6 2 criteria and weights for computer replacement decision

... 397 39 373 28 9 61 5 68 4 353 057 761 827 749 9 46 925 115 505 087 743 24 2 165 3 06 088 988 122 487 897 448 535 61 6 434 29 4 22 1 370 493 23 8 62 9 41 358 128 178 418 947 520 058 1501 823 498 4 06 334 307 ... 940 5 36 870 910 715 827 880 894 784 850 073 741 820 25 2 69 8 790 431 65 5 760 61 0 61 2 730 789 569 700 968 5 26 670 Block number j 10 11 1 69 5 0 56 031 27 3 60 0 077 751 115 24 3 134 894 400 522 63 0 933 ... 991 68 9 29 0 68 3 23 0 0 32 093 784 390 041 908 115 65 7 5 86 620 26 4 524 789 935 850 48 27 1 Multiplier a of the generator 1 69 4 409 69 5 521 770 721 304 319 863 449 974 771 69 880 877 994 5 96 6 02 446...

Ngày tải lên: 14/08/2014, 11:21

... 10 −1 2 2 2 10 20 40 20 10 −1 2 6 −4 6 10 20 10 2 6 − 12 −10 −10 10 −5 −10 20 − 12 −10 2 −5 −10 6 −4 (a) (b) 18 18 36 16 4 −5 −3 −5 −10 −7 −8 −1 −3 −5 −4 −3 (c) (d) Figure 6. 14 Let the ... 16: else 17: wumpus(u) ← 18: end if 19: end for 20 : w ← vertex w ∈ neighbourhood (v ) that maximizes wumpus(w ) 21 : if wumpus(w) ≥ ts and arrows left then 22 : wumpus(w) ← 23 : return shoot, u 24 : ... 6- 6 The right-hand rule modelled in the FSM of Figure 6. 6(c) benefits left-handers (including the authors) Is it possible to define an equivalent rule for right-handers? 6- 7 Figure 6. 6(c) describes...

Ngày tải lên: 14/08/2014, 11:21

... Human Vision A 19 - n 10 -08 400 440 480 520 560 60 0 64 0 68 0 Wavelength (nm) ~ Figure nm ~~ A bichromaticyellow: equal light components at 530 nm and 63 0 the sensation of “yellow,” the exact ... 2. 2, 2. 3, and 2. 4 We discuss some of these in greater detail in Chapter 2. 5 COORDINATE SYSTEMS BASED ON HUMAN VISUAL MODELS Opponent-process models Many models are based on opponent-process formulations ... for example, instead of coding ranges of values as shades of green and red, or shades of yellow and blue, select an axis that is a combination of the red-green and the yellow-blue channels, and...

Ngày tải lên: 22/12/2013, 21:17

... Summary and Notes 109 109 110 111 1 12 120 122 1 26 1 26 129 1 32 135 135 137 137 1 42 145 145 149 150 1 52 10 COLOR ICONS: MERGING COLOR AND TEXTURE PERCEPTION FOR INTEGRATED VISUALIZATION OF MULTIPLE ... M-type cones Abnormal L-type cones Achromatic and chromatic grating detection Size and Perceived Color 13 14 16 18 19 20 23 24 25 28 30 Chapter 2. 1 2. 2 2. 3 2. 4 Continuous variations in Pseudo-perceptual ... Vision Color deficiencies Color-Luminance Interactions Summary and Notes COLOR ORGANIZATION AND COLOR MODELS 2. 1 2. 2 2. 3 2. 4 2. 5 2 .6 2. 7 2. 8 Introduction to Color Modeling Overview of Color Specification...

Ngày tải lên: 22/12/2013, 21:17

... 100 101 1 02 103 104 1 06 108 109 109 110 111 1 12 1 12 6. 1 6. 2 6. 3 6. 4 6. 5 6. 6 6. 7 6. 8 6. 9 6. 10 6. 11 6. 12 6. 13 6. 14 6. 15 6. 16 6.17 6. 18 Decision- making and pattern recognition Prediction and production ... 23 1 23 1 23 2 23 3 23 3 23 4 23 8 23 9 List of Algorithms 2. 1 2. 2 2. 3 2. 4 2. 5 2 .6 2. 7 2. 8 2. 9 2. 10 2. 11 2. 12 3.1 3 .2 3.3 3.4 3.5 3 .6 3.7 3.8 3.9 3.10 3.11 3. 12 3.13 3.14 3.15 4.1 4 .2 4.3 4.4 4.5 4 .6 ... 397 39 373 28 9 61 5 68 4 353 057 761 827 749 9 46 925 115 505 087 743 24 2 165 3 06 088 988 122 487 897 448 535 61 6 434 29 4 22 1 370 493 23 8 62 9 41 358 128 178 418 947 520 058 1501 823 498 4 06 334 307...

