Can you prove the properties of convolution?

In summary, convolution is a mathematical operation used in scientific research to analyze complex systems and signals by breaking them down into simpler components. It is important in image and signal processing for tasks such as feature extraction and noise reduction. Some limitations of using convolution in scientific research include the assumption of linearity and sensitivity to noise and data errors. Careful consideration must be given when selecting the appropriate convolution function for a given research question.
  • #1
Chris L T521
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Thanks to those who participated in last week's POTW! Here's this week's problem.

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Problem: Recall that the convolution of $f$ and $g$ is defined by the integral

\[(f\ast g)(t) = \int_0^{t}f(t-\tau)g(\tau)\,d\tau.\]

Establish the commutative, distributive, and associative properties of convolution, i.e.

(1) $f\ast g = g\ast f$
(2) $f\ast (g_1 + g_2) = f\ast g_1 + f\ast g_2$
(3) $f\ast(g\ast h) = (f\ast g)\ast h$.

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  • #2
This week's question was correctly answered by Sudharaka and Badhi. You can find their solutions below.

Sudharaka's solution:

\[(f*g)(t)=\int_{0}^{t}f(t-\tau)g(\tau)\,d\tau\]

Substituting \(u=t-\tau\) we get,\begin{eqnarray}(f*g)(t)&=&-\int_{t}^{0}f(u)g(t-u)\,du\\&=&\int_{0}^{t}f(u)g(t-u)\,du\\&=&(g*f)(t)\end{eqnarray}\[\therefore f*g=g*f~~~~~~~~~~~~~(1)\]\begin{eqnarray}[f*(g_{1}+g_{2})](t)&=&\int_{0}^{t}f(t-\tau)(g_{1}+g_{2})(\tau)\,d\tau\\&=&\int_{0}^{t}f(t-\tau)[g_{1}(\tau)+g_{2}(\tau)]\,d\tau\\&=&\int_{0}^{t}f(t-\tau)g_{1}(\tau)\,d\tau+\int_{0}^{t}f(t-\tau)g_{2}(\tau)\,d\tau\\&=&(f*g_{1})(t)+(f*g_{2})(t)\end{eqnarray}\[\therefore f*(g_{1}+g_{2})=(f*g_{1})+(f*g_{2})~~~~~~~~~~~~~(2)\]\begin{eqnarray}[(f*g)*h](t)&=&\int_{0}^{t}(f*g)(t-\tau)h(\tau)\,d\tau\\&=&\int_{0}^{t}\left[\int_{0}^{t-\tau}f(t-\tau-u)g(u)\,du\right]h(\tau)\,d\tau\\\end{eqnarray}Substituting \(w=u+\tau\) we get,\[[(f*g)*h](t)=\int_{0}^{t}\left[\int_{\tau}^{t}f(t-w)g(w-\tau)\,dw\right]h(\tau)\,d\tau\]Changing the order of integration,\begin{eqnarray}[(f*g)*h](t)&=&\int_{0}^{t}\left[\int_{0}^{w}f(t-w)g(w-\tau)h(\tau)\,d\tau\right]\,dw\\&=&\int_{0}^{t}f(t-w)\left[\int_{0}^{w}g(w-\tau)h(\tau)\,d\tau\right]\,dw\\&=&\int_{0}^{t}f(t-w)(g*h)(w)\,dw\\&=&[f*(g*h)](t)\end{eqnarray}\[\therefore (f*g)*h=f*(g*h)~~~~~~~~~~~~~(3)\]

BAdhi's solution:

