 2 ), )9<;,, > ), /777?? /777 3 % 3

/ % ( + 9::1 9; / <;= % < 8 2 ), )9<;,, > ), /777?? /777 3 % 3 ! # % & % () +()+./ % % % %&% 4 %%6 /7778 % (+::/. REPLACE TIS LINE WIT YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK ERE TO EDIT) < A Novel Imagig Algoihm fo Fousig

More information

Probability Density Functions of Envelope and Phase of the Sum of a PSK Modulated Carrier and Narrowband Gaussian Noise

Probability Density Functions of Envelope and Phase of the Sum of a PSK Modulated Carrier and Narrowband Gaussian Noise robabiliy Desiy Fuios of Eveloe a hase of he Su of a SK oulae Carrier a Narrowba Gaussia Noise Auhor: Lohar Frieerihs AUDENS Teleouiaios Cosulig Dae: 6.6. wih orreios a eesios of 8.7.8 Coes age. Soe. Uoulae

More information

Communication Systems Lecture 25. Dong In Kim School of Info/Comm Engineering Sungkyunkwan University

Communication Systems Lecture 25. Dong In Kim School of Info/Comm Engineering Sungkyunkwan University Commuiaio Sysems Leure 5 Dog I Kim Shool o Io/Comm Egieerig Sugkyukwa Uiversiy 1 Oulie Noise i Agle Modulaio Phase deviaio Large SNR Small SNR Oupu SNR PM FM Review o Agle Modulaio Geeral orm o agle modulaed

More information

1 Fundamental Solutions to the Wave Equation

1 Fundamental Solutions to the Wave Equation 1 Fundamental Solutions to the Wave Equation Physial insight in the sound geneation mehanism an be gained by onsideing simple analytial solutions to the wave equation One example is to onside aousti adiation

More information

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues Alied Maheaical Sciece Vol. 8 o. 5 747-75 The No-Tucaed Bul Aival Queue M x /M/ wih Reei Bali Sae-Deede ad a Addiioal Seve fo Loe Queue A. A. EL Shebiy aculy of Sciece Meofia Uiveiy Ey elhebiy@yahoo.co

More information

Latticed pentamode acoustic cloak (supplementary Info)

Latticed pentamode acoustic cloak (supplementary Info) Lattied petamode aousti loak (supplemetay Ifo) Yi Che, Xiaoig Liu ad Gegkai Hu Key Laboatoy of yamis ad Cotol of Flight Vehile, Miisty of Eduatio, Shool of Aeospae Egieeig, Beiig Istitute of Tehology,

More information

EE/ME/AE324: Dynamical Systems. Chapter 7: Transform Solutions of Linear Models

EE/ME/AE324: Dynamical Systems. Chapter 7: Transform Solutions of Linear Models EE/ME/AE324: Dynamical Systems Chapter 7: Transform Solutions of Linear Models The Laplace Transform Converts systems or signals from the real time domain, e.g., functions of the real variable t, to the

More information

ECE-314 Fall 2012 Review Questions

ECE-314 Fall 2012 Review Questions ECE-34 Fall 0 Review Quesios. A liear ime-ivaria sysem has he ipu-oupu characerisics show i he firs row of he diagram below. Deermie he oupu for he ipu show o he secod row of he diagram. Jusify your aswer.

More information

Lower Bounds for Cover-Free Families

Lower Bounds for Cover-Free Families Loe Bouds fo Cove-Fee Families Ali Z. Abdi Covet of Nazaeth High School Gade, Abas 7, Haifa Nade H. Bshouty Dept. of Compute Sciece Techio, Haifa, 3000 Apil, 05 Abstact Let F be a set of blocks of a t-set

More information

Communications II Lecture 4: Effects of Noise on AM. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved

Communications II Lecture 4: Effects of Noise on AM. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved Commuiaio II Leure 4: Effe of Noie o M Profeor Ki K. Leug EEE ad Compuig Deparme Imperial College Lodo Copyrigh reerved Noie i alog Commuiaio Syem How do variou aalog modulaio heme perform i he preee of

More information

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is Exercise 7 / page 356 Noe ha X i are ii from Beroulli(θ where 0 θ a Meho of momes: Sice here is oly oe parameer o be esimae we ee oly oe equaio where we equae he rs sample mome wih he rs populaio mome,

More information

What is a Communications System?

What is a Communications System? Wha is a ommuiaios Sysem? Aual Real Life Messae Real Life Messae Replia Ipu Sial Oupu Sial Ipu rasduer Oupu rasduer Eleroi Sial rasmier rasmied Sial hael Reeived Sial Reeiver Eleroi Sial Noise ad Disorio

More information

Design of Optimal Waveforms in MIMO Radar Systems Based on the Generalized Approach to Signal Processing

Design of Optimal Waveforms in MIMO Radar Systems Based on the Generalized Approach to Signal Processing Desig of Opimal Wavefoms i MIMO Rada Sysems Based o e Geealized Appoa o Sigal Poessig VYACESLAV TUZLUKOV Depame of Ifomaio ad Commuiaio Egieeig, Sool of Eleois Egieeig, College of IT Egieeig Kyugpook Naioal