Ngày tải lên: 08/03/2014, 09:20

... 1 .27 2. 45 ± 0 .27 6. 14 ± 4.89 4 .29 ± 1.13 16. 57 ± 0.37 9 .23 ± 5.01 11. 56 ± 4.91 3 .63 ± 2. 43 0 .65 ± 0.54 3 .22 ± 4 .29 1. 86 ± 0.81 0 .68 ± 0 .61 3. 46 ± 2. 74 10. 42 ± 1 .29 75.13 ± 36. 20 17. 02 ± 6. 24 26 . 37 ... 8.13 23 .00 ± 5.37 1 .63 ± 1. 82 3 .29 ± 0. 16 5.31 ± 1.43 5.50 ± 2 .61 3.31 ± 0.95 5.10 ± 2. 50 4.57 ± 2 .65 4 .60 ± 0.57 13.35 ± 7.38 1.43 ± 0 .61 b 5.38 ± 8. 12 8. 12 ± 0.70 41.79 ± 5.58 23 .11 ± 2. 36 12. 20 ... 12. 20 ± 2. 78b 1.05 ± 0.91 4 .65 ± 0.33 4. 42 ± 1.59 2 .68 ± 0.13 1.58 ± 0.80 1.77 ± 1 .28 14. 26 ± 1.58 7 .22 ± 2. 73 5.07 ± 1.58 34. 42 ± 23 .31 0.40 ± 0.33 3.45 ± 2. 06 1.59 ± 1. 16 1.13 ± 0 . 62 1.41 ±...

Ngày tải lên: 21/06/2014, 17:20

... 100 101 1 02 103 104 1 06 108 109 109 110 111 1 12 1 12 6. 1 6. 2 6. 3 6. 4 6. 5 6. 6 6. 7 6. 8 6. 9 6. 10 6. 11 6. 12 6. 13 6. 14 6. 15 6. 16 6.17 6. 18 Decision- making and pattern recognition Prediction and production ... 23 1 23 1 23 2 23 3 23 3 23 4 23 8 23 9 List of Algorithms 2. 1 2. 2 2. 3 2. 4 2. 5 2 .6 2. 7 2. 8 2. 9 2. 10 2. 11 2. 12 3.1 3 .2 3.3 3.4 3.5 3 .6 3.7 3.8 3.9 3.10 3.11 3. 12 3.13 3.14 3.15 4.1 4 .2 4.3 4.4 4.5 4 .6 ... 397 39 373 28 9 61 5 68 4 353 057 761 827 749 9 46 925 115 505 087 743 24 2 165 3 06 088 988 122 487 897 448 535 61 6 434 29 4 22 1 370 493 23 8 62 9 41 358 128 178 418 947 520 058 1501 823 498 4 06 334 307...

Ngày tải lên: 27/06/2014, 08:20

... 100 101 1 02 103 104 1 06 108 109 109 110 111 1 12 1 12 6. 1 6. 2 6. 3 6. 4 6. 5 6. 6 6. 7 6. 8 6. 9 6. 10 6. 11 6. 12 6. 13 6. 14 6. 15 6. 16 6.17 6. 18 Decision- making and pattern recognition Prediction and production ... 23 1 23 1 23 2 23 3 23 3 23 4 23 8 23 9 List of Algorithms 2. 1 2. 2 2. 3 2. 4 2. 5 2 .6 2. 7 2. 8 2. 9 2. 10 2. 11 2. 12 3.1 3 .2 3.3 3.4 3.5 3 .6 3.7 3.8 3.9 3.10 3.11 3. 12 3.13 3.14 3.15 4.1 4 .2 4.3 4.4 4.5 4 .6 ... 20 21 27 29 30 32 33 35 36 39 39 40 50 51 52 54 55 55 56 59 59 60 61 64 66 68 70 77 77 83 84 87 90 101 107 137 xviii 6. 2 6. 3 7.1 7 .2 7.3 9.1 9 .2 9.3 9.4 9.5 10.1 10 .2 A.1 A .2 A.3 LIST...

Ngày tải lên: 14/08/2014, 11:21

... t +m 20 : end for 21 : x ← m 22 : for i ← (c − 2) Middle horizontal 23 : y←w 24 : for j ← (c − 1) 25 : Hx,y ← Displacement(H, x, y, −w, 0, +w, 0, 0, −w, 0, +w , d) 26 : y ← y+m 27 : end for 28 : ... changes 21 : return R ∪ {p, q } 22 : else Rank exchange 23 : p ← copy p 24 : peerWinner(p ) ← false 25 : rank(p ) ← rank(q) 26 : rank(q ) ← rank(p) 27 : return R ∪ {p , q } 28 : end if 29 : end if Random ... half is interleaved on top ( 123 4 567 8 → 5 1 62 7384) and in an out-shuffle the top half is interleaved on top ( 123 4 567 8 → 15 26 3 748) Take an ordinary deck of 52 cards and sort it into a recognizable...

