pierreqi/scilab_code · Datasets at Hugging Face

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// Find the diffusion length // Basic Electronics // By Debashis De // First Edition, 2010 // Dorling Kindersley Pvt. Ltd. India // Example 2-24 in page 101 clear; clc; close; // Given data C_D=1.5*10^-6; // Diffusion capacitance in F D_p=13; // Constant eta=2; // Constant V_t=0.026; // Voltage at room temperature in V I=1*10^-3; // Current in mA // Calculation L_p=sqrt((C_D*D_p*eta*V_t)/I); printf("Diffusion length = %0.3e m",L_p); // Result // Diffusion length = 31.84*10^-3 m

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function y = dosc(x) y = sin(x) - x^2/2 endfunction //Método de la bisección, toma minimo, maximo, funcion, epsilon y epsilon funcion function med = bisecc(mini,maxi,fun,eps,epsf) if(fun(maxi).*fun(mini) > 0) error('Intervalos del mismo signo'); end; m = ((mini+maxi)/2); while(maxi-m > eps | abs(fun(m)) > epsf ) m = ((mini+maxi)/2); if((fun(maxi) < 0 & fun(m) > 0) | (fun(maxi) > 0 & fun(m) < 0)) mini = m; else maxi = m; end; end; med = m; endfunction; //Método Secante, toma primer elemento, segundo, funcion, epsilon y epsilon funcion function seca = secante(fst,snd,func,eps,epsf) if(func(fst).*func(snd) > 0) error('Intervalo incorrecto') end seca = snd - (func(snd) * (snd-fst )/(func(snd)-func(fst))) fst = snd snd = seca while(abs(func(seca)) > epsf | abs(snd-fst) > eps) seca = snd - (func(snd) * (snd-fst )/(func(snd)-func(fst))) fst = snd snd = seca end endfunction //Método de la falsa posición, toma minimo, maximo, funcion, epsilon y epsilon funcion function c = falsapp(a,b,fun,eps,epsf) if(fun(b).*fun(a) > 0) error('Intervalos del mismo signo'); end; c = b - fun(b)*(b-a)/(fun(b)-fun(a)) while(b-c > eps | abs(fun(c)) > epsf ) if((fun(b) < 0 & fun(c) > 0) | (fun(b) > 0 & fun(c) < 0)) a = c; else b = c; end; c = b - fun(b)*(b-a)/(fun(b)-fun(a)) end; endfunction; function y = serie5(x, n) if(n == 0) y = x; return;end; y = serie5(2** (x-1), n-1); return; endfunction function y = serie6(x, c, n) //cotas x = 2 y c = 1, que onda lo de sqrt(z)? if(n == 0) y = x; return;end; y = serie6((x+c*((x^2)-5)),c,n-1); return; endfunction function y = ide(x) y = x endfunction // Ejercicio 8 // ge(f,x,n) es la funcion generica para iterar sobre una funcion dada // g1 a g4 son las funciones del ejercicio function y = g1(x) y = %e^x/3 endfunction function y = g2(x) y = (%e^x - x)/2 endfunction function y = g3(x) y = log(3*x) endfunction function y = g4(x) y = %e^x - 2*x endfunction function y = comp(f, x) y = f(x) endfunction function y = ge(x, n) // Funcion, Punto, Cantidad iteraciones if(n == 0) y = x; return;end; y = ge(g3(x), n-1); return; endfunction // Ejercicio 9 function n = fnueve(X) n = [1 + X(1)^2 - X(2)^2 + %e^X(1)*cos(X(2)); 2*X(1)*X(2) + %e^X(1)*sin(X(2))]; endfunction //Método de Newton function y = newt_mult(fn, X, N) Xn = X; mprintf("X0 = %f\n", Xn) for i = 1:N J = numderivative(fn, Xn); J = 1/J; y = Xn - J*fn(Xn); Xn = y //mprintf("X%d = %0.5f |-| %0.5f\n", i, Xn(1), Xn(2)) end endfunction // Ejercicio 10 function n = fdiez(X) n = [X(1)^2 + X(1)*X(2)^3 - 9; 3*X(1)^2*X(2) - 4 - X(2)^3]; endfunction Xa = [1.2; 2.5] Xb = [-2; 2.5] Xc = [-1.2; -2.5] Xd = [2; -2.5] // Ejercicio 11 function y = fonce(X) y = 2*X(1) + 3*X(2)^2 + %e^(2*X(1)^2 + X(2)^2) endfunction function y = newt_mult_fin(fn, X, eps) //Función, punto inicial, error y = X; Xn = X; i = 0; mprintf("X0 = %f\n", Xn) while(norm(y-Xn) > eps | i == 0) // Norma euclideana Xn = y; J = numderivative(fn, Xn); J = 1/J; y = Xn - J*fn(Xn); i = i+1; mprintf("X%d = %0.12f |-| %0.12f\n", i, y(1), y(2)) end [Jac, Hes] = numderivative(fn, y, [] , 2, "blockmat"); mprintf("Una matriz es definida positiva si sus autovalores son positivos.\n") mprintf("Autovalores del hessiano de fn: ") disp(spec(Hes)) endfunction function ploty(fn,l,in,r) // Funcion, Limite Izq, Intervalo, Limite Der //xdel(winsid()); x = [l:in:r]; y = fn(x); n = size(x); yy = zeros(1,n(2)); plot(x,yy) plot(x,y) a = gca(); a.auto_scale = "off"; endfunction

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/728/CH5/EX5.1/Ex5_1.txt

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Ex5_1.txt

//Caption:Determine the minimum distance between two end plates //Exa:5.1 clc; clear; close; //Given: a=3;//in cm c=3*10^10;//in cm/s f=10*10^9;//in Hz P_01=2.405; d=%pi/sqrt(f^2*4*%pi^2/c^2-(P_01/a)^2); disp(d,'Minimum distance (in cm) =');

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//laplace// printf("since S2 is the referance stator winding , Es2=KVcos0 \n where Es2 & Er are rms voltages \n') k=1 Theta=60; disp(Theta,"Theta=") V=28; disp(V,"V(applied)=") printf("Es2=V*cos(Theta) \n") Es2=k*V*cos(Theta*(%pi/180)); disp(Es2,"Es2=") printf("Es1=k*V*cos(Theta-120)\n") Es1=k*V*cos((Theta-120)*(%pi/180)); // Given Theta=60 in degrees disp(Es1,"Es1=') printf("Es3=k*V*cos(Theta+120) \n") Es3=k*V*cos((Theta+120)*(%pi/180)); disp(Es3,"Es3=') printf("Es31=sqrt(3)*k*Er*sin(Theta)") Es31=sqrt(3)*k*V*sin(Theta*(%pi/180)); disp(Es31,"Es31=') printf("Es12=sqrt(3)*k*Er*sin((Theta-120)") Es12=sqrt(3)*k*V*sin((Theta-120)*(%pi/180)); disp(Es12,"Es12=') printf("Es23=sqrt(3)*k*Er*sin((Theta+120)") Es23=sqrt(3)*k*V*sin((Theta+120)*(%pi/180)); disp(Es23,"Es23=')

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clc; clear; s = poly(0, 's'); g = (10*s + 2000) / (s^3 + 202*s^2 + 490*s + 18001); G = syslin('c', g); scf(); show_margins(G, 'bode'); K = 9 * 18001 / 2000; g1 = K*g; G1 = syslin('c', g1); disp(sprintf("The proportional added to get sse of 10%s = %.4f",'%', K)); [gm, fg] = g_margin(G1); [phm, fp] = p_margin(G1); disp(sprintf("The gain margin of G1(s) is %.8f", gm)); disp(sprintf("The phase margin of G1(s) is %.8f at crossover frequency %.8f Hz",... phm, fp)); g2 = g1 * (s+1); G2 = syslin('c', g2); [gm2, fg2] = g_margin(G2); [phm2, fp2] = p_margin(G2); disp(sprintf("The gain margin of G2(s) is %.8f", gm2)); disp(sprintf("The phase margin of G2(s) is %.8f at crossover frequency %.8f Hz",... phm2, fp2)); G2_cl = G2 /. syslin('c', 1, 1); [z, p, k] = tf2zp(G2_cl); disp(p); scf(); show_margins(G2);

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AFISMC_obsv_beam_new.sce

clc clear A=[-209.443458108676,450.728478793718,0,0,0,0;-450.728478793718,-209.443458108676,0,0,0,0;0,0,-2.25680753735172,467.128469627320,0,0;0,0,-467.128469627320,-2.25680753735172,0,0;0,0,0,0,-0.460680331454473,87.5968670596031;0,0,0,0,-87.5968670596031,-0.460680331454473]; B=[-9.64474367821996,2.65660382188316;-7.76311006325836,-5.61613187293423;-0.117927290005266,0.0527206875641465;0.215603654718808,-0.128623447556356;0.0737096142170821,0.0391127484963023;-0.0970049785583168,-0.0786358112919118]; C=[1.88477251668799,-1.14284793707382,5.77230310808448,47.9525873535994,-5.11774457916239,-49.0010462763406]; H=-B(:,1); dum1=size(A); dum2=size(B); dum3=size(H); dum4=size(C); n=dum1(1,1); m=dum2(1,2); m_H=dum3(1,2); q=dum4(1,1); DeltaA=eye(n,n)*0.01; DeltaB=zeros(n,m); DeltaB(1,1)=0; Bp=inv(B'*B)*B'; Gama=eye(n)-B*Bp; DeltaAm=Bp*DeltaA; DeltaAu=Gama*DeltaB; DeltaBm=Bp*DeltaA; DeltaBu=Gama*DeltaB; bm=norm(DeltaBm,2); bu=norm(DeltaBu,2); a=norm(DeltaA,2); au=norm(DeltaAu,2); am=norm(DeltaAm,2); gm=0; cm=0; function [LME, LMI, OBJ]=AFSMC(XLIST) [X,Kh,eps1,eps2,eps3,eps4,eps5,eps6,eps7,eps8,eps9,gama]= XLIST(:) LME=list(X-X') LMI=list(-([X*A'+A*X+Kh'*B'+B*Kh+(eps1+eps2+eps3+eps4+eps5+eps6+eps7+eps8+eps9)*eye(n,n),zeros(n,m_H),(1+cm)*X'*C',au*X',gm*X'*Bp'*B',am*bu/(1-bm)*X',gm*bu/(1-bm)*X'*Bp',bu/(1-bm)*Kh',gm*X',zeros(n,n+m+n);zeros(m_H,n),-gama^2*eye(m_H,m_H),zeros(m_H,q+n+n+n+m+m+n),H'*Bp'*B',bu/(1-bm)*H'*Bp',H';(1+cm)*C*X,zeros(q,m_H),-eye(q,q),zeros(q,n+n+n+m+m+n+n+m+n);au*X,zeros(n,m_H+q),-eps1*eye(n,n),zeros(n,n+n+m+m+n+n+m+n);gm*B*Bp*X,zeros(n,m_H+q+n),-eps2*eye(n,n),zeros(n,n+m+m+n+n+m+n);am*bu/(1-bm)*X,zeros(n,m_H+q+n+n),-eps4*eye(n,n),zeros(n,m+m+n+n+m+n);gm*bu/(1-bm)*Bp*X,zeros(m,m_H+q+n+n+n),-eps5*eye(m,m),zeros(m,m+n+n+m+n);bu/(1-bm)*Kh,zeros(m,m_H+q+n+n+n+m),-eps7*eye(m,m),zeros(m,n+n+m+n);gm*X,zeros(n,m_H+q+n+n+n+m+m),-eps8*eye(n,n),zeros(n,n+m+n);zeros(n,n),B*Bp*H,zeros(n,q+n+n+n+m+m+n),-eps3*eye(n,n),zeros(n,m+n);zeros(m,n),bu/(1-bm)*Bp*H,zeros(m,q+n+n+n+m+m+n+n),-eps6*eye(m,m),zeros(m,n);zeros(n,n),H,zeros(n,q+n+n+n+m+m+n+n+m),-eps9*eye(n,n)]),X,eps1,eps2,eps3,eps4,eps5,eps6,eps7,eps8,eps9,gama,0.9-gama) OBJ=gama endfunction X0=ones(n,n); Kh0=zeros(m,n); eps1_0=1; eps2_0=1; eps3_0=1; eps4_0=1; eps5_0=1; eps6_0=1; eps7_0=1; eps8_0=1; eps9_0=1; gama_0=1; Init_guess=list(X0,Kh0,eps1_0,eps2_0,eps3_0,eps4_0,eps5_0,eps6_0,eps7_0,eps8_0,eps9_0,gama_0); Ans_LMI=lmisolver(Init_guess, AFSMC); X0=Ans_LMI(1); Kh0=Ans_LMI(2); gama_0=Ans_LMI(12); K0=Kh0*X0^-1; R0=eye(n,n); Lh0=zeros(n,q); eps1_0=1; eps2_0=1; eps3_0=1; eps4_0=1; eps5_0=1; eps6_0=1; eps7_0=1; eps8_0=1; eps9_0=1; eps10_0=1; save('sys.dat',A,B,C,H,n,m,q,m_H,a,am,au,bm,bu,gm,cm,Bp); save('init_vals1.dat',X0,Kh0,K0,R0,Lh0,eps1_0,eps2_0,eps3_0,eps4_0,eps5_0,eps6_0,eps7_0,eps8_0,eps9_0,eps10_0,gama_0); for ii=1:1 clear load('init_vals1.dat','X0','Kh0','K0','R0','Lh0','eps1_0','eps2_0','eps3_0','eps4_0','eps5_0','eps6_0','eps7_0','eps8_0','eps9_0','eps10_0','gama_0'); load('sys.dat','A','B','C','H','n','m','q','m_H','a','am','au','bm','bu','gm','cm','Bp'); gama=gama_0; function [LME, LMI, OBJ]=AFSMCobsv(XLIST) [X,Kh,R,Lh,eps1,eps2,eps3,eps4,eps5,eps6,eps7,eps8,eps9,eps10]= XLIST(:) LME=list(X-X',R-R') LMI=list(-([X'*A'+A*X+Kh'*B'+B*Kh+(eps1+eps2+eps3+eps4+eps5+eps6+eps7)*eye(n,n),-B*K0,H,au*X',gm*X',am*bu/(1-bm)*X',bu/(1-bm)*Kh',gm*X',zeros(n,n+n+m+m+n+n);-K0'*B',A'*R+R*A+C'*Lh'+Lh*C+(eps8*am^2+eps9*au^2)*eye(n,n),R*H,zeros(n,n+n+n+m+n),a*Bp'*B',am*bu/(1-bm)*eye(n,n),bu/(1-bm)*K0',R*B,R,R;H',H'*R,-gama*eye(m_H,m_H),zeros(m_H,n+n+n+m+n+n+n+m+m+n+n);au*X,zeros(n,n+m_H),-eps2*eye(n,n),zeros(n,n+n+m+n+n+n+m+m+n+n);gm*X,zeros(n,n+m_H+n),-eps3*eye(n,n),zeros(n,n+m+n+n+n+m+m+n+n);am*bu/(1-bm)*X,zeros(n,n+m_H+n+n),-eps4*eye(n,n),zeros(n,m+n+n+n+m+m+n+n);bu/(1-bm)*Kh,zeros(m,n+m_H+n+n+n),-eps6*eye(m,m),zeros(m,n+n+n+m+m+n+n);gm*X,zeros(n,n+m_H+n+n+n+m),-eps10*eye(n,n),zeros(n,n+n+m+m+n+n);zeros(n,n),a*B*Bp,zeros(n,m_H+n+n+n+m+n),-eps1*eye(n,n),zeros(n,n+m+m+n+n);zeros(n,n),am*bu/(1-bm)*eye(n,n),zeros(n,m_H+n+n+n+m+n+n),-eps5*eye(n,n),zeros(n,m+m+n+n);zeros(m,n),bu/(1-bm)*K0,zeros(m,m_H+n+n+n+m+n+n+n),-eps7*eye(m,m),zeros(m,m+n+n);zeros(m,n),B'*R',zeros(m,m_H+n+n+n+m+n+n+n+m),-eps8*eye(m,m),zeros(m,n+n);zeros(n,n),R',zeros(n,m_H+n+n+n+m+n+n+n+m+m),-eps9*eye(n,n),zeros(n,n);zeros(n,n),R',zeros(n,m_H+n+n+n+m+n+n+n+m+m+n),-eps10*eye(n,n)]),X,R,eps1,eps2,eps3,eps4,eps5,eps6,eps7,eps8,eps9,eps10) OBJ=[] endfunction Init_guess=list(X0,Kh0,R0,Lh0,eps1_0,eps2_0,eps3_0,eps4_0,eps5_0,eps6_0,eps7_0,eps8_0,eps9_0,eps10_0); Ans_LMI=lmisolver(Init_guess,AFSMCobsv); X0=Ans_LMI(1); Kh0=Ans_LMI(2); R0=Ans_LMI(3); Lh0=Ans_LMI(4); eps1_0=Ans_LMI(5); eps2_0=Ans_LMI(6); eps3_0=Ans_LMI(7); eps4_0=Ans_LMI(8); eps5_0=Ans_LMI(9); eps6_0=Ans_LMI(10); eps7_0=Ans_LMI(11); eps8_0=Ans_LMI(12); eps9_0=Ans_LMI(13); eps10_0=Ans_LMI(14); Kind=K0-Kh0*X0^-1; K0=Kh0*X0^-1; L0=R0^-1*Lh0; save('init_vals1.dat',X0,Kh0,K0,R0,Lh0,eps1_0,eps2_0,eps3_0,eps4_0,eps5_0,eps6_0,eps7_0,eps8_0,eps9_0,eps10_0,gama_0); end for ii=1:1 clear load('init_vals1.dat','X0','Kh0','K0','R0','Lh0','eps1_0','eps2_0','eps3_0','eps4_0','eps5_0','eps6_0','eps7_0','eps8_0','eps9_0','eps10_0','gama0'); load('sys.dat','A','B','C','H','n','m','q','m_H','a','am','au','bm','bu','gm','cm','Bp'); function [LME, LMI, OBJ]=AFSMCobsv(XLIST) [X,Kh,R,Lh,eps1,eps2,eps3,eps4,eps5,eps6,eps7,eps8,eps9,eps10,gama]= XLIST(:) LME=list(X-X',R-R',K0*X-Kh) LMI=list(-([X'*A'+A*X+Kh'*B'+B*Kh+(eps1+eps2+eps3+eps4+eps5+eps6+eps7)*eye(n,n),-B*K0,H,au*X',gm*X',am*bu/(1-bm)*X',bu/(1-bm)*Kh',gm*X',zeros(n,n+n+m+m+n+n);-K0'*B',A'*R+R*A+C'*Lh'+Lh*C+(eps8*am^2+eps9*au^2)*eye(n,n),R*H,zeros(n,n+n+n+m+n),a*Bp'*B',am*bu/(1-bm)*eye(n,n),bu/(1-bm)*K0',R*B,R,R;H',H'*R,-gama*eye(m_H,m_H),zeros(m_H,n+n+n+m+n+n+n+m+m+n+n);au*X,zeros(n,n+m_H),-eps2*eye(n,n),zeros(n,n+n+m+n+n+n+m+m+n+n);gm*X,zeros(n,n+m_H+n),-eps3*eye(n,n),zeros(n,n+m+n+n+n+m+m+n+n);am*bu/(1-bm)*X,zeros(n,n+m_H+n+n),-eps4*eye(n,n),zeros(n,m+n+n+n+m+m+n+n);bu/(1-bm)*Kh,zeros(m,n+m_H+n+n+n),-eps6*eye(m,m),zeros(m,n+n+n+m+m+n+n);gm*X,zeros(n,n+m_H+n+n+n+m),-eps10*eye(n,n),zeros(n,n+n+m+m+n+n);zeros(n,n),a*B*Bp,zeros(n,m_H+n+n+n+m+n),-eps1*eye(n,n),zeros(n,n+m+m+n+n);zeros(n,n),am*bu/(1-bm)*eye(n,n),zeros(n,m_H+n+n+n+m+n+n),-eps5*eye(n,n),zeros(n,m+m+n+n);zeros(m,n),bu/(1-bm)*K0,zeros(m,m_H+n+n+n+m+n+n+n),-eps7*eye(m,m),zeros(m,m+n+n);zeros(m,n),B'*R',zeros(m,m_H+n+n+n+m+n+n+n+m),-eps8*eye(m,m),zeros(m,n+n);zeros(n,n),R',zeros(n,m_H+n+n+n+m+n+n+n+m+m),-eps9*eye(n,n),zeros(n,n);zeros(n,n),R',zeros(n,m_H+n+n+n+m+n+n+n+m+m+n),-eps10*eye(n,n)]),X,R,eps1,eps2,eps3,eps4,eps5,eps6,eps7,eps8,eps9,eps10,gama) OBJ=gama endfunction Init_guess=list(X0,Kh0,R0,Lh0,eps1_0,eps2_0,eps3_0,eps4_0,eps5_0,eps6_0,eps7_0,eps8_0,eps9_0,eps10_0,gama0); Ans_LMI=lmisolver(Init_guess,AFSMCobsv); X0=Ans_LMI(1); Kh0=Ans_LMI(2); R0=Ans_LMI(3); Lh0=Ans_LMI(4); eps1_0=Ans_LMI(5); eps2_0=Ans_LMI(6); eps3_0=Ans_LMI(7); eps4_0=Ans_LMI(8); eps5_0=Ans_LMI(9); eps6_0=Ans_LMI(10); eps7_0=Ans_LMI(11); eps8_0=Ans_LMI(12); eps9_0=Ans_LMI(13); eps10_0=Ans_LMI(14); gama0=Ans_LMI(15); Kind=K0-Kh0*X0^-1; K0=Kh0*X0^-1; L0=R0^-1*Lh0; save('init_vals1.dat',X0,Kh0,K0,R0,Lh0,eps1_0,eps2_0,eps3_0,eps4_0,eps5_0,eps6_0,eps7_0,eps8_0,eps9_0,eps10_0,gama0); end