More information

The Structures of Fuzzifying Measure

The Structures of Fuzzifying Measure Sesors & Trasduers Vol 7 Issue 5 May 04 pp 56-6 Sesors & Trasduers 04 by IFSA Publishig S L hp://wwwsesorsporalom The Sruures of Fuzzifyig Measure Shi Hua Luo Peg Che Qia Sheg Zhag Shool of Saisis Jiagxi

More information

SOME NEW SEQUENCE SPACES AND ALMOST CONVERGENCE

SOME NEW SEQUENCE SPACES AND ALMOST CONVERGENCE Faulty of Siees ad Matheatis, Uivesity of Niš, Sebia Available at: http://www.pf.i.a.yu/filoat Filoat 22:2 (28), 59 64 SOME NEW SEQUENCE SPACES AND ALMOST CONVERGENCE Saee Ahad Gupai Abstat. The sequee

More information

Digital Signal Processing. Homework 2 Solution. Due Monday 4 October Following the method on page 38, the difference equation

Digital Signal Processing. Homework 2 Solution. Due Monday 4 October Following the method on page 38, the difference equation Digital Sigal Proessig Homework Solutio Due Moda 4 Otober 00. Problem.4 Followig the method o page, the differee equatio [] (/4[-] + (/[-] x[-] has oeffiiets a0, a -/4, a /, ad b. For these oeffiiets A(z

More information

Algebra 2A. Algebra 2A- Unit 5

Algebra 2A. Algebra 2A- Unit 5 Algeba 2A Algeba 2A- Ui 5 ALGEBRA 2A Less: 5.1 Name: Dae: Plymial fis O b j e i! I a evalae plymial fis! I a ideify geeal shapes f gaphs f plymial fis Plymial Fi: ly e vaiable (x) V a b l a y a :, ze a

More information

Time-Space Model of Business Fluctuations

Time-Space Model of Business Fluctuations Time-Sace Moel of Business Flucuaions Aleei Kouglov*, Mahemaical Cene 9 Cown Hill Place, Suie 3, Eobicoke, Onaio M8Y 4C5, Canaa Email: Aleei.Kouglov@SiconVieo.com * This aicle eesens he esonal view of

More information

6.3.3 Parameter Estimation

6.3.3 Parameter Estimation 130 CHAPTER 6. ARMA MODELS 6.3.3 Parameter Estimatio I this sectio we will iscuss methos of parameter estimatio for ARMAp,q assumig that the orers p a q are kow. Metho of Momets I this metho we equate

More information

Consider the time-varying system, (14.1)

Consider the time-varying system, (14.1) Leue 4 // Oulie Moivaio Equivale Defiiios fo Lyapuov Sabiliy Uifomly Sabiliy ad Uifomly Asympoial Sabiliy 4 Covese Lyapuov Theoem 5 Ivaiae- lie Theoem 6 Summay Moivaio Taig poblem i ool, Suppose ha x (

More information

2007 Spring VLSI Design Mid-term Exam 2:20-4:20pm, 2007/05/11

2007 Spring VLSI Design Mid-term Exam 2:20-4:20pm, 2007/05/11 7 ri VLI esi Mid-erm xam :-4:m, 7/5/11 efieτ R, where R ad deoe he chael resisace ad he ae caaciace of a ui MO ( W / L μm 1μm ), resecively., he chael resisace of a ui PMO, is wo R P imes R. i.e., R R.

More information

Principles of Communications Lecture 12: Noise in Modulation Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University

Principles of Communications Lecture 12: Noise in Modulation Systems. Chih-Wei Liu 劉志尉 National Chiao Tung University Priiples of Commuiatios Leture 1: Noise i Modulatio Systems Chih-Wei Liu 劉志尉 Natioal Chiao ug Uiversity wliu@twis.ee.tu.edu.tw Outlies Sigal-to-Noise Ratio Noise ad Phase Errors i Coheret Systems Noise

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Commuicaio heory 7. Represeaio of Digially Modulaed Sigals II Z. Aliyazicioglu Elecrical ad Compuer Egieerig Deparme Cal Poly Pomoa Represeaio of Digial Modulaio wih Memory Liear Digial Modulaio

More information

Physics 218, Spring March 2004

Physics 218, Spring March 2004 Today in Physis 8: eleti dipole adiation II The fa field Veto potential fo an osillating eleti dipole Radiated fields and intensity fo an osillating eleti dipole Total satteing oss setion of a dieleti

More information

Comparing Different Estimators for Parameters of Kumaraswamy Distribution

Comparing Different Estimators for Parameters of Kumaraswamy Distribution Compaig Diffee Esimaos fo Paamees of Kumaaswamy Disibuio ا.م.د نذير عباس ابراهيم الشمري جامعة النهرين/بغداد-العراق أ.م.د نشات جاسم محمد الجامعة التقنية الوسطى/بغداد- العراق Absac: This pape deals wih compaig

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

Research Design - - Topic 2 Inferential Statistics: The t-test 2010 R.C. Gardner, Ph.D. Independent t-test

