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Mind Map: Sampling Theorem | Statistics for SSC CGL

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FAQs on Mind Map: Sampling Theorem - Statistics for SSC CGL

1. What is the Sampling Theorem in signal processing?
Ans. The Sampling Theorem states that a continuous signal can be completely represented in its samples and fully reconstructed if it is sampled at a rate that is at least twice the highest frequency present in the signal. This minimum sampling rate is known as the Nyquist rate.
2. Why is the Nyquist rate important in digital signal processing?
Ans. The Nyquist rate is crucial because it determines the minimum sampling frequency required to avoid aliasing, which occurs when higher frequency signals are misrepresented as lower frequency signals. Sampling below the Nyquist rate can lead to loss of information and distortion in the reconstructed signal.
3. What are the consequences of violating the Sampling Theorem?
Ans. Violating the Sampling Theorem by sampling below the Nyquist rate can result in aliasing, where different signals become indistinguishable when sampled. This leads to inaccurate representation and reconstruction of the original signal, causing distortion and loss of critical information.
4. How can one prevent aliasing when sampling a signal?
Ans. To prevent aliasing, one can use an anti-aliasing filter to remove frequency components above half the sampling rate before sampling the signal. Additionally, ensuring that the sampling frequency is at least twice the highest frequency of interest in the signal helps maintain fidelity.
5. What role does quantization play in the Sampling Theorem?
Ans. Quantization is the process of mapping a range of values into discrete values during the digital representation of a sampled signal. It is an essential step after sampling, as it affects the precision and quality of the signal representation. Proper quantization levels help minimize errors and improve the accuracy of the reconstructed signal.
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