The following series of lectures
about the signals and systems gives the brief idea about signals and their
analysis for beginners. These lectures also help to understand the difference
between time domain, frequency domain and spacial domain. A clear idea will be
presented about importance of Fourier series and Fourier transforms to analyze
the signals. Further, design of stable systems for signal processing using
z-transforms. Finally a small real time project called digital sthescope will
be presented to analyze the heart beat signals along with other noise signals.

**i.**

**Introduction to signals**

**What is a signal?**

A signal is
any physical phenomenon (like gesture, action, electrical impulse, sound, image
and video) through which one can convey information to other. In other words,
signals are functions of one or more independent variables typically carry some
type of information. The graphical representation of signal is shown in the figure
1.1

figure 1.1 A one dimension continous time signal representation |

**Types of signals**:

Generally, Signals are classified
into two types

1.

**Continuous signals**:
These signals are functions of continuous
independent variable or real number. Again these continuous signals are categorized
into two.

**a.**

**One dimension continuous signals:**

One dimension continuous signals are the function of continuous
single independent variable. For, example a speech signal is a continuous one-dimension
signal, where time (t) is the independent variable. Hence speech signal is
referred as continuous time one- dimensional signal. Human speech signal is
shown in figure 1.2

figure 1.2 human speech signal representation |

**b.**

**Multi-dimension continuous signals:**

Multi-dimension continuous signals are the function of
multiple continuous independent variables. For, example image brightness is a function
that varies horizontally and vertically.

brightness(horiz,verti);

2.

**Discrete signals**:
These signals are functions of integer
number or discrete independent variable. The discrete signals are also
categorized into two.

**a.**

**One dimension discrete signals:**

One dimension discrete signals are the function of discrete single
independent variable. The representation of discrete time signal is shown below
figure 1.3.

figure 1.3 one dimension discrete signal representation |

**b.**

**Multi-dimension discrete signals:**

Multi-dimension
discrete signals are the function of multiple discrete independent variables. For
example, the representation of spatial antenna array is a two dimension
discrete signal x[n,m].

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