Consultant evaluation and lab 3

8th-Semester-Spring-2025/clinic-consultant/labs/lab-3/lab3q1.m

@@ -0,0 +1,49 @@
+% value of a
+a=0.9;
+
+% signal x(n)
+for n=0:200
+    x(n+1) = a^n;
+end
+figure(1)
+n=0:1:200;
+stem(n,x)
+xlabel('n');
+ylabel('x(n)');
+
+% question part (e)
+n=1;
+d=[1 -a];
+[h1,w]=freqz(n,d,256);
+h1mag=abs(h1);
+figure(2)
+plot(w,h1mag,'b','linewidth',2)
+xlabel('Frequency');
+ylabel('Magnitude Response');
+
+% partial dtft for k =3
+[h2,w]=freqz(x(1:4),1,256);
+h2mag=abs(h2);
+hold
+plot(w,h2mag,'r','linewidth',2)
+
+% partial dtft for k =10
+[h3,w]=freqz(x(1:11),1,256);
+h3mag=abs(h3);
+plot(w,h3mag,'m','linewidth',2)
+
+% partial dtft for k =20
+[h4,w]=freqz(x(1:21),1,256);
+h4mag=abs(h4);
+plot(w,h4mag,'k','linewidth',2)
+
+% supremum coefficients of the error
+for k=1:200
+   [hk,w]=freqz(x(1:k+1),1,256);
+   ek=abs(h1-hk);
+   coeff(k)=max(ek);
+end
+figure(3)
+k=1:1:200;
+stem(k,coeff)
+

8th-Semester-Spring-2025/clinic-consultant/labs/lab-3/lab3q1.py

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+import numpy as np
+import scipy as sp
+import matplotlib.pyplot as plt
+
+DATA_POINTS = 200
+
+def u(n):
+    return np.heaviside(n, 1)
+
+def main():
+    a = 0.9
+    n = np.arange(DATA_POINTS)
+    x = a**n * u(n)
+
+    # Plot samples of x[n]
+    plt.figure()
+    plt.stem(n, x)
+    plt.xlabel("n")
+    plt.ylabel(r"$x[n]$")
+    
+    # Plot the analytical DTFT
+    numerator = 1
+    denominator = [1, -a]
+    omega, h_0 = sp.signal.freqz(numerator, denominator, 256)
+    
+    plt.figure()
+    plt.plot(omega, np.abs(h_0), label="Analytical DTFT")
+    plt.xlabel("Frequency")
+    plt.ylabel("Magnitude Response")
+    
+    # Plot the truncated DTFT for K = 3, 10, 20
+    for K in (3, 10, 20):
+        omega, h = sp.signal.freqz(x[:K], 1, 256)
+        plt.plot(omega, np.abs(h), label=f"Truncated DTFT ($K = {K}$)")
+    plt.legend()
+
+    # Calculate the maximum error between the truncated DTFT
+    # and the analytical DTFT for values of K from 1 to 200
+    k = np.arange(DATA_POINTS)
+    coeffs = np.zeros_like(k, dtype=np.float32)
+
+    for i_k in k:
+        omega, h_k = sp.signal.freqz(x[:i_k], 1, 256)
+        err_k = np.abs(h_0 - h_k)
+        coeffs[i_k] = np.max(err_k)
+    
+    # Plot the maximum error previously calculated
+    plt.figure()
+    plt.stem(k, coeffs)
+    plt.xlabel("k")
+    plt.ylabel("Supremum coefficients of the error")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()

