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systems analysis and design using matlab
Control Of Color Imaging Systems Analysis And Design 1st Edition Lalit K. Mestha - Solutions
Convert the following L*a*b* values to ROMMRGB: L*a*b=[60_0_0] and L*a*b= [40 -16 50]
Find the L*a*b* with respect to paper of a color patch with absolute L*a*b*¼[53 -16 15] . Assume that the paper L*a*b* ¼ [90 0 0].
Assuming a D50 light source, compute the tristimulus values of perfect white.
10.7, obtain the printer gamut for five different paper types. Use measured paper reflectance spectra for each type.
Find DEa*b and DE2000 color difference between the following two color patches L*a*b*2 ¼ [65 120 70] and L*a*b*1 ¼ [68 117 68]. These color patches are shown in Figure A.10
10.6 For a four-color printing system, using the techniques described in Section
10.4 A square sheet 2m on a side situated in the x–y plane and centered on the origin has a charge of 1 nC uniformly distributed over its surface.(a) Find the electric potential V at a distance of 1 m perpendicular from center of the square.(b) Find the electric field ~E at the same point.
9.14 For a four-input three-output controller with TC target as one of the actuator, design the LQR to emphasize the TC target. Show the equation for the steadystate value and plot it as a function of weights used in TC target, with all other weights unchanged.
9.13 Describe the relationship between TC control and a typical inventory management system. Compare the controllers.
9.12 Simulate the TC performance for a system with the state feedback controller and estimator in Example 9.10 when the TC system has a fixed transport lag of 3 and 10 cycles. Apply the Smith predictor and a PI controller and simulate the performance again. Comment on the results.
9.11 For TC system with PI controller ~umin¼0 and ~umax¼a reasonable limit, simulate the situation with integrator windup. Design a antiwindup compensator with high gain L and simulate the integrator recovery and the TC performance with antiwindup compensator.
9.10 Design the gain matrix for the PI controller of Example 9.10 using the LQR method. Compare your TC performance with the pole-placement technique.Comment on the results.
9.9 Consider a scenario where the level 1 loop of Example 9.1 is closed for every print and the level 2 loop of Example 9.6 is closed for every 5 prints.Let level 1 loop be designed for a dead beat response (i.e., closed-loop eigenvalues¼0) and level 2 loop has the following closed-loop poles:a.
9.8 Obtain an inversion using control-based inversion for Problem 9.7. Use identity as the reference TRC. Process magenta separated image through the inverted TRC and printer model.
9.6 For a level 2 controller (Figure 9.8), design the antiwindup compensator gain matrix and show the performance with and without the compensator.
9.4 Consider the development control system of Example 9.5. The development system is characterized by the Jacobian matrix B, at the nominal operating point.Find the expression for the steady-state actuator U. Is the steady-state actuator vector dependent on the gain matrix? Explain the reasons for
9.3 Consider the electrostatic control system of Figure 9.4. The charging system is characterized by the Jacobian matrix B, at the nominal operating point.a. Write the open- and closed-loop transfer functions of the system. Use any convenient signals as inputs and outputs.b. Determine the
9.2 Determine the controllability and observability matrices and then ranks for Problem 9.1. What are the eigenvalues of the open-loop system with and without the integrator in the loop? Is the system open loop stable in both cases? Close the loop with the gain matrix of Problem 9.1. Determine the
As an example, we find the optimal gray levels by solving Equation 9.134 for spatial TRC data. The quality of TRCs derived from equispaced levels is compared with the quality derived from levels determined by using the optimal gray level method. A system is assumed with a small number of gray
Use Equation 9.63 as the TC system and the PI controller with closed-loop poles l1¼0.6, l2¼0.65 and Smith predictor Equations 9.93 through 9.96 to simulate the performance of TC system with a dispenser lag of (a) 3 cycles and (b) 10 cycles.Assume no images are printed during the entire simulation
Let the vector, U0, contain the input patch gray levels (or area coverages) for controlling DMA on the photoconductor for cyan separation shown in Table 9.1.The vector x contains the normalized values of the measurements, the vector xd contains the tone values of the desired reference TRC. Both
Let the vector U0 contain the input patch gray levels (or area coverages) for controlling DMA on the photoconductor for the magenta separation shown in Table 9.1 below. The vector x contains the normalized values of the measurements corresponding to each gray level. This data was produced for a
Let the desired closed-loop poles for level 2 controller be located at p1, p2, p3.(i) Using Equation 9.45 for the Jacobian matrix of Example 9.5 find the gain matrix that will provide equal poles p1¼p2¼p3¼0.3. Use the MATLAB pole-placement algorithm from Ref. [12]. Find the gain matrix using
Let the charge on the photoconductor at the exposure station be decayed by 30 V(i.e., Vh at the exposure station is equal to 550þ30 V). Simulate the transient performance of the controller shown in Example 9.3.