Research Design - - Topic 2 Inferential Statistics: The t-test 2010 R.C. Gardner, Ph.D. Independent t-test Research Desig - - Topic Ifereial aisics: The -es 00 R.C. Garer, Ph.D. Geeral Raioale Uerlyig he -es (Garer & Tremblay, 007, Ch. ) The Iepee -es The Correlae (paire) -es Effec ize a Power (Kirk, 995, pp

More information

Lecture 3 : Concentration and Correlation

Lecture 3 : Concentration and Correlation Lectue 3 : Cocetatio ad Coelatio 1. Talagad s iequality 2. Covegece i distibutio 3. Coelatio iequalities 1. Talagad s iequality Cetifiable fuctios Let g : R N be a fuctio. The a fuctio f : 1 2 Ω Ω L Ω

More information

THIS PAGE DECLASSIFIED IAW EO IRIS u blic Record. Key I fo mation. Ma n: AIR MATERIEL COMM ND. Adm ni trative Mar ings.

THIS PAGE DECLASSIFIED IAW EO IRIS u blic Record. Key I fo mation. Ma n: AIR MATERIEL COMM ND. Adm ni trative Mar ings. T H S PA G E D E CLA SSFED AW E O 2958 RS u blc Recod Key fo maon Ma n AR MATEREL COMM ND D cumen Type Call N u b e 03 V 7 Rcvd Rel 98 / 0 ndexe D 38 Eneed Dae RS l umbe 0 0 4 2 3 5 6 C D QC d Dac A cesson

More information

A cooperative tranmission strategy on WSN based on virtual MIMO

A cooperative tranmission strategy on WSN based on virtual MIMO 29 Ieaioal Cofeee o Compue Egieeig ad Appliaios IPCSI vol2 (2 (2 IACSI Pess Sigapoe A oopeaive amissio saegy o WSN based o viual I WEI Yu-wei auly of eleomeaial egieeig Guagdog Uivesiy of eology Guagzou

More information

Consider a Binary antipodal system which produces data of δ (t)

Consider a Binary antipodal system which produces data of δ (t) Modulaion Polem PSK: (inay Phae-hi keying) Conide a inay anipodal yem whih podue daa o δ ( o + δ ( o inay and epeively. Thi daa i paed o pule haping ile and he oupu o he pule haping ile i muliplied y o(

More information

Angle Modulation: NB (Sinusoid)

Angle Modulation: NB (Sinusoid) gle Moulaio: NB Siuoi I uay, i he eage igal i a pue iuoi, ha i, a a i o o PM o FM The, i whee a p a o PM o FM : pea equey eviaio Noe ha i ow a oulaio ie o agle oulaio a i he aiu value o phae eviaio o boh

More information

Chapter 1 Fundamentals in Elasticity

Chapter 1 Fundamentals in Elasticity Fs s ν . Po Dfo ν Ps s - Do o - M os - o oos : o o w Uows o: - ss - - Ds W ows s o qos o so s os. w ows o fo s o oos s os of o os. W w o s s ss: - ss - - Ds - Ross o ows s s q s-s os s-sss os .. Do o ..

More information

Recursion. Algorithm : Design & Analysis [3]

Recursion. Algorithm : Design & Analysis [3] Recusio Algoithm : Desig & Aalysis [] I the last class Asymptotic gowth ate he Sets Ο, Ω ad Θ Complexity Class A Example: Maximum Susequece Sum Impovemet of Algoithm Compaiso of Asymptotic Behavio Aothe

More information

BBU Codes Overview. Outline Introduction Beam transport Equation How to solve (BBU-R, TDBBUU, bi, MATBBU etc.) Comparison of BBU codes

BBU Codes Overview. Outline Introduction Beam transport Equation How to solve (BBU-R, TDBBUU, bi, MATBBU etc.) Comparison of BBU codes BBU Codes Oveview asau Sawamua ad Ryoichi Hajima JAERI Outie Itoductio Beam tasot Equatio How to sove BBU-R, DBBUU, bi, ABBU etc. Comaiso of BBU codes Itoductio ERL cuet imited by Beam Beaku asvese defect

More information

CSE 202: Design and Analysis of Algorithms Lecture 16

CSE 202: Design and Analysis of Algorithms Lecture 16 CSE 202: Desig ad Aalysis of Algorihms Lecure 16 Isrucor: Kamalia Chaudhuri Iequaliy 1: Marov s Iequaliy Pr(X=x) Pr(X >= a) 0 x a If X is a radom variable which aes o-egaive values, ad a > 0, he Pr[X a]

More information

Factorial Designs. Prof. Daniel A. Menasce Dept. of fcomputer Science George Mason University. studied simultaneously.