8th-Semester-Spring-2025/clinic-consultant/labs/lab-3/lab3q2.m

@@ -0,0 +1,54 @@
+% value of a
+a=0.9;
+
+% signal x(n)
+for n=0:200
+    x(n+1) = n*(a^n);
+end
+figure(1)
+n=0:1:200;
+stem(n,x)
+xlabel('n');
+ylabel('x(n)');
+
+% question part (e)
+n=[0 a];
+d=[1 -2*a a*a];
+[h1,w]=freqz(n,d,256);
+h1mag=abs(h1);
+figure(2)
+plot(w,h1mag,'b','linewidth',2)
+xlabel('Frequency');
+ylabel('Magnitude Response');
+
+% partial dtft for k =3
+[h2,w]=freqz(x(1:4),1,256);
+h2mag=abs(h2);
+hold
+plot(w,h2mag,'r','linewidth',2)
+
+% partial dtft for k =10
+[h3,w]=freqz(x(1:11),1,256);
+h3mag=abs(h3);
+plot(w,h3mag,'m','linewidth',2)
+
+% partial dtft for k =20
+[h4,w]=freqz(x(1:21),1,256);
+h4mag=abs(h4);
+plot(w,h4mag,'g','linewidth',2)
+
+% partial dtft for k =40
+[h5,w]=freqz(x(1:41),1,256);
+h5mag=abs(h5);
+plot(w,h5mag,'k','linewidth',2)
+
+% supremum coefficients of the error
+for k=1:200
+   [hk,w]=freqz(x(1:k+1),1,256);
+   ek=abs(h1-hk);
+   coeff(k)=max(ek);
+end
+figure(3)
+k=1:1:200;
+stem(k,coeff)
+

8th-Semester-Spring-2025/clinic-consultant/labs/lab-3/lab3q2.py

@@ -0,0 +1,58 @@
+import numpy as np
+import scipy as sp
+import matplotlib.pyplot as plt
+
+
+DATA_POINTS = 200
+
+
+def u(n):
+    return np.heaviside(n, 1)
+
+
+def main():
+    a = 0.9
+    n = np.arange(DATA_POINTS)
+    x = n*(a**n)*u(n)
+
+    # Plot samples of x[n]
+    plt.figure()
+    plt.stem(n, x)
+    plt.xlabel("n")
+    plt.ylabel(r"$x[n]$")
+    
+    # Plot the analytical DTFT
+    numerator = [0, a]
+    denominator = [1, -2*a, a**2]
+    omega, h_0 = sp.signal.freqz(numerator, denominator, 256)
+    
+    plt.figure()
+    plt.plot(omega, np.abs(h_0), label="Analytical DTFT")
+    plt.xlabel("Frequency")
+    plt.ylabel("Magnitude Response")
+    
+    # Plot the truncated DTFT for K = 3, 10, 20
+    for K in (3, 10, 20):
+        omega, h = sp.signal.freqz(x[:K], 1, 256)
+        plt.plot(omega, np.abs(h), label=f"Truncated DTFT ($K = {K}$)")
+    plt.legend()
+
+    # Calculate the maximum error between the truncated DTFT
+    # and the analytical DTFT for values of K from 1 to 200
+    k = np.arange(DATA_POINTS)
+    coeffs = np.zeros_like(k, dtype=np.float32)
+
+    for i_k in k:
+        omega, h_k = sp.signal.freqz(x[:i_k], 1, 256)
+        err_k = np.abs(h_0 - h_k)
+        coeffs[i_k] = np.max(err_k)
+    
+    # Plot the maximum error previously calculated
+    plt.figure()
+    plt.stem(k, coeffs)
+    plt.xlabel("k")
+    plt.ylabel("Supremum coefficients of the error")
+    plt.show()
+
+if __name__ == "__main__":
+    main()

8th-Semester-Spring-2025/clinic-consultant/labs/lab-3/lab3q3.m

@@ -0,0 +1,52 @@
+% value of a
+wc=0.4*pi;
+
+% signal x(n)
+for n=-50:50
+if (n == 0)
+    x(n+51) = wc/pi;
+else
+    x(n+51) = sin(wc*n)/(pi*n);
+end
+end
+figure(1)
+n=-50:1:50;
+stem(n,x)
+xlabel('n')
+ylabel('x(n)')
+
+% Ideal lpf
+n=0;
+for w=0:0.05*pi:pi;
+    n=n+1;
+    wfreq(n)=w;
+    if  (w <= wc)
+        hlpf(n)=1;
+    else
+        hlpf(n)=0;
+    end
+end
+figure(2)
+plot(wfreq,hlpf,'b','linewidth',2)
+xlabel('Frequency')
+ylabel('DTFT of Ideal Lowpass Filter')
+
+% partial dtft for k =10
+k=10;
+[h2]=freqz(x(51-k:51+k),1,wfreq);
+h2mag=abs(h2);
+hold
+plot(wfreq,h2mag,'r','linewidth',2)
+
+% partial dtft for k =20
+k=20;
+[h3]=freqz(x(51-k:51+k),1,wfreq);
+h3mag=abs(h3);
+plot(wfreq,h3mag,'m','linewidth',2)
+
+% partial dtft for k =30
+k=30;
+[h4]=freqz(x(51-k:51+k),1,wfreq);
+h4mag=abs(h4);
+plot(wfreq,h4mag,'k','linewidth',2)
+