For the open-loop electrostatic system shown in Example 9.1, find the gain matrix K to place the closed-loop poles within the unit circle on the real axis at 0.2 and 0.3.
6.6 Repeat Problem 6.3 using(a) Tetrahedral interpolation(b) Shepard interpolation(c) Moving matrix
6.2 Consider the 1-D LUT shown in Table 6.18.(a) Use linear interpolation to estimate the value of the function at x¼3.8.(b) Repeat part a using(i) The Shepard technique (use L1 norm and m¼1)(ii) The moving matrix with parameters m¼2 and e¼0
Consider the 1-D function y¼P(x) given by the LUT shown in Table 6.9.Use the moving-matrix approach, interpolate, and compute the corresponding value of P(x) at x¼0.5. Use m¼2 and e¼104.
Consider aCMY printer with the 333 forward printermap LUT(CMY!L* a* b*)given in Table 6.8. Use the Shepard interpolation algorithm to find(a) The CMY corresponding to L*¼50, a*¼30, and b*¼40. Use m¼2 and p¼2.(b) The CMY corresponding to L*¼50, a*¼80, and b*¼14. Use m¼2 and p¼2.
Consider the transformation from device-dependent CMY to device-independent L*a*b* given by the LUT shown in Table 6.6.Using tetrahedral interpolation, find the L*a*b* values corresponding to the CMY¼[67 128 54] color patch.
Consider the 1-D LUT given in Table 6.2. Use linear interpolation to interpolate the value of the function at x¼5.4.
=+How software developers use design patterns to avoid duplicating effort by applying
=+the knowledge and experience of other developers to a particular problem. A pattern
=+describes a particular way of doing something that has proved effective in real world projects.
=+• How patterns are used to record knowledge, to document solutions and to aid reuse.
=+• when the client can call the function
=+• what parameters should be passed
=+• what the function does
=+• what kind of result is returned (type and value)
=+• what effect the function has on global data
=+• what action the function takes if there is a problem
=+1. What is a ‘class invariant’? Choose only one option.
=+(a) A class whose source code is versioned and therefore cannot be changed.
=+(b) A class whose objects have constant fields.
=+(c) A condition that will always be true for an instance of the class.
=+2. What is meant by the term ‘design by fear’? Choose only one option.(a) Design is scary.(b) You cannot know when to trust the code.(c) You design a system too quickly because of time pressures.
=+3. What is ‘Design by Contract’? Choose only one option.
=+(a) Designing code as if there were a contract between an object that sends a message and the object that receives it.
=+(b) Reinforcing the contract between every pair of objects by increasing the amount of error-checking.
=+(c) Protecting your software using a contract with a firewall.
=+(d) Designing a software system under contract.
Solve the following second-order DE with initial conditions y(0) ¼ 1 and dy(t) dt t=0 = 4:
Solve the following second-order DE with initial conditions y(0) ¼ 1 and dy(t)dt jt¼0¼ 4:where u(t) is the unit step function. dy(t) dt +3dy(t) dt +2y(t) = eu(t)
Find the Laplace transform and ROC of the signal x(t) ¼ eatu(t).
Find the Laplace transform and ROC of the signal x(t) ¼ u(t), where u(t) is the unit step function
Find the Laplace transform and ROC of the unit impulse signal x(t) ¼ d(t).
Find the inverse Laplace transform of the following function of the right-sided signal x(t) if X(s) === S (s+1)(s+2)
Solve the following second-order DE using Laplace transform. The initial conditions are y(0) ¼ 1 and dy(t)dt jt¼0¼ 4.where u(t) is the unit step function. dy(t). dy(t) +3 dt dt +2y(t)=eu(t)
Solve the following second-order DE with initial conditions y(-2) ¼ =1 and y(-1) ¼ =-1: y(n) = 0.75y(n-1)- 0.125y(n-2)
Consider the following second-order DE:Find the output signal y(n) if the input signal is u(n) ¼ 2(0:4)n for n 0. Assume zero initial conditions, that is, let y(2) ¼ y(1) ¼ 0. = y(n) 0.75y(n-1)-0.125y(n-2)+u(n)
Find the z-transform of the one-sided signal x(n) = anu(n).