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clear; clc; //To find Approx Value function[A]=approx(V,n) A=round(V*10^n)/10^n;//V-Value n-To what place funcprot(0) endfunction //Exampl_14.8 //Solution : Program to Determine the Phase equlibrium data for the System A12=4.62424; A21=3.35629; alpha12=3.78608; alpha21=1.81775; B11=-996; B22=-1245; B12=-567; P1_sat=103.264;//[kPa] P2_sat=5.633;//[kPa] T=308.15;//[K] R=8314; //G_E/RT = T1 = A21*x1 + A12*x2 - Q //V1=exp[(x2^2)*[A12 + (2*(A21-A12)*x1) - Q - (x1*(dQ/dx1))]] //V2=exp[(x1^2)*[A21 + (2*(A12-A21)*x2) - Q + (x2*(dQ/dx1))]] //Q=(alpha12*x1*alpha21*x2)/((alpha12*x1) + (alpha21*x2)) //dQ/dx1=dQ_x1=(alpha12*alpha21*(alpha21*x2^2 - alpha12*x1^2))/((alpha12*x1 + alpha21*x2)^2) //P=(x1*V1*P1_sat)/si1 + (x2*V2*P2_sat)/si2 //d12=2B12-B11-B22 //si1=exp[((B11*(P-P1_sat)) + (P*y2^2*d12)))/RT] //si2=exp[((B22*(P-P2_sat)) + (P*y1^2*d12)))/RT] //y1=(x1*V1*P1_sat)/(si1*P) //y2=(x2*V2*P2_sat)/(si2*P) //BUBL P x1=[0.01:0.01:0.99]; x2=1-x1; for(i=1:99) si1=1;//Assumed si2=1;//Assumed dP=100; while(dP>0.0001) Q=approx(((alpha12*x1(i)*alpha21*x2(i))/((alpha12*x1(i)) + (alpha21*x2(i)))),4); dQ_x1=approx((alpha12*alpha21*((alpha21*((x2(i))^2)) - (alpha12*((x1(i))^2))))/(((alpha12*x1(i)) + (alpha21*x2(i)))^2),4); V1=approx(exp((x2(i)^2)*(A12 + (2*(A21-A12)*x1(i)) - Q - (x1(i)*dQ_x1))),4); V2=approx(exp((x1(i)^2)*(A21 + (2*(A12-A21)*x2(i)) - Q + (x2(i)*dQ_x1))),4); Pi=approx((((x1(i)*V1*P1_sat)/si1) + ((x2(i)*V2*P2_sat)/si2)),4); y1=approx((x1(i)*V1*P1_sat)/(si1*Pi),4); y2=approx((x2(i)*V2*P2_sat)/(si2*Pi),4); d12=(2*B12)-B11-B22; si1=approx(exp(((B11*(Pi-P1_sat))+(Pi*(y2^2)*d12))/(R*T)),4); si2=approx(exp(((B22*(Pi-P2_sat))+(Pi*(y1^2)*d12))/(R*T)),4); Pf=approx(((x1(i)*V1*P1_sat)/si1) + ((x2(i)*V2*P2_sat)/si2),4); dP=abs(Pf-Pi); end P(i)=Pf; y(i)=y1; end for(i=1:99) if(P(i)>104.61) P(i)=%nan; end end x1(100)=1; y(100)=1; P(100)=P1_sat; subplot(1,2,1) P_=[5.633 104.6]; x=[0 0.0117]; plot2d(x,P_,rect=[0,0,0.02,104.6]) P_=[104.6 104.6]; x=[0 0.02]; plot(x,P_,'r') P_=[5.633 5.633]; y1=[0 0.02]; plot(y1,P_,'b') P_=[104.6 120]; xa=[0.0117 0.0117]; plot(xa,P_,'g--') legend('P vs x1','P* = 104.6kPa','P vs y1','x1_a=0.0117') xtitle('P-x-y','x1,y1','P/kPa') P_=[100 120]; y1=[0.02 0.02]; plot(y1,P_,'w') subplot(1,2,2) P_=[104.6 104.6]; x=[0.943 0.96]; plot(x,P_,'r') P_=[104.3 104.6]; y1=[0.946 0.946]; plot(y1,P_,'b--') P_=[104.6 104.8]; xb=[0.95 0.95]; plot(xb,P_,'g--') plot2d(x1,P,rect=[0.943,103,1,105]) plot2d(y,P,style=3,rect=[0.943,103,1,105]) legend('P*=104.6kPa','y1*=0.946','x1_b=0.95','P vs x1','P vs y1') xtitle('P-x-y(Ether Rich Region)','x1,y1','P/kPa') //End

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/3745/CH1/EX1.62/Ex1_62.sce

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Ex1_62.sce

// Ex 62 Page 403 clc;clear;close; // Given R=150;//ohm Vrms=200;//V Rd1=65;//ohm Rd2=140;//ohm Vm=Vrms/sqrt(2);//V //v=Vm*sin(theta) Rf=R+Rd1;//ohm Rb=R+Rd2;//ohm //i_f=v/Rf;//A //i_b=v/Rb;//A Irms=1/2/%pi*(integrate('(sqrt(2)*sin(theta))**2','theta',0,%pi)+integrate('(sqrt(2)/3*sin(theta))**2','theta',%pi,2*%pi)) Iav=1/2/%pi*(integrate('sqrt(2)*sin(theta)','theta',0,%pi)+integrate('sqrt(2)/3*sin(theta)','theta',%pi,2*%pi)) printf("reading of ammeter 1= %.2f A",Irms) printf("\n reading of ammeter 2 = %.2f A",Iav) P=1/2*(Vrms**2/Rf+Vrms**2/Rb);//W printf("\n\n Power taken from the mains = %.1f W",P) Pc=Irms**2*R;//W Pd=P-Pc;//W printf("\n Power dissipated in rectifying device = %d W",Pd) //Answer wrong in the textbook.

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kiks_draw_remoterobot.sci

function [] = kiks_draw_remoterobot(id,kx,ky,ang,n,r) // Display mode mode(0); // Display warning for floating point exception ieee(1); // ----------------------------------------------------- // (c) 2000-2004 Theodor Storm <theodor@tstorm.se> // http://www.tstorm.se // ----------------------------------------------------- global("KIKS_MMPERPIXEL","KIKS_ARENA_HDL","KIKS_REMOTEKHEPDIOD_HDL","KIKS_REMOTEKHEPWHL_HDL","KIKS_REMOTEKHEP_HDL","KIKS_MMPERPIXEL","KIKS_NR_HDL","KIKS_LINVIS_GR_HDL","KIKS_LINVIS_HDL","KIKS_ROBOT_MATRIX","KIKS_RBT_BODY","KIKS_RBT_LAMP","KIKS_RBT_DIOD","KIKS_RBT_HDL","KIKS_RBTSENS_HDL","KIKS_RBTWHL_HDL","KIKS_RBTLMP_HDL","KIKS_RBTDIOD_HDL","KIKS_WALL_WIDTH","KIKS_WALL_RENDER","KIKS_PROX_DIR","KIKS_PROX_ANG"); scale = mtlb_double(r)/29; KIKS_WALL_WIDTH_SCALED = mtlb_double(KIKS_WALL_WIDTH)/mtlb_double(KIKS_MMPERPIXEL); KIKS_WALL_RENDER_SCALED = mtlb_double(KIKS_WALL_RENDER)/mtlb_double(KIKS_MMPERPIXEL); if ~isempty(n) then // !! L.15: Unknown function kiks_remotekheppatch not converted, original calling sequence used KIKS_REMOTEKHEP_HDL = mtlb_i(KIKS_REMOTEKHEP_HDL,id,kiks_remotekheppatch(id)); // !! L.16: Matlab function sprintf not yet converted, original calling sequence used // !! L.16: Matlab function patch not yet converted, original calling sequence used KIKS_REMOTEKHEPWHL_HDL = mtlb_i(KIKS_REMOTEKHEPWHL_HDL,id,patch("Facecolor",[0.5,0.5,0.6],"EdgeColor","none","Erase","xor","tag",sprintf("remoteKhep %d",id))); // !! L.17: Matlab function sprintf not yet converted, original calling sequence used // !! L.17: Matlab function patch not yet converted, original calling sequence used KIKS_REMOTEKHEPDIOD_HDL(id,1) = patch("Facecolor",[0.5,0.5,0.6],"EdgeColor","none","Erase","xor","tag",sprintf("remoteKhep %d",id)); // !! L.18: Matlab function sprintf not yet converted, original calling sequence used // !! L.18: Matlab function patch not yet converted, original calling sequence used KIKS_REMOTEKHEPDIOD_HDL(id,2) = patch("Facecolor",[0.5,0.5,0.6],"EdgeColor","none","Erase","xor","tag",sprintf("remoteKhep %d",id)); end; // ! L.21: mtlb($) can be replaced by $() or $ whether $ is an M-file or not // ! L.21: mtlb($) can be replaced by $() or $ whether $ is an M-file or not // !! L.21: Matlab function set not yet converted, original calling sequence used // L.21: Name conflict: function name changed from set to %set %set(mtlb_e(KIKS_REMOTEKHEP_HDL,id),"xdata",mtlb_a(mtlb_s(mtlb_a(mtlb_double(KIKS_RBT_BODY(1,mtlb_imp(1,3,mtlb_double(mtlb($)))))*(mtlb_double(r)/mtlb_double(KIKS_MMPERPIXEL)),floor(mtlb_double(kx)/mtlb_double(KIKS_MMPERPIXEL))),floor(KIKS_WALL_WIDTH_SCALED)),floor(KIKS_WALL_RENDER_SCALED)),"ydata",mtlb_a(mtlb_s(mtlb_a(mtlb_double(KIKS_RBT_BODY(2,mtlb_imp(1,3,mtlb_double(mtlb($)))))*(mtlb_double(r)/mtlb_double(KIKS_MMPERPIXEL)),floor(mtlb_double(ky)/mtlb_double(KIKS_MMPERPIXEL))),floor(KIKS_WALL_WIDTH_SCALED)),floor(KIKS_WALL_RENDER_SCALED))); whl_xcoord = ([-15,-15,20]/mtlb_double(KIKS_MMPERPIXEL))*scale; whl_ycoord = ([-10,10,0]/mtlb_double(KIKS_MMPERPIXEL))*scale; // !! L.25: Matlab function set not yet converted, original calling sequence used // L.25: Name conflict: function name changed from set to %set %set(mtlb_e(KIKS_REMOTEKHEPWHL_HDL,id),"xdata",mtlb_a(mtlb_s(mtlb_a(mtlb_s(whl_xcoord*cos(-mtlb_double(ang)),whl_ycoord*sin(-mtlb_double(ang))),floor(mtlb_double(kx)/mtlb_double(KIKS_MMPERPIXEL))),floor(KIKS_WALL_WIDTH_SCALED)),floor(KIKS_WALL_RENDER_SCALED)),"ydata",mtlb_a(mtlb_s(mtlb_a(mtlb_a(whl_xcoord*sin(-mtlb_double(ang)),whl_ycoord*cos(-mtlb_double(ang))),floor(mtlb_double(ky)/mtlb_double(KIKS_MMPERPIXEL))),floor(KIKS_WALL_WIDTH_SCALED)),floor(KIKS_WALL_RENDER_SCALED))); endfunction

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Mux8Way16.tst

// This file is part of the materials accompanying the book // "The Elements of Computing Systems" by Nisan and Schocken, // MIT Press. Book site: www.nand2tetris.org // File name: projects/00/Mux8Way16.tst load Mux8Way16.hdl, output-file Mux8Way16.out, compare-to Mux8Way16.cmp, output-list a%X1.4.1 b%X1.4.1 c%X1.4.1 d%X1.4.1 e%X1.4.1 f%X1.4.1 g%X1.4.1 h%X1.4.1 sel%D2.1.2 out%X1.4.1; set a 0, set b 0, set c 0, set d 0, set e 0, set f 0, set g 0, set h 0, set sel 0, eval, output; set sel 1, eval, output; set sel 2, eval, output; set sel 3, eval, output; set sel 4, eval, output; set sel 5, eval, output; set sel 6, eval, output; set sel 7, eval, output; set a %X1234, set b %X2345, set c %X3456, set d %X4567, set e %X5678, set f %X6789, set g %X789a, set h %X89ab, set sel 0, eval, output; set sel 1, eval, output; set sel 2, eval, output; set sel 3, eval, output; set sel 4, eval, output; set sel 5, eval, output; set sel 6, eval, output; set sel 7, eval, output;

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ex_4.sce

//Example (pg no.136) // x1 + 2(x2) = 3 //2(x1) + 4(x2) = 6 A=[1 2;2 4] //coefficient matrix of above equations b=[3 6]' x=A\b //for corresponding homogenous system // x1 + 2(x2) = 0 //2(x1) + 4(x2) = 0 A=[1 2;2 4] //coefficient matrix of above equations b=[0 0]' x=A\b

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/2048/DEPENDENCIES/armac1.sci

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armac1.sci

// Scilab description of an ARMAX process // Form: // A(q) y(t) = [B(q)/F(q)] u(t-nk) + [C(q)/D(q)] e(t) // [A(q)*F(q)*D(q)] y(t) = [B(q)*D(q)] u(t-nk) + [C(q)*F(q)]e(t) // A1(q) = [A(q)*F(q)*D(q)] // B1(q) = [B(q)*D(q)] // D1(q) = [C(q)*F(q)] function process_ar = armac1(a,b,c,d,f,sig) ny = 1; nu =1; a1 = convol(convol(a,f),d); b1 = convol(b,d); d1 = convol(c,f); process_ar = armac(a1,b1,d1,ny,nu,sig); endfunction;

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/2384/CH8/EX8.9/ex8_9.sce

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ex8_9.sce

// Exa 8.9 clc; clear; close; format('v',7) // Given data N = 500; R = 4;// in ohm d_mean = 0.25;// in m a = 700;// in mm^2 a = a * 10^-6;// in m V = 6;// in V miu_r = 550; miu_o = 4*%pi*10^-7; l_i = %pi*d_mean;// in m S = l_i/(miu_o*miu_r*a);// in AT/Wb I = V/R;// in A // Calculation of mmf mmf = N*I;// in AT // total flux phi = mmf/S;// in Wb phi = phi * 10^6;// in µWb disp(phi,"The total flux in the ring in µWb is"); // Note: In the book the value of flux calculated correct in µWb but at last they print only in Wb, so the answer in the book is wrong.

