sum from{-infinity } to{infinity } ({1} over {2 * pi } )(int (abs{X(func e^{jw})})^2 )dw
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.
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.
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).
By definition a continuous signal is just that continuous to have no amplitude is to mean it doesn't exists
An analog signal is one which is continuous in time as well as continuous in amplitude . Example : sine wave, cosine wave. An Digital signal is one which is continuous in discrete in time. Example : square waves.
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.
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.
bjbl,
An analog signal is characterized by continuous amplitudes and continuous time.
sampling theorem is used to know about sample signal.
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.
No
The frequency domain of a voice signal is normally continuous because voice is a nonperiodic signal.
it is continuous
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).
circular convolution is used for periodic and finite signals while linear convolution is used for aperiodic and infinite signals. In linear convolution we convolved one signal with another signal where as in circular convolution the same convolution is done but in circular pattern ,depending upon the samples of the signal
Sampling in digital communication is the process of converting a continuous signal into a discrete signal by taking periodic measurements of the amplitude of the continuous signal at specific intervals. This process enables the representation of analog signals in a digital format, allowing for efficient transmission, storage, and processing. The sampling rate must be high enough to capture the essential characteristics of the signal, adhering to the Nyquist theorem to prevent aliasing. Proper sampling is crucial for maintaining the integrity and quality of the transmitted information.