Factorial Designs. Prof. Daniel A. Menasce Dept. of fcomputer Science George Mason University. studied simultaneously. Desig of Expeimets: Factoial Desigs Pof. Daiel A. Measce Dept. of fcompute Sciece Geoge Maso Uivesity Basic Cocepts Factoial desig: moe tha oe facto is studied simultaeously. k umbe of factos umbe of levels

More information

Applications of force vibration. Rotating unbalance Base excitation Vibration measurement devices

Applications of force vibration. Rotating unbalance Base excitation Vibration measurement devices Applicaios of foce viaio Roaig ualace Base exciaio Viaio easuee devices Roaig ualace 1 Roaig ualace: Viaio caused y iegulaiies i he disiuio of he ass i he oaig copoe. Roaig ualace 0 FBD 1 FBD x x 0 e 0

More information

Chapter 5. Long Waves

Chapter 5. Long Waves ape 5. Lo Waes Wae e s o compaed ae dep: < < L π Fom ea ae eo o s s ; amos ozoa moo z p s ; dosac pesse Dep-aeaed coseao o mass

More information

Fourier transform. Continuous-time Fourier transform (CTFT) ω ω

Fourier transform. Continuous-time Fourier transform (CTFT) ω ω Fourier rasform Coiuous-ime Fourier rasform (CTFT P. Deoe ( he Fourier rasform of he sigal x(. Deermie he followig values, wihou compuig (. a (0 b ( d c ( si d ( d d e iverse Fourier rasform for Re { (

More information

Supplementary Information

Supplementary Information Supplemeay Ifomaio No-ivasive, asie deemiaio of he coe empeaue of a hea-geeaig solid body Dea Ahoy, Daipaya Saka, Aku Jai * Mechaical ad Aeospace Egieeig Depame Uivesiy of Texas a Aligo, Aligo, TX, USA.

More information

Channel Aware Target Tracking in Multi-Hop Wireless Sensor Networks

Channel Aware Target Tracking in Multi-Hop Wireless Sensor Networks 4h Ieraioal Coferee o Iformaio Fusio Chiago Illiois USA July 5-8 Chael Aware arge raig i uli-o Wireless Sesor Newors Xiaou Yag Ruii Niu Egi asazade Pramod K. Varshey Shool of Iformaio Egieerig Chag a Uiversiy

More information

Statistical analysis of a new correlation peak detection method for unimodal autocorrelation

Statistical analysis of a new correlation peak detection method for unimodal autocorrelation Compue Commuiaio & Coaoaio (o. 3 ssue 5 SS 9-8(Pi 9-36(Oie Sumied o Ja. 3 5 DOC: 9-36-5--4-5 Saisia aaysis o a ew oeaio pea deeio mehod o uimoda auooeaio Ádám Kapp (Coespodee auho ad Lásó Pap Depame o

More information

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise

M-ary Detection Problem. Lecture Notes 2: Detection Theory. Example 1: Additve White Gaussian Noise Hi ue Hi ue -ay Deecio Pole Coide he ole of decidig which of hyohei i ue aed o oevig a ado vaiale (veco). he efoace cieia we coide i he aveage eo oailiy. ha i he oailiy of decidig ayhig ece hyohei H whe

More information

STIFFNESS EVALUATION OF SOME QUASI-ISOTROPIC FIBRE- REINFORCED COMPOSITE LAMINATES

STIFFNESS EVALUATION OF SOME QUASI-ISOTROPIC FIBRE- REINFORCED COMPOSITE LAMINATES The d Ieaioal Cofeee o Compuaioal Mehais ad Viual gieeig COMC 009 9 0 OCTOBR 009 Basov Romaia STIFFSS VALUATIO OF SOM QUASI-ISOTROPIC FIBR- RIFORCD COMPOSIT LAMIATS H. Teodoesu-Daghiesu S. Vlase A. Chiu

More information

Parameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data

Parameter Estimation and Hypothesis Testing of Two Negative Binomial Distribution Population with Missing Data Avlble ole wwwsceceeccom Physcs Poce 0 475 480 0 Ieol Cofeece o Mecl Physcs Bomecl ee Pmee smo Hyohess es of wo Neve Boml Dsbuo Poulo wh Mss D Zhwe Zho Collee of MhemcsJl Noml UvesyS Ch zhozhwe@6com Absc

More information

ECE 350 Matlab-Based Project #3

ECE 350 Matlab-Based Project #3 ECE 350 Malab-Based Projec #3 Due Dae: Nov. 26, 2008 Read he aached Malab uorial ad read he help files abou fucio i, subs, sem, bar, sum, aa2. he wrie a sigle Malab M file o complee he followig ask for

More information

β A Constant-G m Biasing

β A Constant-G m Biasing p 2002 EE 532 Anal IC Des II Pae 73 Cnsan-G Bas ecall ha us a PTAT cuen efeence (see p f p. 66 he nes) bas a bpla anss pes cnsan anscnucance e epeaue (an als epenen f supply lae an pcess). Hw h we achee

More information

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example

Representing Knowledge. CS 188: Artificial Intelligence Fall Properties of BNs. Independence? Reachability (the Bayes Ball) Example C 188: Aificial Inelligence Fall 2007 epesening Knowledge ecue 17: ayes Nes III 10/25/2007 an Klein UC ekeley Popeies of Ns Independence? ayes nes: pecify complex join disibuions using simple local condiional

More information

Engineering Accreditation. Heat Transfer Basics. Assessment Results II. Assessment Results. Review Definitions. Outline

Engineering Accreditation. Heat Transfer Basics. Assessment Results II. Assessment Results. Review Definitions. Outline Hea ansfe asis Febua 7, 7 Hea ansfe asis a Caeo Mehanial Engineeing 375 Hea ansfe Febua 7, 7 Engineeing ediaion CSUN has aedied pogams in Civil, Eleial, Manufauing and Mehanial Engineeing Naional aediing

More information

Camera Models class 8

Camera Models class 8 Camea Models class 8 Mulile View Geomey Com 29-89 Mac ollefeys Mulile View Geomey couse schedule (subjec o change) Jan. 7, 9 Ino & moivaion ojecive 2D Geomey Jan. 4, 6 (no class) ojecive 2D Geomey Jan.

