Causal signal is defined in electrical eng.

• krindik
In summary, the conversation discusses the definition and interpretation of causal signals in electrical engineering. Causal signals are defined as having values only for positive time, while non-causal signals have values for negative time. The physical meaning of non-causal signals is questioned, and examples are requested. The conversation also touches on the concept of causality in the Kramers-Kronig relation, where the response functions only depend on past or present stimuli.

krindik

Hi,

As I read, A Causal signal is defined in electrical eng. context as

$f(t) = \{^{f(t), t\geq{0}}_{0, t<0}$

However what does it mean to have f(t) when t<0 ? ie. non-causal signal?
Is there any meaning when t<0 for any signal? In that sense all signals are causal. Isn't it?

Could u pls expain and point me correct direction.

Thanks

In fact, I'm referring to Causal Signals

Pls see below for there definitions,
http://cnx.org/content/m11495/latest/

However, my question is regarding the actual physical interpretation of these signals.

what is the meaning of an anti-causal or non-causal signal which has values for negative time?

what's the meaning of negative time? is it with respect to the point in time which we start to observe the system?

can u give some examples of such signals?

Maybe it would help to keep in mind that the point t = 0 corresponds to 'now'. In terms of constitutive relations (e.g. Kramers-Kronig), it means that the present state of the material can only depend on *past* states of the material and incident fields, not future values.

The same type of formulation was used by Wheeler and Feynman (wheeler-feynman abosrber theory)

http://www.npl.washington.edu/npl/int_rep/dtime/node2.html [Broken]

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So when we refer to non-causal it means that the signal has existed before now until future and an anti-causal signal means it has existed in the past.

So when we interpret Kramers-kronig relatioship we say that $\chi^{'} \, ,\chi^{''}$ depend on each others present and future values but not on any of past values?
http://en.wikipedia.org/wiki/Kramers-Kronig_relations" [Broken]

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I'm not familiar with the way you are using non-causal as opposed to anti-causal. My understanding is that causal signals can influence present effects, non-causal signals cannot.

As for the Kramers-Kronig relation, I think you have a sign error: the response functions only depend on past (or present) stimuli.

1. What is a causal signal in electrical engineering?

A causal signal in electrical engineering refers to a signal that follows a cause-and-effect relationship, where the output of a system is only affected by its past inputs. In other words, the current output value of the signal only depends on the previous input values and not on any future inputs.

2. How is a causal signal different from a non-causal signal?

A non-causal signal is a signal that is not constrained by any cause-and-effect relationship, meaning that the current output value of the signal can be affected by both past and future inputs. This is in contrast to a causal signal, which only depends on past inputs.

3. What are some examples of causal signals in electrical engineering?

Examples of causal signals in electrical engineering include digital signals, where the output value at any given time is determined by the previous input values, and filters, where the output signal is only affected by the input signal and not by any future inputs.

4. What are the benefits of using causal signals in electrical engineering?

One of the main benefits of using causal signals in electrical engineering is that it allows for more predictable and stable systems. By only considering past inputs, engineers can design systems with a better understanding of how the system will respond to different inputs, making it easier to troubleshoot and maintain.

5. Can a non-causal signal be converted into a causal signal?

In most cases, it is not possible to convert a non-causal signal into a causal signal. However, there are some techniques, such as causality filtering, that can be used to approximate a causal signal from a non-causal signal. These techniques involve manipulating the signal's time domain or using advanced signal processing algorithms.