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ex6.sce

//ques6 //isothermal steady state processes clear clc //from table A.2 P1=8;//pressure at state 1 in MPa P2=0.5;//pressure at state 2 in MPa T1=150;//Temperature at state 1 in K Pr1=P1/3.39;//Reduced pressure at state 1 Pr2=P2/3.39;//Reduced pressure at state 2 Tr1=T1/126.2;//Reduced temperature T2=125;//temperature at state 2 //from compressibility chart h1*-h1=2.1*R*Tc //from zero pressure specific heat data h1*-h2*=Cp*(T1a-T2a) //h2*-h2=0.5*R*Tc //this gives dh=h1-h2=-2.1*R*Tc+Cp*(T1a-T2a)+0.15*R*Tc R=0.2968;//gas constant for given substance Tc=126.2;//K, Constant temperature Cp=1.0416;//heat enthalpy at constant pressure in kJ/kg dh=(2.35)*R*Tc+Cp*(T2-T1);// printf('Enthalpy change = %.2f kJ/kg \n',dh); //change in entropy //ds= -(s2*-s2)+(s2*-s1*)+(s1*-s1) //s1*-s1=1.6*R //s2*-s2=0.1*R //s2*-s1*=Cp*log(T2/T1)-R*log(P2/P1) //so ds=1.6*R-0.1*R+Cp*log(T2/T1)-R*log(P2/P1); printf(' Entropy Change = %.4f kJ/kg.K ',ds);

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exa_2_2.sce

// Example 2.2 clc; clear; close; // Given data guagePressure= 1500;// in kN/m^2 atmPressure= 100;// in kN/m^2 P1= guagePressure+atmPressure;// in kN/m^2 V1= 0.1;// in m^3 V2= 0.4;// in m^3 // Formula P1*V1 = P2*V2 P2= P1*V1/V2;// in kN/m^2 NewGuagePressure= P2-atmPressure;// in kN/m^2 disp(NewGuagePressure,"New guage pressure in kN/m^2 is : ")

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/671/CH7/EX7.14/7_14.sce

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7_14.sce

uo=(4*%pi)*1E-7 ur=1600 lc=160/100 lg=0.8/1000 A=5/10000 N=1200 Rc=lc/(uo*ur*A) Rg=lg/(uo*A) R=Rc+Rg L=N*N/R disp(L)

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data10_9.sci

//(Springs) Example 10.9 //Diameter of the safety valve dia (mm) dia = 50 //Blow off pressure of the valve Pb (MPa) Pb = 1.5 //Initial compression of the spring delta1 (mm) delta1 = 25 //Maximum lift of the valve l (mm) l = 10 //Spring index C C = 6 //Ultimate tensile strength of the spring Sut (N/mm2) Sut = 1500 //For plain ends, endtype = 1 //For plain ends(ground), endtype = 2 //For square ends, endtype = 3 //For square ends(ground), endtype = 4 endtype = 4 //Modulus of rigidity of the spring G (N/mm2) G = 81370 //The permissible shear stress for the spring wire is r% of the Sut r = 30 //Gap between adjacent turns of the spring g (mm) g = 2

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/workspace/MissionC1.sce

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Exia-epickiwi/exolife

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MissionC1.sce

//Load scripts from folder funcprot(0) getd("../scripts"); //Global variables imgPos = "../images/"; //The position of the source images renderPos = "render/"; //The folder where the render images will be saved //Load image imgin = readpbm(imgPos+"Contours.pbm"); imgout = normalisation(contours(imgin)) //Show the coordinates of the brightest color of the image loaded writepbm(imgout,renderPos+"MissionC1.pbm")

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ex4_13.sce

// Example 4.13, Page No-226 clear clc // TF is H(S)= 4/(s^2 + 3.3*s + 0.9) // This is a theorotical problem

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/UFRGS/calcNumerico/area2/P2_numerico_oberdan/M9 - Riemann-Simpson-Trapezio-erros/riemmann.sci

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andredxc/Files

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riemmann.sci

clear /** Somas de riemmann a esquerda * a: limite esquerdo * b: limite direito * n: numero de iteracoes * * S: area apos integrar a funcao */ function S=riemmann(a,b,n) h=(b-a)/n; x=linspace(a,b,n+1); S=0; for i=1:n A=f(x(i))*h; S=S+A; end endfunction // Devolve a integral definida da funcao para comparacao com metodos iterativos function v=integral(limiteEsquerda, limiteDireita, funcao) v = intg(limiteEsquerda, limiteDireita, funcao); endfunction // valor da integral fica em riemmann(inicio, fim, numero_intervalos) // valor da funcao fica em f(valor_x) //q1 Funcao a ser integrada e' dada por y function y=f(x) y=cos(2*x); endfunction disp("Questão 1"); disp(riemmann(2,3,40)); disp("---------"); //q5 Funcao a ser integrada e' dada por y function y=f(x) y=cos(4*x+11); endfunction disp("Questão 5"); disp(riemmann(0,1,10000000)) //tentativa e erro. disp("---------"); //q8 Funcao a ser integrada e' dada por y function y=f(x) y=cos(4*x+11); endfunction disp("Questão 8"); disp(riemmann(2,3,3799)) //tentativa e erro. disp("---------");

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EX17_42.sce

clc;funcprot(0);//EXAMPLE 17.42 // Initialisation of Variables Cpw=4.18;..............//Specific heat of water in kJ/kgK n=1;................//No of cylinders N=350;.......//Engine rpm pmi=2.8;..........//Mean effective pressure in bar bl=590;..........//Brake load in N mf=4.3;............//Fuel consumption in kg mw=500;..............//Mass of cooling water tw1=25;...............//Water inlet temperature in C tw2=50;................//Water outlet temperature in C ma=33;..................//Mass of air used per kg of fuel in kg tr=25;.................//Room temperature in C tg=400;.................//Exhaust temperature in C D=0.22;.................//Engine bore in m L=0.28;.................//Engine stroke in m Db=1;......................//Brake drum diameter in m C=43900;...................//Calorirfic value of fuel in kJ/kg Cps=2.09;..................//Specific heat of steamm in exhaust in kJ/kgK Cpg=1.0;...................//Specific heat of dry exhaust gases in kJ/kgK k=1;....................//Two stroke engiine perh=15;...................//Percentage of hydrogen //Calculations IP=(n*pmi*N*D*D*L*k*10*(%pi/4))/6;...................//Indicated power in kW disp(IP,"Indicated power in kW:") BP=(bl*%pi*Db*N)/(60*1000);......................//Brake power in kW disp(BP,"Brake power in kW:") //Heat supplied hf=(mf/60)*C;................//heat supplied by fuel hip=IP*60;...........//Heat equivalent of BP in kJ/min hcw=(mw/60)*Cpw*(tw2-tw1);..........//Heat carried away by cooling water mg=(mf+(mf*ma))/60;....................//Mass of exhaust gases in kg/min mst=9*(perh/100)*(mf/60);..................//Mass of steam formed mdg=mg-mst;..............................//Mass of dry exhaust gases per min hg=(mdg)*Cpg*(tg-tr);..........//Heat carried by exhaust gasses hst=mst*(417.5+2257.9+(Cps*(400-99.6)));....................//Heat carried by exhaust steam, the obtained values are from steam tables at NTP mg=mf+(ma*mf);....................//Mass of exhaust gases in kg/min ha=round(hf)-round(hip+hg+hst+hcw);............//Unaccounted heat pf=100;pip=(hip/hf)*100;pcw=(hcw/hf)*100;pg=(hg/hf)*100;pa=(ha/hf)*100;pst=(hst/hf)*100; printf("\n\n") printf("HEAT BALANCE TABLE\n") printf("_______________________________________________________________________\n") printf("Item kJ Percent\n") printf("_______________________________________________________________________\n") printf("Heat supplied by fuel %d %f\n",hf,pf) printf("Heat absorbed in IP %d %f\n",hip,pip) printf("Heat taken away by cooling water %d %f\n",hcw,pcw) printf("Heat carried away by dry exhaust gases %d %f\n",hg,pg) printf("Heat carried away by steam in exhaust gases %d %f\n",hst,pst) printf("Unaccounted heat %d %f\n",ha,pa) printf("_____________________________________________________________________")

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clear all; clc; s_p = 200;//steam pressure in lb/in^2 l = 4;//length in inches b = 4;//breadth in inches p = 14000;//permissible streaa in lb/in^2 P = s_p*l*b;//Pull on each bolt in lb-wt A = P/p ;//necessary area of bolt-section d = sqrt(4*A/%pi) ;//minimum diameter in inches printf('The minimum diameter d of each stay bolt = %0.2f inch',d);

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//developed in windows XP operating system 32bit //platform Scilab 5.4.1 clc;clear; //example 3.17w //calculation of time taken and position of the arrival on opposite bank //given data dyaxis=.5//displacement(in km) along Y axis vrg=2//velocity(in km/h) of the river with respect to ground vmr=3////velocity(in km/h) of the man with respect to river theta1=30//angle(in degree) of vmr with Y axis theta2=90//angle(in degree) of vrg with Y axis //calculation vyaxis=(vmr*cosd(theta1))+(vrg*cosd(theta2));//velocity along Y axis i.e taking y component in equation vmg=vmr+vrg t=dyaxis/vyaxis; vxaxis=(-vmr*sind(theta1))+(vrg*sind(theta2));//velocity along X axis i.e taking x component in equation vmg=vmr+vrg dxaxis=vxaxis*t; printf('time taken by the swimmer to cross the river is %3.2f hour',t); printf('\ndisplacement of the swimmer along X axis is %3.4f km',dxaxis);

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h=100; t=10; P=0:10:100; N_o=grand(1,10000,"exp",2); S=10^(P/10); for i=1:1:length(S) K=(S(i)*h^2/N_o); X(i)=sum(K<t)/10000; end plot2d("ln",X,S,style=2); plot(X,S);

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//Part (a) r= 0.113; //bond length, nm Mc= 1.99*(10^(-26)); //mass of C12, kg Mo= 2.66*(10^(-26)); //mass of O16, kg Mco= (Mc*Mo)/(Mc+Mo); //mass of CO, kg I= Mco*((r*(10^(-9)))^2); //moment of inertia, kg.m^2 J=1; //lowest rotational state h= 6.63*(10^(-34)); //Planck's constant, J.s hbar= h/(2*(%pi)); //reduced Planck's constant, J.s E1= (J*(J+1)*(hbar^2))/(2*I); //energy corresponding to state J=1, J e= 1.6*(10^(-19)); //charge of an electron, C E1= E1/e; //converting to eV disp(E1,"The energy of CO molecule, in eV, is: ") //Result // The energy of CO molecule, in eV, is: // 0.0004787 //Part(b) w= sqrt((2*E1*e)/(I)); //angular velocity, rad/s disp(w,"The angular velocity, in rad.sec, is: ") //Result // The angular velocity, in rad.sec, is: // 1.027D+12

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clc; clear; k=2 lambda=5*10^-5 //wavlength in cm theta=30 //angle in degrees //calculations e=(k*lambda)/sind(theta) //in cm mprintf("No. of lines per centimeter = %.0e",(1/e)) //The answer provided in the textbook is wrong

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a 0.8 #intensite ambiante dans scene d 250 #distance camera image #sources lumineuses #l 400 400 100 0.5 #l -100 -150 -200 0.7 #l 150 -100 500 1.2 l 250 -300 -150 0.7 #l 150 -100 -300 1.2 #l -150 200 -300 0.7 l -150 200 500 1.2

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//to find transfer function using mason gain formula printf("syms R1 R2 C1 C2 \n //gains of forward path\n P1=1/(R1*R2*C1*C2*s^2);//forward path1 gain\n //gain of individual loops\n L1=-1/(R1*C1*s);\n L2=-1/(R2*C1*s);\n L3=-1/(R2*C2*s);\n //gain of two non touching loops\n g1=1/(s^2*R1*R2*C1*C2);\n //since all the loops touches the forward path1 so\n d1=1\n d=1-(L1+L2+L3)+g1;\n G=(P1*d1)/d;\n transfer function C/R=G")

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//Variable Declaration Tant=35 //Antenna noise temperature(kelvin) Te1=150 //Receiver noise temperature(kelvin) L=5 //Cable Loss (dB) T0=290 G1=10**5 //LNA Gain F=12 //Receiver Noise figure(dB) //Calculation L=10**(L/10) //Converting L into ratio F=10**(F/10) //Converting F into ratio Ts=Tant+Te1+(L-1)*T0/G1+L*(F-1)*T0/G1 //Noise Temperature referred to the input(Kelvin) //Result printf("The noise temperature referred to the input is %.0f Kelvini",Ts)

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//Moved to it's proper location in Ch 13

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clc(); clear; //To determine the density of free electrons rho=9000; //density in kg/m^3 w=65; //atomic weight v=1; //volume in m^3 n=(rho*v)/(w/(6.022*10^26)); //number of atoms a=1.4; //average number of free electrons per atom rhoe=n*a //density of free electrons per atom in electrons/m^3 printf("The density of free electrons is %e electrons/m^3",rhoe);

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// Scilab Code Ex11.3: Page-250 (2010) h = 6.626e-034; // Planck's constant, Js k = 1.38e-023; // Boltzmann constant, J/K // Stimulated Emission = Spontaneous Emission <=> exp(h*f/(k*T))-1 = 1 i.e. // f/T = log(2)*k/h = A A = log(2)*k/h; // Frequency per unit temperature, Hz/K printf("\nThe stimulated emission equals spontaneous emission iff f/T = %4.2e Hz/K", A); // Result // The stimulated emission equals spontaneous emission iff f/T = 1.44e+010 Hz/K

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Chap14_Ex1_R1.sce

// Y.V.C.Rao ,1997.Chemical Engineering Thermodynamics.Universities Press,Hyderabad,India. //Chapter-14,Example 1,Page 489 //Title: Standard Gibbs free energy change and equilibrium constant //================================================================================================================ clear clc //INPUT //The water gas shift reaction is given by : CO2(g)+H2(g)--->CO(g)+H2O(g) T=298.15;//temperature in K del_Gf=[-137.327;-228.600;-394.815;0];//the standard Gibbs free energy of formation of CO(g),H2O(g),CO2(g) and H2(g) in kJ n=[1;1;-1;-1];//stoichiometric coefficients of CO(g),H2O(g),CO2(g) and H2(g) respectively (no unit) R=8.314;//universal gas constant in J/molK //CALCULATION //calculation of the standard Gibbs free energy of reaction at 298.15K using Eq.(14.1) in kJ del_G=(n(1,:)*del_Gf(1,:))+(n(2,:)*del_Gf(2,:))+(n(3,:)*del_Gf(3,:))+(n(4,:)*del_Gf(4,:)); Ka=exp((-(del_G*10^3))/(R*T));//calculation of the equilibrium constant using Eq.(14.9) (no unit) //OUTPUT mprintf('The standard Gibbs free energy of the water gas shift reaction at 298.15K=%0.3f kJ \n',del_G); mprintf('The equilibrium constant of the water gas shift reaction at 298.15K=%0.3e \n',Ka); //===============================================END OF PROGRAM===================================================

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//PME3402 - Laboratório de Medição e Controle Discreto / Atividade Aula 2 //Tarefa 2 //Grupo 4 - Integrantes: //Caique de Oliveira Kobayashi - 9793461 //Heitor Fontana de Godoy - 10335677 //Lucas Hattori Costa - 10335847 //Lucas Pinheiro Paiva Cavalcante - 10274270 //Pedro Henrique Pavelski - 10335621 clc clear xdel( winsid() ) pi = %pi M = csvRead('C:\Users\Usuario\Documents\GitHub\Lab_Medicoes_2\ex2\Sons\Caique_o_fechado.wav') scf(0) disp(M)

