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//Enter the number of trails below numberOfTrails=10; //Create two new arrays One will //store the //X positions of the mouse and the //other will //hold the Y positions of the mouse pastMouseXPosition=new Array; pastMouseYPosition=new Array; //Load the first six values of each //array with

(A41)

for x [0, 1] The solutions are located at x = 008, x = 025, x = 045, x = 068 and x = 093 Niching problem 5 (modified Himmelblau function): Maximize f (x1 , x2 ) = 200 (x2 + x2 11)2 (x1 + x2 7)2 1 2 (A42)

5

with x1 , x2 [ 5, 5] Maxima are located at ( 281, 313), (30, 20), (358, 185) and ( 378, 328)

Many real-world problems require the simultaneous optimization of a number of objective functions Some of these objectives may be in con ict with one another For example, consider nding optimal routes in data communications networks, where the objectives may include to minimize routing cost, to minimize route length, to minimize congestion, and to maximize utilization of physical infrastructure There is an important trade-o between the last two objectives: minimization of congestion is

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//Put the mouse position into the //past mouse position arrays pastMouseXPosition[countframe] =_xmouse; pastMouseYPosition[countframe] =_ymouse; //Set the x,y position of the mouse //trails according to the //previous x,y positions of the //mouse for (i=1;i<=numberOfTrails;i++) { setProperty("trail"+i,_x, pastMouseXPosition[countframe-i-1]); setProperty("trail"+i,_y, pastMouseYPosition[countframe -i-1]); } countframe=countframe+1; //Don t let the array get too big //Start over after 1000 frames if (countframe>=999){ gotoAndPlay(1); }

Why would you want to do this In the case of letterboxed material, the answer is fairly simple By removing unwanted material from what is captured, the amount of captured data is significantly reduced If you are saving your video to disk, this means:

achieved by reducing the utilization of links A reduction in utilization, on the other hand, means that infrastructure, for which high installation and maintenance costs are incurred, is under-utilized This chapter provides a theoretical overview of multi-objective optimization (MOO), focusing on de nitions that are needed in later chapters The objective of this chapter is by no means to give a complete treatment of MOO The reader can nd more in-depth treatments in [150, 195] Section A81 de nes the multi-objective problem (MOP), and discusses the meaning of an optimum in terms of MOO Section A82 summarizes weight aggregation approaches to solve MOPs Section A83 provides de nitions of Pareto-optimality and dominance, and a lists a few example problems

A81

Less disk space is required for saving the material of interest The throughput, both on capture and in saving to disk, is smaller, and thus capture is less demanding on the host machine When encoding for streaming, either live or from a previously saved avi file, the amount of CPU processing necessary to encode the video is less because there is less data to encode Finally, because there is less data to encode, more valuable encoding bits can be applied to the material of interest, and, thus, quality improves

Let S Rnx denote the nx -dimensional search space, and F S the feasible space With no constraints, the feasible space is the same as the search space Let x = (x1 , x2 , , xnx ) S, referred to as a decision vector A single objective function, fk (x), is de ned as fk : Rnx R Let f(x) = (f1 (x), f2 (x), , fnk (x)) O Rnk be an objective vector containing nk objective function evaluations; O is referred to as the objective space The search space, S is also referred to as the decision space Using the notation above, the multi-objective optimization problem is de ned as: Definition A10 Multi-objective problem: minimize f(x) subject to gm (x) 0, m = 1, , ng hm (x) = 0, m = ng + 1, , ng + nh x [xmin , xmax ]nx

Insert a keyframe in Frame 3 and type or paste it in:

(A43)

Other reasons for cropping include:

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