More information

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1 Samplig Example Le x = cos( 4π)cos( π). The fudameal frequecy of cos 4π fudameal frequecy of cos π is Hz. The ( f ) = ( / ) δ ( f 7) + δ ( f + 7) / δ ( f ) + δ ( f + ). ( f ) = ( / 4) δ ( f 8) + δ ( f

More information

Institute of Actuaries of India

Institute of Actuaries of India Isiue of cuaries of Idia Subjec CT3-robabiliy ad Mahemaical Saisics May 008 Eamiaio INDICTIVE SOLUTION Iroducio The idicaive soluio has bee wrie by he Eamiers wih he aim of helig cadidaes. The soluios

More information

ELEG 635 Digital Communication Theory. Lecture 10

ELEG 635 Digital Communication Theory. Lecture 10 ELEG 635 Digital Cmmuiati hery Leture Ergdi Radm Presses CPM reeiver Sigal Parameter Estimati Carrier Syhrizati Ageda Ergdi Radm Presses - A radm press is said t be ergdi if time averagig is equivalet

More information

COMP26120: Introducing Complexity Analysis (2018/19) Lucas Cordeiro

COMP26120: Introducing Complexity Analysis (2018/19) Lucas Cordeiro COMP60: Itroduig Complexity Aalysis (08/9) Luas Cordeiro luas.ordeiro@mahester.a.uk Itroduig Complexity Aalysis Textbook: Algorithm Desig ad Appliatios, Goodrih, Mihael T. ad Roberto Tamassia (hapter )

More information

Recall from last week:

Recall from last week: Recall fom last week: Length of a cuve '( t) dt b Ac length s( t) a a Ac length paametization ( s) with '( s) 1 '( t) Unit tangent vecto T '(s) '( t) dt Cuvatue: s ds T t t t t t 3 t ds u du '( t) dt Pincipal

More information

8.1 8.2 9.1 9.2 9.3 10.1 10.2 11.1 12.1 12.2 13.1 14.2 15.1 15.2 16.1 17.1 17.2 8.1 8.2 9.1 9.2 10.1 10.2 10.3 11.1 12.1 12.2 13.1 13.2 14.1 14.1 14.2 15.1 16.1 16.2 16.3 16.4 16.5 16.6 W A W C 12 U A

More information

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff Laplace ransfom: -ranslaion rule 8.03, Haynes Miller and Jeremy Orloff Inroducory example Consider he sysem ẋ + 3x = f(, where f is he inpu and x he response. We know is uni impulse response is 0 for

More information

Z Transforms. Lesson 20 6DT. BME 333 Biomedical Signals and Systems - J.Schesser

Z Transforms. Lesson 20 6DT. BME 333 Biomedical Signals and Systems - J.Schesser Z rasforms Lesso 6D BME 333 Biomedical Sigals ad Systems Z rasforms A Defiitio I a sese similar to the L excet it is associated with discrete time fuctios. Let s assume we hae a cotiuous time fuctio, f

More information

Executive Committee and Officers ( )

Executive Committee and Officers ( ) Gifted and Talented International V o l u m e 2 4, N u m b e r 2, D e c e m b e r, 2 0 0 9. G i f t e d a n d T a l e n t e d I n t e r n a t i o n a2 l 4 ( 2), D e c e m b e r, 2 0 0 9. 1 T h e W o r

More information

5. Limit Theorems, Part II: Central Limit Theorem. ECE 302 Fall 2009 TR 3 4:15pm Purdue University, School of ECE Prof.

5. Limit Theorems, Part II: Central Limit Theorem. ECE 302 Fall 2009 TR 3 4:15pm Purdue University, School of ECE Prof. 5. Limit Theorems, Part II: Cetral Limit Theorem ECE 302 Fall 2009 TR 3 4:15pm Purdue Uiversity, School of ECE Prof. Ilya Pollak WLLN ad CLT X 1,, X i.i.d. with fiite mea μ ad variace σ 2 WLLN ad CLT X

More information

Formation of A Supergain Array and Its Application in Radar

Formation of A Supergain Array and Its Application in Radar Formatio of A Supergai Array ad ts Applicatio i Radar Tra Cao Quye, Do Trug Kie ad Bach Gia Duog. Research Ceter for Electroic ad Telecommuicatios, College of Techology (Coltech, Vietam atioal Uiversity,

More information

Chapter 15 VECTORS EXERCISE 15A. Scale: 1 cm 10 km h 1 1 cm 10 ms 1 N. 35 ms km h-1 W S. Scale: N cm 10 m. 60 ms -1.