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THE OPTIMIZATION ALGORITHM HAS CHANGED TO THE EM ALGORITHM. ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 1 2 3 4 5 ________ ________ ________ ________ ________ 1 0.529298D+00 2 -0.768207D-02 0.415135D-02 3 0.366046D-01 -0.304514D-02 0.392577D+00 4 -0.312172D-02 0.435699D-05 -0.210729D-02 0.325538D-02 5 0.144862D-02 0.149009D-03 -0.154729D-03 0.364884D-04 0.405951D-02 6 0.876992D-03 0.362265D-04 0.604377D-03 0.106137D-04 0.123335D-03 7 -0.243409D-04 0.125888D-03 0.198624D-02 0.989486D-05 0.453959D-03 8 -0.114703D-03 0.317640D-04 -0.120898D-02 0.995436D-04 0.265720D-03 9 -0.137879D+00 0.262662D-01 -0.431811D-01 -0.483974D-02 0.859430D-01 10 0.137409D+00 0.128867D-01 0.241869D-03 0.211800D-01 0.182522D+00 11 -0.195606D+00 0.321229D-01 -0.215995D+00 -0.145780D-01 -0.240237D-01 12 -0.667330D-02 -0.172287D-02 -0.120255D+01 0.541088D-01 -0.332322D-01 13 0.848360D-01 0.164504D-01 0.105074D+00 0.128407D-02 0.629735D-02 14 -0.645725D-02 0.104647D-02 -0.477924D+00 0.180438D-01 0.514726D-02 15 -0.744670D+00 -0.328461D-01 -0.189230D+01 0.128862D-01 -0.110705D+00 16 -0.409549D-01 -0.406011D-02 -0.279968D-01 -0.173911D-02 -0.253842D-02 17 -0.188004D-02 -0.102072D-02 0.850797D-02 -0.209734D-03 -0.101924D-02 18 -0.169078D+01 -0.130581D-01 0.447863D+00 -0.402089D-01 -0.214831D-01 19 -0.240336D+00 0.161514D-01 0.137087D+00 -0.292112D-02 -0.182245D-01 20 0.354640D+00 -0.257067D-01 -0.329882D+01 -0.570872D-01 0.336717D-01 21 0.196643D+00 -0.163247D-01 -0.723778D-01 0.160550D-02 0.154292D-01 22 0.600020D-02 -0.449255D-03 -0.151300D-02 0.101787D-02 0.132760D-03 23 0.466003D-01 0.345854D-03 -0.459398D-01 -0.121649D-01 -0.163454D-02 24 -0.235782D-02 0.103798D-02 0.597148D-02 -0.636993D-03 -0.228462D-03 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 6 7 8 9 10 ________ ________ ________ ________ ________ 6 0.878539D-03 7 0.106435D-02 0.378663D-02 8 -0.946239D-04 0.703794D-04 0.177566D-02 9 0.401522D-02 0.538152D-01 -0.521214D-03 0.568848D+02 10 -0.178257D-01 -0.134501D-01 0.394212D-02 0.245282D+01 0.257555D+02 11 -0.196162D-01 -0.664520D-02 0.166789D-01 0.351721D+01 -0.152265D+01 12 -0.262180D-01 0.212416D-01 0.143340D-01 0.606020D+01 0.152563D+01 13 0.639567D-01 0.149385D+00 -0.181401D-01 0.210371D+01 0.433088D+00 14 -0.143700D-01 -0.125584D-01 0.156291D+00 0.182679D+01 0.178938D+01 15 0.238549D-01 -0.387319D-01 -0.297734D-01 -0.198786D+02 -0.128590D+02 16 -0.781850D-03 -0.130439D-02 -0.745018D-03 0.164408D+01 -0.513956D+00 17 -0.197787D-03 -0.146072D-03 0.867791D-05 -0.190118D+00 -0.563960D-01 18 -0.298828D-01 -0.500451D-01 -0.141807D-02 -0.116309D+02 0.324863D+01 19 -0.125656D-01 0.379577D-02 0.134380D-01 -0.623094D+00 -0.187492D+01 20 0.526258D-01 0.394471D-01 -0.969277D-01 -0.362490D+00 0.434816D+00 21 0.954471D-02 -0.867733D-02 -0.139738D-01 0.102457D+01 0.169645D+01 22 -0.212235D-03 -0.584706D-03 0.121048D-03 0.653528D-01 -0.299006D-01 23 -0.565197D-03 -0.177919D-02 -0.446140D-04 0.355967D-01 -0.961392D-02 24 -0.132037D-03 0.419008D-04 -0.163465D-03 0.695239D-02 -0.416682D-01 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 11 12 13 14 15 ________ ________ ________ ________ ________ 11 0.425246D+02 12 0.569392D+01 0.123726D+03 13 -0.394670D+01 0.358369D+00 0.177481D+02 14 0.317067D+01 -0.111325D+01 -0.259392D+01 0.579877D+02 15 0.871122D+00 0.299160D+01 -0.355475D+00 0.426727D+00 0.633308D+03 16 0.446141D+00 0.728614D+00 -0.102112D+00 -0.231325D+00 0.172694D+01 17 -0.718168D-02 -0.271584D-01 -0.332380D-01 0.186206D-01 -0.276451D+01 18 -0.290868D+01 0.474002D+01 -0.432719D+01 -0.280990D+01 0.798564D+02 19 0.129923D+01 -0.210278D+01 -0.115290D+01 0.236712D+01 0.918438D+00 20 -0.230678D+01 -0.170487D+02 0.754425D+01 -0.173048D+02 0.117985D+03 21 -0.187421D+00 0.163709D+01 0.555569D+00 -0.240594D+01 -0.414738D+01 22 -0.839646D-01 -0.215208D-01 -0.145245D-01 0.341256D-01 -0.535394D+00 23 0.151959D+00 -0.386695D+00 -0.839136D-01 0.294295D+00 0.328419D+00 24 0.198220D-01 -0.128000D+00 -0.203569D-01 -0.911847D-01 -0.522204D+00 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 16 17 18 19 20 ________ ________ ________ ________ ________ 16 0.884003D+00 17 -0.600594D-01 0.291749D-01 18 -0.189335D+01 -0.462346D+00 0.410651D+03 19 -0.212423D-01 -0.389906D-02 0.590636D+01 0.617924D+01 20 0.306373D+00 -0.628166D+00 0.845062D+01 -0.752994D+00 0.489071D+03 21 0.304368D-01 0.123754D-01 -0.162248D+01 -0.572961D+01 -0.747387D+00 22 0.520968D-02 0.440568D-02 -0.187204D+01 -0.314697D-01 -0.463814D-01 23 0.141889D-01 0.934757D-02 -0.139352D+01 -0.341476D-01 0.429335D+01 24 0.108942D-01 0.376551D-02 0.347632D-02 0.104333D-01 -0.236905D+01 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 21 22 23 24 ________ ________ ________ ________ 21 0.730074D+01 22 -0.138957D-01 0.182919D-01 23 0.115832D+00 -0.812041D-02 0.630271D+00 24 -0.351714D-01 -0.217152D-03 -0.326087D-01 0.249239D-01 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 1 2 3 4 5 ________ ________ ________ ________ ________ 1 1.000 2 -0.164 1.000 3 0.080 -0.075 1.000 4 -0.075 0.001 -0.059 1.000 5 0.031 0.036 -0.004 0.010 1.000 6 0.041 0.019 0.033 0.006 0.065 7 -0.001 0.032 0.052 0.003 0.116 8 -0.004 0.012 -0.046 0.041 0.099 9 -0.025 0.054 -0.009 -0.011 0.179 10 0.037 0.039 0.000 0.073 0.564 11 -0.041 0.076 -0.053 -0.039 -0.058 12 -0.001 -0.002 -0.173 0.085 -0.047 13 0.028 0.061 0.040 0.005 0.023 14 -0.001 0.002 -0.100 0.042 0.011 15 -0.041 -0.020 -0.120 0.009 -0.069 16 -0.060 -0.067 -0.048 -0.032 -0.042 17 -0.015 -0.093 0.079 -0.022 -0.094 18 -0.115 -0.010 0.035 -0.035 -0.017 19 -0.133 0.101 0.088 -0.021 -0.115 20 0.022 -0.018 -0.238 -0.045 0.024 21 0.100 -0.094 -0.043 0.010 0.090 22 0.061 -0.052 -0.018 0.132 0.015 23 0.081 0.007 -0.092 -0.269 -0.032 24 -0.021 0.102 0.060 -0.071 -0.023 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 6 7 8 9 10 ________ ________ ________ ________ ________ 6 1.000 7 0.584 1.000 8 -0.076 0.027 1.000 9 0.018 0.116 -0.002 1.000 10 -0.119 -0.043 0.018 0.064 1.000 11 -0.101 -0.017 0.061 0.072 -0.046 12 -0.080 0.031 0.031 0.072 0.027 13 0.512 0.576 -0.102 0.066 0.020 14 -0.064 -0.027 0.487 0.032 0.046 15 0.032 -0.025 -0.028 -0.105 -0.101 16 -0.028 -0.023 -0.019 0.232 -0.108 17 -0.039 -0.014 0.001 -0.148 -0.065 18 -0.050 -0.040 -0.002 -0.076 0.032 19 -0.171 0.025 0.128 -0.033 -0.149 20 0.080 0.029 -0.104 -0.002 0.004 21 0.119 -0.052 -0.123 0.050 0.124 22 -0.053 -0.070 0.021 0.064 -0.044 23 -0.024 -0.036 -0.001 0.006 -0.002 24 -0.028 0.004 -0.025 0.006 -0.052 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 11 12 13 14 15 ________ ________ ________ ________ ________ 11 1.000 12 0.078 1.000 13 -0.144 0.008 1.000 14 0.064 -0.013 -0.081 1.000 15 0.005 0.011 -0.003 0.002 1.000 16 0.073 0.070 -0.026 -0.032 0.073 17 -0.006 -0.014 -0.046 0.014 -0.643 18 -0.022 0.021 -0.051 -0.018 0.157 19 0.080 -0.076 -0.110 0.125 0.015 20 -0.016 -0.069 0.081 -0.103 0.212 21 -0.011 0.054 0.049 -0.117 -0.061 22 -0.095 -0.014 -0.025 0.033 -0.157 23 0.029 -0.044 -0.025 0.049 0.016 24 0.019 -0.073 -0.031 -0.076 -0.131 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 16 17 18 19 20 ________ ________ ________ ________ ________ 16 1.000 17 -0.374 1.000 18 -0.099 -0.134 1.000 19 -0.009 -0.009 0.117 1.000 20 0.015 -0.166 0.019 -0.014 1.000 21 0.012 0.027 -0.030 -0.853 -0.013 22 0.041 0.191 -0.683 -0.094 -0.016 23 0.019 0.069 -0.087 -0.017 0.245 24 0.073 0.140 0.001 0.027 -0.679 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 21 22 23 24 ________ ________ ________ ________ 21 1.000 22 -0.038 1.000 23 0.054 -0.076 1.000 24 -0.082 -0.010 -0.260 1.000

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//Ex:6.2 clc; clear; close; X_c=3.18; R=100; V_rip=1*(X_c/sqrt(R^2+X_c^2)); printf("Ripple voltage = %f V",V_rip);

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//Part A Chapter 6 Example 3 clc; clear; close; R=8.314/32;//kJ/kgK p1=125;//kPa p2=375;//kPa T1=27+273;//K T2=T1;//K delta_S=-R*log(p2/p1);//kJ/K;//kJ/kgK disp("Change in entropy = "+string(delta_S)+" kJ/K");

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function[x, k] = newton(foncjac, tol, Kmax, x0) if Kmax - floor(Kmax) ~= 0 | Kmax < 0 error('Kmax must be an int'); end if tol < 0 | abs(tol) < %eps error('wrong tol'); end for k = 1:Kmax [f, J] = foncjac(x0); correction = J\-f; x = x0 + correction; if (norm(x - x0) / norm(x)) < tol return x; else x0 = x; end end error('didnt converge'); endfunction

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t=(0:0.01:5*%pi)'; tc=(2*%pi)/10; fc=1/tc; k=(squarewave(t)+1)*(1/2); y=k.*cos(2*%pi*fc*t); k1=((-1)*squarewave(t)+1)*(1/2); ta=(2*%pi)/2; fa=1/ta; y1=k1.*cos(2*%pi*fa*t); p=y+y1; plot(t,p);

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clear // // // //Variable declaration Hc=200*10**3 //critical magnetic field(A/m) Tc=12 //critical temperature(K) H0=250*10**3 //critical magnetic field(A/m) //Calculation T=Tc*sqrt(1-(Hc/H0)**2) //maximum critical temperature(K) //Result printf("\n maximum critical temperature is %0.3f K",T)

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// comparing real numbers to infinity %inf==%inf %inf<=%inf 1<=%inf 1>-%inf // using isinf A=[0 %nan 1 %inf 2 -%inf %nan ] A==%inf isinf(A)

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clear; clc; // Stoichiometry // Chapter 3 // Material Balances Without Chemical Reaction // Example 3.4 // Page 62 printf("Example 3.4, Page 62 \n \n"); // solution m = 1 //[kg] dry neem leaves (basis) m1 = .01/100 //[kg] beta cartene content of leaves Ex = (m1*100)/.41 //[kg] extract quantity Tc1 = Ex*.155 //[kg] Alpha Tocopherol in the extract Tc2 = .46/100 //[kg] Alpha Tocopherol in the neem leaves R = (Tc1*100)/Tc2 // recovery of Alpha Tocopherol printf("(a) \n \nmass of extract phase per kg of dry leaves is "+string(Ex)+" kg \n \n \n(b) \n \npercent recovery of Alpha Tocopherol is "+string(R)+".")

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//By Manas,FOSSEE,IITB //function which designs an iir digital filter using analog filter designs and bilinear transformation . hz=iir(3,'bp','butt',[.15 .25],[0 0]); [hzm,fr]=frmag(hz,256); plot2d(fr',hzm') xtitle('Discrete IIR filter band pass 0.15<fr<0.25 ',' ',' '); q=poly(0,'q'); //to express the result in terms of the delay operator q=z^-1 hzd=horner(hz,1/q)

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clc; clear all; disp("evaporation rate calculation") U=2.8;// m/s L=300/1000;//m rho=1.205;//kg/m^3 v=15.06*10^(-6);//m^2/s D=4.166*10^(-5);//m^2/s Re=U*L/v;// Reynolds No. Re if Re<5*10^5 disp("flow is laminar") end Sc=v/D;// Schmidt No. Sc Sh=0.664*((Re)^0.5)*(Sc)^(0.33); Sh L=320/1000;//m hm=Sh*D/L;// m/s disp("m/s",hm,"mass transfer coefficient = ") disp("mass transfer based on pressure difference ") T=15+273;//K R=287; hmp=hm/(R*T);// m/s A=0.32*0.42;//m^2 pw1=0.017*10^(5); pw2=0.0068*10^(5); mw=hmp*A*(pw1-pw2)*3600; disp("kg/h",mw,"mass diffusion of water =")

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// Exa 1.10 clc; clear; close; // Given data format('v',7) V_CC= 9;// in volt V_EE= 9;// in volt V_BE= 0.7;// in volt (Assuming value) R_C= 47;// in k ohm R_C= R_C*10^3;// in ohm R_E= 43;// in k ohm R_E= R_E*10^3;// in ohm Ri_1= 20;// in ohm Ri_2= Ri_1;// in ohm v_in1= 2.5;// in mv v_in1=v_in1*10^-3;// in volt Bita_1= 75; Bita_2= Bita_1; I_CQ = (V_EE-V_BE)/(2*R_E+Ri_1/Bita_1);// in amp I_E= I_CQ;// in amp V_CEQ= V_CC + V_BE - I_CQ*R_C;// in volt re_desh= (26*10^-3)/I_E;// in ohm // However, voltage gain of single-input, unbalanced-output differential amplifier is given by so A_d = R_C/(2*re_desh); v_out= A_d*v_in1;// in volt disp(v_out,"Output voltage in volt")

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//Chemical Engineering Thermodynamics //Chapter 3 //First Law of Thermodynamics //Example 3.11 clear; clc; //Given H1 = 680.6;//Enthalpy of entering steam at 6Kgf/cm^2 &200 deg cel in Kcal/Kg u1 = 60;//velocity at which steam entered the nozzle in m/sec u2 = 600;//velocity at which steam left the nozzle in m/sec g = 9.8; Hg = 642.8; Hlq = 110.2;//Enthalpy of saturated vapour & saturated liquid at 1.46 Kgf/cm^2 respectively //To calculate the quality of exit steam H2 = H1+((u1^2)-(u2^2))/(2*g*427);//enthalpy of leaving steam in Kcal/Kg x = (H2-Hlq)/(Hg-Hlq); mprintf('The quality of exit steam is %f percent',x*100); //end

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//To Calculate the Capacitance of the capacitor //Example 31_1 clear; clc; Q=60*10^-6;//Charge on the capacitor V=12;//Potential difference between the plates C=Q/V;//Formula for finding the capacitance of the capacitor printf("Capacitance of the capacitor=%f *10^-6 F",C*10^6);

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// stack representation L=[] // empty stack L=[1,L] // add 1 to the stack L=[2,L] // add 2 to the stack L=[3,L] // add 3 to the stack x=L(1),L(1)=[] // "unstacking" // queue representation F=[] // empty queue F=[F,1] // add 1 to the queue F=[F,2] // add 2 to the queue F=[F,3] // add 3 to the queue x=F(1),F(1)=[] // remove from the queue

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//Ex:5.2 clc; clear; close; i=15*10^-3; R=(21-2.2)/i; v=18.8;//in volts P=i*v*1000; printf("Resistor %d ohms of %d mW",R,P);

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clear; clc; rb=75000e3; ro=50e6; v1=11e3; v2=66e3; xa=.25*rb/ro; xb=.75; xt=.1; v=1; xeq=inv(inv(xa)+inv(xb))+xt; i=v/xeq; i=round(i*100)/100; ia=i*xb/(xa+xb); ib=i*xa/(xa+xb); ia=round(ia*100)/100; ilt=rb/(sqrt(3)*v1); iht=rb/(sqrt(3)*v2); i=i*iht; i=fix(i) ia=ia*ilt; ilt=rb/(1.73*v1); ib=ib*ilt; ia=round(ia); ib=round(ib/10)*10; mprintf("sub transient current generator A=%dA \n generator B=%dA \n HT side=%dA",ia,ib,i);

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//Chapter 12: Polymers and Polymerization //Problem: 3 clc; //Declaration of Variables d1 = 920 // density,in kg per m cube d2 = 961.97 // density,in kg per m cube dp = 44 // density % // Solution mprintf("dp = [d2 * (p - d1)] * [100/p * (d2 - d1)]\n") p = 937.98 mprintf(" Density of sample is %.2f kg per m cube", p)

