(New page: == The Basics of Linearity == According to the definition of linearity given in class, x1(t) -> system -> y1(t) -> *a -> ay1(t) } } -> + -> ay1(t) + by2(t...)
 
Line 5: Line 5:
 
                                 } -> + -> ay1(t) + by2(t)
 
                                 } -> + -> ay1(t) + by2(t)
 
x2(t) -> system -> y2(t) -> *b -> by2(t)  }
 
x2(t) -> system -> y2(t) -> *b -> by2(t)  }
 +
 +
 +
Now, according to the problem statement,
 +
 +
exp(2jt) -> system -> exp(-2jt) -> *1 -> exp(-2jt)  }
 +
                                } -> + -> exp(-2jt) + exp(2jt)
 +
exp(-2jt) -> system -> exp(2jt) -> *1 ->  exp(2jt)  }

Revision as of 11:15, 16 September 2008

The Basics of Linearity

According to the definition of linearity given in class,

x1(t) -> system -> y1(t) -> *a -> ay1(t) }

                               } -> + -> ay1(t) + by2(t)

x2(t) -> system -> y2(t) -> *b -> by2(t) }


Now, according to the problem statement,

exp(2jt) -> system -> exp(-2jt) -> *1 -> exp(-2jt) }

                               } -> + -> exp(-2jt) + exp(2jt)

exp(-2jt) -> system -> exp(2jt) -> *1 -> exp(2jt) }

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Sees the importance of signal filtering in medical imaging

Dhruv Lamba, BSEE2010