Chapter 15 VECTORS EXERCISE 15A. Scale: 1 cm 10 km h 1 1 cm 10 ms 1 N. 35 ms km h-1 W S. Scale: N cm 10 m. 60 ms -1. Chate VECTORS EXERCISE a Sale: Sale: m 0 km h m 0 ms W E ms - 0 km h- S W S E W 0 0 E Sale: m 0 m 60 ms - m unway S Sale: m 0 ms a Sale: m newtons Sale: m newtons 7 newtons W S E 60 newtons W S E a Sale:

More information

/ / MET Day 000 NC1^ INRTL MNVR I E E PRE SLEEP K PRE SLEEP R E

/ / MET Day 000 NC1^ INRTL MNVR I E E PRE SLEEP K PRE SLEEP R E 05//0 5:26:04 09/6/0 (259) 6 7 8 9 20 2 22 2 09/7 0 02 0 000/00 0 02 0 04 05 06 07 08 09 0 2 ay 000 ^ 0 X Y / / / / ( %/ ) 2 /0 2 ( ) ^ 4 / Y/ 2 4 5 6 7 8 9 2 X ^ X % 2 // 09/7/0 (260) ay 000 02 05//0

More information

Degree of Approximation of Fourier Series

Degree of Approximation of Fourier Series Ieaioal Mahemaical Foum Vol. 9 4 o. 9 49-47 HIARI Ld www.m-hiai.com h://d.doi.og/.988/im.4.49 Degee o Aoimaio o Fouie Seies by N E Meas B. P. Padhy U.. Misa Maheda Misa 3 ad Saosh uma Naya 4 Deame o Mahemaics

More information

4/3/2017. PHY 712 Electrodynamics 9-9:50 AM MWF Olin 103

4/3/2017. PHY 712 Electrodynamics 9-9:50 AM MWF Olin 103 PHY 7 Eleodnais 9-9:50 AM MWF Olin 0 Plan fo Leue 0: Coninue eading Chap Snhoon adiaion adiaion fo eleon snhoon deies adiaion fo asonoial objes in iula obis 0/05/07 PHY 7 Sping 07 -- Leue 0 0/05/07 PHY

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

Solutions Manual 4.1. nonlinear. 4.2 The Fourier Series is: and the fundamental frequency is ω 2π

Solutions Manual 4.1. nonlinear. 4.2 The Fourier Series is: and the fundamental frequency is ω 2π Soluios Maual. (a) (b) (c) (d) (e) (f) (g) liear oliear liear liear oliear oliear liear. The Fourier Series is: F () 5si( ) ad he fudameal frequecy is ω f ----- H z.3 Sice V rms V ad f 6Hz, he Fourier

More information

Exponential Stability of Gradient Systems with Applications to Nonlinear-in-Control Design Methods

Exponential Stability of Gradient Systems with Applications to Nonlinear-in-Control Design Methods Poeedigs of he 7 Ameia Cool Cofeee Maio Maquis Hoel a imes Squae New Yo Ciy, USA, uly -3, 7 FC4. Eoeial Sabiliy of Gadie Sysems wih Aliaios o Noliea-i-Cool Desig Mehods Eugee Lavesy, Chegyu Cao, ad Naia

More information

Stenciling. 5 th Week, Reflection without Using the Stencil Buffer

Stenciling. 5 th Week, Reflection without Using the Stencil Buffer Secilig 5 h Week, 9 Reflecio wihou Usig he Secil Buffe Blockig he Reflecio Usig he Secil Buffe Secil Buffe A off-scee buffe fo secial effecs Haig same esoluio as he back buffe a eh buffe To block eeig

More information

A New Class of Ternary Zero Correlation Zone Sequence Sets Based on Mutually Orthogonal Complementary Sets

A New Class of Ternary Zero Correlation Zone Sequence Sets Based on Mutually Orthogonal Complementary Sets IOSR Joural of Electroics ad Commuicatio Egieerig (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 0, Issue 3, Ver. I (May - Ju.205), PP 08-3 www.iosrjourals.org A New Class of Terary Zero Correlatio

More information

Linear Quadratic Regulator (LQR) - State Feedback Design

Linear Quadratic Regulator (LQR) - State Feedback Design Linear Quadrai Regulaor (LQR) - Sae Feedbak Design A sysem is expressed in sae variable form as x = Ax + Bu n m wih x( ) R, u( ) R and he iniial ondiion x() = x A he sabilizaion problem using sae variable

More information

Transverse Wakefield in a Dielectric Tube with Frequency Dependent Dielectric Constant

Transverse Wakefield in a Dielectric Tube with Frequency Dependent Dielectric Constant ARDB-378 Bob Siemann & Alex Chao /4/5 Page of 8 Tansvese Wakefield in a Dielectic Tube with Fequency Dependent Dielectic Constant This note is a continuation of ARDB-368 that is now extended to the tansvese

More information

( ) :. : - 1. : - 2. : - 3. : - 4., - :. - :1 -.,M /M/1. :2 -. :* - 3.,M /M/ M, /M/c/, K M/M/c :4 - :. : - 1. : - 2.

