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University of Texas at Dallas - EE - 6334
The University of Texas at DallasErik Jonsson School PhoTECTHE DIFFRACTION INTEGRAL (1) Optical eld at a point in image space due to a perfectly spherical wave Aeikf /f converging to the Gaussian focal point: iAeikf eiks dS u(r1) = S f s r1 = vector to
University of Texas at Dallas - EE - 6310
The University of Texas at DallasErik Jonsson School PhoTECOVERVIEW OF OPTICAL COMMUNICATION SYSTEMSNotes prepared for EE 6310 by Professor Cyrus D. Cantrell AugustDecember 2003c C. D. Cantrell (06/2003)The University of Texas at DallasErik Jonsson
University of Texas at Dallas - EE - 6310
The University of Texas at DallasErik Jonsson School of Engineering & Computer ScienceOPTICAL DETECTORS AND RECEIVERSNotes prepared for EE 6310 by Professor Cyrus D. Cantrell AugustDecember 2003c C. D. Cantrell (06/2003)The University of Texas at Dal
University of Texas at Dallas - EE - 6481
The University of Texas at DallasErik Jonsson School of Engineering & Computer ScienceCOMPUTATIONAL SCIENCE & ENGINEERINGNotes prepared for EE 6481 by Professor Cyrus D. Cantrell MayAugust 2005c C. D. Cantrell (05/2005)The University of Texas at Dall
University of Texas at Dallas - EE - 6481
The University of Texas at DallasErik Jonsson School of Engineering & Computer ScienceVECTOR OPTIMIZATION TECHNIQUESNotes prepared for EE 6481 by Professor Cyrus D. Cantrell MayAugust 2005c C. D. Cantrell (04/2005)The University of Texas at DallasEr
University of Texas at Dallas - EE - 6328
The University of Texas at DallasErik Jonsson School PhoTECOPTICS OUTLINE (1) Maxwells equations Standard assumptions: Linear, homogeneous, isotropic medium; waves that can be described by a classical eld Vector boundary value problems; reciprocity Vect
University of Texas at Dallas - EE - 2310
The University of Texas at DallasErik Jonsson School of Engineering & Computer ScienceHOW DID WE GET HERE? Where were we, and what sort of future did we see? "Computers in the future may have only 1,000 vacuum tubes and perhaps only weigh 1 1 tons."-Pop
University of Texas at Dallas - EE - 6334
The University of Texas at DallasErik Jonsson School PhoTECCOHERENCE THEORY (1) Coherence is the ability to form interference fringes Fringe visibility: Imax Imin V := Imax + Imin V is dimensionless, and 0 V 1 For two coherent beams of equal intensity,
University of Texas at Dallas - EE - 6481
The University of Texas at DallasErik Jonsson School PhoTECGAUSSIAN QUADRATURE (1) Objective: Approximate an integral as a sumb a m-1f (x) w(x) dx i=0wi f (xi)as accurately as possible, given m points w is a positive-valued weight function Tools: P
University of Texas at Dallas - EE - 4301
The University of Texas at DallasErik Jonsson School of Engineering & Computer ScienceGOALS OF AN ENGINEERING EDUCATION Engineering programs must demonstrate to ABET that their graduates have:(a) an ability to apply knowledge of mathematics, science, a
University of Texas at Dallas - EE - 6328
The University of Texas at DallasErik Jonsson School PhoTECA MODEL FOR (3), PART 1 Assumptions: Model atomic nucleus as a point charge, +Ze Model electrons as a continuous negative charge distribution (r) Assume spherical symmetry: (r) = (r) Apply a un
University of Texas at Dallas - EE - 6310
The University of Texas at DallasErik Jonsson School PhoTECOPTICAL AMPLIFIERSNotes prepared for EE 6310 by Professor Cyrus D. Cantrell AugustDecember 2003c C. D. Cantrell (06/2003)THE UNIVERSITY OF TEXAS AT DALLASErik Jonsson School of Engineering a
University of Texas at Dallas - EE - 6317
The University of Texas at DallasErik Jonsson School PhoTECDIFFRACTION (1) Diffraction is the name given to the deviation of light from rectilinear propagation A property of all waves Usually most evident at the edges of shadows Vital for a quantitative
Duke - ECE - 593
