# Midterm Q1b: Signal Transformation Solutions

• stanigator
In summary, the solution for question 1b of the midterm is not to flip the function along the y-axis, but rather to shift it by 4 after flipping it about the y-axis. This is because flipping about the axis does not apply to the shift, and in order to compute h(y(t)) where y(t) = -t-2, you need to first shift by 2 and then flip about the y-axis.
stanigator
In question 1b of the midterm in the pdf attached below, I don't understand why the solution should be what it stated in the other pdf attached rather than what I did in the picture, which basically flips 1a along y-axis.

#### Attachments

• CIMG0221.jpg
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• mid1.pdf
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• mid1_solB.pdf
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Flipping about the axis does not apply to the shift.
You have h(t), and you want to compute h(y(t)) where y(t) = -t-2.
h(-y(t)) $$\neq$$ h(y(-t))
The former is what you did, which is to compute y(t+2) and then flip, but that gives you y(-t+2), so then you need to shift by 4 to get y(-t-2).

## 1. What is signal transformation and why is it important in science?

Signal transformation is the process of converting one form of input signal into another form of output signal. This is important in science because it allows us to analyze and interpret data in different ways, making it easier to understand and draw conclusions from.

## 2. What are some common solutions for signal transformation?

Some common solutions for signal transformation include filtering, amplification, modulation, and demodulation. These methods are often used in various scientific fields, such as telecommunications, medicine, and engineering.

## 3. How do scientists determine which signal transformation solution to use?

The choice of signal transformation solution depends on the specific needs and goals of the experiment or study. Scientists consider factors such as the type of input signal, the desired output signal, and the accuracy and precision required for their research.

## 4. Can signal transformation solutions be used in real-time applications?

Yes, signal transformation solutions can be used in real-time applications. With advancements in technology, signal transformation can now be performed quickly and efficiently, making it possible to analyze and manipulate signals in real-time.

## 5. How does signal transformation contribute to scientific advancements?

Signal transformation plays a crucial role in scientific advancements as it allows us to collect and analyze data in a more efficient and accurate manner. This enables researchers to make new discoveries and improve existing technologies, leading to progress and innovation in various scientific fields.

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