Design of a computer based system to process an analog signal

i

KIGALI INSTITUTE OF SCIENCE AND TECHNOLOGY (KIST)

Avenue de l’armée

PO BOX 3900 Kigali – Rwanda

www.kist.ac.rw

FACULTY OF ENGINEERING

DEPARTMENT OF ELECTRICAL AND ELECTRONICS

PROGRAM OF ELECTRONICS AND TELECOMMUNICATION

PROJECT REPORT ON

Submitted by:

GASHEMA Gaspard (GS 20060092)

IYAKAREMYE Dieudonné (GS 20060189)

Under guidance of:

Supervisor: TWIRINGIYIMANA Remy

Submitted in partial fulfillment of the requirements for the award of

BACHELOR OF SCIENCE DEGREE IN

ELECTRICAL AND ELECTRONICS ENGINEERING (EEE)

September, 2010

DESIGN OF A COMPUTER-BASED

SYSTEM TO PROCESS AN ANALOG

SIGNAL

ii

CERTIFICATION

This is to certify that the work presented in this report entitled: “DESIGN OF COMPUTER

BASED SYSTEM TO PROCESS AN ANALOG SIGNAL ” is an original work of

GASHEMA Gaspard and IYAKAREMYE Dieudonné; and it has not been submitted to any

university or elsewhere in any form for the award of any degree.

Supervisor Head Of Department of Electrical and Electronic

Engineering:

TWIRINGIYIMANA Remy ZIMULINDA François

Signature: ……………………… Signature:…………………………………………

Date…:………………………… Date …..……………………………………..

iii

DECLARATION

We, GASHEMA Gaspard and IYAKAREMYE Dieudonné, hereby declare that, the work

presented in this report is our own contribution. To the best of our knowledge, this same work

has never been presented or submitted to any other Universities or institutions of higher learning

for the award of any degree.

We therefore declare that, this work is our own contribution for the partial fulfillment of the

award of the degree of Electronics and Telecommunication Engineering in KIST.

GASHEMA Gaspard IYAKAREMYE Dieudonné

REG No.: GS20060092 REG N

o.: GS20060189

Signature: ……………… Signature: ………………

Date: …………………… Date: ……………………

This report has been submitted for examination with the approval of the following supervisor:

TWIRINGIYIMANA Remy

Department of Electrical and Electronics Engineering, KIST

Signature: ………………………………………..

Date: ……………………………………………..

iv

DEDICATION

This project is dedicated:

To the Almighty God

To our families

To our friends

v

ACKNOWLEDGEMENT

We are thankful to the Almighty God for the given gift of life and guidance, especially during

this project. Also we are grateful for members of our families and relatives. Our thanks go to KIST and SFAR for their financial contribution for carrying out our studies. We sincerely thank

Mr. TWIRINGIYIMANA Remy, for his kind guidance and for his provision of necessary

facilities to carry out this project work. Thanks to several former lecturers and classmates who

broadened our knowledge and technical skills to fulfill the requirement to this project. We wish

to extend our deep sense of gratitude to our beloved parents for their encouragement throughout

our studies.

God bless you all.

vi

ABSTRACT

This project has the aim of designing a system that can make decision electronically thereby

speeding up the operations made and improving electronic system by using digital system. The

work to be done was concentrated on acquiring analog signal and processing of this acquired

signal using a personal computer. Digital filters were designed using Matlab programming to

process the acquired signals. This was achieved through the use of electronic equipments such as

signal generator, oscilloscope, computer and interfacing circuit. In fact, the main task to be

carried out in this project is to design a computer-based system to acquire and process an analog

signal generated by front end devices such as function generator. There were two ways to realize

the practice of this project. The first one consisted the use of zelscope software as oscilloscope

which analyzes signal originates from mobile phone. The mobile phone was used to play music

(audio signal) in order to be analyzed by zelscope on PC screen. On other hand, this audio signal

from mobile phone were acquired and analyzed with matlab programming Language. Finally,

the analyzed signal by matlab had to be compared with that obtained when using zelscope. The

second one concerned with acquiring real time signal generated from function generator and

compare the processed signal on PC with the signal displayed on oscilloscope. This work was

carried out in KIST laboratory building (KIST4) in electronics lab (Second Floor- room 33)

vii

TABLE OF CONTENTS

CERTIFICATION ............................................................................................................................................... i

