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13.11. Example 3: Piecewise Convolution
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Today, we will begin by analyzing two functions: f(t) and g(t). Can anyone explain what a piecewise function is?
A piecewise function is defined by different expressions based on the input value, right?
Exactly! So, for our piecewise functions, f(t) is defined as 1 for t between 0 and 1. What about g(t)?
g(t) is just t, but that means it increases linearly across its range.
Right! Understanding the definitions is crucial since they'll guide us in computing the convolution.
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Let's write down the convolution integral. Who remembers the formula?
It's the integral of f(τ)g(t-τ) dτ from 0 to t, right?
Correct! Now, how do we set this up for our specific functions regarding their ranges?
We consider t values separately! For 0 ≤ t ≤ 1, both functions behave differently compared to when t > 1.
Exactly! Let's work through Case 1 and establish what (f ∗ g)(t) is for that range.
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Now, we've calculated the convolution for t in the first case. What do we get?
We find (f ∗ g)(t) = t²/2 for 0 ≤ t ≤ 1!
Perfect! But now, what changes for t > 1?
Then we only consider f(τ) from 0 to 1 due to its piecewise definition.
Exactly! Now verifying this integration, we can derive the second part: t - 1/2. What does that lead us to?
We can compile both results into a piecewise function!
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Alright, can we summarize the final piecewise result that we obtained?
Sure! (f ∗ g)(t) = t²/2 for 0 ≤ t ≤ 1 and t - 1/2 for t > 1!
Well articulated! This clearly demonstrates how function behavior affects convolution. Well done!
Overview
Short Summary
This section illustrates the process of piecewise convolution using functions defined over specific intervals, detailing each step involved in the calculations.
Medium Summary
In this section, we explore the example of piecewise convolution between two functions: f(t), defined as 1 for 0≤t≤1, and g(t), defined as t for the same interval. The convolution is computed for two cases, where the ranges of t differ, illustrating the integral calculations distinctly for the two scenarios.
Detailed Summary
Example 3: Piecewise Convolution
In this section, we analyze the convolution of two piecewise functions, f(t) and g(t), specifically designed to demonstrate how piecewise definitions impact the convolution process:
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f(t) is defined as:
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g(t) is defined as:
The goal is to compute the convolution of f and g, symbolized as (f ∗ g)(t).
Finding (f ∗ g)(t)
The formula for convolution is given by:
In solving this, we need to consider two scenarios based on the piecewise nature of f(t):
- Case 1: 0 ≤ t ≤ 1
- For this scenario, f(\tau) equals 1 for all \tau within the limits of integration:
- Evaluating this integral yields:
- Case 2: t > 1
- In this case, f(\tau) remains equal to 1 only from τ=0 to τ=1. Therefore, the limits of the integral adjust accordingly:
- This results in:
Piecewise Result
Combining both cases, we can express the final result of the convolution as a piecewise function:
This example illustrates the importance of considering the nature of the functions when performing convolution, particularly when applying it to real-world engineering scenarios.
Reference YouTube Videos
Audio Book
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Create a free accountLet:
f(t)=\begin{cases} 1, & 0 \leq t \leq 1 \\ 0, & t > 1 \end{cases}$$g(t)=t$$
Detailed Explanation
In this example, we have two functions. The function f(t) is defined as 1 between time 0 and 1, and 0 afterwards, which means it only has a significance in that small time window. On the other hand, g(t) is simply the function that equals t, meaning its value increases constantly as time progresses. These functions will be used to calculate their convolution over different time intervals.
Examples & Analogies
Imagine f(t) as a light switch that is turned on (value 1) only for the first second of a process, representing a brief burst of energy, while g(t) represents a continuously increasing temperature gauge. During the brief moment when the switch is on, we will analyze how that quick burst affects the temperature gauge over time.
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Create a free accountWe compute:
Since f(\tau)=1 for \tau \in[0,t]:
\int_0^t (t-\tau) d\tau = \frac{t^2}{2}$$Detailed Explanation
In this case (when t is between 0 and 1), we substitute f(τ) = 1, simplifying our integral to just g(t - τ) over the range from 0 to t. This results in the integral that computes the area under the graph of g(t) from 0 to t, giving us , which captures the cumulative effect of g(t) during this time period.
Examples & Analogies
Think of this as pouring a little bit of syrup (g(t)) into a container (the integral) every second for the first one second. The total volume of syrup collected in that second is represented by - showing how much can accumulate even in that short burst.
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Create a free accountNow f(\tau)=1 only from \tau =0 to 1:
\int_0^1 (t-\tau) d\tau = t\tau - \frac{\tau^2}{2} \Big|_0^1 = t - \frac{1}{2}$$Detailed Explanation
In this case (when t is greater than 1), f(τ) remains 1 only from 0 to 1, while g(t - τ) adjusts accordingly. The integral thus computes the area under g(t) again, but this time the upper limit of the integral is capped at 1, reflecting that f(τ) is not active beyond that point. The result is , indicating how the initial burst (from f(t)) has an effect even after it has turned off.
Examples & Analogies
Imagine a hose (g(t)) which starts spraying water continuously after the switch is pushed. Even after the brief activation of the switch, the water sprays for a while, but it is limited by the initial pressure (1 unit of f(τ)) established only for a short moment. As time passes beyond that initial burst, the total effect on the system can be computed until the constraints of the burst influence the overall outcome.
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Create a free accountThus:
(f \ast g)(t)=\begin{cases} t^2, & 0 \leq t \leq 1 \\ t - \frac{1}{2}, & t > 1\end{cases}$$Detailed Explanation
The final result encapsulates everything we've discussed in the previous chunks. It summarizes the impact of both functions through time, reflecting how their interactions yield different results depending on the interval being analyzed. During the onset (0 ≤ t ≤ 1), the effect is quadratic due to the consistent influence of f(t). Beyond that, the growth pattern changes due to the diminishing influence of f(t), giving us a linear effect based on the performance of g(t).
Examples & Analogies
This final expression can be seen as the overall temperature perceived in a room where warm energy was rapidly introduced for just a moment. Initially, during the effective input time, the temperature rises quite dramatically, followed by a gradual increase as the room continues to heat with time. By summarizing the impact mathematically, we can better predict and analyze behaviors in a variety of systems.
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Key concepts
Core takeaways and short definitions to help you quickly recall the key ideas from this section.
- Piecewise Convolution:
The operation defining the output as a combination of integrals based on the definitions of each segment of the functions.
- Integral Limits:
Distinct intervals must be considered based on the piecewise nature of the functions.
Examples
Memory aids
Once, there were functions f and g that only worked together in their defined ranges. They combined through a magical integral to form a new existence.
Fabulous Integrals Produce Results - Remember that convolution requires integrating each piece appropriately.