Practical Digital Signal Processing

Edmund Lai

• Publisher: Newnes
• Year: 2003
• Print ISBN: 9780750657983
• eBook ISBN: 9780080473840
• Topic: Technology & Engineering
• Subtopic: Industrial Design
• Index: Technology & Engineering

The aim of this book is to introduce the general area of Digital Signal Processing from a practical point of view with a working minimum of mathematics. The emphasis is placed on the practical applications of DSP: implementation issues, tricks and pitfalls. Intuitive explanations and appropriate examples are used to develop a fundamental understanding of DSP theory, laying a firm foundation for the reader to pursue the matter further. The reader will develop a clear understanding of DSP technology in a variety of fields from process control to communications.* Covers the use of DSP in different engineering sectors, from communications to process control* Ideal for a wide audience wanting to take advantage of the strong movement towards digital signal processing techniques in the engineering world * Includes numerous practical exercises and diagrams covering many of the fundamental aspects of digital signal processing

1

Introduction

Digital signal processing (DSP) is a field which is primarily technology driven. It started from around mid 1960s when digital computers and digital circuitry became fast enough to process large amounts of data efficiently.

When the term ‘digital’ is used, often it loosely refers to a finite set of distinct values. This is in contrast to ‘analog’, which refers to a continuous range of values. In digital signal processing we are concerned with the processing of signals which are discrete in time (sampled) and in most cases, discrete in amplitude (quantized) as well. In other words, we are primarily dealing with data sequences – sequences of numbers.

Such discrete (or digital) signals may arise in one of the following two distinct circumstances:

• The signal may be inherently discrete in time (and/or amplitude)

• The signal may be a sampled version of a continuous-time signal

Examples of the first type of data sequences include monthly sales figures, daily highest/lowest temperatures, stock market indices and students examination marks. Business people, meteorologists, economists, and teachers process these types of data sequences to determine cyclic patterns, trends, and averages. The processing usually involves filtering to remove as much ‘noise’ as possible so that the pattern of interest will be enhanced or highlighted.

Examples of the second type of discrete-time signals can readily be found in many engineering applications. For instance, speech and audio signals are sampled and then encoded for storage or transmission. A compact disc player reads the encoded digital audio signals and reconstructs the continuous-time signals for playback.

1.1 Benefits of processing signals digitally

A typical question one may ask is why process signals digitally? For the first type of signals discussed previously, the reason is obvious. If the signals are inherently discrete in time, the most natural way to process them is using digital methods. But for continuous-time signals, we have a choice.

Analog signals have to be processed by analog electronics while computers or microprocessors can process digital signals. Analog methods are potentially faster since the analog circuits process signals as they arrive in real-time, provided the settling time is fast enough. On the other hand, digital techniques are algorithmic in nature. If the computer is fast and the algorithms are efficient, then digital processing can be performed in ‘real-time’ provided the data rate is ‘slow enough’. However, with the speed of digital logic increasing exponentially, the upper limit in data rate that can still be considered as real-time processing is becoming higher and higher.

The major advantage of digital signal processing is consistency. For the same signal, the output of a digital process will always be the same. It is not sensitive to offsets and drifts in electronic components.

The second main advantage of DSP is that very complex digital logic circuits can be packed onto a single chip, thus reducing the component count and the size and reliability of the system.

1.2 Definition of some terms

DSP has its origin in electrical/electronic engineering (EE). Therefore the terminology used in DSP are typically that of EE. If you are not an electrical or electronic engineer, there is no problem. In fact many of the terms that are used have counterparts in other engineering areas. It just takes a bit of getting used to.

For those without an engineering background, we shall now attempt to explain a few terms that we shall be using throughout the manual.

• Signals

We have already started using this term in the previous section. A signal is simply a quantity that we can measure over a period of time. This quantity usually changes with time and that is what makes it interesting. Such quantities could be voltage or current. They could also be the pressure, fluid level and temperature. Other quantities of interest include financial indices such as the stock market index. You will be surprised how much of the concepts in DSP has been used to analyze the financial market.

• Frequency

Some signals change slowly over time and others change rapidly. For instance, the (AC) voltage available at our household electrical mains goes up and down like a sine function and they complete one cycle in 50 times or 60 times a second. This signal is said to have a frequency of 50 or 60 hertz (Hz).

• Spectrum

While some signals consist of only a single frequency, others have a combination of a range of frequencies. If you play a string on the violin, there is a fundamental tone (frequency) corresponding to the musical note that is played. But there are other harmonics (integer multiples of the fundamental frequency) present. This musical sound signal is said to have a spectrum of frequencies. The spectrum is a frequency (domain) representation of the time (domain) signal. The two representations are equivalent.

• Low-pass filter

Filters let a certain range of frequency components of a signal through while rejecting the other frequency components. A low-pass filter lets the ‘low-frequency’ components through. Low-pass filters have a cutoff frequency below which the frequency components can pass through the filter. For instance, if a signal has two frequency components, say 10 hz and 20 hz, applying a low-pass filter to this signal with a cutoff frequency of 15 hz will result in an output signal, which has only one frequency component at 10 hz; the 20 hz component has been rejected by the filter.

• Band-pass filter

Band-pass filters are similar to low-pass filters in that only a range of frequency components can pass through it intact. This range (the passband) is usually above the DC (zero frequency) and somewhere in the mid-range. For instance, we can have a band-pass filter with a passband between 15 and 25 Hz. Applying this filter to the signal discussed above will result in a signal having only a 20 Hz component.

• High-pass filter

These filters allow frequency components above a certain frequency (cutoff) to pass through intact, rejecting the ones lower than the cutoff frequency.

This should be enough to get us going. New terms will arise from time to time and they will be explained as we come across them.

1.3 DSP systems

DSP systems are discrete-time systems, which means that they accept digital signals as input and output digital signals (or information extracted). Since digital signals are simply sequences of numbers, the input and output relationship of a discrete-time system can be illustrated as in Figure 1.1. The output sequence of sample y(n) is computed from the input sequence of sample x(n) according to some rules, which the system (H) defines.

Figure 1.1 A discrete-time system

There are two main methods by which the output sequence is computed from the input sequence. They are called sample-by-sample processing and block processing respectively. We shall encounter both types of processing in later chapters. Most systems can be implemented with either processi...

Table of contents

• Cover image
• Title page
• Table of Contents
• Titles in the series
• Copyright
• Preface
• Chapter 1: Introduction
• Chapter 2: Converting analog to digital signals and vice versa
• Chapter 3: Time-domain representation of discrete-time signals and systems
• Chapter 4: Frequency-domain representation of discrete-time signals
• Chapter 5: DSP application examples
• Chapter 6: Finite impulse response filter design
• Chapter 7: Infinite impulse response (IIR) filter design
• Chapter 8: Digital filter realizations
• Chapter 9: Digital signal processors
• Chapter 10: Hardware and software development tools
• Binary encoding of quantization levels
• Practical sessions
• Index