Kumar Appaiah kumanna

Teaching tips

Kumar Appaiah, Associate Professor, Department of Electrical Engineering, IIT Bombay

I would like to preface this with the information that I chose to become a teacher primarily because I was very positively influenced by my teachers, especially at the undergraduate level. The happiness of seeing my students graduate and do great things in industry is a reward that has no parallels, and I am very grateful for this

	import numpy as np
	import matplotlib.pyplot as plt

	N = 2048
	N_CP = 3

	h = [1, 0.4-0.3j, 0.2+0.4j, -0.3+0.4j]

	# Generate N random QPSK symbols
	x = np.sign(np.random.randn(N)) + 1j * np.sign(np.random.randn(N))

	import numpy as np
	import matplotlib.pyplot as plt

	T = 1
	delta_f = 1 / T
	M = 8
	N = 8
	OVERSAMPLING = 128
	t = np.arange(-10 * N * M * T, 10 * N * T * M, 1 / OVERSAMPLING)

	import numpy as np

	N = 8
	h = np.array([1+1j, 0.5-0.4j, 0.2 + 0.3j])
	x = np.array([1, 3, 2, 4, 7, -3, 4, 5])
	assert(len(x) == N)

	# Convolution
	y = np.convolve(x, h)
	print(y)

	import numpy as np
	import matplotlib.pyplot as plt

	# Ts = 0.01
	# N = 400
	t = np.arange(0, 4, 0.01)
	x = np.cos(2 * np.pi * t)
	# plt.plot(t, x)
	# plt.show()

	import numpy as np
	import matplotlib.pyplot as plt
	import scipy.signal

	SAMPLE_RATE = 44100
	DURATION_SEC = 2
	FREQ1 = 1000
	FREQ2 = 1050
	T = 1 / SAMPLE_RATE

	import numpy as np
	import matplotlib.pyplot as plt

	N = 100
	bpsk_data = np.sign(np.random.randn(N))

	h = np.array([1, 0.25, -0.25])

	y = np.convolve(h, bpsk_data)

	import numpy as np
	import matplotlib.pyplot as plt
	import scipy

	np.set_printoptions(edgeitems=30, linewidth=100000, formatter=dict(float=lambda x: "%.3g" % x))

	# We will create an ideal truncated filter

	Ts = 44100
	# 0.5 seconds

	import numpy as np

	M = 8
	N = 8
	triplets = [
	(1 - 0.3j, 0, 0),
	(0.3 + 0.4j, 1, 1),
	(0.7 - 0.6j, 2, 1),
	]