DECLARATION .............................................................................................................................................. iii

DEDICATION ................................................................................................................................................. iv

ACKNOWLEDGEMENT ................................................................................................................................... v

ABSTRACT ..................................................................................................................................................... vi

TABLE OF CONTENTS................................................................................................................................... vii

LIST OF FIGURES AND TABLES ...................................................................................................................... ix

LIST OF TABLES .............................................................................................................................................. x

LIST OF ABBREVIATIONS AND SYMBOLES .................................................................................................... xi

CHAPTER 1: INTRODUCTION ......................................................................................................................... 1

1.1. General introduction .......................................................................................................................... 1

1.2. Structure of project report .................................................................................................................. 1

1. 3. Statement of the problem .................................................................................................................. 1

1.4. Significance and justification ............................................................................................................. 1

1.5. Objectives .......................................................................................................................................... 2

1.5.1. Main objective ............................................................................................................................ 2

1.5.2. Specific objectives ....................................................................................................................... 2

1.6. Scope and limitation of the project .................................................................................................... 2

viii

1.7. Methodology ...................................................................................................................................... 2

CHAPTER 2: LITERATURE REVIEW ................................................................................................................. 4

2.1. Electrical instrumentation signals ...................................................................................................... 4

2.1.1. Analog and digital signals ............................................................................................................ 4

2.1.2 Basic analog signal measurements .............................................................................................. 4

2.2.1. DSP applications ....................................................................................................................... 11

2.2.2. The Basic DSP Operations ......................................................................................................... 15

2.3. Data Acquisition Toolbox ................................................................................................................ 15

2.3.1. Basic Steps for Acquiring Data .................................................................................................. 15

2.3.2. Acquiring Data with a Sound Card ............................................................................................ 15

2.4. Computer sound cards ...................................................................................................................... 19

2.4.1. Introduction to computer sound cards ..................................................................................... 19

2.4.2. Analog versus Digital object related to sound card .................................................................. 20

2.4.3. Conditions required to interface any signal to computer sound card ...................................... 22

2.4.4. Chip Chat Sound Card Technical Specifications ........................................................................ 22

CHAPTER 3: SYSTEM DESIGN ...................................................................................................................... 25

3.1. Example of analyzing data by matlab .............................................................................................. 25

3.1.1. Signal analysis ........................................................................................................................... 25

3.2. Data Acquisition with MATLAB Programming ............................................................................. 27

3.3. Digital Filter Design ........................................................................................................................ 29

3.3.1. FIR digital filter .......................................................................................................................... 31

3.3.2. Band Pass filter Design Specifications ....................................................................................... 32

3.3.3.Low pass filter Design Specifications ......................................................................................... 33

3.4. The design using zelscope ............................................................................................................... 34

3.5. Design using physical devices ......................................................................................................... 36

3.5.1. Designing methodology and process ........................................................................................ 36

3.5.2. Procedure of experiment .......................................................................................................... 39

CHAPTER4. ANALYSIS OF RESULTS AND DISCUSSION ................................................................................. 41

4.1. Analysis of results ............................................................................................................................ 41

4.1.1. Results about Zelscope ............................................................................................................. 41

4.1.2. Description of interface circuit ................................................................................................. 44

4.2. Discussion ........................................................................................................................................ 48

4.2.1. Discussion about the use of Zelscope ....................................................................................... 48

ix

4.2.2. Discussion about oscilloscope ................................................................................................... 48

CHAPTER5. CONCLUSION AND RECOMMENDATIONS ................................................................................ 50

5.1. Conclusion ....................................................................................................................................... 50

5.2. Recommendations ............................................................................................................................ 50

REFERENCES: ............................................................................................................................................... 52

APPENDICES ................................................................................................................................................ 53

LIST OF FIGURES AND TABLES

Figure2. 1: Analog Oscilloscope Block Diagram[9] ...................................................................................... 5

Figure2. 2: Triggering Stabilizes a Repeating Waveform[9] ......................................................................... 6