Ngày tải lên: 14/08/2014, 11:21

... + 21 : score(q) ← score(q) + w 22 : else 23 : score(p) ← score(p) + t 24 : score(q) ← score(q) + t 25 : end if 26 : end for 27 : if n is odd then 28 : player Rn−1 receives a bye 29 : end if 30: end for ... −∞ β = 2 D α = +5 α = 2 β = +∞ 2 α = 2 β = +∞ β = 2 +5 A MAX MIN α = 2 β = +∞ E (b) MAX F MIN α = 2 β = 2 α = 2 β = +∞ −5 MAX A α = +1 β = +∞ MIN G (c) H α = +1 MAX MIN α = 2 β = +1 ... all u ∈ children(v) 23 : if e < β then β ← e end if 24 : t ← Expecti-Alpha-Beta(u, α, β) 25 : if t < e then e ← t end if 26 : if e ≤ α then return e end if 27 : end for 28 : return e 29 : else v is a max...

Ngày tải lên: 14/08/2014, 11:21

... Algorithms and Networking for Computer Games Jouni Smed and Harri Hakonen  20 06 John Wiley & Sons, Ltd 1 16 DECISION- MAKING Previous primitives World Primitive events and states Possible actions ... 1/n 4 -22 In Copper Noughts and Crosses all the chance moves are marked and fixed before the coin is tossed for both of them Let us change this rule: The player marks the first empty square and then ... the destination, exit the final waypoint, and proceed to the destination Algorithms and Networking for Computer Games Jouni Smed and Harri Hakonen  20 06 John Wiley & Sons, Ltd 98 PATH FINDING...

Ngày tải lên: 14/08/2014, 11:21

... 16: if M3 ∈ M3 then / 17: M3 ← M3 ∪ {M3 } 18: end if 19: end if 20 : end for 21 : end for 22 : if < e < then Normalization 23 : for all M ∈ M3 24 : m3 (M) ← m3 (M)/(1 − e) 25 : end for 26 : end if 27 : ... M1 For pairs of members between M1 and M2 8: for all M2 ∈ M2 9: M3 ← M1 ∩ M2 10: p ← m1 (M1 ) · m2 (M2 ) 11: m3 ( ) ← m3 ( ) − p 12: if M3 = ∅ then Excess for imaginary m3 (∅) 13: e ← e+p 14: ... functions and X and Y be the subsets of for which m1 and m2 have non-zero values The combined mass function m3 is X∩Y =Z m1 (X) · m2 (Y ) (7 .6) m3 (Z) = − X∩Y =∅ m1 (X) · m2 (Y ) An implementation for...

Ngày tải lên: 14/08/2014, 11:21

... 19: P ← P \ {q} 20 : end if 21 : end for 22 : end for 23 : until |P | = 24 : w ← the single value in P 25 : return w (B) per message This requires more organizing between the nodes, and, consequently, ... t = 1: p (2, 2) Received at t = 2: t = 1: p(3, 1), v(4, 2) t = 0: p(0, 0), v (2, 2) Figure 9 .6 Dead reckoning comprises prediction and convergence Open circles represent the predicted information ... ongoing and the players can form teams and participate from all over the world Devise a design for this game and list the possible problems stemming from the physical and logical platforms 8-9...

Ngày tải lên: 14/08/2014, 11:21

... players, 60 turns, and 1% gossip (b) 10 players, 60 turns, and 10% gossip CHEATING PREVENTION 22 5 (c) 100 players, 60 turns, and 1% gossip (d) 10 players, 360 turns, and 1% gossip 10 -6 What countermeasures ... commitments (Baughman and Levine 20 01) The commitment must meet two requirements: it cannot be used to infer the 2 16 CHEATING PREVENTION p1 p2 p1 t p2 t s=t s=t t +2 s=t +2 t +3 t +3 s=t +2 t +5 t +4 t ... before sending it to player r Thus, when the message comes out from the delegate, player p cannot tamper with its contents CHEATING PREVENTION p1 21 9 p2 p1 C1 p2 C1 C1 C1 C2 1 C2 A1 C2 A1 C2...

