Why is the function y=x(2t) not time-invariant?

In summary, the conversation discusses a function y=x(2t) and its property of time-invariance. The input and output signals, x1(t) and y1(t), are shown, as well as the effects of shifting the input by 2. The conversation also discusses the use of a time scaling system and its implications on time invariance. Ultimately, the group is stuck on understanding the concept and its application in the given scenario.
  • #1
Bassalisk
947
2
I am a bit confused.

http://pokit.org/get/bedc0ac7e1d17e01d7d58b021b81663c.jpg

The function is y=x(2t)

and the point of it is to show the property of time-invariance.(which we should fail in this example, because it isn't time invariant.)

Input signal is x1(t)

Output signal is shown for that signal y1(t)

But when you shift it, x1(t-2) it doesn't give y1(t-2).

I am both ok and not ok with that. First let's go over the math.

If I shift my signal x1(t) by -2

I get [itex] y_2(t)=x(2(t-2)) [/itex]

Is that correct?

But that is [itex] y_2(t)=x(2(t-2))=x(2t-4) [/itex] and that is original signal, shifted by 4, and then scaled by 2, which is not what it should be.

y2(0)=x(-4) which is 0, not 1.

I am probably stuck on something simple, but nevertheless I am stuck.
 
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  • #2
bumpity
 
  • #3
i don't know anything about that notation.

is it analog or digital?
 
  • #4
jim hardy said:
i don't know anything about that notation.

is it analog or digital?

I believe its analog.
 
  • #5
This might help.

Its a quote from Oppenheim, signals and systems.

This system represents a time scaling. That is, y(t) is a time-compressed (by a factor of 2) version of x(t).
Intuitively, then, any time shift in the input will also be compressed by a factor of 2, and it is for this reason
that the system is not time invariant.

To demonstrate this by counterexample, consider the input x1(t) shown in the Figure 1 47(a) and the resulting output y1(t) depicted in figure (b).

If we then shift the input by 2 i.e. consider x2(t)=x1(t-2) as shown in figure (c) - we obtain the resulting output

y2(t)=x2(2t) shown in figure (d)

Comparing figures (d) and (e) we see that the y2(t) <>y1(t-2)
 
  • #6
it looks like the time axis is compressed towards the origin between a) and b), and between c) and d). Doing that means that every point on the time axis is mapped to a new point, except the origin, which remains unchanged. This makes t=0 special. I think the idea of a time invariant system is that no time t is special, and you are free to choose your origin wherever you like.

What are these images supposed to represent? Input and output signals of some system? I think the output y requires knowledge of the future of x which violates causality
 
  • #7
i'm a plodder and haven't ruled out that the paradox might stem from 2 X 2 = 2 + 2

is operator y half of operator x?
and offset also 2?
That's too many twos for me.

what if operator y were one-third of operator x ?
or 1/e ?

please excuse if that's a dumb question but my simple mind has to rule out the dumb things first. And i do just plod.

thanks for your tolerance.

old jim
 
  • #8
I am stuck just like you guys. I think I will have just skip this one, and accept it.

This represents a system with function y=x(2t).
 
  • #9
This is wrong:

If I shift my signal x1(t) by -2

I get y2(t)=x(2(t−2))
----------------

The function is H{x(t)}=x(2t)
What it means is take any occurrence of t in your input, and replace it with 2t.

So we look at x2(t)=x1(t-2)
And we want to get its output: we replace any t with 2t:
so we get y2(t)=x2(2t)=x1(2t-2)=x1(2(t-1))

Which agrees with the plots in your post

Think hard about it, and do more examples, I remember these kind of things were really a pain in the *** in my signals & systems course
 
  • #10
elibj123 said:
This is wrong:

If I shift my signal x1(t) by -2

I get y2(t)=x(2(t−2))
----------------

The function is H{x(t)}=x(2t)
What it means is take any occurrence of t in your input, and replace it with 2t.

So we look at x2(t)=x1(t-2)
And we want to get its output: we replace any t with 2t:
so we get y2(t)=x2(2t)=x1(2t-2)=x1(2(t-1))

Which agrees with the plots in your post

Think hard about it, and do more examples, I remember these kind of things were really a pain in the *** in my signals & systems course

I will. This was just a brainer. I didn't have a lot of time to tackle this. I did in the end, get a very good mark on this course 9/10.

Thank you, I will give it some deep thought tomorrow. Gotta study Theory of information now :D
 

Related to Why is the function y=x(2t) not time-invariant?

1. What are signals in math?

Signals in math refer to any type of data or information that is represented as a function of time or space. This can include things like sound waves, electrical currents, or even stock market prices. In math, signals are typically represented using equations or graphs.

2. What is the difference between analog and digital signals?

Analog signals are continuous and can take on any value within a certain range, while digital signals are discrete and can only take on specific values. Analog signals are often represented by smooth curves, while digital signals are represented by a series of discrete points. In math, analog signals are typically described using functions, while digital signals are described using sequences or series.

3. How are signals used in real-world applications?

Signals are used in a wide variety of real-world applications, from communication systems to medical imaging. For example, cell phones use signals to transmit and receive information, while MRI machines use signals to create images of the human body. In math, signals are used to model and analyze these real-world systems.

4. What is the Fourier transform and how is it related to signals?

The Fourier transform is a mathematical tool used to analyze signals and decompose them into their individual frequency components. This is useful in understanding the different frequencies present in a signal and how they contribute to its overall behavior. The Fourier transform is commonly used in signal processing and can help identify patterns and trends in data.

5. How can I better understand signals and avoid math confusion?

One way to better understand signals is to practice visualizing them using graphs or diagrams. This can help make abstract concepts more concrete and easier to understand. It is also helpful to review fundamental math concepts such as functions, sequences, and series, which are commonly used to describe signals. Seeking out additional resources, such as textbooks or online tutorials, can also aid in understanding signals and reducing math confusion.

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