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THE OPTIMIZATION ALGORITHM HAS CHANGED TO THE EM ALGORITHM. ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 1 2 3 4 5 ________ ________ ________ ________ ________ 1 0.254114D+00 2 -0.329455D-02 0.196425D-02 3 0.121788D+00 -0.262772D-02 0.281165D+00 4 -0.201290D-02 0.887240D-03 -0.528054D-02 0.229735D-02 5 -0.425259D-03 -0.207082D-04 0.123844D-02 0.380227D-04 0.262262D-02 6 -0.186682D-03 0.655832D-04 0.146532D-03 -0.121259D-03 -0.386094D-03 7 -0.665925D-03 0.107984D-03 -0.592615D-03 -0.670200D-04 0.528680D-03 8 0.960528D-03 -0.119453D-04 0.566942D-03 0.172302D-03 -0.540302D-05 9 -0.167603D+00 -0.134576D-02 -0.118886D+00 0.109523D-01 0.248955D-01 10 -0.246365D+00 -0.415916D-02 0.191995D-01 -0.201862D-02 0.124062D+00 11 0.623366D-01 0.809937D-02 0.118389D+00 0.863066D-02 0.298419D-01 12 -0.472021D+00 -0.369682D-02 -0.800358D+00 0.322672D-01 0.111100D+00 13 -0.689804D-01 0.787281D-02 0.191885D-01 -0.112781D-01 -0.107956D-01 14 -0.183244D+00 -0.140391D-02 -0.523412D+00 0.263070D-01 0.315289D-02 15 -0.137123D+01 0.992568D-02 -0.668055D+00 0.644651D-02 -0.108978D+00 16 -0.921077D-02 -0.829671D-02 0.721046D-02 -0.463072D-02 0.323115D-03 17 -0.167284D-02 -0.324577D-03 -0.181369D-02 -0.483887D-04 -0.153951D-03 18 -0.526684D+00 -0.501606D-02 -0.107685D+01 0.313948D-01 -0.616948D-02 19 -0.758680D-01 0.262296D-03 -0.255280D-01 0.458605D-02 0.212350D-02 20 -0.855305D+00 0.334640D-01 -0.252106D+01 0.511490D-01 0.293709D-02 21 0.934451D-01 -0.909549D-02 0.999443D-01 -0.176276D-01 -0.144329D-02 22 -0.241555D-02 -0.124378D-04 -0.259703D-02 0.269701D-03 -0.421765D-03 23 0.426627D-01 -0.629577D-02 0.493423D-01 -0.126216D-01 -0.935423D-03 24 -0.375048D-02 0.207436D-03 -0.136665D-02 0.559563D-04 -0.460613D-03 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 6 7 8 9 10 ________ ________ ________ ________ ________ 6 0.980643D-03 7 0.657208D-03 0.321399D-02 8 -0.309786D-04 -0.329590D-03 0.318493D-02 9 0.156599D-01 0.436734D-01 0.211864D-01 0.328404D+02 10 -0.257549D-01 0.131238D-01 0.712901D-02 0.389664D+00 0.139671D+02 11 0.274355D-01 0.434922D-01 0.206439D-01 0.838680D+01 0.582666D+00 12 -0.406606D-01 0.439148D-01 0.169019D-02 0.801116D+01 0.116209D+02 13 0.585354D-01 0.899392D-01 -0.577683D-03 0.173874D+01 0.221349D+00 14 -0.903721D-02 -0.389331D-01 0.230266D+00 0.191456D+01 0.300248D+01 15 0.226797D-01 -0.803617D-02 -0.829908D-01 -0.448734D+01 -0.845216D+01 16 0.295518D-03 0.769410D-03 -0.120869D-02 0.368531D+00 0.788204D-01 17 -0.123263D-03 -0.266286D-03 0.577168D-03 -0.536464D-01 0.504363D-02 18 -0.688696D-01 -0.147557D+00 -0.474499D-01 -0.846995D+01 -0.746594D-01 19 -0.878079D-02 0.110649D-01 -0.387112D-02 -0.117559D+01 0.285878D+00 20 -0.179157D-01 -0.430719D-01 -0.231491D+00 -0.715998D+01 -0.214530D+01 21 0.964579D-02 -0.146701D-01 0.121837D-02 0.103876D+01 0.123572D-01 22 0.395049D-04 -0.104567D-03 0.674379D-03 0.476435D-01 -0.214170D-01 23 0.138612D-02 -0.230792D-02 -0.555035D-02 -0.276305D+00 0.693180D-01 24 -0.648053D-04 -0.477397D-04 0.900972D-03 0.101206D+00 -0.821701D-02 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 11 12 13 14 15 ________ ________ ________ ________ ________ 11 0.316118D+02 12 0.655638D+00 0.119308D+03 13 0.980473D+00 0.265799D-01 0.102550D+02 14 0.846112D-02 0.351534D+01 0.295901D+00 0.514783D+02 15 -0.601322D+01 -0.169971D+01 0.255923D+01 -0.443509D+01 0.160931D+03 16 0.329298D-01 0.375981D+00 -0.478191D-02 -0.938865D-01 0.625546D-01 17 -0.246809D-01 -0.711525D-01 -0.209058D-01 0.470341D-01 -0.715176D+00 18 -0.414434D+01 -0.258544D+01 -0.729377D+01 -0.540073D+01 0.668654D+02 19 -0.735496D+00 0.264981D+01 -0.507408D+00 -0.112868D+01 0.209242D+01 20 0.208456D+01 -0.257475D+02 -0.570598D+01 -0.211410D+02 0.469761D+02 21 0.108858D+01 -0.197543D+01 0.373765D+00 0.873786D+00 -0.268481D+01 22 -0.630325D-01 -0.354030D-01 0.401251D-02 0.809843D-01 -0.251732D+00 23 -0.169773D-01 0.659033D+00 -0.654569D-01 -0.476432D+00 -0.231907D-01 24 -0.598973D-01 -0.740237D-01 0.292760D-01 0.100172D+00 -0.144203D+00 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 16 17 18 19 20 ________ ________ ________ ________ ________ 16 0.323294D+00 17 -0.122367D-01 0.100821D-01 18 -0.174443D+00 -0.266784D+00 0.137250D+03 19 0.155640D+00 -0.280495D-01 0.314225D+00 0.362127D+01 20 -0.218598D+00 -0.167697D+00 0.114701D+03 -0.403893D+00 0.301234D+03 21 0.188502D+00 0.106297D-01 0.576207D+00 -0.322753D+01 0.704364D+00 22 -0.843629D-02 0.453721D-02 -0.572077D+00 -0.245657D-01 -0.557150D+00 23 0.131623D+00 -0.892880D-02 0.291029D+00 0.554793D-01 0.187950D+01 24 -0.703131D-02 0.283850D-02 -0.463680D+00 -0.187403D-01 -0.136296D+01 ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES 21 22 23 24 ________ ________ ________ ________ 21 0.398011D+01 22 -0.177295D-01 0.785202D-02 23 0.494400D+00 -0.254901D-01 0.622899D+00 24 -0.263928D-01 0.725928D-02 -0.589932D-01 0.167163D-01 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 1 2 3 4 5 ________ ________ ________ ________ ________ 1 1.000 2 -0.147 1.000 3 0.456 -0.112 1.000 4 -0.083 0.418 -0.208 1.000 5 -0.016 -0.009 0.046 0.015 1.000 6 -0.012 0.047 0.009 -0.081 -0.241 7 -0.023 0.043 -0.020 -0.025 0.182 8 0.034 -0.005 0.019 0.064 -0.002 9 -0.058 -0.005 -0.039 0.040 0.085 10 -0.131 -0.025 0.010 -0.011 0.648 11 0.022 0.033 0.040 0.032 0.104 12 -0.086 -0.008 -0.138 0.062 0.199 13 -0.043 0.055 0.011 -0.073 -0.066 14 -0.051 -0.004 -0.138 0.076 0.009 15 -0.214 0.018 -0.099 0.011 -0.168 16 -0.032 -0.329 0.024 -0.170 0.011 17 -0.033 -0.073 -0.034 -0.010 -0.030 18 -0.089 -0.010 -0.173 0.056 -0.010 19 -0.079 0.003 -0.025 0.050 0.022 20 -0.098 0.044 -0.274 0.061 0.003 21 0.093 -0.103 0.094 -0.184 -0.014 22 -0.054 -0.003 -0.055 0.064 -0.093 23 0.107 -0.180 0.118 -0.334 -0.023 24 -0.058 0.036 -0.020 0.009 -0.070 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 6 7 8 9 10 ________ ________ ________ ________ ________ 6 1.000 7 0.370 1.000 8 -0.018 -0.103 1.000 9 0.087 0.134 0.066 1.000 10 -0.220 0.062 0.034 0.018 1.000 11 0.156 0.136 0.065 0.260 0.028 12 -0.119 0.071 0.003 0.128 0.285 13 0.584 0.495 -0.003 0.095 0.018 14 -0.040 -0.096 0.569 0.047 0.112 15 0.057 -0.011 -0.116 -0.062 -0.178 16 0.017 0.024 -0.038 0.113 0.037 17 -0.039 -0.047 0.102 -0.093 0.013 18 -0.188 -0.222 -0.072 -0.126 -0.002 19 -0.147 0.103 -0.036 -0.108 0.040 20 -0.033 -0.044 -0.236 -0.072 -0.033 21 0.154 -0.130 0.011 0.091 0.002 22 0.014 -0.021 0.135 0.094 -0.065 23 0.056 -0.052 -0.125 -0.061 0.024 24 -0.016 -0.007 0.123 0.137 -0.017 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 11 12 13 14 15 ________ ________ ________ ________ ________ 11 1.000 12 0.011 1.000 13 0.054 0.001 1.000 14 0.000 0.045 0.013 1.000 15 -0.084 -0.012 0.063 -0.049 1.000 16 0.010 0.061 -0.003 -0.023 0.009 17 -0.044 -0.065 -0.065 0.065 -0.561 18 -0.063 -0.020 -0.194 -0.064 0.450 19 -0.069 0.127 -0.083 -0.083 0.087 20 0.021 -0.136 -0.103 -0.170 0.213 21 0.097 -0.091 0.059 0.061 -0.106 22 -0.127 -0.037 0.014 0.127 -0.224 23 -0.004 0.076 -0.026 -0.084 -0.002 24 -0.082 -0.052 0.071 0.108 -0.088 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 16 17 18 19 20 ________ ________ ________ ________ ________ 16 1.000 17 -0.214 1.000 18 -0.026 -0.227 1.000 19 0.144 -0.147 0.014 1.000 20 -0.022 -0.096 0.564 -0.012 1.000 21 0.166 0.053 0.025 -0.850 0.020 22 -0.167 0.510 -0.551 -0.146 -0.362 23 0.293 -0.113 0.031 0.037 0.137 24 -0.096 0.219 -0.306 -0.076 -0.607 ESTIMATED CORRELATION MATRIX FOR PARAMETER ESTIMATES 21 22 23 24 ________ ________ ________ ________ 21 1.000 22 -0.100 1.000 23 0.314 -0.364 1.000 24 -0.102 0.634 -0.578 1.000

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clc; clear; //Example 3.38 v=23.13*10^-6 ; //[m^2/s] k=0.0321 ; //[W/m.K] Beta=2.68*10^-3; //[K^-1] Tw=443 ;//[K] T_inf=303 ; //[K] dT=Tw-T_inf; //[K] g=9.81 ; //[m/s^2] Npr=0.688; //Prandtl number D=100 ; //Diameter [mm] D=D/1000 //Diameter [m] Nra=(g*Beta*dT*(D^3)*Npr)/(v^2) Nnu=0.53*(Nra^(1.0/4.0)) //Nusselt number h=Nnu*k/D //[W/(m^2.K)] h=7.93 //Approximation e=0.90; //Emissivity sigma=5.67*10^-8 ; //Q=Q_conv+Q_rad //Total heat loss //for total heat loss per meter length Q_by_l=h*%pi*D*dT+sigma*e*%pi*D*(Tw^4-T_inf^4) //[W/m] printf("Total heat loss per metre length of pipe is %f W/m",Q_by_l)

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//Chapter-1, Example 1.13, Page 25 //============================================================================= clc; clear; //INPUT DATA m=80000;//mass of water lifted by pump in Kg/min g=9.81;//gravity constant in m/sec^2 h=2;//pump is in operation for two hours a day d=30;//pump is in operation for 30 days T=h*d;//total time for which pump is in operation in hrs n=70;//efficeincy in percentage h=12;//the height in m to which pump lifts water C=50;//cost of energy in paise/Kwh //CALCULATIONS P=m*g*h;//potential energy possessed by water per minute or workdone by motor pump/minute measured in joules P=P/60;//potential energy possessed by water per minute or workdone by motor pump/minute measured in joules/sec or watts. O=P/1000;//output power of motor in Kw n=n/100; E=O/n;//input power of motor in Kw Et=E*T;//total energy supplied or energy consumption in Kwh C=C/100;//cost of energy in Rs/Kwh Ct=C*Et;//Total cost of energy //OUTPUT mprintf("Thus the total cost of energy is Rs %4.0f",Ct); //=================================END OF PROGRAM==============================

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clear ; clc; // Example 6.1 printf('Example 6.1\n\n'); printf('Page No. 142\n\n'); // given L = 2.5;// Length of tubes in metre Do = 10*10^-3;// Internal diameter of tubes in metre m = 3.46;// mass flow rate in kg/s Th = 120;// Temperature of condening steam in degree celcius Tl_i = 20;// Inlet temperature of liquid in degree celcius Tl_o = 80;// Outlet temperature of liquid in degree celcius Cp = 2.35*10^3;// Specific heat capacity of liquid in J/kg-K U = 950;// Overall heat transfer coefficent in W/m^2-K T1 = Th- Tl_i;// in degree celcius T2 = Th- Tl_o;// in degree celcius Tm = ((T2-T1)/log(T2/T1));// logarithmic mean temperature of pipe in degree celcius a = %pi*Do*L;//Surface area per tube in m^2 A = ((m*Cp*(Tl_o - Tl_i))/(U*Tm));// in m^2 N = A/a; printf('The number of tubes required is %3.0f',N)

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//Chapter-3,Example 16,Page 61 clc; close; //Reaction.....U(235) + n(1) ---> Kr(95) + Ba(139) + 2*n(1) + Q m_U= 235.124 // Isotopic mass of Uranium in a.m.u. m_n= 1.0099 // mass of neutron in a.m.u. m_Kr= 94.945 // Isotopic mass of Kripton in a.m.u. m_Ba=138.954 // Isotopic mass of Ba in a.m.u. Q_value= (m_U + m_n - (m_Kr + m_Ba + 2*m_n))*931 // in electron volt since 1 a.m.u. =931 MeV //since mass is decreased after reaction // Q value is positive printf('the Q value for the reaction is %.3f MeV',Q_value) //mistake in textbook

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function[img_ret]=cv_threshold_mean(image,maxValue) pyImport adaptive_threshold img_ret=adaptive_threshold.adaptive_thresh_mean(image,maxValue) endfunction

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// Scilab code Ex5.16: Pg 166 (2008) clc; clear; R_1 = 30; // Resistance, ohm R_2 = 70; // Resistance, ohm R_in = 200; // Internal resistance of meter, ohm V = 12; // Supply voltage, V // Using voltage divider rule, we have V_2t = (R_2 /(R_1 + R_2))*V // True value of p.d across resistance R_2, V // Since the rsistances R_2 and R-in are parallel, so their equivalent resistance is given their parallel combination R_BC = (R_2 * R_in)/(R_2 + R_in); // Resistance, ohms // Using the potential divider technique, V_2i = (R_BC / ( R_BC + R_1 ))*V // Indicated value of p.d across by voltmetre, volts err = (( V_2i-V_2t ) / V_2t)*100 // Percentage error in the reading printf("\nThe p.d. indicated by the meter = %3.1f V", V_2i); printf("\nThe percentage error in the reading = %4.2f percent", err); // Result // The p.d. indicated by the meter = 7.6 V // The percentage error in the reading = -9.50 percent

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//calculating resistance, reactance and impedance of choke coil I=7.5//current flowing through the circuit V1=110//voltage across non-inductive resistor R=V1/I V2=180//voltage across choke coil Z=V2/I Zt=230/I//impedance of whole circuit r=(Zt^2-R^2-Z^2)/(2*R) Xl=sqrt(Z^2-r^2) mprintf("Reactance of coil=%f ohm\nResistance of coil=%f ohm\nImpedance of coil=%f ohm\n",Xl,r,Z) //calculating total resistance and impedance of the circuit Rt=r+R Zt=sqrt(Rt^2+Xl^2) mprintf("Total resistance of circuit=%f ohm\nTotal impedance of circuit=%f ohm\n",Rt,Zt) //calculating power absorbed by the coil P1=I^2*r mprintf("Power absorbed by the coil=%f W\n",P1) //calculating power drawn by circuit P2=I^2*(r+R) mprintf("Power drawn by the circuit=%f W\n",P2) //calculating power factor of whole circuit pf=Rt/Zt mprintf("Power factor of the whole circuit=%f lagging",pf) //answers vary from the textbook due to round off error

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// Grob's Basic Electronics 11e // Chapter No. 03 // Example No. 3_13 clc; clear; // How much current is needed for a 600-W, 120-V toaster? // Given data V = 120; // Applied Voltage=120 Volts P = 600; // Power of toaster=600 Watts I = P/V; disp (I,'The Current I in Amps')

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//Ex5_6 clc Vdc = 15 disp("Vdc = "+string(Vdc)+"V")//applied D.C. voltage //Half Wave Rectifier Vm = %pi*Vdc PIV = Vm disp("Vm = Vdc*pi = "+string(Vm)+"V")//D.C. voltage for half wave rectifier disp("PIV = Vm = "+string(PIV)+"V")//peak inverse voltage for half wave rectifier //Full Wave Rectifier Vm = %pi*Vdc/2 PIV = 2*Vm disp("Vm = Vdc*pi/2 = "+string(Vm)+"V")//D.C. voltage for full wave rectifier disp("PIV = 2*Vm = "+string(PIV)+"V")//peak inverse voltage for full wave rectifier //Bridge Rectifier Vm = %pi*Vdc/2 PIV = Vm disp("Vm = Vdc*pi/2 = "+string(Vm)+"V")//D.C. voltage for bridge rectifier disp("PIV = Vm = "+string(PIV)+"V")//peak inverse voltage for bridge rectifier

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clc clear //Initialization of variables p1=14.7 //psia t1=60 //F p2=60 //psia t2=440 //F m=10 //lb/sec //calculations disp("From mollier charts,") h2=216.3 //Btu/lb h1=124.3 //Btu/lb W21=h2-h1 power=W21*m hp=power*3600/2545 cp=0.237 W212=cp*(t2-t1) power2=W212*m hp2=power2*3600/2545 //results printf("Power required = %d hp",hp) printf("\n Power required = %d hp",hp2) printf("\n Work done = %.1f Btu/lb",W212)

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//Section-10,Example-3,Page no.-CT.42 //To calculate Entropy change(dl_S). clc; R=8.314 C_v=(3/2)*R C_p=C_v+R n=5 T_1=323 T_2=298 P_2=380 P_1=760 R=8.314 dl_S=n*((C_p*log(T_2/T_1))+(R*log(P_1/P_2))) disp(dl_S,'Entropy change(JK^-1)')

