In a real-world environment, changing granularity requirements might dictate changes in primary key selection, and those changes might ultimately require the use of keys.

Write a function that draws a pool ball. this function should take as parameters, the color, the number that should go on the pool ball, and the location of the center of the pool ball. the radius of the pool balls should be pool_ball_radius, and the font of the number should be pool_ball_font. the text of the pool ball font should be white. drawpoolball(color. orange, 5, 100, 100); drawpoolball(color. green, 6, 50, 200); drawpoolball(color. red, 3, 150, 350); drawpoolball(color. blue, 2, 250, 140); to center the numbers on the pool ball, you should use the getwidth() and getheight() methods. you are allowed to call these methods on your text object, such as txt.

You will be given two character arrays of the same size, one will contain a number of ships. ships will move around the character array based on which way they are facing and the route they are on. routes are given in the other array. the route consists of '-' and '|' for straight paths, '\' and '/' for curves, and '+' for intersections. there are ships on these routes. ships always face a direction, '^' for up, '> ' for right, 'v' for down, and '< ' for left. any time the ships hit a '\' or a '/' it will turn as you would expect a ship to turn (e. g. a '^' that moves into a '/' will turn right). at an intersection, ships will always continue straight through. all ships move at the same speed, ships take turns moving and all ships move during one 'tick'. the one in the most top left goes first, followed by those to its right, then the ones in the next row. it iterates along the rows and then down the columns. each ship moves one space on its turn moving along the route. your function needs to return the position of the first collision between two ships and the number of ticks before the crash occurred.

Using least squares fitting, you are to fit the data sets to the following models and solve for the parameters ai , where i is the index of the parameter. the input/output data for the systems are linked in the bblearn site. for each of the systems use matlab to plot the supplied data vs. the model fit on one plot. include your code in the solutions. (a) linear fit "lineardata. mat" y=a1x^3 + a2x^2 + a3x + a4 (b) plant fit "plantdata. mat g(s) = a1/(s + a2)