Consider the sequence x(n) = an, where 0 Im(z) ROC of X(z) a
Find the z-transform of the following sequence: xin)=2(7)"un) +3()"un)
Find the z-transform of the following sequence x(n) ={(0.6)" n3 (0.6)" n4
Convolve the following two sequences = x(n) [231] and h(n) = [1-232]
Convolve the following two sequences using z-transform: x(n) = (0.5)" u(n) h(n) = (0.7)u(n)
Find the z-transform of the sequence x(n) = nau(n)
Find the initial and the final values of the one-sided signal x(n), if X(z) = z2 z(z -0.7) 1.25z+0.25
Find the inverse z-transform of the following function: X(z) === ROC: |z|> 0.3 (z 0.2)(z 0.3)'
Find the inverse z-transform of the function X(z) ¼ z z0:2 using power series expansion.
Find the inverse z-transform of z3-2.3z2 +0.84z X(z) = z3 1.4z2 +0.63z -0.09
Find the inverse z-transform of the irrational function X(z) = log(1+az1) ROC: [z]>a ==
Find the inverse z-transform of the following function: z X(z) = ROC: z> 0.3 (z -0.2)(z -0.3)'
Consider a SISO system described by the second-order DE:Find the output of this system if the input signal is a unit step function x(n) ¼ u(n)with the initial conditions y(1) ¼ y(0) ¼ 0. y(n+2)+0.4y(n+1)+ 0.03y(n) = x(n)
Find the DTFT of the five-point discrete signal: x(n) [2-3 7-3 2]
Find the inverse DTFT of X( jv) given by = X(jw)-e-for-
Find the z-transform of the sequence x(n1,n2)= a b 1, 20 otherwise
Find the z-transform of the 2-D sequence x(n1,n2) = { 10 otherwise
Find the inverse DSFT of X( jv1, jv2) given bywhere D is the dashed area shown in Figure 3.12. 1 X(jon, jw2) 0 if (1,02) ED otherwise
Find the eigenvalues and eigenvectors of the 2 * 2 matrix A: A= [ -2 2 -24 12
Find the eigenvalues and eigenvectors of the 3 * 3 matrix A: 2.6 1.3 -2.5] A: 0.8 5.4 0.8 1.4 -5 -1
Find the characteristic polynomial of the following 3 * 3 matrix: -2 4 A = 1 6 23 3 1 -1 -5
Find the modal matrix of 2.6 1.3 -2.5" A= 0.8 5.4 -5 0.8 1.4 -1
Diagonalize 3 * 3 matrix A given by 2.6 1.3 -2.5 A=08 5.4 -5 0.8 1.4 -1
Diagonalize matrix A: A = 5 2 UNT 1 0 40 -1 1 6
Diagonalize the 2 * 2 matrix A: A ^= [44] -4
Check the following symmetric matrices for their definiteness: (a) A = [5 ] -2 5 1 (b) B = [2] (c) C = 1 -2 -6 -4 -4 -6
Find the SVD of matrix A given by 2 46 A = -26 -4
Consider the 256 256 cameraman image. If we decompose the image into its SVD components, each component is an image of size 256 256. The ith component of the image is s2i uivi and the whole image isPlot of the 256 singular values of the image normalized with respect to the largest singular value
Find the p ¼ 1, p ¼ 2, p¼1, and Frobenius norm of the following matrix: A = 4 -6
Figure 3.18 shows 2-D scatter data obtained by plotting two neighboring pixels x ¼ f (i, j) and y ¼ f (i þ 1, j) of the LENA image. The estimated covariance matrix of this data set is R = [0.2529 [0.2512 0.2512 0.2525
In this example, the gray-scale cameraman image is used to obtain the principal components (eigenimages). These eigenimages are used as basis functions for compressing the image. The image is partitioned into 8 8 block and the 64 64 sample covariance matrix is formed. There are 64 basis
Show that matrix A satisfies its characteristic polynomial A= 1
Find the matrix polynomial |f(A) A8-8A6 +245 +2A3-3A2 + A+7/ if
Find f (A) = eAt if 0 A= ^= [ 2 - 3 ] 1 -3
Find f (A) = Ak if [0.75 0.5 -0.75 A= 0.5 3 -3 0.5 2 -2
Find SeTdT if
Find eAt if -3 -1 A = 0 -4
Find Ak, if 0.1 0.4 -0.1 A= -0.15 0.6 -0.05 -0.2 0.4 0.2
Let x 2 R3 and f (x) ¼ x31þ 3x21þ 2x2 7x2x3 þ 4. Find af(x) ax
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