1. Consider the difference equation shown below. y(n) = 0.0899x(n) + 0.0257x(n − 1) - 0.0257x(n − 2) - 0.0899x(n − 3) + 1.6148y(n − 1) - 1.9395y(n − 2) + 1.2807y(n-3) - 0.5852y(n − 4) Solve the following: A. System Function, H(z) B. Poles and zeros position in the z-plane C. Expression of the impulse response, h(n). Verify your answer by comparing the stem plots of the first 50 samples of h(n) vs. using impz () function D. Frequency response plot of the system. Comment on the plot of the frequency response in relation to the position of the poles and zeros.
Quantization and Resolution
Quantization is a methodology of carrying out signal modulation by the process of mapping input values from an infinitely long set of continuous values to a smaller set of finite values. Quantization forms the basic algorithm for lossy compression algorithms and represents a given analog signal into digital signals. In other words, these algorithms form the base of an analog-to-digital converter. Devices that process the algorithm of quantization are known as a quantizer. These devices aid in rounding off (approximation) the errors of an input function called the quantized value.
Probability of Error
This topic is widely taught in many undergraduate and postgraduate degree courses of:
answer A and B.
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Consider the difference equation shown below.
y(n) = 0.0899x(n) + 0.0257x(n − 1) — 0.0257x(n − 2) — 0.0899x(n − 3)
+ 1.6148y(n − 1) — 1.9395y(n − 2) + 1.2807y(n − 3) −0.5852y(n − 4)
Solve the following:
A. System Function, H(z)
B. Poles and zeros position in the z-plane
C. Expression of the impulse response, h(n). Verify your answer by comparing the
stem plots of the first 50 samples of h(n) vs. using impz () function
D. Frequency response plot of the system. Comment on the plot of the frequency
response in relation to the position of the poles and zeros."
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