from the definition,
$$(f\ast g)(t)=\int_0^tf(t-\tau)g(\tau)\,d \tau$$(1) Proving that $f\ast g=g\ast f$$f\ast g =\int_0^tf(t-\tau )g(\tau )\,d \tau$with the substitution,
$$\begin{align*}t-\tau=v\\
\,dv=-\,d\tau\\
\end{align*}$$then,
$$\tau=0 \implies v=t, \qquad \tau=t \implies v=0$$
$\begin{align*}
f\ast g &=\int_0^tf(t-\tau )g(\tau )\,d \tau\\
&=-\int_t^0f(v)g(t-v)\,dv\\
\end{align*}
$Since the definite integral is independent from the variable, we can substitute $v$ with $\tau$then the equation becomes,$\begin{align*}f\ast g &=-\int_t^0f(\tau )g(t-\tau )\, d\tau\\
&=\int_0^tf(\tau )g(t-\tau )\, d\tau\\
&=\underbrace{\int_0^tg(t-\tau )f(\tau )\, d\tau }_{g\ast f}\\
\end{align*}$thus proves the commutative property,
$$f\ast g=g\ast f$$(2) proving, $f\ast (g_1+g_2)=f\ast g_1 +f\ast g_2$$\begin{align*}
f\ast(g_1+g_2)&=\int_0^tf(t-\tau )\left[g_1(\tau )+g_2(\tau )\right]\, d\tau \\
&=\int_0^t f(t-\tau )g_1(\tau ) + f(t-\tau)g_2(\tau )\, d\tau \\
&=\underbrace{\int_0^t f(t-\tau )g_1(\tau )\, d\tau }_{f\ast g_1} + \underbrace{\int_0^t f(t-\tau )g_2(\tau )\, d\tau }_{f\ast g_2}\\
\end{align*}
$thus proves the distributive property,
$$f\ast (g_1+g_2)=f\ast g_1 +f\ast g_2$$(3) proving that, $f\ast (g\ast h)=(f\ast g)\ast h$$\begin{align*}
f\ast (g\ast h) &= (g\ast h)\ast f \qquad \qquad \left(from \;proof: (1)\right)\\
&=\int_0^t\left[ (g\ast h)(t-\tau )\right] f(\tau ) \,d\tau \\
&=\int_0^tf(\tau )\left[ \int_0^{t-\tau} g(t- \tau -u)h(u) \,du\right]\,d\tau\\
&=\int_0^t\int_0^{t-\tau}f(\tau )g(t-\tau -u)h(u)\,du\,d\tau \\
\end{align*}$by changing the order of the integral, the change of boundaries of $\tau$ and $u$ are,$0<\tau<t,\: 0<u<(t-\tau) \longrightarrow 0<u<t, \: 0<\tau<(t-u) $then,$
\begin{align*}
f\ast (g\ast h) &=\int_0^t\int_0^{t-u}f(\tau )g(t-\tau -u)h(u)\,d\tau\,du\\
\end{align*}$since $h(u)$ is independent from $\tau$,$\begin{align*}
f\ast (g\ast h) &=\int_0^th(u)\underbrace{\left[ \int_0^{t-u}f(\tau )g(t-u-\tau )\,d\tau\right]}_{(g\ast f)(t-u)} \,du\\
&=\int_0^th(u)[(g\ast f)(t-u)]\,du\\
&=(g\ast f)\ast h
\end{align*}$from $proof : (1) $,$f\ast (g\ast h)=(f\ast g)\ast h$Thus proves the associative property,$$f\ast (g\ast h)=(f\ast g)\ast h$$
 

FAQ: Can you prove the properties of convolution?

What is convolution and why is it important in scientific research?

Convolution is a mathematical operation that combines two functions to produce a third function that represents how the shape of one function is modified by the other. It is important in scientific research because it allows us to analyze and understand complex systems and signals by breaking them down into simpler components.

Can you explain the properties of convolution in simple terms?

The properties of convolution include commutativity, associativity, distributivity, and shifting. Commutativity means that the order of the functions being convolved does not change the result. Associativity means that the grouping of functions being convolved does not change the result. Distributivity means that convolution is distributive over addition. Shifting means that if one of the functions being convolved is shifted, the result is also shifted.

How is convolution used in image and signal processing?

In image and signal processing, convolution is used to extract features from images and signals, such as edge detection, blurring, and noise reduction. It is also used to filter and enhance images and signals, as well as to compress and store data.

Can you provide an example of how convolution is applied in real-world scenarios?

One example of how convolution is applied in real-world scenarios is in speech recognition technology. Convolution is used to analyze and process spoken words, breaking them down into smaller components to identify patterns and recognize the words being spoken.

What are some limitations of using convolution in scientific research?

Some limitations of using convolution in scientific research include the assumption of linearity, which may not hold true for all systems, and the sensitivity to noise and errors in the data. Additionally, the choice of the convolution function can greatly affect the results, so careful consideration must be given when selecting the appropriate function for a given research question.

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