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LINEARITY

Linearity, in my definition, means that superposition always works. In other words, summation of inputs yield summation of outputs.

Example of Linearity and its proof

$ \,y(t)=x(2t)\, $


Proof:

$ x1(t) \to System \to y1(t)=x1(2t) \to Scalar multiplication(*a) \to ax1(2t) $

$ x2(t) \to System \to y2(t)=x2(2t) \to Scalar multiplication(*b) \to bx2(2t) $

$ ax1(2t) and bx2(2t) \to SUM \to '''ax1(2t)+bx2(2t)''' $


$ x1(t) \to Scalar multiplication(*a) \to ax1(t) $

$ x2(t) \to Scalar multiplication(*b) \to bx2(t) $

$ ax1(t) and bx2(t) \to SUM \to \to System \to'''ax1(2t)+bx2(2t)''' $

Those two yielded the same outputs thus it is linear.


Example of non-linearity and its proof

$ \,y(t)=e^{x(t)}\, $


Proof:

$ x(t) \to System \to y(t)=e^{x(t)} \to Time Shift(t0) \to z(t)=y(t-t0) $

$ \, =e^{x(t-t0)}\, $


$ x(t) \to Time Shift(t0) \to y(t)=x(t-t0) \to System \to z(t)=e^{y(t)} $

$ \, =e^{x(t-t0)}\, $


Both cascades yielded the same outputs, thus $ \,y(t)=e^{x(t)}\, $ is time invariant.

Alumni Liaison

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva