Standard signals
The following definition introduces basic signals that play a prominent role in this course:
-
step:
𝟙 -
ramp:
𝟙 -
rectangular pulse (of width
): -
triangular pulse (of width
): -
sinusodial:
-
sinc (sine cardinal):
-
exponential:
Notes:
Common abbreviations are:
Norms
We can define signal norms in a very similar way to vector norms:
Definition:
-norm: If , then we say that and call absolutely integrable.
-norm: If , then we say that and call square integrable.
-norm: If , then we say that and call it bounded
Example: Standard signals' norms
- if
, then
- if
with 𝟙 , then
- if
then 𝟙
Operations on Signals
(Amplitude) Scaling
Given
with
Example:
𝟙
Addition
Given
Multiplication
Given
Time Scale (pace change)
Given
Commutativity property:
Time Shift
Given
Commutativity property:
Periodic Signals
A signal
Defintion:
A real-valued signal
that can be written as is called a sinusoid or real harmonic signal. Then
is the amplitude, the angular frequency, and the initial phase of the signal .
Such sinusoids have a period of
Lemma:
Suppose that
is integrable on and that is periodic with period . Then for every , there holds:
Energy and Power
It is customary in signal analysis to use “energy” instead of norm:
Definition:
The energy
of a signal is defined as If
(finite energy content), then the signal is said to be an energy signal.
The rectangular and traingular pulses are examples of energy signals. For a signal
Definition:
The power
of a signal is defined as Signals that have finite power are called power signals.
Example:
In the case of a
-periodic signal, the power signal is finite, and it equals the average energy over one period: Let there be
such that :
Example:
The power of the sinusoid
with period , is:
Convolution
Loosely speaking a convolution is a linear combination of shifted copies of signal. For instance:
Is an example of convolution of signal
are known as convolutions, and so is its integral version which we take to be its definition.
Definition:
The convolution or convolution product of two signals
and is denoted as and is defined as:
It is an interesting fact that convolution products commute:
Convolutions are very common in applications, and are, for instance, useful if we want to remove noise from signals, detect edges in pictures, soften pictures, etc.
Example: Sliding window averaging and noise reduction
For a given signal
we construct the signal by averaging around over an interval of a fixed length , i.e., we consider Averaging
this way filters out highly fluctuating noise. It is to be expected then, that is somewhat smoother that , but as long as is not too large the graph of the averagd should retain roughly the same shape as the graph of .
Screenshot_20240609_111610_Samsung Notesveraged withThe signal
can be written as the convolution of with a suitable function : for
Example: Convolution with step:
Convolution with the unit step amount to integration:
𝟙 𝟙 𝟙 𝟙 For the step signal, we get zero for every
, therefore the second integral evaluates to : 𝟙
The Delta Function
In applications we often encounter signals that a very short duration but nevertheless have a definite impact. Such signals are called impulses. The standard are called impulses. The standard impulse is the so-called Dirac delta function also known as the unit impulse. The delta function
As
A series of
for and
However, the area enclosed by the spike and the
and we think of the delta function as a “function” that is zero everywhere except at
The delta function is usually depicted as
The delta function
The idea to see the function as a pike in this sense is helpful, but mathematically it is far from sound. After all,
and the integral of a function that is zero everywhere except for one point, is zero.
Lemma:
If
is continuous at , then
Properties of the delta function
Delta functions can be added, they can be multiplied with regular functions, they can be integrated etc.
The Sifting Property
The scaled and shifted delta function
For
Shifted and scaled
This is very much like a shifted copy of
Shifted delta function
We can now generalize the previous Lemma:
Lemma:
If
is continuous, then:
An immediate special case is that
This property is known as the sifting property of the delta function. It is the property that out of all values
Products with delta functions
Lemma:
If
is continuous at , then
Property | Condition | Notes |
---|---|---|
Sifting | ||
Convolution | ||
Scaling | ||
Properties and rules of calculus for the delta function
Exercises
Question 1
Consider the continuous time signal
Triangle signal
Construct
Solution:
The above mentioned signal, broken down to three
signals. Try to match the signals to their corresponding graph representation.
According to signal norm:
We have:
Question 2
Let
i.e.
find the convolution
Solution:
One of the properties of Convolution is:
We’ll calculate each one individually, using the The Sifting Property:
and we end up with