Fourier series analysis is useful in signal processing as, by conversion from one domain to the other, you can apply filters to a signal using software, instead of hardware. As an example, you can build a low pass filter by converting to frequency domain, chopping off the high frequency components, and then back converting to time domain. The sky is the limit in terms of what you can do with fourier series analysis.
Laplace = analogue signal Fourier = digital signal Notes on comparisons between Fourier and Laplace transforms: The Laplace transform of a function is just like the Fourier transform of the same function, except for two things. The term in the exponential of a Laplace transform is a complex number instead of just an imaginary number and the lower limit of integration doesn't need to start at -∞. The exponential factor has the effect of forcing the signals to converge. That is why the Laplace transform can be applied to a broader class of signals than the Fourier transform, including exponentially growing signals. In a Fourier transform, both the signal in time domain and its spectrum in frequency domain are a one-dimensional, complex function. However, the Laplace transform of the 1D signal is a complex function defined over a two-dimensional complex plane, called the s-plane, spanned by two variables, one for the horizontal real axis and one for the vertical imaginary axis. If this 2D function is evaluated along the imaginary axis, the Laplace transform simply becomes the Fourier transform.
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"Practical application" can best be defined by contrasting it to "theoretical application". A practical application is the real or tangible use of a thing or a concept, whereas the outcome of a theoretical application is nontangibe results not subject to objective measurement because the thing or theory has not actually been put to use.
This question does not make any sense. "Real world" load, from the perspective of the power producing generator, can be reduced to a lumped resistance in series with a reactive component (either inductive or capacitive). In reality, the load connected to that generator has series elements and parallel elements.
A Fourier series is a set of harmonics at frequencies f, 2f, 3f etc. that represents a repetitive function of time that has a period of 1/f. A Fourier transform is a continuous linear function. The spectrum of a signal is the Fourier transform of its waveform. The waveform and spectrum are a Fourier transform pair.
Victor L. Shapiro has written: 'Fourier series in several variables with applications to partial differential equations' -- subject(s): Partial Differential equations, Functions of several real variables, Fourier series
Laplace = analogue signal Fourier = digital signal Notes on comparisons between Fourier and Laplace transforms: The Laplace transform of a function is just like the Fourier transform of the same function, except for two things. The term in the exponential of a Laplace transform is a complex number instead of just an imaginary number and the lower limit of integration doesn't need to start at -∞. The exponential factor has the effect of forcing the signals to converge. That is why the Laplace transform can be applied to a broader class of signals than the Fourier transform, including exponentially growing signals. In a Fourier transform, both the signal in time domain and its spectrum in frequency domain are a one-dimensional, complex function. However, the Laplace transform of the 1D signal is a complex function defined over a two-dimensional complex plane, called the s-plane, spanned by two variables, one for the horizontal real axis and one for the vertical imaginary axis. If this 2D function is evaluated along the imaginary axis, the Laplace transform simply becomes the Fourier transform.
we use fourier transform to convert our signal form time domain to frequency domain. This tells us how much a certain frequency is involve in our signal. It also gives us many information that we cannot get from time domain. And we can easily compare signals in frequency domain.
Spectral analysis of a repetitive waveform into a harmonic series can be done by Fourier analyis. This idea is generalised in the Fourier transform which converts any function of time expressed as a into a transform function of frequency. The time function is generally real while the transform function, also known as a the spectrum, is generally complex. A function and its Fourier transform are known as a Fourier transform pair, and the original function is the inverse transform of the spectrum.
Fourier transform and Laplace transform are similar. Laplace transforms map a function to a new function on the complex plane, while Fourier maps a function to a new function on the real line. You can view Fourier as the Laplace transform on the circle, that is |z|=1. z transform is the discrete version of Laplace transform.
But what is reality? It is just a series of electrical signals in your brain? Is this "real?" Come, Neo. We must go to Zion and defeat Agent Smith.
Fourier analysis is used in many places. Three examples are digital filtering, where a signal is converted to frequency domain, certain bands are removed or processed, and then converted back to time domain; your cell phone or its headset, if it has advanced noise cancellation technology; and the telephone system itself, where digital filtering is used to minimize bandwidth demands.
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In real life application, isometric drawing is used in the design of the video games.
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