## What is the difference between Fourier transform and Fourier series?

The Fourier series is used to represent a periodic function by a discrete sum of complex exponentials, while the Fourier transform is then used to represent a general, nonperiodic function by a continuous superposition or integral of complex exponentials.

What is the difference between Fourier transform and Fast Fourier transform?

Main Differences Between FFT and DFT FFT is a much efficient and fast version of Fourier transform whereas DFT is a discrete version of Fourier transform. FFT is an implementation of DFT whereas DFT establishes a relationship between the time domain and the frequency domain representation.

What is the Fourier transform used for?

The Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components. The output of the transformation represents the image in the Fourier or frequency domain, while the input image is the spatial domain equivalent.

### What is Fourier series and Fourier transform in signal and system?

The main drawback of Fourier series is, it is only applicable to periodic signals. To overcome this shortcoming, Fourier developed a mathematical model to transform signals between time (or spatial) domain to frequency domain & vice versa, which is called ‘Fourier transform’.

What exactly is Fourier transform?

What is the Fourier transform? At a high level the Fourier transform is a mathematical function which transforms a signal from the time domain to the frequency domain. This is a very powerful transformation which gives us the ability to understand the frequencies inside a signal.

What are the applications of Fast Fourier Transform?

It covers FFTs, frequency domain filtering, and applications to video and audio signal processing. As fields like communications, speech and image processing, and related areas are rapidly developing, the FFT as one of the essential parts in digital signal processing has been widely used.

## What do you mean by Fourier transformation?

A Fourier transform is a mathematical technique for converting a time function into one expressed in terms of frequency. A Fourier transform is a circuit analysis technique that decomposes or separates a waveform or function into sinusoids of different frequency which sum to the original waveform.

What does Fourier series represent?

A Fourier series is a way of representing a periodic function as a (possibly infinite) sum of sine and cosine functions. It is analogous to a Taylor series, which represents functions as possibly infinite sums of monomial terms. A sawtooth wave represented by a successively larger sum of trigonometric terms.

Is the Fourier transform unique?

It is unique. If the function f(t) is piecewise continious and square integrable the fourier coffiecients are unique.

### What are the disadvantages of Fourier tranform?

The major disadvantage of the Fourier transformation is the inherent compromise that exists between frequency and time resolution. The length of Fourier transformation used can be critical in ensuring that subtle changes in frequency over time, which are very important in bat echolocation calls, are seen.

What are the different types of the Fourier transform?

aperiodic spectrum This is the most general form of continuous time Fourier transform.

• discrete aperiodic spectrum This is the Fourier series expansion of a periodic signal with time period .
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• Why there is a need of Fourier transform?

Fourier Transform is used in spectroscopy, to analyze peaks, and troughs. Also it can mimic diffraction patterns in images of periodic structures, to analyze structural parameters. Similar principles apply to other ‘transforms’ such as Laplace transforms, Hartley transforms.

## What is the philosophical meaning of Fourier series?

A Fourier series is a way to represent complex waves, such as sound, as a series of simple sine waves. The series breaks down a wave into a sum of sines and cosines. This means that elements of a wave can be isolated from each other.