8th-Semester-Spring-2025/clinic-consultant/labs/lab-3/lab3q5.m

@@ -0,0 +1,15 @@
+for n=0:99
+    y1(n+1) = (1 - (0.95)^(n+1))/0.05;
+    y2(n+1) = n + 1;
+end
+figure(1)
+n=0:1:99;
+stem(n,y1)
+xlabel('n')
+ylabel('y(n)')
+figure(2)
+n=0:1:99;
+stem(n,y2)
+xlabel('n')
+ylabel('y(n)')
+

8th-Semester-Spring-2025/cloud-hardware/chapter-4-questions/chapter-4-questions.md

@@ -0,0 +1,37 @@
+---
+title: ECE09488 Assignment #4
+author: Aidan Sharpe
+date: March 6th, 2025
+geometry: margin=1in
+---
+
+### 1. What type of cloud network traffic incurs fees?
+c. Egress traffic from a database
+
+### 2. Which of the following is a legitimate CIDR block for a subnet? Choose TWO.
+a. 172.300.7.0/25
+b. 192.168.4.0/24
+
+### 3. Which of the following concepts is ensured by redundant routers and switches?
+b. HA
+
+### 4. Which IP address belongs within the CIDR block 172.25.1.0/23?
+a. 172.25.2.10
+
+### 5. Which tier is most protected?
+c. Data tier
+
+### 6. Which cloud stack layer corresponds to IaaS services?
+b. Network layer
+
+### 7. Which technology is used to improve vNIC performance?
+d. SR-IOV (single root input/output virtualization)
+
+### 8. How do you change applicable routes in an Azure VNet?
+d. Override system routes with custom routes
+
+### 9. What factor is improved by SR-IOV?
+b. Performance
+
+### 10. Which cloud platform's VPC or VNet can extend beyond a single region?
+c. GCP

8th-Semester-Spring-2025/frontiers/notes/technical-overview.md

@@ -0,0 +1,9 @@
+## Macromolecule storage techniques
+- Several KBs have been stored successfully in DNA chains
+- Synthetic molecules also work
+- Technology is mostly limited by slow reading and writing speeds
+- Sequencing methods include tandem mass spectrometry (MS/MS), enzyme-based approaches, and nanopore threading.
+- Using synthetic polymers allows the molecular structure to be tuned to facilitate sequencing using "routine analytical instruments".
+
+## Synthesis of coded macromolecules
+- Solid-phase iterative chemistry

