Combination of discrete/continuous signals

In summary, the conversation discusses the topic of signals which are both discrete and continuous, and how this is not covered in typical engineering literature. The conversation also suggests methods for dealing with such signals, including using a 2D continuous signal representation and applying linear time-invariant systems to them. The conversation ends with a note that there is no specific application or situation being discussed, just a general curiosity about this topic.
  • #1
MathematicalPhysicist
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I am looking for literature in theoretical engineering that covers a topic of a signal which is both discrete and continuous.

For example ##x[n,t) = t/n## where ##t## ranges over ##[0,\infty)\cap \mathbb{R}## and ##n## is discrete, i.e takes values in ##\mathbb{Z}##.

I believe that this isn't covered in the usual books of Oppenhiemer, but I may be wrong.
Thanks in advance!
 
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  • #2
It isn't covered in the typical signals and systems books I am familiar with, but most things you would want to do with such a signal should be pretty straightforward to write down. For example, if ##x## is an input to a linear time-invariant system with impulse response ##h[n,t)##, then the output would be
$$
y[n,t) = \int_{-\infty}^\infty \sum_{m=-\infty}^{\infty} h[m,\tau) x[n-m,t-\tau) \, d\tau
$$
Likewise, to get into the frequency domain you can do a combination of a 1D continuous Fourier transform and a 1D discrete time Fourier transform. etc.

If you are not comfortable with that, one direct approach is to treat it like a 2D continuous signal defined by
$$
x_c(v,t) = \sum_{n=-\infty}^\infty x[n,t) \delta(v-n).
$$
Then if you have a continuous linear time-invariant system with impulse response ##h_c(v,t)##, the output is
$$
\begin{eqnarray*}
y(v,t) & = & \int_{-\infty}^\infty \int_{-\infty}^\infty h_c(\nu,\tau) x_c(v-\nu,t-\tau) \, d\tau \, d\nu \\
& = & \int_{-\infty}^\infty \int_{-\infty}^\infty h_c(\nu,\tau) \sum_{m=-\infty}^\infty x[m,t-\tau) \delta(v-\nu-m) \, d\tau \, d\nu \\
& = & \int_{-\infty}^\infty h_c(v-m,\tau) \sum_{m=-\infty}^\infty x[m,t-\tau) \, d\tau \\
& = & \int_{-\infty}^\infty \sum_{m=-\infty}^{\infty} h_c(m,\tau) x[v-m,t-\tau) \, d\tau ,
\end{eqnarray*}
$$
which is of course only defined when ##v## is an integer. You can of course take 2D continuous Fourier transforms of ##x_c## as well. etc.

Is there some particular application or messy situation you are looking at?

jason
 
Last edited:
  • #3
@jasonRF not anything in particular.
I just wonder what has already been done in this topic of combined signals.
 

FAQ: Combination of discrete/continuous signals

1. What is the difference between discrete and continuous signals?

Discrete signals are represented by a series of distinct, separate values, while continuous signals are represented by an infinite number of values within a given range. Discrete signals can only take on specific values, while continuous signals can take on any value within a range.

2. Can discrete and continuous signals be combined?

Yes, discrete and continuous signals can be combined in certain cases. For example, in digital signal processing, discrete signals can be converted to continuous signals through a process called interpolation, and then the two signals can be combined.

3. How is the combination of discrete and continuous signals useful?

The combination of discrete and continuous signals allows for more complex and accurate representations of real-world phenomena. For example, in audio signals, discrete values can represent individual samples of sound, while continuous values can represent the smooth changes in pitch and volume.

4. What are some examples of systems that use a combination of discrete and continuous signals?

Some examples include digital communication systems, digital audio and video systems, and digital control systems. These systems often use discrete signals for encoding and decoding information, while continuous signals are used for transmission and processing.

5. How is the combination of discrete and continuous signals handled in mathematics and computer science?

In mathematics and computer science, discrete and continuous signals are often represented using different mathematical models. Discrete signals can be represented using sequences or series, while continuous signals can be represented using functions or differential equations. These different models allow for the analysis and manipulation of both types of signals in different contexts.

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