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//Example 10.15 //Fourth Order Runge Kutta Method //Page no. 324 clc;clear;close; deff('y=f(x,y)','y=x^2+y^2') y=1;h=0.1; for i=1:2 x=(i-1)*h K1=h*f(x,y); K2=h*f(x+h/2,y+K1/2); K3=h*f(x+h/2,y+K2/2); K4=h*f(x+h,y+K3); disp(K4,'K4 =',K3,'K3 =',K2,'K2 =',K1,'K1 =') y=y+(K1+2*K2+2*K3+K4)/6 printf('\ny(%g) = %.13f\n\n\n\n',x+h,y) end

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//example3.12 clc disp("R1=0.9 ohm, R2=0.03 ohm, X1=5 ohm, X2=0.13 ohm") disp("K=N2/N1=1/6 as N1:N2 is 6:1") r=0.03+(0.9*(1/6)^2) format(6) disp(r,"Therefore, (R_2e)[in ohm]=R2+R1''=R2+(K^2)*R1=0.03+(1/6)^2*0.9=") x=0.13+(5*(1/6)^2) format(8) disp(x,"(X_2e)[in ohm]=X2+X1''=X2+(K^2)*X1=0.13+(5*(1/6)^2)=") disp("I_sc = 200 A") disp("(Z_2e)=(V_sc)/(I_sc) i.e. sqrt((R_2e^2)+(X_2e^2))=(V_sc)/200") v=200*0.27444 disp(v,"V_sc(in V)=200*0.27444=") v=54.8895*6 disp(v,"i) V1(in V)=(V_sc)/K=54.8895/(1/6)=") disp("(W_sc)=(V_sc)*(I_sc)*cos(phi_sc) and (W_sc)=(I_sc^2)*(R_2e)") disp("Therefore, (200^2)*0.055 = 54.8895*200*cos(phi_sc)") s=((0.055*200)/(54.8895)) format(4) disp(s,"Therefore, cos(phi_sc)[lagging]=")

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//a function to choose randomly a number nb_config of observations //to remove in the range [1,l] (l being the total number of observations ; i.e., the number of line) //inputs : // - nb_config : number of observations to be removed // - l : total number of observations //outputs : // - idx_config : randomly chosen observations to be removed function idx_config = choose_random_index(nb_config,l) //random permutation r = grand(1,"prm",1:l); //choose nb_config first elem in the permutation idx_config = r(1:nb_config); endfunction //remove idx_config from teh set of observations rows //inputs : // - rows : the initial set of observations // - idx_config : indexes of observations to be removed //outputs : // - red_rows : reduced set of observations (initial set from which observations have been removed) function red_rows = remove_idx(rows, idx_config) red_rows = rows; red_rows(idx_config,:) = []; endfunction //a function to save a matrix in a specified filename //inputs : // - m : matrix of data to be saved // - path : the path to the directory in which data will be saved // - filename : the name of the file containing data to be saved function save_result(m,path,filename) csvWrite(m,path+filename); endfunction //a function to normalize and take care of missing values //the normalization is in [0;1], //missing values are replaced with '0' (at worst will add a bin) //each column are treated separately in turn and replace previous values //inputs : // - data : all data that will be processed (even columns which are not of interest) // - idx_col : indexes of columns of interest //outputs : // - d : matrix with all columns but columns are interest are normalized and missing value are replaced function d=normalize_and_fill(data,idx_col) //copy before replacing needed columns d = data; //for each column of interest, check if no value miss and if normalize in [0;1] for i = 1:prod(size(idx_col)) //consider specific column c = data(:,idx_col(i)); //remove possible"-nan" replacing them by '0' perf_red = c; perf_red(find(c == "-nan"))='0'; ////normalize //find columns which are not between [0;1] //normalize columns temp=strtod(perf_red); if(find(temp > 1 | temp < 0) ~= []) ma = max(temp); mi = min(temp); temp = (temp-mi)/(ma-mi); end //replace column with possible changes // d=d'; // d(idx_col(i),:) = temp'; // d=d'; d(:,idx_col(i)) = string(temp); end endfunction function [red_set, test_set] = decouple(data,list_tc,idx_config) test_set = []; red_set = []; for i=1:prod(size(list_tc)) tc_meas = data(find(data(:,1) == list_tc(i)),:); test_set = [test_set;tc_meas(idx_config,:)]; tc_meas(idx_config,:)=[]; red_set= [red_set;tc_meas]; end endfunction //a function to load all csv files from a given directory // inputs : // - path : the path where files containing observations over executions // - fileregex : a simple regex containing data to process //files containing data to process must be in a csv format with columns separated by ';' //decimal float values given by a '.' and every cell will be intepreted as a string // outputs : // - x : a matrix containing every read data contained in files function x=load_csv_files(path,fileregex) x=[]; //not sorted list specifically csv_files=listfiles(path+fileregex+'.csv'); //nb file to retrieve nb_iter = size(csv_files,1); //for each file; read data and put them in a matrix to be returned for i=1:nb_iter ////@DEBUG : display the name of the current file to be read //disp(csv_files(i)) //read the file and remove first element (name of the different columns) curr=read_csv(csv_files(i)); if(size(curr,2) == 1) curr = csvTextScan(curr,';','.','string'); end if(nb_iter ~= 1) curr(1,:)=[]; //concatenate to previous data x=[x;curr]; else x = curr; end end endfunction //a function to create histograms needed to compute dispersion scores //it also computates associate dispersion scores to videos //scores and histograms are stored in the given file //inputs : // - data : data to build histograms and dispersion scores // - path : the path to the folder where results will be saved // - filename : the file name containing results (histograms + dispersion score) // - idx_col : the column indexes containing property of interest // - is_rand : a boolean representing whether we should use random test cases or a predefined set function [best_i,best_j,best_k,best_l,best_m] = process_data(data,path,filename,idx_col,is_rand) //scan csv data to have proper format (string and double are mixted) -> //everything in string with decimal noted '.' //formatted_data = csvTextScan(data,";",".",'string'); formatted_data = data; formatted_data = normalize_and_fill(formatted_data,idx_col); //unique first column (filename) unique_text_file = unique(formatted_data(:,1)); //prepare output file (column header) result=["filename","metric","hist"]; idx_config =[]; if(is_rand) //compute the maximum number of observations (parameter to remove observations) l_max = 0; // //arbitrary high constant value l_min = 100000; for(i=1:size(unique_text_file,1)) rows = formatted_data(find(formatted_data(:,1) == unique_text_file(i)),:); l = size(rows,1); if(l > l_max) l_max = l; end if(l < l_min) l_min = l; end end //find for each unique filename corresponding rows //for j = 0 : 100 //number of config to put in test set j = 30; //take indexes at random to be removed //cannot remove more than the max number of observations if(j>l_max) j = l_max-1; end //choose the index to put appart idx_config = choose_random_index(j,l_max); else // test_filename = ["../../../../../results/video_synth/results_executions/motiv_metrics_product_1.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_2.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10_2.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10_7.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_16.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_24.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_32.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_70.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_89.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_208.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10_105.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_44.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_213.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_212.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_206.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_121.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_125.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_115.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_107.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_124.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_59.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_122.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_128.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_110.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_111.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_216.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_116.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_132.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_106.csv"]; test_filename = ["../../../../../results/video_synth/results_executions/motiv_metrics_product_1.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_2.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10_2.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10_7.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_16.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_24.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_32.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_70.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_89.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_208.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10_105.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_212.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_111.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_34_216.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_116.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_11_208.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_11_303.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_10_107.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_11_212.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_101.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_107.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_108.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_114.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_127.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_131.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_19_132.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_44.csv"; "../../../../../results/video_synth/results_executions/motiv_metrics_product_69.csv"]; csv_files=listfiles("../../../../../results/video_synth/results_executions/"+"motiv_metrics_*"+".csv"); for i =1:size(test_filename,1) idx_config = [idx_config find(csv_files == test_filename(i))]; end end //extract property (properties) of interest data_prop = formatted_data(:,[1 idx_col]); //decouple data into two sets : the test set and the other set //test set will be used to check that our score makes sense [red_set, test_set] = decouple(data_prop,unique_text_file,idx_config); fd=mopen("../../../../../data/video_synth/CV_programs/used_configurations.txt",'r'); used_config = mgetl(fd,-1); mclose(fd); //disp(size(used_config)); //save config used for test used_config_test = used_config(idx_config,:); save_result(used_config_test,"../../../../../data/video_synth/generalization/","used_configuration_test.txt"); //for each test case in reduced set, compute metric and histogram for i=1:size(unique_text_file,1) red_set2 = red_set(find(red_set(:,1) == unique_text_file(i)),:); if(red_set2 ~= []) [measure,hist] = compute_metric(red_set2); hist = strcat(hist,' '); result=[result;unique_text_file(i),measure, hist]; end end //save results of histograms and measures for a given set save_result(result,"../../../../../data/video_synth/generalization/",filename+"_gener.csv"); save_result(test_set,"../../../../../data/video_synth/generalization/",filename+"_test_set.csv") nb_cols = prod(size(idx_col)); // retrieve the best set of 5 histograms [best_measure,best_i,best_j,best_k,best_l,best_m] = compose_5hist_best(result(2:$,3),nb_cols); disp("afte composition : "); disp(best_measure); disp(best_i); disp(best_j); disp(best_k); disp(best_l); disp(best_m); //retrieve lines corresponding to test cases in test set meas_test1 = test_set(find(test_set(:,1) == unique_text_file(best_i)),:); meas_test2 = test_set(find(test_set(:,1) == unique_text_file(best_j)),:); meas_test3 = test_set(find(test_set(:,1) == unique_text_file(best_k)),:); meas_test4 = test_set(find(test_set(:,1) == unique_text_file(best_l)),:); meas_test5 = test_set(find(test_set(:,1) == unique_text_file(best_m)),:); //save those information save_result(meas_test1,"../../../../../results/video_synth/generalization/","tc1.csv"); save_result(meas_test2,"../../../../../results/video_synth/generalization/","tc2.csv"); save_result(meas_test3,"../../../../../results/video_synth/generalization/","tc3.csv"); save_result(meas_test4,"../../../../../results/video_synth/generalization/","tc4.csv"); save_result(meas_test5,"../../../../../results/video_synth/generalization/","tc5.csv"); endfunction // computes the histogram of observations and associated dispersion score // the dispersion score is computed as follows: disp(S) = (#bin of histogram ~= 0 / # of programs) // which is the ratio of activated bins to the number of programs to execute // inputs : // - m : a matrix containing observations to build histogram and dispersion score // - idx_col : index of columns of interest containing observations to take into account // outputs : // - measure : the computed dispersion score based on observations // - hist : histogram associated to the dispersion score function [measure, hist]=compute_metric(m,idx_col) measure=[]; //retrieve right data -> data-first column perf=m; perf(:,1) = []; //convert to double d=strtod(perf); ////prepare histogram //number of bins nb_bins = size(perf,1); cf =[]; ind=[]; //for each column to process for i = 1:size(idx_col,2) //compute histogram [tmp_cf,tmp_ind] = histc([0:nb_bins]/nb_bins,d(:,i)); //add to final histogram and frequencies cf = [cf,tmp_cf']; ind=[ind,tmp_ind]; end //finalize dispersion score and convert to string measure = size(unique(ind,'r'),1); measure = measure/nb_bins; measure = string(measure); //histogram also converted to string hist = string(cf); endfunction //function to create the set of 5 videos which gives the highest dispersion score //regarding a property of interest combining different observations : // histograms are kept separated and are processed as one multi-dimensional histogram //inputs : // - histograms : the set of all histograms available // - nb_cols : number of execution performed to compute histograms //outputs : // - measure : the dispersion scores of each possible set // - i : the index of the first video of each possible set // - j : the index of the second video of each possible set // - k : the index of the third video of each possible set // - l : the index of the fourth video of each possible set // - m : the index of the fifth video of each possible set function [best_measure,best_i,best_j,best_k,best_l,best_m] = compose_5hist_best(histograms,nb_cols) best_measure = 0; best_i = 1; best_j = 2; best_k = 3; best_l = 4; best_m = 5; z=1 for i = 1 : size(histograms,1) for j = i+1 : size(histograms,1) for k = j+1 : size(histograms,1) for l = k+1 :size(histograms,1) for m = l+1 : size(histograms,1) hist1 = csvTextScan(histograms(i),' ','.',"double"); hist2 = csvTextScan(histograms(j),' ','.',"double"); hist3 = csvTextScan(histograms(k),' ','.',"double"); hist4 = csvTextScan(histograms(l),' ','.',"double"); hist5 = csvTextScan(histograms(m),' ','.',"double"); // hist2 = histograms(j); // hist3 = histograms(k); // hist4 = histograms(l); // hist5 = histograms(m); //resize so that cols are kept (no mix of different dimensions) hist1 = matrix(hist1,nb_cols,-1); hist2 = matrix(hist2,nb_cols,-1); hist3 = matrix(hist3,nb_cols,-1); hist4 = matrix(hist2,nb_cols,-1); hist5 = matrix(hist3,nb_cols,-1); //hist1T and hist2T are transposed of hist1 and hist2 respectively //hist1T and hist2T -> 1 line = 1 observation over all columns of interest hist1T = hist1'; hist2T = hist2'; hist3T = hist3'; hist4T = hist4'; hist5T = hist5'; //with different dimensions, a bin is not activated if // every bin of each dimension is not activated v=[]; [v1,v2]=find(hist1T~=0); v=[v;unique(v1)']; [v1,v2]=find(hist2T~=0); v=[v;unique(v1)']; [v1,v2]=find(hist3T~=0); v=[v;unique(v1)']; [v1,v2]=find(hist4T~=0); v=[v;unique(v1)']; [v1,v2]=find(hist5T~=0); v=[v;unique(v1)']; ///////dispersion score is still ///////the number of bins activated to the number of executions //measure = # of activated bins measure = size(unique(v),1); //normalize nb_bins = max([size(hist1,2),size(hist2,2),size(hist3,2),size(hist4,2),size(hist5,2)]); measure = measure/nb_bins; if(measure > best_measure) then best_measure = measure; best_i = i; best_j = j; best_k = k; best_l = l; best_m = m; end end end end end end endfunction function data = display_results(filename,best_i,best_j,best_k,best_l,best_m) data = read_csv("../../../../../data/video_synth/generalization/"+filename+"_test_set.csv"); reshaped_d = matrix(data(:,2),[30,-1]); col=[best_i best_j best_k best_l best_m]; //retrieve column of interest d_interest = reshaped_d(:,col); //convert from string to double d_interest = strtod(d_interest); //x= [1:size(col,2)]; //plot2d(x,d_interest,style=[-1 -1 -1 -1 -1], rect=[0 -0.2 30 1]) plot2d(d_interest,style=[-1 -1 -1 -1 -1], rect=[0 -0.2 30 1]); endfunction //load all csv files from the current directory //and rewrite them into a single one without headers //one after an other all_data=load_csv_files("../../../../../data/video_synth/","all_data"); //cols = [9]; //cols = [10]; cols = [11,12,13]; [i,j,k,l,m] = process_data(all_data,"../../../../../results/video_synth/generalization/","metrics_hist_composite_reduced",cols,%f); ////@DEBUG //i= 1; //j= 15; //k = 35; //l = 36; //m = 37; d = display_results("metrics_hist_composite_reduced",i,j,k,l,m);

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//radius of the outer conductor //given clc C=70D-12//F/m Zo=75//ohm L=Zo^2*C//inductance epsilon_r=2.3 a=0.292//mm//radius of inner conductor b=a*10^(Zo*sqrt(epsilon_r)/138)//Zo=(138/sqrt(epsilon_r))*log(b/a) b=round(b*1d+4)/1d+4///rounding off decimals disp(b,'the radius of the outer conductor')

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clc; clear; rand('seed',0); N = [2,2,1]; x=[0.89, 0.79; 0.85, 0.74; 0.84, 0.72; 1, 1; 0.04, 0.07; 0.03, 0.02; 0.02, 0.01;0.01, 0.01; 0.0086, 0.0053; 0.0061, 0.0026; 0.0044, 0.009; 0.008, 0.0087]'; t=[1 1 1 1 0 0 0 0 0 0 0 0]; disp(size(x)) lp=[0.1, 0]; W=ann_FF_init(N); T=400; W=ann_FF_Std_online(x,t,N,W,lp,T); a=ann_FF_run(x,N,W); disp(x); disp(W);

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//Resistance R, Voltage V close(); clear; clc; R1 = 6;//ohm R2 = 1; R3 = 2; R4 = 3; R5 = 10; V1 = 10;//V V2 = 20; //Solving Nodal equations A = [1/R1+1/R2+1/R3 -1/R3;-1/R3 1/R3+1/R4+1/R5]; C = [V1/R1;V2/R5]; B = inv(A)*C; V3 = B(1,1); V4 = B(2,1); I = (V4-V2)/R5; mprintf('I = %0.2f A',I);

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clear // //this is a derivation by substitution problem //al1=al0/(1+al0*t1) //al2=al0/(1+al0*t2) //where t1 and t2 are different temperatures al0,al1 and al2 are temperature coefficients //substitute al0 in al2 //on deriving and solving for al2 we get, printf("\n al2=al1/(1+al1*(t1-t2))")

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EXP-1_60002190039_DURVANG_VIJAY_PARAB_plotting_elementary_signals.sce