( ) :. : - 1. : - 2. : - 3. : - 4., - :. - :1 -.,M /M/1. :2 -. :* - 3.,M /M/ M, /M/c/, K M/M/c :4 - :. : - 1. : - 2. 75 35 75 8 ' / ' ' * / 3 4 * 5 I 6 II 3 ' 4 5 * 6 7 / 8 * Hazard 3 4 5 6 * 7 *8 *9 75 3 8 3 4 M/M/ *3 4 M/M/c/K M/M/c M/M/ 3 75 ' 8 4 75 / [] D ' D ' D T ' T T T [ T T T S { } D D T S T 8 5 75 / = = =

More information

Bayesian Methods: Introduction to Multi-parameter Models

Bayesian Methods: Introduction to Multi-parameter Models Bayesia Methods: Itroductio to Multi-parameter Models Parameter: θ = ( θ, θ) Give Likelihood p(y θ) ad prior p(θ ), the posterior p proportioal to p(y θ) x p(θ ) Margial posterior ( θ, θ y) is Iterested

More information

LIPSCHITZ ESTIMATES FOR MULTILINEAR COMMUTATOR OF MARCINKIEWICZ OPERATOR

LIPSCHITZ ESTIMATES FOR MULTILINEAR COMMUTATOR OF MARCINKIEWICZ OPERATOR Reseh d ouiios i heis d hei Siees Vo. Issue Pges -46 ISSN 9-699 Puished Oie o Deee 7 Joi Adei Pess h://oideiess.e IPSHITZ ESTIATES FOR UTIINEAR OUTATOR OF ARINKIEWIZ OPERATOR DAZHAO HEN Dee o Siee d Ioio

More information

B. Maddah ENMG 622 ENMG /20/09

B. Maddah ENMG 622 ENMG /20/09 B. Maddah ENMG 6 ENMG 5 5//9 Queueig Theory () Distributio of waitig time i M/M/ Let T q be the waitig time i queue of a ustomer. The it a be show that, ( ) t { q > } =. T t e Let T be the total time of

More information

ELEG 635 Digital Communication Theory. Lecture 11

ELEG 635 Digital Communication Theory. Lecture 11 EEG 635 Digital Cmmuiati Thery eture 11 110801 Ageda Carrier Syhrizati Phase k p (P) Deisi Direted ps N-Deisi Direted ps Timig Syhrizati Deisi Direted ps N-Deisi Direted ps frmati Thery Sha's aw iear Blk

More information

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002 ECE 330:541, Stochastic Sigals ad Systems Lecture Notes o Limit Theorems from robability Fall 00 I practice, there are two ways we ca costruct a ew sequece of radom variables from a old sequece of radom

More information

Stochastic Control for Asset Management

Stochastic Control for Asset Management Joual of Mahemaial Fiae 3 3 59-69 h://dxdoiog/436/jmf335 Published Olie Febuay 3 (h://wwwsiog/joual/jmf) Sohasi Cool fo Asse Maageme James J Kug ig-keug og E-Chig u 3 Deame of Ieaioal Busiess Mig Chua

More information

A Two-Level Quantum Analysis of ERP Data for Mock-Interrogation Trials. Michael Schillaci Jennifer Vendemia Robert Buzan Eric Green

A Two-Level Quantum Analysis of ERP Data for Mock-Interrogation Trials. Michael Schillaci Jennifer Vendemia Robert Buzan Eric Green A Two-Level Quaum Aalysis of ERP Daa for Mock-Ierrogaio Trials Michael Schillaci Jeifer Vedemia Rober Buza Eric Gree Oulie Experimeal Paradigm 4 Low Workload; Sigle Sessio; 39 8 High Workload; Muliple

More information

VARIATIONAL ITERATION METHOD: A COMPUTATIONAL TOOL FOR SOLVING COUPLED SYSTEM OF NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS

VARIATIONAL ITERATION METHOD: A COMPUTATIONAL TOOL FOR SOLVING COUPLED SYSTEM OF NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS Joral of Sciece a Ars Year 6 No. 336 pp. 43-48 6 ORIGINAL PAPER ARIATIONAL ITERATION METHOD: A COMPTATIONAL TOOL FOR SOLING COPLED SYSTEM OF NONLINEAR PARTIAL DIFFERENTIAL EQATIONS MORF OYEDNSI OLAYIOLA

More information

Professor Fearing EE C128 / ME C134 Problem Set 7 Solution Fall 2010 Jansen Sheng and Wenjie Chen, UC Berkeley

Professor Fearing EE C128 / ME C134 Problem Set 7 Solution Fall 2010 Jansen Sheng and Wenjie Chen, UC Berkeley Professor Fearing EE C8 / ME C34 Problem Set 7 Solution Fall Jansen Sheng and Wenjie Chen, UC Berkeley. 35 pts Lag compensation. For open loop plant Gs ss+5s+8 a Find compensator gain Ds k such that the

More information

Recitation PHYS 131. must be one-half of T 2

Recitation PHYS 131. must be one-half of T 2 Reitation PHYS 131 Ch. 5: FOC 1, 3, 7, 10, 15. Pobles 4, 17, 3, 5, 36, 47 & 59. Ch 5: FOC Questions 1, 3, 7, 10 & 15. 1. () The eloity of a has a onstant agnitude (speed) and dietion. Sine its eloity is

