A sine wave is a simple vertical line in the frequency domain because the horizontal axis of the frequency domain is frequency, and there is only one frequency, i.e. no harmonics, in a pure sine wave.
frequency transfer function deals with transfer in frequency domain, transfer function alone can be referring to any type of transfer in different domain e.g time domain
The element law of a capacitor in frequency domain is based on Ohm's Law, which is capacitance times voltage is equal to current. The higher frequency, the lower the capacitance and vice versa.
Time domain basically means plotting a curve of amplitude over thr time axis. A given function or signal can be converted between the time and frequency domains with a pair of mathematical operators called a transform. An example is the Fourier transform, which decomposes a function into the sum of a (potentially infinite) number of sine wave frequency components. The 'spectrum' of frequency components is the frequency domain representation of the signal. The inverse Fourier transform converts the frequency domain function back to a time function.
Convolution is particularly useful in signal analysis. See related link.
Convolution in the time domain is equivalent to multiplication in the frequency domain.
Convolution TheoremsThe convolution theorem states that convolution in time domain corresponds to multiplication in frequency domain and vice versa:Proof of (a):Proof of (b):
If we need to add two signals in time domain, we perform convolution. A better way, is to convert the two signals from time domain to frequency domain. This can be achieved by FAST FOURIER TRANFORM. Once both the signals have been converted to frequency domain, they can simply be multiplied. Since Convolution in time domain is similar to multiplying in Frequency domain. Once both the signals have been multiplied, they can be converted back to time domain by Inverse Fourier Transform method. Thus achieving accurate results.
Check this : https://ccrma.stanford.edu/~jos/sasp/img2442.png
A convolution is an integral that expresses the amount of overlap of one function as it is shifted over another function.You can use correlation to compare the similarity of two sets of data. Correlation computes a measure of similarity of two input signals as they are shifted by one another. The correlation result reaches a maximum at the time when the two signals match bestThe difference between convolution and correlation is that convolution is a filtering operation and correlation is a measure of relatedness of two signalsYou can use convolution to compute the response of a linear system to an input signal. Convolution is also the time-domain equivalent of filtering in the frequency domain.
the convolution of a signal is to filter the components of the signal. The convolution does not mean the masking. Masking means it is going to remove all the masked components(both high and low frequency components).But convolution is going to remove any one (either low r high frequency) depending upon the filter response.
time domain is respected to the time and frequency domain is respected to the frequency
The solution to the Heat equation using Fourier transform is given by the convolution of the initial condition with the fundamental solution of the heat equation, which is the Gaussian function. The Fourier transform helps in solving the heat equation by transforming the problem from the spatial domain to the frequency domain, simplifying the calculations.
A sine wave is a simple vertical line in the frequency domain because the horizontal axis of the frequency domain is frequency, and there is only one frequency, i.e. no harmonics, in a pure sine wave.
Design of filtering and control systems is usually easier in the frequency domain than in the time domain.
the use of frequency domain will prove better results were the latency is not a problem. also u can do batch processing in frequency domain hence the overall efficiency of hardware can be effectively used.....
for finding convolution of periodic signals we use circular convolution