EA3 Week 6
State Variables & Coupled Diff Eqs
Spring 2017
RQ (Which are True?)
1. The initial conditions of a system
A. are not essential to solve state equations
B. are essential to predict response of system over time
C. must be given at time = zero
2
EA3 Week 6
State Variables & Coupled Diff Eqs
Spring 2017
RQ (Which are True?)
1. The initial conditions of a system
A. are not essential to solve state equations
B. are essential to predict response of system over time
C. must be given at time = zero
2
EA3 Week 10
Electrical Systems RLC Circuits, Resonance,
Mechanical Analogies
Spring 2017
Quiz 3 this week!
RQ (T/F)
1. An analog volume control knob is probably
A. a potentiometer
B. an inductor
C. a variable resistor
2. With respect to the RLC circuit
EA3 Week 9
Electrical Systems
RC Circuits & State Equations
Spring 2017
RQ (T/F)
1. Counting equations and unknowns
A. is hopeless as there are so many
B. is essential to avoid hopeless algebra
C. helps avoid missing a loop or node equation
2. The di
EA3 Week 9
Electrical Systems
RC Circuits & State Equations
Spring 2017
RQ (T/F)
1. Counting equations and unknowns
A. is hopeless as there are so many
B. is essential to avoid hopeless algebra
C. helps avoid missing a loop or node equation
2. The di
EA3 Week 8
Electrical Systems
Spring 2017
RQ (T/F)
1. The conserved quantity in an electrical system is
A. voltage
C. current
B. charge
D. time
2. Voltage
E. is a through variable
F. is measured with a two probed meter
G. is an effort variable analogous
EA3 Week 10
Electrical Systems RLC Circuits, Resonance,
Mechanical Analogies
Spring 2017
Quiz 3 this week!
RQ (T/F)
1. An analog volume control knob is probably
A. a potentiometer
B. an inductor
C. a variable resistor
2. With respect to the RLC circuit
EA3 Week 8
Electrical Systems
Spring 2017
RQ (T/F)
1. The conserved quantity in an electrical system is
A. voltage
C. current (a through variable)
B. charge
D. time
2. Voltage
E. is a through variable
F. is measured with a two probed meter
G. is an effor
function PSNRs = computePSNRs(imgVec, imageDatabase)
%
%
%
This function accepts a vectorized image, imgVec, and a database (matrix)
of vectorized images, imageDatabase as an input and computes the PSNR
between imgVec and each image in the database.
% Ini
function MSE = calcMSE(x1, x2)
%
%
%
This function accepts two column vectors, x1 and x2 as an input, calculates
the Mean Squared Error (MSE) between them and assigns the result to the
output variable MSE
% Makes vectors of the two inputs.
V1 = makeVector
function plotIndices(scrambledIndices, correctIndices)
%
%
%
%
n
%
x
This function accepts the initial scrambledIndices and the final
correctIndices as inputs and creates a figure with two plots for
comparison.
n
=
x
=
is the number of columns of scramble
function minPos = findMinimumErrorPosition(imgVec, imageDatabase)
%
%
%
%
%
This function accepts a vectorized image, imgVec and a database (matrix)
of vectorized images, imageDatabase, as an input and finds the position
(column) in the database that prod
function PSNR = calcPSNR(x1, x2, maxX)
% This function accepts two column vectors, x1 and x2, and a maximum value for
% their elements as an input and calculates the Peak Signal-to-Noise Ratio
% (PSNR) between them. Then it assigns the calculated value to
function [newDatabase, indices] = unScrambleDatabase(imagePath, database)
%
%
%
%
%
This function should read all the images from the folder Player Images and
search for their position (column) in the scrambled database. Then it
should unscramble the data
function vecOut = makeVector(matrixIn)
% This function accepts a matrix, matrixIn, as an input
% and returns the vectorized version of this matrix, vecOut as an output.
% Outputs an error if matrix is not numeric type.
if isnumeric(matrixIn) = false
error
function [image] = readImage(imgName)
%
%
%
%
%
This function accepts the string, imgName, as an input and reads the image
from the hard-drive. If the image has color (3 Channels - RGB) it converts
it to grayscale and normalizes it. The resulting image is
% Homework Program 2
%
%Name:
David Wolff
%Section:
30
%Date:
9/30/16
%Get Inputs:
r = input('Input a value of r (columns) : ');
u = input('Input a value of u (rows): ');
n = input('Input a value of n (# of people) : ');
%Create Matrix A:
A = randi(u,n,r)
function [coords,basis] = find_coords(A)
%FIND_COORDS is a function that calculates the coordinates of A
%
% Given a matrix A, find a basis for the column space and write the
% coordinates of all the columns in terms of these basis vectors.
%
% Input:
%
T
function check_dep(A,B)
%CHECK_DEP checks the linear dependence between two matrices
%
% There are two inputs to check_dep. The matrix A and the matrix B. The
% rows of matrix A and B should be the same. The output are the solution
% vectors for the linea
function mc_span_visualize(A)
%MC_SPAN_VISUALIZE visualizes the span of the matrix A
%
% A is a matrix that should be 2 or 3 dimensional
%
%
%
%
%
Homework Program 5
%
%
m
n
Store the number of rows of "A" in a variable "m", and the number of
columns in t
function [E, Image] = mandelbrot(limits, nx, ny, max_esctime)
%MANDELBROT compute visualization of the mandelbrot set.
%
%
Inputs:
% limits: This is a 4-element vector specifying a rectangular region in the
complex plane. It has the form
% [XMIN XMAX YMIN
function mc_soln_visualize(A,b)
%MC_SOLN_VISUALIZE takes in two functions to show Ax=b
%
% A is a matrix that should be 2 or 3 dimensional
% b is a vector that you are finding a solution for.
%
%
%
%
%
Homework Program 5
%
%
m
n
Store the number of rows o
function Vnew = transform_mesh(A,V,T,C)
%TRANSFORM_MESH performs a linear transformation to matrices of a
%three-dimensional object.
%Applies a linear transformation to a
%triangle mesh, a particular type of three-dimensional graphics model.
%In such a mo
function Image = show_mandelbrot(E,limits)
% SHOW_MANDELBROT Show a color image of the Mandelbrot set.
%
% Inputs:
%
E = matrix of escape times
%
limits = vector of rectangular region limits, as in axis()
%
% Outputs:
%
Image = ny-by-nx-by-3 "truecolor" i
function sig = gen_sig(amplitudes, noise_level)
%GEN_SIG is a function that takes the amplitude and noise_level and outputs
%the noisy signal, y(t).
%
% Inputs:
%
The first input is amplitudes, which is a 1 by 4 array that
%
contains the amplitudes to fin
Practice Final Exam
Engineering Analysis 1
Name
Solution
Section
Clearly circle or box your solutions.
You may leave answers as fractions, where appropriate.
1
1. (16 points total)
(a) The questions below are independent of each other
and use
the
follow
Practice Final Exam
Engineering Analysis 1
Name
Section
Clearly circle or box your solutions.
Check that your exam booklet has 11 pages
You may leave answers as fractions, where appropriate.
1
1. (16 points total)
(a) The questions below are independent o