Object RecognitionLecture 19Wireless Sensor NetworksEE 493/593OverviewClassification (Data Learning)Supervised: labeled training data Unsupervised: unlabeled training dataDecision (Hypothesis Testing)Distance based: distance definition (Euclidean,
uofl.edu - ECE - 520
University of Louisville Electrical and Computer EngineeringInstructor: Dr. Mohamed N. Ahmed Summer 2007EE520 Project 4Due 7/17/2007Digital Filter Design: Commonly Used Windows1. Generate the five different windows w[n] of length N=256: Rectangular,
uofl.edu - ECE - 520
University of Louisville Electrical and Computer EngineeringInstructor: Dr. Mohamed N. Ahmed Summer 2007EE520 Project 2Due 6/21/2007Fourier Transform1. Generate and plot 512 samples of the following periodic signals with period 100 milliseconds ( f s
uofl.edu - ECE - 520
University of Louisville Electrical and Computer EngineeringInstructor: Dr. Mohamed N. Ahmed Summer 2007EE520 Project 3Due 7/10/2007Circular and Linear Convolution1. Generate and plot a random signal x[ n] of length N = 1024 having uniform distributi
uofl.edu - ECE - 520
University of Louisville Electrical and Computer EngineeringInstructor: Dr. Mohamed N. Ahmed Summer 2007ECE 520 - Digital Signal Processing Hw # 3 Due 7/17/20071. Determine the Z-transform, including region of convergence, for the following sequences1
uofl.edu - ECE - 520
5-1Chapter 5 Digital Filter Design5.1 IntroductionDigital filters (discrete-time filters) are examples of LTI systems. They are used to alter, in a desired fashion, the frequency spectrum of systems of various types. It is not necessarily the case that
uofl.edu - ECE - 520
4-1Chapter 4 The Z-Transform4.14.1.aIntroductionThe Z-transformA signal processing system is illustrated in Figure 4.1.xc (t) x[n] y[n] yr (t)C/DDiscrete-Time System h[n]D/CTTFigure 4.1: Signal Processing System: Some tools are needed for ana
uofl.edu - ECE - 520
1-1Chapter 1 An Overview of Analog Signals and Systems1.1 IntroductionA number of important signal processing tasks can be modeled as an input-output system; that is, the input signal is mapped into a desired output signal by a certain mapping function
uofl.edu - ECE - 520
7-1Chapter 7 On Power Spectral EstimationIn this section, we discuss a few aspects of the power spectral estimation problem. The results will be used in signal/image restoration. We will start with the one-dimensional case before the 2-D.7.11-D spectr
uofl.edu - ECE - 520
6-1Chapter 6 The Discrete Fourier TransformGoal: Given a finite-duration sequence x[n], 0 n N - 1, we want to obtain a Fourier representation for x[n]. This representation, known as the Discrete Fourier Transform (DFT), will enable us to analyze importa
uofl.edu - ECE - 520
2-1Chapter 2 Sampling2.1 SamplingIn this chapter, we study the representation of a continuous-time signal by its samples. This is provided in terms of the sampling theorem. Consider three continuous-time signals x1 (t), x2 (t), and x3 (t)shown in Figur
uofl.edu - ECE - 520
% This sample is created for helping students to learn and understand Matlab easily, who % are taking ECE 520/521-Digital Signal Processing (DSP) in ECE at University of % Louisville, in summer, 2007. close all; % close all the previous figure windows cle
University of Florida - ECH - 4224
Department of Chemical Engineering Unit Operations LaboratoryContinuous Filtration ResourcesFoust, A.S., L.A. Wenzel, C.W. Clump, L. Maus, and L. B. Anderson. Principles of Unit Operations, Second Edition. John Wiley & Sons: New York, 1980. (UF Librarie
University of Florida - ECH - 4404
1.Standard Operating Procedure for the activity: 2. Chemical(s) UsedEthanolBatch Distillation 4. Health HazardsInhalation (cough, drowsiness, headache, fatigue), Eyes (redness, pain, burning), Skin (dryness), Ingestion (burning sensation, confusion, d
Indiana - ORACLE - 102
Oracle DatabaseRelease Notes 10g Release 2 (10.2) for Linux on POWERB25146-03 April 2006This document contains important information that was not included in the platform-specific or product-specific documentation for this release. This document supple
Indiana - ORACLE - 102
