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We will view signals as elements of certain mathematical spaces. The spaces have a common structure, so it will be useful to think of them in theabstract.

Metric spaces

Definition 1

A set is a (possibly infinite) collection of distinct objects.

  • The empty set: = { } (plays a role akin to zero)
  • Binary numbers: { 0 , 1 }
  • Natural numbers: N = { 1 , 2 , 3 , . . . }
  • Integers: Z = { . . . , - 2 , - 1 , 0 , 1 , 2 , . . . } ( Z is short for “Zahlen”, German for “numbers”)
  • Rational numbers: Q ( Q for “quotient”)
  • Real numbers: R
  • Complex numbers: C

In this course we will assume familiarity with a number of common set operations. In particular, for the sets A = { 0 , 1 } , B = { 1 } , C = { 2 } , we have the operations of:

  • A B = { 0 , 1 } , B C = { 1 , 2 }
  • A B = { 1 } , B C =
  • A B = { 0 }
  • A c = U A , A c = { 2 }
  • A 2 = A × A = { ( 0 , 0 ) , ( 0 , 1 ) , ( 1 , 0 ) , ( 1 , 1 ) }

In order to be useful a set must typically satisfy some additional structure. We begin by defining a notion of distance.

Definition 2

A metric space is a set M together with a metric (distance function) d : M × M R such that for all x , y , z M

  • d ( x , y ) = d ( y , x ) (symmetry)
  • d ( x , y ) 0 (non-negative)
  • d ( x , y ) = 0 iff x = y (positive semi-definite)
  • d ( x , z ) d ( x , y ) + d ( y , z ) (triangle inequality).
  • ( M is arbitrary) d ( x , y ) = 0 if x = y , 1 if x y .
  • ( M = R ) d ( x , y ) = | x - y |
  • ( M = R N ) d ( x , y ) = i = 1 N | x i - y i | 2
  • ( M = R N ) d ( x , y ) = i = 1 N | x i - y i |
  • ( M = R N ) d ( x , y ) = i = 1 N | x i - y i | p 1 / p
  • ( M = R N ) d ( x , y ) = max i = 1 , . . . , N | x i - y i |
  • ( M = real (or complex) valued functions defined on [ a , b ] ) d p ( x , y ) = a b | x ( t ) - y ( t ) | p d t 1 / p

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Source:  OpenStax, Digital signal processing. OpenStax CNX. Dec 16, 2011 Download for free at http://cnx.org/content/col11172/1.4
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