Hoosier Burger Bob and Thelma Mellankamp have come to realize that the current p
ID: 3787086 • Letter: H
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Hoosier Burger
Bob and Thelma Mellankamp have come to realize that the current problems with their inventory control, customer ordering, and management reporting systems are seriously affecting Hoosier Burger’s day-to-day operations. At the close of business one evening, Bob and Thelma decide to hire the Build a Better System (BBS) consulting firm. Harold Parker and Lucy Chen, two of BBS’s owners, are frequent Hoosier Burger customers. Bob and Thelma are aware of the excellent consulting service BBS is providing to the Bloomington area.
Build a Better System is a medium-size consulting firm based in Bloomington, Indiana. Six months ago, BBS hired you as a junior systems analyst for the firm. Harold and Lucy were impressed with your résumé, course work, and systems analysis and design internship. During your six months with BBS, you have had the opportunity to work alongside several senior systems analysts and observe the project management process.
On a Friday afternoon, you learn that you have been assigned to the Hoosier Burger project and that the lead analyst on the project is Juan Rodriquez. A short while later, Juan stops by your desk and mentions that you will be participating in the project management process. Mr. Rodriquez has scheduled a meeting with you for 10:00 a.m. on Monday to review the project management process with you. You know from your brief discussion with Mr. Rodriquez that you will be asked to prepare various planning documents, particularly a Gantt chart and a network diagram.
QUESTION: After reviewing the Gantt chart and a network diagram, Mr. Rodriquez feels that alternative generation should take only one-half week and that implementation may take three weeks. Modify your charts to reflect these changes.
Activity Activity No. Requirements collection 2 Requirements structuring 3 Alternative generation 4 Logical design 6 Implementation Time Immediate weeks PredecessorsExplanation / Answer
Linear and circular convolution ar basically totally different operations. However, there ar conditions beneath that linear and circular convolution ar equivalent. Establishing this equivalence has necessary implications. for 2 vectors, x and y, the circular convolution is up to the inverse separate Fourier remodel (DFT) of the merchandise of the vectors' DFTs. Knowing the conditions beneath that linear and circular convolution ar equivalent permits you to use the DFT to expeditiously reason linear convolutions.
The linear convolution of Associate in Nursing N-point vector, x, Associate in Nursingd an L-point vector, y, has length N + L - one.
For the circular convolution of x and y to be equivalent, you need to pad the vectors with zeros to length a minimum of N + L - one before you are taking the DFT. once you invert the merchandise of the DFTs, retain solely the primary N + L - one components.
Create 2 vectors, x and y, and reason the linear convolution of the 2 vectors.
x = [2 one two 1];
y = [1 two 3];
clin = conv(x,y);
The output has length 4+3-1.
Pad each vectors with zeros to length 4+3-1. get the DFT of each vectors, multiply the DFTs, and procure the inverse DFT of the merchandise.
xpad = [x zeros(1,6-length(x))];
ypad = [y zeros(1,6-length(y))];
ccirc = ifft(fft(xpad).*fft(ypad));
The circular convolution of the zero-padded vectors, xpad and ypad, is cherish the linear convolution of x and y. you keep all the weather of ccirc as a result of the output has length 4+3-1.
Plot the output of linear convolution and also the inverse of the DFT product to point out the equivalence.
subplot(2,1,1)
stem(clin,'filled')
ylim([0 11])
title('Linear Convolution of x and y')
subplot(2,1,2)
stem(ccirc,'filled')
ylim([0 11])
title('Circular Convolution of xpad and ypad')
Pad the vectors to length twelve and procure the circular convolution victimisation the inverse DFT of the merchandise of the DFTs. Retain solely the primary 4+3-1 components to provide identical result to linear convolution.
N = length(x)+length(y)-1;
xpad = [x zeros(1,12-length(x))];
ypad = [y zeros(1,12-length(y))];
ccirc = ifft(fft(xpad).*fft(ypad));
ccirc = ccirc(1:N);
The Signal process Toolbox™ code includes a perform, cconv, that returns the circular convolution of 2 vectors. you'll get the linear convolution of x and y victimisation circular convolution with the subsequent code.
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