Ngày tải lên: 14/08/2014, 11:21

... effect, 20 2 20 5, 21 1 Bye, 57–58, 60 62 C programming language, 22 9, 24 5, 2 46 C++, 22 9, 2 46 Card trick, 28 , 44 Centralized architecture, 177, 181, 1 86, 22 1, 22 5 Algorithms and Networking for Computer ... 24 1 if, 23 4, 23 5 indices, 2 36 logical operator, 23 3 mod, 23 3 neighbourhood, 24 1 nil, 23 1, 23 7 or else, 23 3 others, 23 5 preamble, 24 2 predecessors, 24 1 rationale, 22 9 23 0 repeat, 2 36 return, 2 36 ... all, 23 5 and then, 23 3 arithmetic operator, 23 3 case, 23 4 children, 24 1 columns, 24 0 copy, 23 1 div, 23 3 INDEX domain, 24 0 else, 23 4 ends, 24 1 enumeration, 23 8 error, 2 36 false, 23 1, 23 7 for, 23 5...

Ngày tải lên: 14/08/2014, 11:21

... survey and measurement were conducted 29 0 fog f 29 2 03 -2 86 05 -28 4 10 -28 4 14 -28 4 29 8 29 4 16- 29 4 Lift Lobby Storey Storey Storey Storey Storey Storey Storey Storey 2 96 10 -2 96 Storey 10 Storey 11 -2 96 ... illustrated in Figure 3.7 3.5 .2 • Objective measurement Equipment used in the measurement 31 28 8 2 86 28 4 28 2 28 0 04 -28 0 27 8 15 -27 8 2 76 16- 2 76 Figure Units where indoor survey and measurement were conducted ... Storey 11 -2 96 06- 29 8 14 -29 8 Storey 11 Storey 12 Storey 13 Storey 14 Storey 15 Storey 16 Lift Lobby 32 274 05 -27 4 11 -27 4 27 2 27 0 13 -27 0 26 8 Storey 16 Storey 15 Storey 14 Storey 13 Storey 12 Storey 11...

Ngày tải lên: 05/10/2015, 21:24

... Emphasis using What, Al1 and It Nominalisation 198 20 0 20 2 20 4 Vocabulary SECTION SECTION 23 2 23 4 Substituting one phrase for another IntensiSing and emphasising Exam practice 14 2 36 Unit fifteen Ernp ... prepositions and prepositional phrases Expressing knowledge and belief 23 0 23 8 Verb cornplernentation Entry test SECTION I SECTION SECTION 2 06 20 8 Exam practice 12 210 Unit thirteen 21 2 23 9 OVERVIEW ... twelve 1 96 22 8 Vocabulary 1 92 2 26 SECTION Vocabulary SECTION A SECTION Reference words Problems and solutions Entry test 1 96 OVERVIEW 197 SECTION SECTION SECTION Fronting Introductory There and It...

Ngày tải lên: 05/10/2012, 09:51

... go back to user for more information) Priority Want 47 48 Activity 6. 2: Determining Requirements, Wants, and Constraints Exercise 2: Determining Constraints ! Review case study for constraints ... business challenge and vision statement, and the case study, you should be able to determine the requirements that are critical from business and user perspectives Requirement Activity 6. 2: Determining ... 46 Activity 6. 2: Determining Requirements, Wants, and Constraints Exercise 1: Determining Requirements ! Review case study for requirements (10 minutes) Review...

Ngày tải lên: 22/10/2013, 16:15

... intention (I and we) offers (I and we) official orders asking for advice I think we shall get back early next week W e shall g v e ourfinal decision tornorrow Shall I g v e you a hand? Al1 the candidates ... timefor dinner (For an alphabetical list of modals with their meanings, see pages 64 -67 .) me h i d be late MEANINGS O F MODAL VERBS Here is a quick reference to the meanings of moda1 verbs For ... tentative) Would you mind giving me a hand? You couldn't give me a hand, could you? Do you think you could give me a hand? Can you give me a hand? Give me a hand, would you? (= more direct) Note...

Ngày tải lên: 25/10/2013, 18:20

... 10 preview (before) a Across: mono semi mulri o 10 non 11 over 12 bi 13 under 17 post 18 in 19 mis 20 pre 21 counter 23 out 25 neo 26 ir Down: de mal sub im inter uni on fore 12 be 14dis r e ... England coming to England to come to England come to England coming to England to come to England come to England coming to England coming to England coming to England O (1) regret (2) intend / plan ... be 14dis r e i a n t e i7pseudo 20 pro 21 22 tri 24 un Q (1) impressive (2) irrelevance (3) consultative (4) inconsiderable (5) invaluable (6) worthless Q a Unfortunately, the optimistic feelings...

Ngày tải lên: 25/10/2013, 18:20