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How do you find the inverse Fourier transform from Fourier series coefficients?
To find the inverse Fourier transform from Fourier series coefficients, you first need to express the Fourier series coefficients in terms of the complex exponential form. Then, you can use the inverse Fourier transform formula, which involves integrating the product of the Fourier series coefficients and the complex exponential function with respect to the frequency variable. This process allows you to reconstruct the original time-domain signal from its frequency-domain representation.
Advantage of Z-transform in DSP?
obtaining the frequency respones of discret signal.
What is the history of fourier series?
It is quite complicated, and starts before Fourier. Trigonometric series arose in problems connected with astronomy in the 1750s, and were tackled by Euler and others. In a different context, they arose in connection with a vibrating string (e.g. a violin string) and solutions of the wave equation.Still in the 1750s, a controversy broke out as to what curves could be represented by trigonometric series and whether every solution to the wave equation could be represented as the sum of a trigonometric series; Daniel Bernoulli claimed that every solution could be so represented and Euler claimed that arbitrary curves could not necessarily be represented. The argument rumbled on for 20 years and dragged in other people, including Laplace. At that time the concepts were not available to settle the problem.Fourier worked on the heat equation (controlling the diffusion of heat in solid bodies, for example the Earth) in the early part of the 19th century, including a major paper in 1811 and a book in 1822. Fourier had a broader notion of function than the 18th-century people, and also had more convincing examples.Fourier's work was criticised at the time, and his insistence that discontinuous functions could be represented by trigonometric series contradicted a theorem in a textbook by the leading mathematician of the time, Cauchy.Nonetheless Fourier was right; Cauchy (and Fourier, and everyone else at that time) was missing the idea of uniform convergence of a series of functions. Fourier's work was widely taken up, and also the outstanding problems (just which functions can be represented by Fourier series?; how different can two functions be if they have the same Fourier series?) were slowly solved.Source: Morris Kline, Mathematical Thought from Ancient to Modern Times, Oxford University Press, 1972, pages 478-481, 502-514, 671-678,and 964.
What is the Laplace transform of unit ramp signal?
normally the unit ramp signal is defined as follows... r(t)= t, t>=0 0,otherwise so the laplace of it is given as R(s)=1/s^2
What is orthonormal signal?
A signal is said to be orthonormal when two vector are perpendicular and having unit length.
What is the difference between Discrete-time Fourier transform and Discrete Fourier transform?
The Discrete Fourier Transform (DFT) is a specific mathematical algorithm used to compute the frequency spectrum of a finite sequence of discrete samples. In contrast, the Discrete-time Fourier Transform (DTFT) represents a continuous function of frequency for a discrete-time signal, allowing for the analysis of signals in the frequency domain over an infinite range. Essentially, the DFT is a sampled version of the DTFT, applied to a finite number of samples, whereas the DTFT provides a broader, continuous frequency representation of the signal.
Difference between the DFT and the FFt?
A Discrete Fourier Transform is simply the name given to the Fourier Transform when it is applied to digital (discrete) rather than an analog (continuous) signal. An FFT (Fast Fourier Transform) is a faster version of the DFT that can be applied when the number of samples in the signal is a power of two. An FFT computation takes approximately N * log2(N) operations, whereas a DFT takes approximately N^2 operations, so the FFT is significantly faster simple answer is FFT = Fast DFT
Why discrete Fourier transform is used in digital signal processing?
The Discrete Fourier Transform (DFT) is used in digital signal processing to analyze the frequency content of discrete signals. It converts time-domain signals into their frequency-domain representations, enabling the identification of dominant frequencies, filtering, and spectral analysis. By efficiently transforming data, the DFT facilitates various applications, including audio and image processing, communication systems, and data compression. Its computational efficiency is further enhanced by the Fast Fourier Transform (FFT) algorithm, making it practical for real-time processing tasks.
What is relation between laplace transform and fourier transform?
The Laplace transform is related to the Fourier transform, but whereas the Fourier transform expresses a function or signal as a series of modes ofvibration (frequencies), the Laplace transform resolves a function into its moments. Like the Fourier transform, the Laplace transform is used for solving differential and integral equations.
Difference between fourier series and fourier transform?
The Fourier series is used to represent periodic functions as sums of sine and cosine terms, allowing for the analysis of functions defined on a finite interval. In contrast, the Fourier transform extends this concept to non-periodic functions, transforming them into a continuous spectrum of frequencies. While the Fourier series deals with discrete frequency components, the Fourier transform provides a continuous representation, making it suitable for a broader range of applications in signal processing and analysis.
Is there away to sort an array of data using the fast Fourier transform and finding the highest lower or average value finding his value or even best his position?
The Fast Fourier Transform is an implementation of the Discrete Fourier Transform. The DFT is a method of processing a time-sampled signal (eg, an audio wave) into a series of sines and cosines. As such, it is not a sorting algorithm, so this question does not make any sense.
Difference between fourier transform and z-transform?
Laplace Transforms are used primarily in continuous signal studies, more so in realizing the analog circuit equivalent and is widely used in the study of transient behaviors of systems. The Z transform is the digital equivalent of a Laplace transform and is used for steady state analysis and is used to realize the digital circuits for digital systems. The Fourier transform is a particular case of z-transform, i.e z-transform evaluated on a unit circle and is also used in digital signals and is more so used to in spectrum analysis and calculating the energy density as Fourier transforms always result in even signals and are used for calculating the energy of the signal.
How can a composite signal be decomposed into its individual frequencies?
Fourier analysis Frequency-domain graphs
What is the difference between fourier series and fourier transform with real life example please?
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.
What is Fast Fourier Transform in matlab?
The Fast Fourier Transform (FFT) in MATLAB is an efficient algorithm used to compute the discrete Fourier transform (DFT) and its inverse. It allows for the transformation of a time-domain signal into its frequency-domain representation, facilitating analysis and processing of signals. MATLAB provides built-in functions like fft for performing FFT, making it easy to analyze signal frequencies, perform filtering, and apply other signal processing techniques. The FFT significantly reduces computational complexity compared to directly calculating the DFT, especially for large datasets.
How do you represent the discrete hilbert transform?
The discrete Hilbert transform can be represented using the convolution of a discrete signal with the kernel ( h[n] = \frac{1}{\pi n} ), where the convolution is defined for all integer ( n ). It can also be computed using the Fast Fourier Transform (FFT) by multiplying the frequency components of the signal by ( -i , \text{sgn}(f) ), where ( \text{sgn}(f) ) is the sign function. This approach efficiently computes the transform in the frequency domain and then transforms it back to the time domain using the inverse FFT.
How can a composite signal be decomposed?
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.
