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23 Mathematics 16324 Quantum Mechanics 17125 Creativity 17626 Experts 18127 Unreliable Data 18728 Systems Engineering 19529 You Get What You Measure 20230 You and Your Research 209vIndex 216
PREFACEAfter many years of pressure and encouragement from friends, I decided to write up the graduate course inengineering I teach at the U.S.Naval Postgraduate School in Monterey, California. At first I concentrated onall the details I thought should be tightened up, rather than leave the material as a series of somewhatdisconnected lectures. In class the lectures often followed the interests of the students, and many of the laterlectures were suggested topics in which they expressed an interest Also, the lectures changed from year toyear as various areas developed. Since engineering depends so heavily these days on the correspondingsciences, I often use the terms interchangeably.After more thought I decided that since I was trying to teach “style” of thinking in science andengineering, and “style” is an art, I should therefore copy the methods of teaching used for the other arts—once the fundamentals have been learned. How to be a great painter cannot be taught in words; one learnsby trying many different approaches that seem to surround the subject. Art teachers usually let the advancedstudent paint, and then make suggestions on how they would have done it, or what might also be tried, moreor less as the points arise in the student’s head—which is where the learning is supposed to occur! In thisseries of lectures I try to communicate to students what cannot be said in words—the essence of style inscience and engineering. I have adopted a loose organization with some repetition since this often occurs inthe lectures. There are, therefore, digressions and stories—with some told in two different places—all in thesomewhat rambling, informal style typical of lectures.I have used the “story” approach, often emphasizing the initial part of the discovery, because I firmlybelieve in Pasteur’s remark, “Luck favors the prepared mind.” In this way I can illustrate how theindividual’s preparation before encountering the problem can often lead to recognition, formulation, andsolution. Great results in science and engineering are “bunched” in the same person too often for success tobe a matter of random luck.Teachers should prepare the student for the student’s future, not for the teacher’s past. Most teachersrarely discuss the important topic of the future of their field, and when this is pointed out they usually reply:“No one can know the future.” It seems to me the difficulty of knowing the future does not absolve theteacher from seriously trying to help the student to be ready for it when it comes. It is obvious theexperience of an individual is not necessarily that of a class of individuals; therefore, any one person’sprojection into the future is apt to be somewhat personal and will not be universally accepted. This does notjustify reverting to impersonal surveys and losing the impact of the personal story.Since my classes are almost all carefully selected navy, marine, army, air force, and coast guard studentswith very few civilians, and, interestingly enough, about 15% very highly selected foreign military, thestudents face a highly technical future—hence the importance of preparing them for their future and not justour past.
Page 2 and 3: The Art of Doing Science and Engine
Page 4 and 5: This edition published in the Taylo
Page 8 and 9: The year 2020 seems a convenient da
Page 10 and 11: 1OrientationThe purpose of this cou
Page 12 and 13: ORIENTATION 3Telephone Laboratories
Page 14 and 15: they arise so you will not be left
Page 16 and 17: ORIENTATION 7One of the main tasks
Page 18 and 19: 2Foundations of the Digital (Discre
Page 20 and 21: FOUNDATIONS OF THE DIGITAL (DISCRET
Page 26 and 27: 3History of Computers— HarwareThe
Page 28 and 29: HISTORY OF COMPUTERS—HARDWARE 19b
Page 30 and 31: HISTORY OF COMPUTERS—HARDWARE 21T
Page 32 and 33: HISTORY OF COMPUTERS—HARDWARE 23B
Page 34 and 35: HISTORY OF COMPUTERS—SOFTWARE 25(
Page 36 and 37: HISTORY OF COMPUTERS—SOFTWARE 27A
Page 38 and 39: HISTORY OF COMPUTERS—SOFTWARE 29F
Page 40 and 41: HISTORY OF COMPUTERS—SOFTWARE 31b
Page 42 and 43: 5History of Computer ApplicationAs
Page 44 and 45: HISTORY OF COMPUTER APPLICATIONS 35
Page 50 and 51: asic understanding of when the meth
Page 52 and 53: ARTIFICIAL INTELLIGENCE—I 43chang
Page 54 and 55: ARTIFICIAL INTELLIGENCE—I 45Early
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7Artificial Intelligence—IIIn thi
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ARTIFICIAL INTELLIGENCE—II 49mome
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ARTIFICIAL INTELLIGENCE—II 51have
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ARTIFICIAL INTELLIGENCE—II 53can
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8Artificial Intelligenc—IIII sugg
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9n-Dimensional spaceWhen I became a
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N-DIMENSIONAL SPACE 59Figure 9. IIl
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N-DIMENSIONAL SPACE 61and hence the
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N-DIMENSIONAL SPACE 63where X and Y
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N-DIMENSIONAL SPACE 653. D(x,y)=D(y
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10Coding Theory—IHaving looked at
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CODING THEORY—I 69is uniquely dec
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CODING THEORY—I 71Figure 10.IVFig
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CODING THEORY—I 73I feel it neces
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CODING THEORY—II 75Figure 11.IFig
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CODING THEORY—II 77Figure 11.VThi
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CODING THEORY—II 79in this scheme
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12Error Correcting CodesThere are t
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ERROR CORRECTING CODES 83Figure 12.
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ERROR CORRECTING CODES 85old syndro
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ERROR CORRECTING CODES 87mention it
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13Information TheoryInformation The
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INFORMATION THEORY 91Figure 13.IThi
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INFORMATION THEORY 93Figure 13.IIIc
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INFORMATION THEORY 95Let us critiqu
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14Digital Filters—INow that we ha
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DIGITAL FILTERS—I 99for any other
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DIGITAL FILTERS—I 101Figure 14.IT
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DIGITAL FILTERS—I 103Figure 14.II
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DIGITAL FILTERS—I 105Figure 14.IV
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15Digital Filters— IIWhen digital
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DIGITAL FILTERS—II 109Let us run
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DIGITAL FILTERS—II 111Figure 15.I
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DIGITAL FILTERS—II 113At x=0 this
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16Digital Filters—IIIWe are now r
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DIGITAL FILTERS—III 117Figure 16.
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DIGITAL FILTERS—III 119We next tu
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DIGITAL FILTERS—III 121Figure 16.
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17Digital Filters—IVWe now turn t
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DIGITAL FILTERS—IV 125These recur
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DIGITAL FILTERS—IV 127Figure 17.I
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SIMULATION—I 129Figure 18.IThe fi
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SIMULATION—I 131(though they had
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SIMULATION—I 133Figure 18.IIIinte
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19Simulation—IIWe now take up the
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SIMULATION—II 137The major part o
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SIMULATION—II 139energy or angula
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20Simulation—IIII will continue t
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SIMULATION—III 143Figure 20.IVFig
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SIMULATION—III 145Figure 20.VIdow
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SIMULATION—III 147Figure 20.VIIIW
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SIMULATION—III 149Now to the next
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21Fiber OpticsOne of the reasons fo
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FIBER OPTICS 153Figure 21.IIII did
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FIBER OPTICS 155Figure 21.IVFigure
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22Computer Aided Instruction—CAIB
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COMPUTER AIDED INSTRUCTION—CAI 15
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and they all knew immediately it wa
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23MathematicsAs you live your life
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MATHEMATICS 165Figure 23.IFigure 23
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MATHEMATICS 167illustrates my attit
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MATHEMATICS 169can use the same for
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24Quantum MechanicsMost physicists
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taught QM, no one has found a widel
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QUANTUM MECHANICS 175I do not prete
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CREATIVITY 177I hope the above has
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CREATIVITY 179“memorizing the mat
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26ExpertsAs remarked in an earlier
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EXPERTS 183Figure 26.ILaboratories
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EXPERTS 185Figure 26.IIthe final an
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27Unreliable DataIt has been my exp
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UNRELIABLE DATA 189But how about ba
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UNRELIABLE DATA 191country might ge
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UNRELIABLE DATA 193collection of qu
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28Systems EngineeringParables are o
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SYSTEMS ENGINEERING 197Cramming is
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of traffic performance degrades imm
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SYSTEMS ENGINEERING 201of the natur
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YOU GET WHAT YOU MEASURE 203Figure
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YOU GET WHAT YOU MEASURE 205still o
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YOU GET WHAT YOU MEASURE 207whole o
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30You and Your ResearchI have given
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YOU AND YOUR RESEARCH 211Confidence
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YOU AND YOUR RESEARCH 213know where
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YOU AND YOUR RESEARCH 215believe, i
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INDEX 217Godel’s theorem, 280grow
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