ASN2 - Something Interesting: Exponential: Difference between revisions
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Here's an demonstration of using the expontential function in a Fourier Series example. |
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Fourier Series |
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One way of representing a basis function is with cosine <math> cos(\frac{ 2 \pi nt}{T}) \!</math> |
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. |
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Where the Fourier series is <math> x1(t)= \sum_{n=0}^\infty a_n cos(\frac{ 2 \pi nt}{T}) \!</math> |
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Using an exponential to represent basis functions |
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However, a more convient way is using an exponential funtion <math> e^{\frac{ j2 \pi nt}{T}} \!</math>. |
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To solve for the coefffients <math> a_n \!</math> the solutions for both are almost identical. The benefit of using the eponetialinstead of cosine is that mathematical it is simplier for solving. |
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To solve for the coefficients do the dot product ' '''.''' ' of the basis function and <math> x(t) \!</math> |
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To solve a Fourier series equation for the coefffients <math> a_n \!</math> using the above expressions result in similar solutions but using the eponetial basis function is simplier to solving. To find the coefficients perform the dot product ' '''.''' ' as follows. |
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Using exponential basis function |
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⚫ | <math> x2(t) \!</math> '''.''' <math> e^{\frac{ -j2 \pi mt}{T}} = \int_{-\frac{T}{2}}^{\frac{T}{2}}\sum_{n=0}^\infty a_n e^{\frac{ j2 \pi nt}{T}}e^{\frac{ -j2 \pi mt}{T}} dt =\sum_{n=0}^\infty a_n \int_{-\frac{T}{2}}^{\frac{T}{2}} e^{\frac{ j2 \pi (n-m)t}{T}} dt =\sum_{n=0}^\infty a_n |
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⚫ | <math> x2(t) \!</math> '''.''' <math> e^{\frac{ -j2 \pi mt}{T}} = \int_{-\frac{T}{2}}^{\frac{T}{2}}\sum_{n=0}^\infty a_n e^{\frac{ j2 \pi nt}{T}}e^{\frac{ -j2 \pi mt}{T}} dt =\sum_{n=0}^\infty a_n \int_{-\frac{T}{2}}^{\frac{T}{2}} e^{\frac{ j2 \pi (n-m)t}{T}} dt =\sum_{n=0}^\infty a_n \delta_{mn} \!</math> |
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Then the result is |
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<math>a_m=\int_{-\frac{T}{2}}^{\frac{T}{2}}x2(t)e^{\frac{ -j2 \pi mt}{T}} dt </math> |
<math>a_m=\int_{-\frac{T}{2}}^{\frac{T}{2}}x2(t)e^{\frac{ -j2 \pi mt}{T}} dt </math> |
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<math> x1(t) \!</math> '''.''' <math> cos({\frac{ 2 \pi mt}{T}}) = \int_{-\frac{T}{2}}^{\frac{T}{2}}\sum_{n=0}^\infty a_n cos({\frac{ 2 \pi nt}{T}})cos{\frac{ 2 \pi mt}{T}} dt \!</math> At this point you should use a trig identity |
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applying this trig identity gives |
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<math> \frac{1}{2} \sum_{n=0}^\infty a_n \int_{-\frac{T}{2}}^{\frac{T}{2}} cos({\frac{ j2 \pi (n-m)t}{T}})cos({\frac{ j2 \pi (n+m)t}{T}}) dt =\frac{1}{2}\sum_{n=0}^\infty a_n T \delta_{mn} \!</math> |
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Then the result is |
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<math>a_m=\int_{-\frac{T}{2}}^{\frac{T}{2}}x1(t)cos({\frac{ 2 \pi mt}{T}}) dt </math> |
Latest revision as of 21:52, 13 December 2009
Here's an demonstration of using the expontential function in a Fourier Series example.
One way of representing a basis function is with cosine .
Where the Fourier series is
However, a more convient way is using an exponential funtion .
To solve a Fourier series equation for the coefffients using the above expressions result in similar solutions but using the eponetial basis function is simplier to solving. To find the coefficients perform the dot product ' . ' as follows.
Using exponential basis function
.
Then the result is
Using cosine basis function
. At this point you should use a trig identity
applying this trig identity gives
Then the result is