1. Lecture 2 Signals and Systems (I)
Principles of Communications
Fall 2008
NCTU EE Tzu-Hsien Sang 1

2. Outlines Signal Models & Classifications
Signal Space & Orthogonal Basis
Fourier Series &Transform
Power Spectral Density & Correlation
Signals & Linear Systems
Sampling Theory
DFT & FFT

3. Signal Models and Classifications
The first step to knowledge: classify things.
What is a signal?
Usually we think of one-dimensional signals; can our scheme extend to higher dimensions?
How about representing something uncertain, say, a noise?
Random variables/processes – mathematical models for signals

4. Deterministic signals: completely specified functions of time
Deterministic signals: completely specified functions of time. Predictable, no uncertainty, e.g. , with A and are fixed.
Random signals (stochastic signals): take on random values at any given time instant and characterized by pdf (probability density function). “Not completely predictable”, “with uncertainty”, e.g. x(n) = dice value at the n-th toss.

6. Periodic vs. Aperiodic signals
Phasors and why are we obsessed with sinusoids?

7. Singularity functions (they are not functions at all!!!)
Unit impulse function :
Defined by
It defines a precise sample point of x(t) at the incidence t0:
Basic function for linearly constructing a time signal
Properties: ;

8. What is precisely? some intuitive ways of imaging it:
Unit step funcgtion:

9. Energy Signals & Power Signals
Energy signals: iff
Power signals: iff
Examples:

10. If x(t) is periodic, then it is meaningless to find its energy; we only need to check its power.
Noise is often persistent and is often a power signal.
Deterministic and aperiodic signals are often energy signals.
A realizable LTI system can be represented by a signal and mostly is a energy signal.
Power measure is useful for signal and noise analysis.
The energy and power classifications of signals are mutually exclusive (cannot be both at the same time). But a signal can be neither energy nor power signal.

11. Signal Spaces & Orthogonal Basis
The consequence of linearity: N-dimensional basis vectors:
Degree of freedom and independence: For example, in geometry, any 2-D vector can be decomposed into components along two orthogonal basis vectors, (or expanded by these two vectors)
Meaning of “linear” in linear algebra:

12. A general function can also be expanded by a set of basis functions (in an approximation sense)
or more feasibly
Define the inner product as (“arbitrarily”)
and the basis is orthogonal
then

13. Examples: cosine waves
What good are they?
Taylor’s expansion: orthogonal basis?
Using calculus can show that function approximation expansion by orthogonal basis functions is an optimal LSE approximation.
Is there a very good set of orthogonal basis functions?
Concept and relationship of spectrum, bandwidth and infinite continuous basis functions.

14. Fourier Series & Fourier Transform
Sinusoids (when?):
If x(t) is real,
Notice the integral bounds.

15. Or, use both cosine and sine:
with
Yet another formulation:

16. Some Properties
Linearity If x(t)  ak and y(t)  bk
then Ax(t)+By(t)  Aak + Bbk 
Time Reversal
If x(t)  ak then x(-t)  a-k
Time Shifting
Time Scaling
x(at)  ak But the fundamental frequency changes
Multiplication x(t)y(t) 
Conjugation and Conjugate Symmetry
x(t)  ak and x*(t)  a*-k
If x(t) is real  a-k = ak*

17. Parseval’s Theorem
Power in time domain = power in frequency domain

18. Some Examples

19. Extension to Aperiodic Signals
Aperiodic signals can be viewed as having periods that are “infinitely” long.
Rigorous treatments are way beyond our abilities. Let’s use our “intuition.”
If the period is infinitely long. What can we say about the “fundamental frequency.”
The number of basis functions would leap from countably infinite to uncountably infinite.
The synthesis is now an integration..
Remember, both cases are purely mathematical construction.

20. “The wisdom is to tell the minute differences between similar-looking things and to find the common features of seemingly-unrelated ones…”
Fourier Series
Fourier Transform
Good orthogonal basis functions for a periodic function:
Intuitively, basis functions should be also periodic.
Intuitively, periods of the basis functions should be equal to the period or integer fractions of the target signal.
Fourier found that sinusoidal functions are good and smooth functions to expand a periodic function.
Good orthogonal basis functions for a aperiodic function:
Already know sinusoidal functions are good choice.
Sinusoidal components should not be in a “fundamental & harmonic” relationship.
Aperiodic signals are mostly finite duration.
Consider the aperiodic function as a special case of periodic function with infinite period

21. Synthesis & analysis: (reconstruction & projection)
Given periodic with period , and , it can be synthesized as
: Spectra coefficient, spectra amplitude response
Before synthesizing it, we must first analyze it first and find out
By orthogonality
Given aperiodic with period
, and , we can
synthesize it as
Hence,

22. Frequency components:
Decompose a periodic signal into countable frequency components.
Has a fundamental freq. and many other harmonics.
Discrete line spectra
Power Spectral Density:
and (by Parseval’s equality)
Decompose an aperiodic signal into uncountable frequency components
No fundamental freq. and contain all possible freq.
Continuous spectral density
Energy Spectral Density:
and

23. In real basis functions:
note that
for real x(t).
Exercises!

24. Conditions of Existence
Expansion by orthogonal basis functions can be shown is equivalent to finding using the LSE (or MSE) cost function:
Would as ?
This requires square integrable condition (for the power signal):
and not necessarily
Dirichlet’s conditions:
finite no. of finite discontinuities;
finite no. of finite max & min.;
absolute integrable:
Dirichlet’s condition implies convergence almost everywhere, except at some discontinuities.
This requires square integrable condition (for the energy signal):