Revision as of 17:48, 12 September 2008 by Lee251 (Talk)

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. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang