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What is the receiver location Table 82 shows the results of the iterative approach of eqn (810) initialized at the ECEF origin p = [0, 0, 0] The position estimate converges to millimeter precision in ve iterations This convergence does not imply that the estimated position is accurate to millimeters From prior measurements, the pre-surveyed ECEF antenna position is known to be p = [ 243082917, 470234101, 354660439] m At each iteration, the error between the estimated position and the known position is shown in Table 83 This table shows that while the cost function of eqn (85) is minimized, the position error due to the measurement errors remains Note that the data for this example is from the era when selective availability was active With selective availability now turned o , the magnitude of the error vector displayed in this example would be atypical The measurement matrix H for the last iteration is calculated from eqn (812) to be 04643 07858 04087 10000 09858 00809 01475 10000 H= 01543 09842 00864 10000 00169 05333 08457 10000

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Of course, the robot must be able to survive conditions at its location Also, it must have some way to recover if it falls or gets knocked over

CHAPTER 3 DETERMINISTIC SYSTEMS 0 0 0 0 0 0 cos( ) sin( ) 0 sin( ) cos( ) 0 0 0 1 p u 2 (3102)

Limitations The technology for telepresence has existed for some time But certain logistical problems have always bedeviled engineers committed to developing such systems The most serious limitation is the fact that telemetry cannot, and never will, travel faster than the speed of light in free space This is not a problem over short-range links (a few hundred kilome-

GM where fn = an and fd = ad (R+h)2 are the north and down components of the speci c force vector in the geographic frame, respectively

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6 Let q = S x where q = [ , vn , , h, vd ] Show that the matrix producing this reordering of the state vector is 1 0 0 0 0 0 0 1 0 0 S = 0 0 0 0 1 and that S 1 = S 0 1 0 0 0 0 0 0 1 0 7 Use the similarity transformation matrix S to show that the dynamic model for q is 1 0 0 vn 2 0 R+h (R+h) vd vn 0 fd vn vd 2 R+h (R+h) R+h 1 q = 0 0 vn 2 0 q R+h (R+h) 0 0 0 0 1 vn 2 0 2vn fn 0 2GM 3 R+h (R+h)2 (R+h) 0 0 0 cos( ) sin( ) 0 0 0 1 up + (3103) 2 0 0 0 sin( ) cos( ) 0 where the lines are added to separate the vertical state errors h and vd form the horizontal and tilt errors , vn , and 8 Consider the unforced (ie, up = 0), decoupled vertical error dynam2 ics h vd 0 =

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ters or less), but it is slow in outer space, and nigh impractical in interplanetary operations The moon is approximately 13 light-seconds away from the earth; that means that any command sent to a telechir from the earth to the moon takes 13 s to get there, and any data from the telechir takes another 13 s to get back For planets in the solar system, the delay is on the order of several minutes to several hours On an interstellar scale, telepresence is out of the question The nearest stars are at distances of several light-years Another problem is the resolution of the robot s vision A human being with good eyesight can see things in considerable detail To send images in real-life detail, at reasonable speed, requires a signal with broad bandwidth There are engineering problems (and cost problems) that go along with this However, if one is willing to deal with the cost and accommodate the signal bandwidth, robot vision systems can be designed that offer optical resolution superior to human eyesight Still another limitation is best put as a question: How will a robot be able to feel something and transmit these impulses to the human brain For example, an apple feels smooth, a peach feels fuzzy, and an orange feels shiny yet bumpy How can this sense of texture be realistically transmitted to the human brain

vn 2 (R+h)2

A simple electronic calculator doesn t have AI But a machine that can learn from its mistakes, or that can show reasoning power, does Between these extremes, there is no precise dividing line As computers become more powerful, people tend to set higher standards for what they call AI Things that were once thought of as AI are now ordinary Things that seem fantastic now will someday be humdrum There is a tongue-in-cheek axiom: We can call computer intelligence true AI only as long as it remains a little bit mysterious

2GM (R+h)3

(3104)

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Convert PDF to Word Using C# - C# Corner
Jul 13, 2015 · Convert PDF to Word Using C# PdfBox: This third-party Nuget package will be used to read a PDF file. DocX: This package will be used to write a Word document.

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