More information

Two-Pion Exchange Currents in Photodisintegration of the Deuteron

Two-Pion Exchange Currents in Photodisintegration of the Deuteron Two-Pion Exchange Cuens in Phoodisinegaion of he Deueon Dagaa Rozędzik and Jacek Goak Jagieonian Univesiy Kaków MENU00 3 May 00 Wiiasbug Conen Chia Effecive Fied Theoy ChEFT Eecoagneic cuen oeaos wihin

More information

A note on characterization related to distributional properties of random translation, contraction and dilation of generalized order statistics

A note on characterization related to distributional properties of random translation, contraction and dilation of generalized order statistics PobSa Foum, Volume 6, July 213, Pages 35 41 ISSN 974-3235 PobSa Foum is an e-jounal. Fo eails please visi www.pobsa.og.in A noe on chaaceizaion elae o isibuional popeies of anom anslaion, conacion an ilaion

More information

Matthias Liepe, P4456/7656, Spring 2010, Cornell University Slide 1. Matthias Liepe, P4456/7656, Spring 2010, Cornell University Slide 2

Matthias Liepe, P4456/7656, Spring 2010, Cornell University Slide 1. Matthias Liepe, P4456/7656, Spring 2010, Cornell University Slide 2 5. F ystems ad Patile Aeleatio 5. Waveguides 5..3 Cylidial Waveguides 5. Aeleatig F Cavities 5.. Itodutio 5.. Tavelig wave avity: dis loaded waveguide 5..3 tadig wave avities 5..4 Highe-Ode-Modes 5..5

More information

A note on random minimum length spanning trees

A note on random minimum length spanning trees A ote o adom miimum legth spaig tees Ala Fieze Miklós Ruszikó Lubos Thoma Depatmet of Mathematical Scieces Caegie Mello Uivesity Pittsbugh PA15213, USA ala@adom.math.cmu.edu, usziko@luta.sztaki.hu, thoma@qwes.math.cmu.edu

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

Low-Complexity Hybrid QRD-MCMC MIMO Detection

Low-Complexity Hybrid QRD-MCMC MIMO Detection MITSUBISHI ELECTRIC RESEARCH LABORATORIES h://www.mel.com Low-Comlexiy Hybi QRD-MCMC MIMO Deecion Ronghui Peng Koon Hoo Teo Jinyun Zhang Rong-Rong Chen TR008-086 Decembe 008 Absac In his ae we oose a novel

More information

Control Volume Derivation

Control Volume Derivation School of eospace Engineeing Conol Volume -1 Copyigh 1 by Jey M. Seizman. ll ighs esee. Conol Volume Deiaion How o cone ou elaionships fo a close sysem (conol mass) o an open sysem (conol olume) Fo mass

More information

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mahemaics Prelim Quesio Paper Soluio Q. Aemp ay FIVE of he followig : [0] Q.(a) Defie Eve ad odd fucios. [] As.: A fucio f() is said o be eve fucio if

More information

Narayana IIT/NEET Academy INDIA IIT_XI-IC_SPARK 2016_P1 Date: Max.Marks: 186

Narayana IIT/NEET Academy INDIA IIT_XI-IC_SPARK 2016_P1 Date: Max.Marks: 186 Narayaa IIT/NEET Academy INDIA IIT_XI-IC_SPARK 6_P Date: 5--8 Max.Marks: 86 KEY SHEET PHYSICS B B c 4 B 5 c 6 ac 7 ac 8 ac 9 ad abc bc acd ad 4 5 6 6 7 6 8 4 CHEMISTRY 9 c b c a a 4 bc 5 ab 6 abcd 7 ab

More information

MODIFIED CLASS OF RATIO AND REGRESSION TYPE ESTIMATORS FOR IMPUTING SCRAMBLING RESPONSE

MODIFIED CLASS OF RATIO AND REGRESSION TYPE ESTIMATORS FOR IMPUTING SCRAMBLING RESPONSE Pak. J. tatist. 07 Vol. 33(4), 77-300 MODIFIED CLA OF RATIO AND REGREION TYPE ETIMATOR FOR IMPUTING CRAMBLING REPONE Muhammad Umai ohail, Javid habbi ad hakeel Ahmed Depatmet of tatistis, Quaid-i-Azam

More information

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c

F l a s h-b a s e d S S D s i n E n t e r p r i s e F l a s h-b a s e d S S D s ( S o-s ltiad t e D r i v e s ) a r e b e c o m i n g a n a t t r a c L i f e t i m e M a n a g e m e n t o f F l a-b s ah s e d S S D s U s i n g R e c o v e r-a y w a r e D y n a m i c T h r o t t l i n g S u n g j i n L e, e T a e j i n K i m, K y u n g h o, Kainmd J

More information

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220

ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220 ECE 564/645 - Digital Commuicatio Systems (Sprig 014) Fial Exam Friday, May d, 8:00-10:00am, Marsto 0 Overview The exam cosists of four (or five) problems for 100 (or 10) poits. The poits for each part

More information