Curve fitting
- regression
- interpolation: Its is required in situation where we need to estimate peak of signal based on limited set of discrete values.
- Fourier approximation
Linear curve fitting: this kind of curve fitting introduces discontinuity in the curve
Polynomial curve fitting: removes discontinuity from the curve
Spline curve fitting: sometimes polynomial curves make the curve fitting way too distorted by what is expected. In such scenarios spline is a better options. It behaves more like linear, but removes discontinuity from the curve.
Continuous wave Fourier transform
Discrete wave Fourier transform O(n^2)
Fast Fourier transform O(n. log2(n))
Why discrete FT required in practical applications?
--> Real life data is discrete. And DFT provided fast method of find frequency components. If Continuous FT is to be used, first interpolation is required to be done & then followed by CFT.
Why FFT is required is DFT is already there ?
--> FFT is even fast method of finding frequency content in the discrete data.
Where CFT is required practically?
--> Problems w
Significance of ROOT MEAN SQUARE: Its is derived from AC current. When AC current is passed through resistive load, the dissipate power will be same as the power dissipate when DC current of value equal to RMS of AC current is passed through same resistive load.
Mean
Variance
Standard deviation
- regression
- interpolation: Its is required in situation where we need to estimate peak of signal based on limited set of discrete values.
- Fourier approximation
Linear curve fitting: this kind of curve fitting introduces discontinuity in the curve
Polynomial curve fitting: removes discontinuity from the curve
Spline curve fitting: sometimes polynomial curves make the curve fitting way too distorted by what is expected. In such scenarios spline is a better options. It behaves more like linear, but removes discontinuity from the curve.
Continuous wave Fourier transform
Discrete wave Fourier transform O(n^2)
Fast Fourier transform O(n. log2(n))
Why discrete FT required in practical applications?
--> Real life data is discrete. And DFT provided fast method of find frequency components. If Continuous FT is to be used, first interpolation is required to be done & then followed by CFT.
Why FFT is required is DFT is already there ?
--> FFT is even fast method of finding frequency content in the discrete data.
Where CFT is required practically?
--> Problems w
Significance of ROOT MEAN SQUARE: Its is derived from AC current. When AC current is passed through resistive load, the dissipate power will be same as the power dissipate when DC current of value equal to RMS of AC current is passed through same resistive load.
Mean
Variance
Standard deviation
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