//Plot various elementary signals in continuous and discrete domain //Unit Step clc; clf; clear all; n=-10:10; x=[ zeros(1,10), ones(1,11) ]; a= gca(); subplot(2,1,1) plot2d3(n,x); title( 'Plot of Discrete Time Unit Step ' ); xlabel( ' n ' ); ylabel( ' u[n] ' ); n1=0:10 x1 = [ones(1,11) ]; a = gca(); subplot(2,1,2) plot(n1,x1); title('Plot of Continuous Time unit step '); xlabel(' t '); ylabel(' u(t) '); //Unit Impulse clc ; clf ; clear all; n1= -5:5 x1=[ zeros(1,5),ones(1,1),zeros(1,5)]; subplot(2,1,1) plot2d3(n1,x1); title( 'Plot of discrete unit impulse ' ); xlabel( ' number of samples ' ); ylabel( ' amplitude ' ); n= -5:5 x=[ zeros(1,5),ones(1,1),zeros(1,5)]; subplot(2,1,2) plot(n,x); title( 'Plot of continuous unit impulse ' ); xlabel( ' time ' ); ylabel( ' amplitude ' ); //Unit Ramp clc ; clf ; clear all; n=0:1:10; x=n subplot(2,1,1) plot2d3(n,x); xtitle( 'Plot of Discrete unit ramp signal ' ); xlabel( ' number of samples (n) ' ); ylabel( ' x [ n ] ' ); t=0:1:10; x1=t subplot(2,1,2) plot(t,x1); xtitle( 'Plot of Continuous unit ramp signal ' ); xlabel( ' time ' ); ylabel( ' x ( t ) ' ); //Sinusoidal Signal t=0:0.01:10; a=cos(2*3.14*t); subplot(2,1,1) plot(t,a) xlabel('time') ylabel('cosine x(t)') title('Plot of Continuous cosine wave') t1=0:0.01:10; a1=cos(2*3.14*t); subplot(2,1,2) plot(t1,a1) xlabel('n') ylabel('cosine x[n]') title('Plot of Discrete cosine wave') //Exponential signal clc ; clf ; clear all; t = -2:0.1:2; x= exp(t); subplot(2,1,1) plot(t,x); title( 'Plot of Continuous exponential wave ' ); xlabel( ' t ' ); ylabel( ' x(t) ' ); t = -2:0.1:2; x= exp(t); subplot(2,1,2) plot2d3(t,x); title( 'Plot of Discrete exponential wave ' ); xlabel( ' n ' ); ylabel( ' x[n] ' ); //Signum Function clc ; clf ; clear all; t=-5:0.1:5 a=gca(); x=sign(t); b=gca(); plot2d3(t,x); title('Plot of signum function ' ); xlabel( ' t ' ); ylabel( ' x ' );

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//Section-14,Example-2,Page no.-PC.112 //To calculate the pH in the following cases. clc; V_1=150 //volume of 0.1 NaOH solution V_2=150 //volume of 0.2 HCl solution N_1=0.1 N_2=0.2 V=V_1+V_2 //Total volume of the solution m_eq=(V_2*N_2)-(V_1*N_1) //Total milliequivalents of excess HCl N=m_eq/V C_1=N //Since HCl is a strong acid so[HCl]=[H3O+] pH_1=-log10(C_1) disp(pH_1,'pH of the required solution') pH1=5 C1=10^-5 //[H3O+] pH2=3 C2=10^-3 //[H3O+] C_3=(C1+C2)/2 //[H3O+] pH_2=-log10(C_3) disp(pH_2,'pH of the required solution')

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Example10_1.sce

clear; clc; // Illustration 10.1 // Page: 494 printf('Illustration 10.1 - Page: 494\n\n'); // solution //****Data****// // a:water b:isopropyl ether c:acetic acid xF = 0.30;// [mol fraction] yS = 0;// [mol fraction] S1 = 40;// [kg] B1 = 40;// [kg] //*******// // Equilibrium data at 20 OC: // Wa: Wt. percent of a // Wb: Wt. percent of b // Wc: Wt. percent of c // Data1 = [Wc Wa Wb] // Data1: water layer Data1 = [0.69 98.1 1.2;1.41 97.1 1.5;2.89 95.5 1.6;6.42 91.7 1.9;13.30 84.4 2.3;25.50 71.1 3.4;36.70 58.9 4.4;44.30 45.1 10.6;46.40 37.1 16.5]; // Data2: isopropyl ether layer Data2 = [0.18 0.5 99.3;0.37 0.7 98.9;0.79 0.8 98.4;1.93 1 97.1;4.82 1.9 93.3;11.40 3.9 84.7;21.60 6.9 71.5;31.10 10.8 58.1;36.20 15.1 48.7]; scf(20); plot(Data1(:,3)/100,Data1(:,1)/100,Data2(:,3)/100,Data2(:,1)/100); xgrid(); xlabel("Wt fraction of isopropyl ether"); ylabel("Wt fraction of acetic acid"); // x: Wt fraction of acetic acid in water layer. // y: Wt fraction of acetic acid in isopropyl layer. legend("x Vs fraction ether","y Vs fraction ether"); // The rectangular coordinates of Fig 10.9(a) will be used but only upto x = 0.30 a = gca(); a.data_bounds = [0 0;1 0.3]; // Stage 1: F = 100;// [kg] // From Eqn. 10.4: M1 = F+S1;// [kg] // From Eqn. 10.5: xM1 = ((F*xF)+(S1*yS))/M1; // From Fig. 10.15 (Pg 495): // Point M1 is located on the line FB and with the help of tie line passing through M1: x1 = 0.258;// [mol fraction] y1 = 0.117;// [mol fraction] // From Eqn. 10.8: E1 = (M1*(xM1-x1)/(y1-x1));// [kg] // From Eqn. 10.4: R1 = M1-E1;// [kg] // Stage 2: S2 = 40;// [kg] B2 = 40;// [kg] // From Eqn. 10.15: M2 = R1+B2;// [kg] // From Eqn. 10.16: xM2 = ((R1*x1)+(S2*yS))/M2; // Point M2 is located on the line R1B and the tie line passing through R2E2 through M2: x2 = 0.227; y2 = 0.095; // From Eqn. 10.8: E2 = (M2*(xM2-x2)/(y2-x2));// [kg] // From Eqn. 10.4: R2 = M2-E2;// [kg] // Stage 3: S3 = 40;// [kg] B3 = 40;// [kg] // From Eqn. 10.15: M3 = R2+B3;// [kg] // From Eqn. 10.16: xM3 = ((R2*x2)+(S3*yS))/M3; // Point M3 is located on the line R2B and the tie line passing through R3E3 through M3: x3 = 0.20;// [mol fraction] y3 = 0.078;// [mol fraction] // From Eqn. 10.8: E3 = (M3*(xM3-x3)/(y3-x3));// [kg] // From Eqn. 10.4: R3 = M3-E3;// [kg] Ac = x3*R3; printf("The composited extract is %f kg\n",(E1+E2+E3)); printf("The acid content is %f kg\n",((E1*y1)+(E2*y2)+(E3*y3))); printf("\n"); // If an extraction to give the same final raffinate concentration were to be done in single stage, the point M would be at the intersection of tie line R3E3 and the line BF. x = 0.20;// [mol fraction] xM = 0.12;// [mol fraction] // From Eqn. 10.6: S = F*(xF-xM)/(xM-yS);// [kg] printf("%f kg of solvent would be recquired if the same final raffinate concentration were to be obtained with one stage.\n",S);

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clear; clc; z12=complex(.05,.20); z23=complex(.075,.25); c1=.025; c2=.005; w1= (.1568*10^(-4)); w2= (.1679*10^(-4)); w3= (.0668*10^(-4)); w4= (.0702*10^(-4)); W=[w1 0 0 0; 0 w2 0 0; 0 0 w3 0; 0 0 0 w4]; v1=1.05; v2=1.05; v3=(1.05); h1=(v1/z12); h2=(v2/z12); h3=(v2/z23); h4=(v3/z23); H=[h1 0 0 0; 0 h2 0 0; 0 0 h3 0; 0 0 0 h4]; H1=conj(H); D=H1*W*H; D1=real(D); A=[1 -1 0; -1 1 0; 0 1 -1; 0 -1 1]; B=[-1 0; 1 0; 1 -1; -1 1]; b=[1;-1;0;0]; E=(B')*D; f=E*B; s1=complex(.50,-.12); s2=complex(-.48,.10); s3=complex(.80,-.40); s4=complex(-.78,.38); S=[s1;s2;s3;s4]; vm=(inv(H))*(conj(S)); vb=inv(f)*E*(vm-(b*v1)); V=[v1;vb]; printf("V = ") disp(V);

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Ex7_8.sce

//scilab 5.4.1 clear; clc; printf("\t\t\tProblem Number 7.8\n\n\n"); // Chapter 7 : Mixtures Of Ideal Gases // Problem 7.8 (page no. 329) // Solution //We will take as a basis 100 lbm of mixture. //Dividing colomn 2 by 3 gives us mass/molecular weight or moles of each constituents.The total number of moles in the mixture is the sum of coloumn 4,and the molecular weight of the mixture is the mass of the mixture(100 lbm) divided by the number of moles //In coloumn 5,mole fraction is given by moles/total mole printf("Basis:100 pounds of gas mixture\n\n") printf("gas Mass Molecular Moles Mole Percent \n") printf(" lbm weight MW fraction Volume \n") printf("O2 23.18 32.00 0.724 %f %f \n",(0.724/3.45),(0.724/3.45)*100) printf("N2 75.47 28.02 2.693 %f %f \n",(2.692/3.45),(2.692/3.45)*100) printf("A 1.30 39.90 0.033 %f %f \n",(0.033/3.45),(0.033/3.45)*100) printf("CO2 0.05 44.00 - - - \n") printf(" =100.00 =3.45 =1.00 = 100 \n ") printf(" MWm=100/3.45=28.99 ")

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//Ex19_13 Pg-962 clc dec=175; //binary input oct=dec2oct(dec) //decimal output disp("The octal equivslent of 175 is") disp(oct)

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errcatch(-1,"stop");mode(2);//Example 5.19, page no-317 e=0.2*10^-3 B=0.08 l=10*10^-2 v=e/(B*l) printf("V = %.3f m/sec = %.2f cm/sec",v,v*100) exit();

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fssc=33e3; f0=6e9; a=0.005; favg=f0*(1-0.5*a); t=[0:1e-7:1/(2*fssc)]; foft=favg-2*f0*a*fssc*(t-0.25/fssc); //Triangular thetadelta=f0*a*t/2-f0*a*fssc*(t^2); //Triangular //foft=favg+f0*0.5*a*cos(2*%pi*fssc*t); //Sine //thetadelta=(f0*0.5*a/(2*%pi*fssc))*sin(2*%pi*fssc*t); //Sine tie=thetadelta/favg; printf("Max tie is %0.2f ns\", max(tie)*1e9); xinit("1"); plot2d(t*1e6,foft/1e9); xtitle("f(t)", "Time (uS)", "Frequency (GHz)"); xinit("2"); plot2d(t*1e6,tie*1e9); xtitle("TIE", "Time (uS)", "Interval Error (nS)");

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function L=mulapproxluck(x,p) [nprobs,nsamps]=size(x); ntrials=sum(x,'r'); mu=p*ntrials; z=(x-mu) ./ sqrt(mu); R2=sum(z.^2,'r'); one=ones(1,nsamps); L=cdfgam("PQ",R2/2,((nprobs-1)/2)*one,one); endfunction

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//Exa 1.25 clc; clear; close; //given data BETAmin=80;//unitless BETAmax=120;//unitless IE=400;//in uA VT=25;//in mvolts VEE=15;//in volts VCC=15;//in volts VBE=0.7;//in volts VEB=-0.7;//in vol IE1=IE/2;//in uA IE2=IE1;//in uA IBmax=IE1/(1+BETAmin);//in uA IBmin=IE1/(1+BETAmax);//in uA Iiomax=IBmax-IBmin;//in uA disp(IBmax,"Largest possible input bias current in uA is :"); disp(IBmin,"smallest possible input bias current in uA is :"); disp(Iiomax,"Largest possible input offset current in uA is :");

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call -in 2011-07-21 17:39:00 call my.pr1 -in 2 double quoted -in:int 29647 -in 3 single quoted ;

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// *** ESCRIBA EL CODIGO AQUI! ***

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// Given :- T = 373.15 // initial temperature of saturated liquid in kelvin T0 = 293.15 // in kelvin P0 = 1.014 // in bar // Part(a) // From table A-2 ug = 2506.5 // in kj/kg uf = 418.94 // in kj/kg vg = 1.673 // in m^3/kg vf = 1.0435*(10**(-3)) // in m^3/kg sg = 7.3549 // in kj/kg.k sf = 1.3069 // in kj/kg.k // Calculations // Energy transfer accompanying work etaw = 0 // since p = p0 // Exergy transfer accompanying heat Q = 2257 // in kj/kg,obtained from example 6.1 etah = (1-(T0/T))*Q // Exergy destruction ed = 0 // since the process is accomplished without any irreversibilities deltae = ug-uf + P0*(10**5)*(vg-vf)/(10**3)-T0*(sg-sf) // Results printf( ' Part(a)the change in exergy is %.2f kJ/kg.',deltae) printf( ' The exergy transfer accompanying work is %.2f kJ/kg.',etaw) printf( ' The exergy transfer accompanying heat is %.2f kJ/kg',etah) printf( ' The exergy destruction is %.2f kJ/kg.',ed) // Part(b) Deltae = deltae // since the end states are same Etah = 0 // since process is adiabatic // Exergy transfer along work W = -2087.56 // in kj/kg from example 6.2 Etaw = W- P0*(10**5)*(vg-vf)/(10**3) // Exergy destruction Ed = -(Deltae+Etaw) // Results printf( ' Part(b)the change in exergy is %.2f kJ/kg.',Deltae) printf( ' The exergy transfer accompanying work is %.2f kJ/kg.',Etaw) printf( ' The exergy transfer accompanying heat is %.2f kJ/kg.',Etah) printf( ' The exergy destruction is %.2f kJ/kg.',Ed)

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clc; funcprot(0); // Initialization of Variable function[dms]=degtodms(deg) d = int(deg) md = abs(deg - d) * 60 m = int(md) sd = (md - m) * 60 sd=round(sd*100)/100 dms=[d m sd] endfunction b=40.0;//distance in degrees p=6.0;//disatnce in degrees //calculation a=%pi/2-asin(cos(b*%pi/180)*cos(p*%pi/180)); Bc=a*180/%pi-b; BC=Bc*1.853*60; B=asin(sin(b*%pi/180)/sin(a)) B=degtodms(B*180/%pi); disp(round(BC*100)/100,"distance BC in km"); disp(B,"angle of B deg min sec"); clear()

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Control_levi.sce

//Evaluacion LQG Train // load the data clc clear load("maglevtrainLTI.sod","X","U","sys") Ap=sys.A; Bp=sys.B; Cp=sys.C; Dp=sys.D; Dp=0 Cp=[1 0 0] tri = trzeros(sys) w = logspace(-3,3); svi = svplot(sys,w); scf(1); plot2d("ln", w, 20*log(svi')/log(10)) xgrid(12) xtitle("Valores singulares de la planta inicial","Frequency (rad/s)", "Amplitude (dB)"); ///////////////////////////////////--------------------------- //Planta aumentada con el integrador [ns,nc]=size(Bp); //ns= number of inputs; nc=number of controls Ai=[Ap Bp; 0*ones(nc,ns) 0*ones(nc,nc)]; Bi=[0*ones(ns,nc); eye(nc)]; Ci=[Cp 0*ones(1,1)]; Di=0*ones(nc,nc); sysi=syslin('c',Ai,Bi,Ci,Di); I=eye(nc); /* Plot singular values */ tri = trzeros(sysi) w = logspace(-3,3); svi = svplot(sysi,w); scf(2); plot2d("ln", w, 20*log(svi')/log(10)) xgrid(12) xtitle("Valores singulares de la planta con integrador","Frequency (rad/s)", "Amplitude (dB)"); //Obtenciion de los polos y zeros planta con integrador scf(3); plzr(sysi); //----LQR------// //we use ricatti equation for calculate de gain H C=1*Ci'*Ci; //State Weighting Matrix rho=1; //Cheap control recovery parameter //The smaller the parameter, the better the recovery. R = rho*eye(nc);//Control Weigthing Matrix //now we calculate B B=Bi*inv(R)*Bi'; A=Ai; //Solv the ricatti equation X=riccati(A,B,C,'c','eigen'); //the value of the gain G G=inv(R)*Bi'*X; //Matriz G //----KALMAN FILTER-------/// ll= inv(Cp*inv(-Ap)*Bp+Dp); //Choose ll and lh to match singular values at all frequencies lh = -inv(Ap)*Bp*ll; Lp=[lh, ll]; //ll, lh - for low and high frequency loop shaping pnint = eye(nc,nc) // Process Noise Intensity Matrix mu = 0.1; // Measurement Noise Intesity; Used to adjust Kalman Filter Bandwidth //Small mu - expensive sensor - large bandwidth //Large mu - inexpensive sensor - small bandwidth THETA = mu*eye(nc,nc) // Measurement Noise Intensity Matrix //We use the ricatti equation for calculate de gain H Ch=Lp*Lp'; Ah=Ai'; //calculating Bh Bh=Ci'*inv(THETA)*Ci; //Calculate de solution Xh=riccati(Ah,Bh,Ch,'c','eigen'); //The gain H H=(inv(THETA)*Ci*Xh)'; sysh = syslin('c',Ai,H,Ci,Di); /* Plot singular values*/ trh = trzeros(sysh) w = logspace(-3,3); svh = svplot(sysh,w); scf(4); plot2d("ln", w, 20*log(svh')/log(10)) xgrid(12) xtitle("Valores singulares Malla objetivo G_{KF}", "Amplitude (dB)"); //-------------------------------------- //Compensator LQG Ak = [ Ai-Bi*G-H*Ci 0*ones(ns+nc,nc) G 0*ones(nc,nc) ] Bk = [ H 0*ones(nc,nc) ] Ck = [0*ones(nc, ns+nc) eye(nc,nc) ] Dk = 0*ones(nc,nc); sysk=syslin('c',Ak,Bk,Ck,Dk); /* Plot singular values */ trk = trzeros(sysk) w = logspace(-3,3); svk = svplot(sysk,w); scf(5); plot2d("ln", w, 20*log(svk')/log(10)) xgrid(12) xtitle("Valores singulares compensador","Frequency (rad/s)", "Amplitude (dB)"); //---------------------------------------- //Analysis in open loop Aol = [ Ap Bp*Ck 0*ones(ns+nc+nc,ns) Ak ] Bol = [ 0*ones(ns,nc) Bk ] Col = [ Cp 0*ones(nc,ns+nc+nc) ] Dol = 0*ones(nc,nc); sysol = syslin('c',Aol,Bol,Col,Dol); //Obtencion de los polos y zeros lazo abierto scf(6); plzr(sysol); //---------------------------------------- //Response in closed loop syscl = syslin('c',Aol-Bol*Col, Bol*0.0001, Col, 0*eye(nc,nc)); //Obtencion de los polos y zeros en lazo cerrado scf(7); plzr(syscl); //-------------------------------- //Respuesta al step t=[0:0.1:20]; //input defined by a time function deff('u=timefun(t)','u=1') scf(8); plot2d(t',(csim(timefun,t,syscl))') xtitle("Respuesta del sistema","t(s)","Amplitud(m)");