Oracle DatabaseRelease Notes 10g Release 2 (10.2) for Linux x86B15659-05 February 2006This document contains important information that was not included in the platform-specific or product-specific documentation for this release. This document suppleme
North Dakota State University - G - 440
articlesEight glacial cycles from an Antarctic ice coreEPICA community members**A full list of authors appears at the end of the paper.The Antarctic Vostok ice core provided compelling evidence of the nature of climate, and of climate feedbacks, over
Lake County - CONF - 134
Hedge Effectiveness Forecasting by Roger A. Dahlgran and Xudong MaSuggested citation format: Dahlgran, R. A., and X. Ma. 2008. "Hedge Effectiveness Forecasting." Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and
CofC - CHAPTER - 111
C. Atomic Mass Scale and Molar Mass 1. Atomic Mass (3.1) a. Atomic Mass Unit atomic mass unit or amu: basic mass unit The atomic mass scale is a relative scale: Basis of the atomic mass scale is the carbon-12 isotope Mass of all other elements is measured
University of Montana - MATH - 181
N_W 2.1: S ibV (t^-vci5) -is5. a) avvwl? v4o cZll^Cc v-_^44c)VCS)-V tc )- ID^_ 2. (av A, v =141414 -Z506)- -3ga.V . zy .o l^ ^f l -& _ 1.'t 9ci ^.V = 239 $4cfw_ft 15t-2.ooi)Div)III-ZS0 5M , "jV\ S 4EL4.4.4X411 10I I IV IV .17 r- i2 $
Ole Miss - PHYS - 621
%!PS-Adobe-2.0 %Creator: dvips(k) 5.92b Copyright 2002 Radical Eye Software %Title: Phys_621_Bessel.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: CMBX10 CMR10 CMEX10 CMMI10 CMR7 CMSY10 CMMI7 CMSY7 %EndComments %DVIPSWebPage: (
Ole Miss - PHYS - 621
%!PS-Adobe-2.0 %Creator: dvips(k) 5.92b Copyright 2002 Radical Eye Software %Title: Phys_621_Sph.dvi %Pages: 3 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: CMBX10 CMR10 CMSY10 CMMI10 CMR7 CMSY7 CMEX10 CMMI7 %+ CMSY5 CMR5 %EndComments %DVIP
baruch.edu - CHM - 1000
BERNARD M. BARUCH COLLEGE CHEMISTRY 1000 EXPERIMENT 5 PURIFICATION BY CRYSTALIZATION Introduction: In a typical laboratory preparation, a crystalline solid separated from a reaction mixture is usually contaminated with a small amount of impurities. The pu
baruch.edu - CHM - 1000
MEASUREMENT OF DENSITY Introduction In order to classify and identify materials of a wide variety, scientists use numbers called physical constants (e.g. density, melting point, boiling point, index of refraction) which are characteristic of the material
baruch.edu - CHM - 1000
Assay of the Fat Content of Commercial Meat Products By Walter Scharf and Charles Malerich Natural Sciences/Chemistry Baruch College New York, NY 10010 Introduction Recent publicity by consumer-protection agencies, has focused attention on the unexpectedl
baruch.edu - CHM - 1000
MEASUREMENT OF DENSITY Introduction In order to classify and identify materials of a wide variety, scientists use numbers called physical constants (e.g. density, melting point, boiling point, index of refraction) which are characteristic of the material
baruch.edu - CHM - 1000
NAMING INORGANIC COMPOUNDS Introduction A primary control in chemical experiments is knowing the identity of the initial substances. This knowledge can be communicated by the name of the substances and labels on reagent containers. The following sections
baruch.edu - CHM - 1000
Determination of Vitamin C of Citrus Juices by Dr. Walter Scharf and Dr. Charles Malerich Natural Sciences, Baruch College New York, NY 10010 Introduction-Vitamin C is a water-soluble vitamin that is that is needed daily (The minimum daily requirement for
Allan Hancock College - PHYSICS - 40653
Assignment 2 SolutionsMODERN OPTICS EXAMINATION HINTS With regard to the Modern Optics examination please note: It is 1.5 hours long.There will be three questions of which it is necessary to do all three. Remember that the average assignment mark can rep
LSU - PHYS - 4098
2N5457 / 5458 / 5459 / MMBF5457 / 5458 / 54592N5457 2N5458 2N5459MMBF5457 MMBF5458 MMBF5459GS G STO-92DSOT-23Mark: 6D / 61S / 6LDNOTE: Source & Drain are interchangeableN-Channel General Purpose AmplifierThis device is a low level audio amplif
