Discrete-Time Waveforms: Properties & Conditions

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In summary, the conversation discusses the conditions imposed on a discrete-time waveform in order for it to be written as a linear combination of other discrete-time waveforms. These conditions include forming a complete basis, being non-zero, linearly independent and spanning the space. The example of the unit step function is given as a case where these conditions are met, and it is used in LTI systems to calculate the output of any input function. These conditions are also discussed in further detail in a provided link.
  • #1
RaduAndrei
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I don't know if I write in the right section or not.

I saw that every discrete-time waveform can be written as a linear combination of almost any other discrete-time waveform.

What are the conditions imposed on this other discrete-time waveform?
For example, the unit step function obeys these conditions.

PS: the waveforms are real not complex
 
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  • #2
Well, one of the conditions on this other discrete-time waveform is that any discrete-time waveform can be written as a linear combination of these base waveforms.
In other words that your base waveforms form a complete basis. Just like in linear algebra. There are rules for those bases.

Must say I don't follow your "For example, the unit step function obeys these conditions" ? But I can imagine a time series built up from step functions.
 
  • #3
The unit step function is used in LTI systems. If you know the response of the LTI system to the unit step function, then you can calculate the output to any input function.
You just write the input function as a sum of unit step functions. Then, by homogeneity,additivity and time invariance you can calculate the output just by knowing the response to the unit step function.

In other words, you must write the discrete-time waveform that you want to apply to the system as a linear combination of unit step functions.

A first condition is obviously that this other discrete waveform must be non-zero. The unit step function, for example, is non-zero for n>=0. But there are other conditions as well.
 
  • #4
To make the expansion unique, the base vectors must be linearly independent. (That covers your on-zero).
And there must be N of them if the space has N dimensions (they must span the space).
And that's about it, not much more.

But now I'm repeating the statements in the link I gave.
 
  • #5
Thanks.
 

Related to Discrete-Time Waveforms: Properties & Conditions

1. What are discrete-time waveforms?

Discrete-time waveforms are mathematical representations of signals or data that are sampled at specific time intervals. They are commonly used in digital signal processing and are characterized by a discrete set of values that are defined at equally spaced time intervals.

2. What are the properties of discrete-time waveforms?

The properties of discrete-time waveforms include amplitude, frequency, phase, and time. Amplitude refers to the strength or intensity of the signal, while frequency is the number of cycles or repetitions of the signal per unit time. Phase refers to the relative timing of the signal, and time represents the discrete time intervals at which the signal is sampled.

3. What are the conditions for a discrete-time waveform to be periodic?

A discrete-time waveform is periodic if it repeats itself after a certain number of time intervals. The conditions for a discrete-time waveform to be periodic are that the signal must have a finite length, must repeat itself within that length, and the time interval between each repetition must be constant.

4. How do discrete-time waveforms differ from continuous-time waveforms?

The main difference between discrete-time and continuous-time waveforms is that discrete-time waveforms are sampled at specific time intervals, while continuous-time waveforms are defined at every point in time. This means that discrete-time waveforms have a finite number of values, while continuous-time waveforms have an infinite number of values.

5. What are some common applications of discrete-time waveforms?

Discrete-time waveforms have a wide range of applications, including digital audio and video processing, image processing, digital communications, and control systems. They are also used in scientific and engineering fields for data collection and analysis.

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