Time scaing in discrete time variable?

In summary, the question is whether Y(e^j\omega) can be defined in terms of X(e^j\omega) if y(n) is equal to either x(a*n) or x(n/a). The answer is yes for y(n)=x(a*n), where a is an integer, and the definition is (1/a)*\sumX(exp(j(\omega+2\pim)/a)) where m varies from 0 to a-1. However, it is not possible for y(n)=x(n/a) to be defined in terms of X(e^j\omega) due to the presence of 0's in y(n).
  • #1
ratn_kumbh
10
0
I wanted to know , if x(n) has DTFT X(e^jw)
then can we define Y(e^jw) in terms of X(e^jw)?
where Y(e^jw)is DTFT of y(n)=x(a*n)or y(n)=x(n/a)
. Because in these cases terms of x(n) are either missed or '0' is padded up, so i think it won't be possible to define Y(e^jw) in terms of X(e^jw). can anybody tell i m right or not?
 
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  • #2
continue

OK, i got the answer for y(n)=x(a*n), where a is an integer. Y(e^j[tex]\omega[/tex]) can be defined.

it is (1/a)*[tex]\sum[/tex]X(exp(j([tex]\omega[/tex]+2[tex]\pi[/tex]m)/a)) where m varies from 0 to a-1.

But can anybody please tell; is it possible for y(n)=x(n/a) to define Y(e^j[tex]\omega[/tex]). i am getting confused becoz of 0's which come in y(n) in this case.
 
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  • #3


Time scaling in discrete time variables refers to the manipulation of a signal's time axis by a scaling factor. This can be achieved by multiplying the signal's time index by a constant, a. The resulting signal will have the same values as the original signal, but at different time instances.

In terms of the DTFT, time scaling can be represented as a change in the frequency variable, w. Specifically, if x(n) has a DTFT X(e^jw), then y(n) = x(a*n) will have a DTFT Y(e^j(a*w)). This means that the frequency components of the original signal have been multiplied by a factor of a.

In the case of y(n) = x(n/a), the frequency components will be divided by a, which can result in aliasing if the original signal is not bandlimited. In this case, it may not be possible to define Y(e^jw) in terms of X(e^jw) because the frequency components of the original signal may not be recoverable.

Similarly, if terms of x(n) are missed or '0' is padded up, the resulting signal may not have a well-defined DTFT and therefore it may not be possible to define Y(e^jw) in terms of X(e^jw).

In conclusion, it is not always possible to define Y(e^jw) in terms of X(e^jw) when time scaling is applied to a discrete time variable. This depends on the specific scaling factor and the characteristics of the original signal.
 

1. What is time scaling in discrete time variable?

Time scaling in discrete time variable refers to changing the time scale of a discrete time signal. This can involve either speeding up or slowing down the signal, while still maintaining its overall shape and characteristics.

2. Why is time scaling important in scientific research?

Time scaling allows scientists to manipulate and analyze signals at different speeds, which can help in understanding complex systems and phenomena. It also allows for comparisons between different signals that may have different time scales.

3. How is time scaling achieved in discrete time variable?

Time scaling can be achieved through various techniques, such as interpolation or decimation. Interpolation involves adding or removing data points to change the time scale, while decimation involves selecting only certain data points to keep and discarding the rest.

4. What are some potential applications of time scaling in discrete time variable?

Time scaling has various applications in fields such as signal processing, image and audio editing, and financial analysis. It can also be used in studying natural phenomena, such as climate change and geological events.

5. Are there any limitations to time scaling in discrete time variable?

While time scaling can be a useful tool, it is important to note that it can also introduce errors and distortions in the signal. This is especially true if the time scale is changed significantly, as it can result in loss of important information. It is important for scientists to carefully consider the implications of time scaling in their research.

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