Unformatted text preview: is a f initememory s ystem with a memory of T seconds. Networks containing inductive a nd capacitive elements generally have infinite memory because the
response of such networks a t any instant t is determined by their inputs over the
entire past ( 00, t ). T his is t rue for the R C circuit of Fig. 1.26.
In this book we will generally examine dynamic systems. Instantaneous systems
are a special case of dynamic systems. 1. 7 4 t Causal a nd Noncausal S ystems A c ausal (also known as a p hysical or n onanticipative) s ystem is one for
which t he o utput a t a ny instant to depends only on the value of the input f (t) for
t ::; to. In other words, the value of the o utput a t t he present instant depends only
on t he p ast a nd p resent values of the input f (t), n ot on its future values. To p ut
i t simply, in a causal system the o utput c annot s tart before the input is applied. I f
t he response s tarts before the input, i t means t hat t he system knows t he i nput i n
t he future a nd a cts o n this knowledge before t he i nput is applied. A system t hat
violates the condition of causality is called a n oncausal (or a nticipative) system.
Any practical system t hat operates in real t imet must necessarily be causal.
We do not yet know how t o build a system t hat c an respond t o future inputs (inputs
not yet applied). A noncausal system is a prophetic system t hat knows t he future
input and acts o n i t in the present. Thus, if we apply an input starting a t t = 0
t o a noncausal system, the o utput would begin even before t = o. As a n example,
consider the s ystem specified by y(t) = f (t  2) + f (t + 2) (1.46) tIn realtime operations, the response to an input is essentially simultaneous (contemporaneous) with the input itself. o 2 4 t Fig. 1 .30 A noncausal system and its realization by a delayed causal system.
For the input f (t) i llustrated in Fig. 1.30a, t he o utput y(t), as computed from
Eq. (1.46) (shown in Fig. 1.30b), s tarts even before t he i nput is applied. Equation
(1.46) shows t hat y (t), t he o utput a t t , is given by t he s um of t he i nput values two
seconds before and two seconds after t ( at t  2 and t + 2 respectively). B ut if we
are operating the system in real time a t t, we do not know what t he value of t he
i nput will be two seconds later. T hus i t is impossible t o implement this system in
real time. For this reason, noncausal systems are unrealizable in r eal t ime.
W hy S tudy N oncausal Systems?
From t he above discussion i t may seem t hat noncausal systems have no practical
purpose. This is n ot the case; they are valuable in t he s tudy of systems for several
reasons. First, noncausal systems are realizable when t he i ndependent variable is
o ther t han "time" (e.g., s pace). Consider, for example, an electric charge of density
q(x) placed along t he xaxis for x ?: O. This charge density produces an electric field
E (x) t hat is p resent a t every point on the xaxis from x =  00 t o 00. I n this case the
input...
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This note was uploaded on 04/14/2013 for the course ENG 350 taught by Professor Bayliss during the Spring '13 term at Northwestern.
 Spring '13
 Bayliss
 Signal Processing, The Land

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