Lake County - STAT - 425
> library(alr3) > data(rat) > pairs(rat)Rat data> summary(m1 <- lm(y ~ ., rat)$coefEstimate (Intercept) 0.26592177 BodyWt -0.02124634 LiverWt 0.01429806 Dose 4.17811141Std. Error t value Pr(>|t|) 0.194585255 1.3666080 0.19188433 0.007974304 -2.6643501
Lake County - STAT - 425
Non-constant variance score test for Sleep data > library(alr3) > library(car) > data(sleep1) > names(sleep1) [1] "SWS" "PS" "TS" "BodyWt" "BrainWt" [6] "Life" "GP" "P" "SE" "D" [11] "Species" For this example, we consider the response to be TS, the total
Lake County - STAT - 425
5Weights, Lack of Fit, and More5.1 WEIGHTED LEAST SQUARES R and S-Plus Weighted least squares estimates are most easily obtained using the weights argument for the lm command. In the physics data in alr[5.1], the weights are the inverse squares of the v
Lake County - STAT - 425
Randomized Design and Block Designs Randomized Design: The Coagulation dataset we use is a set of 24 blood coagulation times. 24 animals were randomly assigned to four different diets and the samples were taken in a random order. This data comes from Box,
Lake County - STAT - 425
Nonlinear regression > library(alr3) > data(turk0) > attach(turk0) > plot(A,Gain, xlab="Amount (percent of diet)", ylab="Weight gain, g")> n1 <- nls(Gain ~ th1 + th2*(1-exp(-th3*A), data=turk0, start=list(th1=620,th2=200,th3=10) > summary(n1) Formula: Ga
Lake County - STAT - 424
STAT 424 Exam #1February 15, 2007, 9:00 10:20am Problem 1 : True or false? (Circle the correct answer.) If M is a vector space in Rn , and y M, then y y = 0 where y is the projection of y onto M. (True) If M = spancfw_x1 , . . . , xp and x1 , x2 , . . .
Lake County - STAT - 424
STAT 324 Exam #1 AnswersFebruary 21, 2002 Closed book and note. Calculators are ok.i i i i1.14 points] Consider the model y = 0 + 1 x + 2 x2 + e for i = 1 2 3 4, where (x1 x2 x3 x4 ) = (;3 ;1 1 3). (a) Write down X for the model Y = X + e.0 1 1 ;3 9
Lake County - STAT - 424
STAT 424 Final ExamMay 09, 2007, 8:00 11:00amThis is a closed-book exam so do not refer to your notes, the text, or any other books (please put them on the oor). You may use two sheets of notes or formulas and a calculator, but materials may not be shar
Lake County - STAT - 425
Regression bootstrapsThe function boot.case does casewise bootstraps for regression models. Here is a simplified version of the function1 : function (object, f = coef, B = 999) cfw_ n <- length(resid(object) coef.boot <- NULL for (i in 1:B)cfw_ newSample
Lake County - STAT - 425
Non-constant variance score test for Sleep data > library(alr3) > library(car) > data(sleep1) > names(sleep1) [1] "SWS" "PS" "TS" "BodyWt" "BrainWt" [6] "Life" "GP" "P" "SE" "D" [11] "Species" For this example, we consider the response to be TS, the total
Oregon State University - PH - 642
Duquesne - A - 1984
1478Act 1982-2LAWS OF PENNSYLVANIA(This Joint Resolution No.2 was passed for the first time at the Legislative Session of 1980 as Joint Resolution No.4 and for the second time at the Legislative Session of 1982.) No. 1982-2 A JOINT RESOLUTION HB 202 Pr
Duquesne - A - 1984
PRIOR PRINTER'S NOS. 1, 778, 2065PRINTER'S NO.2606THE GENERAL ASSEMBLY OF PENNSYLVANIAHOUSE BILL No. 1Session of 1979INTRODUCED BY MESSRS. TADDONIO, SWEET, VROON, KOLTER, S. E. HAYES, KOWALYSHYN, HOEFFEL, D. M. FISHER, POLITE, GEIST, PYLES, PETRARCA
Loyola Chicago - CS - 447
Active Mapping: Resisting NIDS Evasion Without Altering TrafficUmesh Shankarushankar@cs.berkeley.edu University of California at BerkeleyVern Paxsonvern@icir.org,ee.lbl.gov ICSI Center for Internet Research and Lawrence Berkeley National LaboratoryAb
Princeton - PH - 509
PH 509 Problem Set 4Instructor: Alexander Polyakov TA: Arvind MuruganDepartment of Physics, Princeton University (Dated: Oct 10,2008 Due: Mon Oct 19, 2008 (Reading: Peskin & Schroeder Chap 4 Problem sessions: Tue Oct 14,08, 2:30 - 4:30 PM)I. PARTICLE D