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//Largest eigen value clear; clc; close(); a = [0 0 0; 0 0 0; 0 0 0] for i=1:3 for j=1:3 a(i,j) = input('Enter the values:') end end disp(a,'A = ') //initial vector u0 = [1 1 1]'; disp(u0,'The initial vector is') v = a*u0 a1 = max(u0) disp(a,'First approximation to eigen value is ') while abs(max(v)-a1)>0.002 disp(v,'Current eigen vector is ') a1 = max(v) disp(a1,'Current eigen value is ') u0 = v/max(v) v = a*u0 end format('v',4) disp(max(v),'The largest eigen value is: ') format('v',5) disp(u0,'The corresponding eigen vector is: ')

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//Example 2.7.12;//pulse width clc; clear; close; //given data : format('v',5) v=200;//in volts il=100;//latch current in mA l=0.2;//inductance in henry dit=v/l;//in amp/sec dt=(il*10^-3)/dit;//in seconds disp("part (a)") disp(dt*10^6,"minimum pulse width required to turn on the SCR is in micro seconds") r=20;//in ohms x=(il*10^-3*r)/v;// t=(log(1-x))*(-l/r);// disp("part (b)") disp(round(t*10^6),"minimum pulse width in micro seconds is") //part b answer is calculated wrong in the textbook

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x = [10:10:1000]; y = 1.2*x+6; plot(x,y); x = [10:10:1000]; y = -1.2*x+6; plot(x,y);

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clc; // page no 444 // prob no 13_2 //Voice transmisssion occupies 30 kHz.Spread spectrum is used to increase BW to 10MHz B1=30*10^3;//BW is 30 kHz B2=10*10^6;//BW is 10 MHz T=300;//noise temp at i/p PN=-110;//signal has total signal power of -110 dBm at receiver k=1.38*10^-23;//Boltzmann's const in J/K //Determination of noise power at B1=30kHz PN1=10*(log10(k*B1*T/10^-3)); disp('dBm',PN1,'The noise power at BW=30 kHz is'); //Determination of noise power at B2=10MHz PN2=10*(log10(k*B2*T/10^-3)); disp('dBm',PN2,'The noise power at BW=10 MHz is'); //Determination of SNR for 30kHz BW SNR1=PN-PN1; disp('dB',SNR1,'The value of SNR for BW=30 kHz is'); //Determination of SNR for 10MHz BW SNR2=PN-PN2; disp('dB',SNR2,'The value of SNR for BW=10 MHz is');

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// Updated(18-7-07) // 11.3 C = [1 0.5]; dC = 1; j=2; A = [1 -0.6 -0.16]; dA = 2; zj = zeros(1,j+1); zj(j+1) = 1; [Fj,dFj,Ej,dEj] = xdync(zj,j,A,dA,C,dC)

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obsv_mat.sci

function o=obsv_mat(a,c) [lhs,rhs]=argn(0) select type(a) case 1 then if rhs=1 then error('2 arguments : a,c'),end [m,n]=size(a) if m<>n then error(20,1),end [mb,nb]=size(c);if nb<>n then error(60),end //-compat next case retained for list/tlist compatibility case 15 then if a(1)<>'lss' then error(91,1),end [a,c]=a([2,4]) [n,n]=size(a) case 16 then if a(1)<>'lss' then error(91,1),end [a,c]=a([2,4]) [n,n]=size(a) else error('(a,c) pair or syslin list') end; o=c;for k=1:n-1, o=[c;o*a],end

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MonaLisa.sce

// Distance Vector dist = (0.75:0.25:15) //Num. elements dist. vector sz = length(dist) //Width Mona Lisa Painting width = 0.53 //Height Mona Lisa Painting height = 0.77 //Focus lenght f = 0.008 //Dimension of a pixel sp = 0.00000408 //Total pixels width & height sensor wp = 1288 hp = 728 //Loop for each distance for i = 1:sz //Calculate angles for each dist alphaW = 2*atan((width/2)/dist(i)) alphaH = 2*atan((height/2)/dist(i)) //Calculate dimensions in camera's sensor camW = (tan(alphaW/2))*2*f camH = (tan(alphaH/2))*2*f //Caculate number of pixels (must be int, it is discrete) pixelsW = int(camW/sp) //Compare pixelsW with total pixels width in sensor if pixelsW > wp then pixelsW = wp end pixelsH = int(camH/sp) //Compare pixelsW with total pixels width in sensor, //truncated if pixelsH is higher than hp if pixelsH > hp then pixelsH = hp end //Vector with all pixels values vecPixels(i) = pixelsW*pixelsH end disp(vecPixels) //Draw graph plot(dist,vecPixels','r') //Write axis-labels and title on the graph xlabel('Distance (m)','fontsize',4) ylabel ('Num. pixels','fontsize',4) title('Mona Lisa graph','fontsize',8,'color','green')

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6_6.sce

clear; clc; printf("\t\t\tExample Number 6.6\n\n\n"); // turbulent heat transfer in a short tube // illustration6.6 // solution p = 101325;// [Pa] pressure of air Ta = 300;// [K] temperature of air d = 0.02;// [m] diameter of tube u = 40;// [m/s] velocity of air L = 0.1;// [m] length of tube dT = 5;// [degree celsius] rise in temperature // we first must evaluate the air properties at 300 K v = 15.69*10^(-6);// [square meter/s] kinematic viscosity k = 0.02624;// [W/m degree celsius] Cp = 1006;// [J/kg K] Pr = 0.70;// prandtl no. rho = 1.18;// [kg/cubic meter] Re_d = u*d/v;// reynolds number disp(Re_d,"reynolds number is "); disp("so the flow is turbulent"); // consulting figure (6-6) for this value of Re_d and L/d = 5 we find Nu_x_by_Nu_inf = 1.15; // or the heat transfer coefficient is about 15 percent higher that it would be for thermally developed flow. // we calculate heat-transfer for developed flow using Nu_d = 0.023*Re_d^(0.8)*Pr^(0.4); // and h = k*Nu_d/d;// [W/square meter degree celsius] // increasing this value by 15 percent h = 1.15*h;// [W/square meter degree celsius] // the mass flow is Ac = %pi*d^(2)/4;// [square meter] m_dot = rho*u*Ac;// [kg/s] // so the total heat transfer is A = %pi*d*L;// [square meter] q_by_A = m_dot*Cp*dT/A;// [W/square meter] printf("\n\n the constant heat flux that must be applied at the tube surface to result in an air temperature rise of 5 degree celsius is %f W/square meter",q_by_A); Tb_bar = (Ta+(Ta+dT))/2;// [K] Tw_bar = Tb_bar+q_by_A/h;// [K] printf("\n\n average wall temperature is %f K",Tw_bar);

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example_4_2b.sce

clear; clc; // A Textbook on HEAT TRANSFER by S P SUKHATME // Chapter 4 // Principles of Fluid Flow // Example 4.2(b) // Page 180 printf("Example 4.2(b), Page 180 \n\n") L = 3 ; //[m] D = 0.01 ; //[m] V = 0.2 ; //[m/s] // (b) V1=0.7; v1 = 1.306 * 10^-6 ; // [m^2/s] printf("(b) If the velocity is increased to 0.7 \n"); // if velocity of water is 0.7 m/s V1=0.7; // [m/s] Re_D1=V1*D/(1.306*10^-6); printf("Reynolds no is %f \n",Re_D1); // flow is again turbulent f1 = 0.079*(Re_D1)^(-0.25); delta_p1 = (4*f1*L*999.7*0.7^2)/(0.01*2); // [Pa] printf("Pressure drop is %f Pa \n",delta_p1); // x1 = (T_w/p)^0.5 = ((f1/2)^0.5)*V ; x1 = ((f1/2)^0.5)*V1 ; y1_plus = 0.005*x1/(v1); printf("y+ at centre line = %f \n",y1_plus); V_max1 = x1*(2.5* log(y1_plus) + 5.5) ; // [m/s] printf("V_max is %f m/s \n",V_max1); ratio1 = V_max1/V1; printf("Vmax/Vbar = %f ",ratio1);

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Ex13_5.sce

clc clear //DATA GIVEN r1=750/2000; //radius of larger pulley in m r2=300/2000; //radius of smaller pulley in m d=1.5; //distance between the centres of pulley in m Tms=14; //maximum safe tension in N/mm b=150; //width of the belt in mm v=540; //speed of the belt in m/min mu=0.25; //coefficient of friction T1=Tms*b; //maximum tension in the belt in N v=v/60; //speed of the belt converted into m/s //(i) for open belt ALPHAo=asin ((r1-r2)/d)*180/(%pi); //alpha in degrees THETAo=180-2*ALPHAo; //angle of lap or contact in deg T2o=T1/(%e^(mu*(THETAo*%pi/180))); //tension on the slack side in N Po=(T1-T2o)*v; //power transmitted by the belt in watts //(ii) for cross belt ALPHAc=asin ((r1+r2)/d)*180/(%pi); //alpha in degrees THETAc=180+2*ALPHAc; //angle of lap or contact in deg T2c=T1/(%e^(mu*(THETAc*%pi/180))); //tension on the slack side in N Pc=(T1-T2c)*v; //power transmitted by the belt in watts printf(' (i) The Maximum Power transmitted by the open belt is: %2.3f kW. \n',(Po/1000)); printf(' (ii) The Maximum Power transmitted by the cross belt is: %2.3f kW. \n',(Pc/1000));

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load ALU.hdl, output-file ALU-64cases.out, compare-to ALU-64cases.cmp, output-list x%B1.16.1 y%B1.16.1 zx%B1.1.1 nx%B1.1.1 zy%B1.1.1 ny%B1.1.1 f%B1.1.1 no%B1.1.1 out%B1.16.1 zr%B1.1.1 ng%B1.1.1 ; set x %B1100110011001100, set y %B1010101010101010; set zx 0, set nx 0, set zy 0, set ny 0, set f 0, set no 0, eval, output; set zx 0, set nx 0, set zy 0, set ny 0, set f 0, set no 1, eval, output; set zx 0, set nx 0, set zy 0, set ny 0, set f 1, set no 0, eval, output; set zx 0, set nx 0, set zy 0, set ny 0, set f 1, set no 1, eval, output; set zx 0, set nx 0, set zy 0, set ny 1, set f 0, set no 0, eval, output; set zx 0, set nx 0, set zy 0, set ny 1, set f 0, set no 1, eval, output; set zx 0, set nx 0, set zy 0, set ny 1, set f 1, set no 0, eval, output; set zx 0, set nx 0, set zy 0, set ny 1, set f 1, set no 1, eval, output; set zx 0, set nx 0, set zy 1, set ny 0, set f 0, set no 0, eval, output; set zx 0, set nx 0, set zy 1, set ny 0, set f 0, set no 1, eval, output; set zx 0, set nx 0, set zy 1, set ny 0, set f 1, set no 0, eval, output; set zx 0, set nx 0, set zy 1, set ny 0, set f 1, set no 1, eval, output; set zx 0, set nx 0, set zy 1, set ny 1, set f 0, set no 0, eval, output; set zx 0, set nx 0, set zy 1, set ny 1, set f 0, set no 1, eval, output; set zx 0, set nx 0, set zy 1, set ny 1, set f 1, set no 0, eval, output; set zx 0, set nx 0, set zy 1, set ny 1, set f 1, set no 1, eval, output; set zx 0, set nx 1, set zy 0, set ny 0, set f 0, set no 0, eval, output; set zx 0, set nx 1, set zy 0, set ny 0, set f 0, set no 1, eval, output; set zx 0, set nx 1, set zy 0, set ny 0, set f 1, set no 0, eval, output; set zx 0, set nx 1, set zy 0, set ny 0, set f 1, set no 1, eval, output; set zx 0, set nx 1, set zy 0, set ny 1, set f 0, set no 0, eval, output; set zx 0, set nx 1, set zy 0, set ny 1, set f 0, set no 1, eval, output; set zx 0, set nx 1, set zy 0, set ny 1, set f 1, set no 0, eval, output; set zx 0, set nx 1, set zy 0, set ny 1, set f 1, set no 1, eval, output; set zx 0, set nx 1, set zy 1, set ny 0, set f 0, set no 0, eval, output; set zx 0, set nx 1, set zy 1, set ny 0, set f 0, set no 1, eval, output; set zx 0, set nx 1, set zy 1, set ny 0, set f 1, set no 0, eval, output; set zx 0, set nx 1, set zy 1, set ny 0, set f 1, set no 1, eval, output; set zx 0, set nx 1, set zy 1, set ny 1, set f 0, set no 0, eval, output; set zx 0, set nx 1, set zy 1, set ny 1, set f 0, set no 1, eval, output; set zx 0, set nx 1, set zy 1, set ny 1, set f 1, set no 0, eval, output; set zx 0, set nx 1, set zy 1, set ny 1, set f 1, set no 1, eval, output; set zx 1, set nx 0, set zy 0, set ny 0, set f 0, set no 0, eval, output; set zx 1, set nx 0, set zy 0, set ny 0, set f 0, set no 1, eval, output; set zx 1, set nx 0, set zy 0, set ny 0, set f 1, set no 0, eval, output; set zx 1, set nx 0, set zy 0, set ny 0, set f 1, set no 1, eval, output; set zx 1, set nx 0, set zy 0, set ny 1, set f 0, set no 0, eval, output; set zx 1, set nx 0, set zy 0, set ny 1, set f 0, set no 1, eval, output; set zx 1, set nx 0, set zy 0, set ny 1, set f 1, set no 0, eval, output; set zx 1, set nx 0, set zy 0, set ny 1, set f 1, set no 1, eval, output; set zx 1, set nx 0, set zy 1, set ny 0, set f 0, set no 0, eval, output; set zx 1, set nx 0, set zy 1, set ny 0, set f 0, set no 1, eval, output; set zx 1, set nx 0, set zy 1, set ny 0, set f 1, set no 0, eval, output; set zx 1, set nx 0, set zy 1, set ny 0, set f 1, set no 1, eval, output; set zx 1, set nx 0, set zy 1, set ny 1, set f 0, set no 0, eval, output; set zx 1, set nx 0, set zy 1, set ny 1, set f 0, set no 1, eval, output; set zx 1, set nx 0, set zy 1, set ny 1, set f 1, set no 0, eval, output; set zx 1, set nx 0, set zy 1, set ny 1, set f 1, set no 1, eval, output; set zx 1, set nx 1, set zy 0, set ny 0, set f 0, set no 0, eval, output; set zx 1, set nx 1, set zy 0, set ny 0, set f 0, set no 1, eval, output; set zx 1, set nx 1, set zy 0, set ny 0, set f 1, set no 0, eval, output; set zx 1, set nx 1, set zy 0, set ny 0, set f 1, set no 1, eval, output; set zx 1, set nx 1, set zy 0, set ny 1, set f 0, set no 0, eval, output; set zx 1, set nx 1, set zy 0, set ny 1, set f 0, set no 1, eval, output; set zx 1, set nx 1, set zy 0, set ny 1, set f 1, set no 0, eval, output; set zx 1, set nx 1, set zy 0, set ny 1, set f 1, set no 1, eval, output; set zx 1, set nx 1, set zy 1, set ny 0, set f 0, set no 0, eval, output; set zx 1, set nx 1, set zy 1, set ny 0, set f 0, set no 1, eval, output; set zx 1, set nx 1, set zy 1, set ny 0, set f 1, set no 0, eval, output; set zx 1, set nx 1, set zy 1, set ny 0, set f 1, set no 1, eval, output; set zx 1, set nx 1, set zy 1, set ny 1, set f 0, set no 0, eval, output; set zx 1, set nx 1, set zy 1, set ny 1, set f 0, set no 1, eval, output; set zx 1, set nx 1, set zy 1, set ny 1, set f 1, set no 0, eval, output; set zx 1, set nx 1, set zy 1, set ny 1, set f 1, set no 1, eval, output;

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// Exa 2.24 clc; clear; close; format('v',5) // Given data R1 = 3;// in ohm R2 = 2;// in ohm R3 = 1;// in ohm R4 = 8;// in ohm R5 = 2;// in ohm V = 10;// in V R = ((R1+R2)*R5)/((R1+R2)+R5);// in ohm Rth = R + R3;// in ohm R_L = Rth;// in ohm disp(R_L,"The value of load resistance in ohm is");

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clc; disp("The filter must attenuate the signal by a factor of 10."); //displaying result f=300*10^6; //frequency in Hz disp(" If R = 100 Ohm ,then the reactance of the capacitor should be about 10 Ohm."); //displaying result c=1/(2*(%pi)*f*10); //calculating capacitance disp(c,"At 300 MHz, this is in Farad = "); //displaying result

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//Example 5_14_u1 clc(); clear; //To calculate the effective temprature of neutrons a=0.352 //units in nm h=1 k=1 l=1 d=a/sqrt(h^2+k^2+l^2) //units in nm theta=28.5 //units in degrees lamda=2*d*sin(theta*(%pi/180)) //units in nm h=6.63*10^-34 //units in m^2 kg s^-1 m=1.67*10^-27 //units in Kgs KB=1.38*10^-23 //units in m^2 kg s^-2 K^-1 lamda=lamda*10^-9 //units in meters T=h^2/(3*m*KB*lamda^2) //units in K printf("The effective temprature of neutrons is T=%dK",T)

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//EXAMPLE 2.94 PG NO-139-140 L=0.6; //LENGTH a=20*10^-4; //AREA MU=(4*%pi*10^-7); R=L/(MU*a); N1=1500; N2=500; i=250; M=(N1*N2)/R; e=M*(i); disp('R = '+string(R)+' '); disp('mutual induction is = '+string(M)+' H'); disp('E.M.F INDUCE is = '+string(e)+' V');