8th-Semester-Spring-2025/weapon-systems/homework/l-6-homework.md

@@ -0,0 +1,94 @@
+---
+title: ECE09426 Lecture 6 Homework
+author: Aidan Sharpe
+date: March 3rd, 2025
+geometry: margin=1in
+---
+
+# Required PDMS for a HAW System
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+P_TX = 7E3
+LOSS_TX = 10**(5/10)
+GAIN_TX = 10**(35/10)
+MAX_RANGE = 60E3
+TARGET_AREA = 1
+
+def PDMS(tx_power, tx_gain, radar_cross_section, tx_loss, dist_source_target, dist_target_missile):
+    tx_p_gain = (tx_power*tx_gain) / (4*np.pi*tx_loss)
+    p_ref = radar_cross_section / (dist_source_target**2)
+    p_rx = 1 / (4*np.pi*dist_target_missile**2)
+
+    return tx_p_gain * p_ref * p_rx
+
+
+def main():
+    pd_min = PDMS(P_TX, GAIN_TX, TARGET_AREA, LOSS_TX, MAX_RANGE, MAX_RANGE)
+    pd_min_db = 10*np.log10(pd_min)
+    print(pd_min_db)
+```
+
+PDMS required = -144.6592668956451
+
+# PDMS for a non-HAW System
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+MAX_RANGE = 60E3
+
+def main():
+    illumination_percent = np.arange(0.1, 1.1, 0.1)
+    max_range = MAX_RANGE/illumination_percent
+
+    plt.plot(100*illumination_percent, max_range)
+    plt.xlabel("Illumination Percent")
+    plt.ylabel("Max Range [km]")
+    plt.show()
+```
+
+![](./illumination-percent-range.png)
+
+
+# Rocket Motor Math
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+g = 9.81
+I_SP = 250
+BURN_TIME = 14
+INITIAL_MASS = 1200
+FINAL_MASS = 700
+
+def v_burnout(I_sp, t_burn, w_launch, w_burnout):
+    return I_sp * g*np.log(w_launch/w_burnout)
+
+
+def main():
+	t = np.linspace(0, BURN_TIME, 500)
+	
+    m_propellant = INITIAL_MASS - FINAL_MASS
+    w_propellant_0 = g*m_propellant
+    w_rocket = g*FINAL_MASS
+
+    weight_flow_rate = w_propellant_0/BURN_TIME
+    v_exit = I_sp*g
+    w_propellant = w_propellant_0 - weight_flow_rate*t
+
+    thrust = weight_flow_rate*v_exit*g
+    w_total = w_rocket + w_propellant
+    
+    acceleration_g = thrust/w_total
+    
+    plt.plot(t, acceleration_g)
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+![](./timed_burn.png)

8th-Semester-Spring-2025/weapon-systems/homework/pdms.py

@@ -0,0 +1,35 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+P_TX = 7E3
+LOSS_TX = 10**(5/10)
+GAIN_TX = 10**(35/10)
+MAX_RANGE = 60E3
+TARGET_AREA = 1
+
+def PDMS(tx_power, tx_gain, radar_cross_section, tx_loss, dist_source_target, dist_target_missile):
+    tx_p_gain = (tx_power*tx_gain) / (4*np.pi*tx_loss)
+    p_ref = radar_cross_section / (dist_source_target**2)
+    p_rx = 1 / (4*np.pi*dist_target_missile**2)
+
+    return tx_p_gain * p_ref * p_rx
+
+
+def main():
+    pd_min = PDMS(P_TX, GAIN_TX, TARGET_AREA, LOSS_TX, MAX_RANGE, MAX_RANGE)
+    pd_min_db = 10*np.log10(pd_min)
+    print(pd_min_db)
+
+    illumination_percent = np.arange(0.1, 1.1, 0.1)
+    max_range = MAX_RANGE/illumination_percent
+
+    plt.plot(100*illumination_percent, max_range)
+    plt.xlabel("Illumination Percent")
+    plt.ylabel("Max Range [km]")
+    plt.savefig("illumination-percent-range.png")
+    plt.show()
+    
+
+
+if __name__ == "__main__":
+    main()

8th-Semester-Spring-2025/weapon-systems/homework/rocket_motor.py

@@ -10,8 +10,11 @@ g = 9.81
 def specific_impulse(v_burnout, w_launch, w_burnout):
     return v_burnout / (g*np.log(w_launch/w_burnout))
 
+def v_burnout(I_sp, t_burn, w_launch, w_burnout):
+    return I_sp * g*np.log(w_launch/w_burnout)
 
-def main():
+
+def exit_velocity():
     plt.figure(figsize=(16,9))
     v_burnout = 1000
     w_rocket = 300
@@ -61,6 +64,32 @@ def main():
     plt.savefig("rocket_motor.png")
     plt.show()
 
+def main():
+    I_sp = 250
+    t_burn = 14
+    t = np.linspace(0, t_burn, 500)
+    m_0 = 1200
+    m_final = 700
+    
+    m_propellant = m_0 - m_final
+    w_propellant_0 = g*m_propellant
+    w_rocket = g*m_final
+
+    weight_flow_rate = w_propellant_0/t_burn
+    v_exit = I_sp*g
+    w_propellant = w_propellant_0 - weight_flow_rate*t
+
+    thrust = weight_flow_rate*v_exit*g
+    w_total = w_rocket + w_propellant
+    
+    acceleration_g = thrust/w_total
+    
+    plt.plot(t, acceleration_g)
+    plt.savefig("timed_burn.png")
+    plt.show()
+
+    
+
 
 if __name__ == "__main__":
     main()