**Introduction**

Mrs. Vinitha Mohan. Her support, encouragement, quiet patience, and unwavering love un‐

grateful to the IntechOpen publishing team specially Mr. Markus Mattila, Publishing Proc‐

Professor, Department of Electronics and Communication Engineering

Dr. Mahalingam College of Engineering and Technology

S.V. Hemesh for bearing my preoccupation, understanding, and love he gave me.

I have no words to thank my son

I am

India

**Dr. R. Sudhakar**

deniably led me to the successful completion of the work.

VIII Preface

ess Manager, who constantly helped me in bringing out this book.

**Chapter 1**

**Provisional chapter**

**Introductory Chapter: Understanding Wavelets**

**Introductory Chapter: Understanding Wavelets**

DOI: 10.5772/intechopen.78388

In this section, let us discuss some fundamentals which are required to understand wavelets. Signals which are coming from a source are normally in time domain. Examples are sinusoidal signal, bio-medical signal, etc. Anytime domain signal can be processed or transformed into frequency domain (spectral domain) using mathematical transformations. Fourier transform is one of the popular or famous transform that will convert a time domain signal into

While plotting time domain signal, we use time in the x-axis and amplitude in the y-axis. The hidden information present in the signal cannot be revealed in the time domain hence a transform domain is required. The frequency content or spectrum of a signal is simply the frequency content (spectral components) of the signal. The frequency spectrum of a signal depicts what are all the frequencies exist in the signal. When plotting frequency domain, we

Normally for any signal, if the frequency content is not changing with respect to time is called as stationary signal. Example can be a sinusoidal signal where the frequency 'X' Hz is not changing irrespective of the cycle. Unfortunately, real time signals are nonstationary signal where the frequency content of the signal is keeping on changing. The best example is biological signals. Suppose when we are looking at an ECG (electrocardiograph) signal. The typical shape of a healthy ECG signal is well known to cardiologists. Any significant deviation from that shape is usually considered to be a symptom of a pathological condition. Doctors analyse these cases not only in time domain, they are using frequency domain also to confirm the

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Sudhakar Radhakrishnan

**1. Introduction**

pathological condition.

Additional information is available at the end of the chapter

frequency domain signal without any loss of generality.

use frequency in the x-axis and amplitude in the y-axis.

Sudhakar RadhakrishnanAdditional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.78388

#### **Introductory Chapter: Understanding Wavelets Introductory Chapter: Understanding Wavelets**

DOI: 10.5772/intechopen.78388
