What the terms orthogonal & basis function denote in case of signals

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Orthogonal basis functions are essential in representing signals, as any signal can be expressed as a summation of these functions. In the context of Fourier series, functions like sin(t), sin(2t), and sin(3t) are orthogonal over the interval [0, π], meaning their inner products equal zero when the functions are distinct. The term "basis" refers to a set of linearly independent functions that span a function space, similar to how vectors span a vector space. An example is the representation of vectors in R2, where any vector can be expressed as a combination of two basis vectors. Understanding orthogonal and basis functions is crucial for analyzing and synthesizing signals in various applications.
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I am a beginer. I have read that any given signal whether it simple or complex one,can be represented as summation of orthogonal basis functions.Here, what the terms orthogonal and basis functions denote in case of signals? Can anyone explain concept with an example?Also,what are the physical implications of basis functions?
 
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ramdas said:
I am a beginer. I have read that
any given signal whether it simple
or complex one,can be
represented as summation of
orthogonal basis functions.Here, what the terms orthogonal
and basis functions denote in
case of signals?
One set of basis functions that is used a lot in Fourier series is the set ##\{\sin(t), \sin(2t), \sin(3t), \dots, \sin(nt), \dots\}##. These functions are orthogonal on the interval ##[0, \pi]##, which means that the inner product of any two distinct functions in this set is zero. In other words, ##\int_0^{\pi} \sin(kt) \sin(mt)~dt = 0##, if ##k \neq m##.

The term basis is linear algebra terminology that has to do with vector spaces (or function spaces, which are nearly the same as vector spaces). For a given space, a basis is a set of vectors (or functions) that are (1) linearly independent and (2) span the space.

For a simple example of these concepts, let's take R2, the plane. This space (it's a vector space) has a natural basis, {<1, 0>, <0, 1>}. Every vector in R2 can be written as a linear combination of the two vectors in the basis. For example, <3, 4> = 3<1, 0> + 4<0, 1>. In a similar way, a function that represents a signal can be written as a linear combination of the basis functions.
ramdas said:
How basis functions can be
explained mathematically and what
are the physical implications of it?
 
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Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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