Unit 4.1: The Laplace Transformation#
The preparatory reading for this section is Chapter 2 of [Karris, 2012] and Chapter 3 of [Hsu, 2020].
Follow along at cpjobling.github.io/eg-150-textbook//laplace_transform/1/laplace
In this unit we will present the Laplace transform and look at some important properties that go with it.
Agenda#
The Laplace Transform#
In Eigenfunctions of Continuous-Time LTI Systems we saw that for a continuous-time LTI system with impulse response
where
Definition#
The function
For a general continuous-time signal
The variable
The Laplace transform defined above is often called the bilateral (or two-sided) Laplace transform in contrast the the unilateral (or one-sided) Laplace transform which is defined as
where
Clearly the bilateral and unilateral tranforms are equivalent only if
In this course, because we are dealing with causal signals and systems, we will be concerned only with unilateral Laplace transform.
The laplace tranform equation is sometimes considered an operator that transforms a signal
and the signal
Laplace transform pairs are tabulated (Common Laplace Transform Pairs) for ease of reference.
Note
By convention, lower-case symbols are used for continuous-time signals and uppercase symbols for their Laplace tranforms.
MATLAB Representation#
The Laplace transform operator is provided in the MATLAB symbolic math toolkit by the function laplace
and can be used as follows:
% set up
format compact
%setappdata(0, "MKernel_plot_format", 'svg')
syms s t x(t) % define Laplace transform variable and time as symbols
X(s) = laplace(x(t))
Region of Convergence#
For a Laplace transfomation to exist, the integral must be bounded. That is
The range of values for the complex variables
Solved Problem 1#
Consider the signal
By hand#
We will work through the analysis in class.
MATLAB analysis#
syms s t a
assume(a,'real')
u0(t) = heaviside(t);
x(t) = exp(-a*t)*u0(t)
fplot(subs(x(t),a,1))

int(x(t)*exp(-s*t),t,0,inf)
assume(s + a > 0)
int(x(t)*exp(-s*t),t,0,inf)
X(s) = laplace(x(t))
The Laplace transform of

Fig. 42 ROC for Example 1#
because
Thus, the ROC for Solved Problem 1 is specified as
In Laplace transform applications, the complex plane is commonly referred to as the s-plane. The horizontal and vertical axes are sometimes referred to as the
Solved Problem 2#
Consider the signal
MATLAB Analysis#
We will work through the analysis in class
x(t) = -exp(-a*t)*u0(-t)
fplot(subs(x(t),a,1))

int(x(t)*exp(-s*t),t,-inf,0)
X(s)=laplace(x(t))
assume(s + a < 0)
int(x(t)*exp(-s*t),t,-inf,0)
By hand analysis#
See ex9.1.
Its Laplace transform
Thus the ROC for Solved Problem 2 is specified as

Fig. 43 ROC for Example 2#
Comparing the results of Solved Problem 1 and Solved Problem 2, we see that that algebraic expressions for
Therefore, in order for the Laplace transform to be unique for each signal
Poles and Zeros of X(s)#
Usually,
The coefficients
The transform
The roots of the numerator polynomial,
Similarly, the zeros of the denominator polynomial,
Therefore, the poles of
The zeros, on the other hand, may lie inside or outside the ROC.
Except for the scale factor
Thus a very compact representation of
Traditionally, an “x” is used to indicate each pole and a “o” is used to indicate each zero.
This is illustrated in Fig. 44 for

Fig. 44 s-plane representation of
MATLAB Analysis#
In MATLAB we represent polynomials as vectors whose elements are the numerical values of the coeffients in decending order of
n = [2 4]; d = [1 4 3];
We factorise using function roots
which determines the zeros of the polynomials:
z = roots(n), p = roots(d), k = 2;
z = -2
p = 2×1 double -3 -1
We can plot these on the pzmap
:
pzmap(p,z)

Note that
Properties of the ROC#
[not examinable]
The properties of the ROC are summarised in Section 3.1 D of schaum and as they are not examinable, we leave their study to the interested student.
Exercises 9#
Exercise 9.1#
Find the Laplace transform of
a).
b).
Solution#
a)
Thus we obtain
b). Similarly
Thus we obtain
Summary#
In this unit we presented the Laplace transform and look at some important properties that go with it. The following topics were covered:
Unit 4.1: Takeaways#
The Laplace transform#
The Laplace transform of a continuous-time signal or system is defined as
The term
For causal signals and systems that are linear-time invariant, we will use the single-sided Laplace transform
Tunctions of time and their Laplace transforms are often presented using the transform-pair notation
Region of convergence#
The Laplace transform exists only if the integral is finite or
The region of the
To be fully defined, the Laplace tranform of
Poles and zeros#
For the signals and systems we are concerned about the Laplace transform takes the form of a rational polynomial in
where
The numerator and denominator polynomials can be factorised into the so-called zero-pole-gain form polynomial
The terms
The terms
The poles and zeros will eaither be real or imaginary. If they are imaginary, they will appear as complex conjugate pairs and can be represented in the polynomial as quadratic factors
As we will see later, any function
The structure of any
MATLAB#
The Laplace transform is available in the MATLAB Symbolic Math Toolbox as function laplace
. To use it we usually specify the values t
and s
as symbols:
syms s t f(t)
F(s) = laplace(f(t))
Warning
The Laplace transform provided by MATLAB is single sided. If you need to find the laplace transform of a signal that is not causal, you need to define the region of convergence, and use the integral. See examples above.
The Laplace transform is representable in MATLAB using polynomials.
The roots of these polynomials can be determined using the roots
function
Factored polynomials can be presented using the roots and the poles and zeros can be plotted on the
Example#
Factorise
and plot the pole-zero map
Nx = [3 9]; Dx = [1 4 7 6]; % coefficients in descending powers of s
Zeros = roots(Nx)
Poles = roots(Dx) % Note the roots are complex!
K = 3
pzmap(Poles,Zeros)
Zeros = -3
Poles = -2.0000 + 0.0000i -1.0000 + 1.4142i -1.0000 - 1.4142i
K = 3

Next Time#
We move on to consider
References#
Hwei P. Hsu. Schaums outlines signals and systems. McGraw-Hill, New York, NY, 2020. ISBN 9780071634724. Available as an eBook. URL: https://www.accessengineeringlibrary.com/content/book/9781260454246.
Steven T. Karris. Signals and systems with MATLAB computing and Simulink modeling. Orchard Publishing, Fremont, CA., 2012. ISBN 9781934404232. Library call number: TK5102.9 K37 2012. URL: https://ebookcentral.proquest.com/lib/swansea-ebooks/reader.action?docID=3384197.