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What is the difference between periodic and aperiodic signals?
A signal which repeats itself after a specific interval of time is called periodic signal. A signal which does not repeat itself after a specific interval of time is called aperiodic signal.A signals that repeats its pattern over a period is called periodic signal,A signal that does not repeats its pattern over a period is called aperiodic signal or non periodic.Both the Analog and Digital can be periodic or aperiodic. but in data communication periodic analog sigals and aperiodic digital signals are used.
Why continuous time signals are represented by samples?
Continuous time signals are represented by samples to enable their processing and analysis using digital systems, which operate with discrete data. Sampling converts the continuous signal into a finite set of values at specific intervals, allowing for easier storage, manipulation, and transmission. This representation also facilitates the use of digital signal processing techniques, making it possible to apply algorithms that enhance, filter, or compress the signal efficiently. Additionally, sampling aligns with the Nyquist theorem, ensuring that the essential information of the continuous signal is preserved in the sampled version.
How do you prevent aliasing while sampling a continuous time signal?
To prevent aliasing when sampling a continuous time signal, you should first apply a low-pass filter to the signal to eliminate frequency components above half the sampling rate (the Nyquist frequency). Then, ensure that the sampling rate is at least twice the highest frequency present in the signal, as dictated by the Nyquist-Shannon sampling theorem. By adhering to these principles, you can accurately reconstruct the original signal from its samples without distortion.
Why sampling theorem is used in digital communication?
The sampling theorem, particularly the Nyquist-Shannon theorem, is crucial in digital communication because it establishes the conditions under which a continuous signal can be accurately represented and reconstructed from its samples. It states that to avoid aliasing and preserve the original signal's integrity, the sampling rate must be at least twice the highest frequency present in the signal. This ensures that the digital representation captures all necessary information, allowing for effective transmission and processing of signals in digital communication systems. By adhering to this theorem, engineers can design systems that maintain high fidelity and minimize distortion.
How does the Sampling rate affect frequency?
In signal processing, sampling is the reduction of a continuous signal to a discrete signal. A common example is the conversion of a sound wave (a continuous signal) to a sequence of samples (a discrete-time signal).
What is the difference between periodic and aperiodic signals?
A signal which repeats itself after a specific interval of time is called periodic signal. A signal which does not repeat itself after a specific interval of time is called aperiodic signal.A signals that repeats its pattern over a period is called periodic signal,A signal that does not repeats its pattern over a period is called aperiodic signal or non periodic.Both the Analog and Digital can be periodic or aperiodic. but in data communication periodic analog sigals and aperiodic digital signals are used.
Why cannot aperiodic signal be represented using fourier series?
An aperiodic signal cannot be represented using fourier series because the definition of fourier series is the summation of one or more (possibly infinite) sine wave to represent a periodicsignal. Since an aperiodic signal is not periodic, the fourier series does not apply to it. You can come close, and you can even make the summation mostly indistinguishable from the aperiodic signal, but the math does not work.
What is the reason of using aperiodic digital signal in data communication?
bjbl,
What is the signal type is characterized by continuous amplitudes in continuous time?
An analog signal is characterized by continuous amplitudes and continuous time.
What is the importance of sampling theorem?
The sampling theorem, also known as the Nyquist-Shannon sampling theorem, is crucial in signal processing as it establishes the conditions under which a continuous signal can be accurately reconstructed from its discrete samples. It states that to avoid information loss, a signal must be sampled at least twice the highest frequency present in the signal. This principle underpins digital audio, telecommunications, and video processing, ensuring that analog signals can be digitized and transmitted without distortion. Overall, it is fundamental for effective data representation and transmission in various technological applications.
Statement of sampling theorem?
sampling theorem is used to know about sample signal.
Why continuous time signals are represented by samples?
Continuous time signals are represented by samples to enable their processing and analysis using digital systems, which operate with discrete data. Sampling converts the continuous signal into a finite set of values at specific intervals, allowing for easier storage, manipulation, and transmission. This representation also facilitates the use of digital signal processing techniques, making it possible to apply algorithms that enhance, filter, or compress the signal efficiently. Additionally, sampling aligns with the Nyquist theorem, ensuring that the essential information of the continuous signal is preserved in the sampled version.
Is Nyquist theorem true for optical fiber?
I cannot see where the Nyquist theorem relates to cables, fiber or not.The theorem I know, the Nyquist-Shannon sampling theorem, talks about the limitations in sampling a continuous (analog) signal at discrete intervals to turn it into digital form.An optical fiber or other cable merely transport bits, there is no analog/digital conversion and no sampling taking place.
How do you prevent aliasing while sampling a continuous time signal?
To prevent aliasing when sampling a continuous time signal, you should first apply a low-pass filter to the signal to eliminate frequency components above half the sampling rate (the Nyquist frequency). Then, ensure that the sampling rate is at least twice the highest frequency present in the signal, as dictated by the Nyquist-Shannon sampling theorem. By adhering to these principles, you can accurately reconstruct the original signal from its samples without distortion.
Why sampling theorem is used in digital communication?
The sampling theorem, particularly the Nyquist-Shannon theorem, is crucial in digital communication because it establishes the conditions under which a continuous signal can be accurately represented and reconstructed from its samples. It states that to avoid aliasing and preserve the original signal's integrity, the sampling rate must be at least twice the highest frequency present in the signal. This ensures that the digital representation captures all necessary information, allowing for effective transmission and processing of signals in digital communication systems. By adhering to this theorem, engineers can design systems that maintain high fidelity and minimize distortion.
Digital signal is continuous?
No
Is the frequency domain plot of a voice signal discrete or continuous?
The frequency domain of a voice signal is normally continuous because voice is a nonperiodic signal.