Figure 2.3: Digital Oscilloscope Block Diagram[9] ....................................................................................... 7

Figure 2.4: Real Time Sampling Diagram [9] ................................................................................................ 8

Figure 2.5: Linear and Sine Interpolation Diagram [9] .................................................................................. 8

Figure 2.6: Equivalent-time Sampling Diagram[9] ........................................................................................ 9

Figure2. 7: Zelscope with two analyzing signal [14] .................................................................................... 9

Figure 2.8: Normal mode, with both channels displayed[14] ....................................................................... 10

Figure 2.9: Broadcasting via satellite[11] ..................................................................................................... 12

Figure 2.10: Dish antenna used for helping in navigation [11]. .................................................................... 13

Figure 2.11: Processing of audio signal [11] ................................................................................................. 14

Figure 2.12: Acquisition of data with sound card[16] ................................................................................... 16

Figure 2.13: Frequency component of Tuning Fork[16] .............................................................................. 18

Figure 2.14: Computer sound card [13] ......................................................................................................... 19

Figure 2.15: Analog signal to be sampled by soundcard[12] ........................................................................ 20

Figure 2.16: An analog-to-digital converter measures sound waves at frequent intervals [12]

. ................... 21

Figure 2.17: A PCI sound card[12] ............................................................................................................... 22

Figure 2.18: mono connector[13] .................................................................................................................. 23

Figure2. 19: Stereo connector[13] ................................................................................................................. 24

Figure 2.20: Jack[13] ..................................................................................................................................... 24

Figure 3.21: Signal analyzed by matlab ....................................................................................................... 26

Figure 3.22: Power Spectral density of data ............................................................................................... 27

x

Figure 3.23: Data Acquired using Matlab acquisition program .................................................................. 28

Figure 3.24: Power spectrum density of data acquired using Matlab acquisition program ........................ 29

Figure 2.25: A conceptual Representation of digital filter .......................................................................... 31

Figure 3.26: Band-pass filter design ........................................................................................................... 32

Figure 3.27: Low pass filter design ............................................................................................................. 33

Figure 3.28: Filtrered Data Acquired using Matlab programming ............................................................. 34

Figure 3.29: Block diagram showing the design using Zelscope ................................................................ 35

Figure 3.30: Photo of signal generator ........................................................................................................ 36

Figure 3.31: Photo of analog oscilloscope .................................................................................................. 37

Figure 3.32: Photo of PC ........................................................................................................................... 37

Figure 3.33: Photo of interface circuit ........................................................................................................ 38

Figure 3.34: Interfacing circuit between PC sound card and signal generator ........................................... 38

Figure 3.35: Block diagram of the design using oscilloscope .................................................................... 39

Figure 3.36: Photo of experiment for complete system .............................................................................. 39

Figure 4.37:Signal analyzed on Zelscope Display ...................................................................................... 41

Figure 4.38: Accquisition of signal “Data” ................................................................................................. 42

Figure 4.39: Power spectral density of data ................................................................................................ 43

Figure 4.40: Filtered signal “data” .............................................................................................................. 44

Figure 4.41: Acquired signal “data” .......................................................................................................... 46

Figure4. 42: Power spectral density of unfiltered signal “data” ................................................................. 47

Figure 4.43: Filtered signal “data”of figure 4.41 ........................................................................................ 48

LIST OF TABLES

Table.1: Results for interface circuit. .......................................................................................................... 45

xi

LIST OF ABBREVIATIONS AND SYMBOLES

: Ohm

A: Ampere

AC: Alternative Current

ADC: Analog to Digital Converter

ADPCM: Adaptive Differential Pulse Code

Modulation

AI: Analog Input

Apass: Attenuation pass band

Astop: attenuation stop band

BPF: Bass Pass Filter

CD: Compact Disc

CH: Channel

CODEC: Coder/Decoder

CRT: Cathode Ray Tube

DAC: Digital to Analog Converter

DC: Direct Current

DSP: Digital Signal Processing

EEE: Electrical and Electronics engineering

ETE: Electronics and Telecommunication

Engineering

FFT: Fast Fourier Transform

Fig:Figure

FIR: Finite Impulse Response

Fpass: Pass band Frequency

FS: Sampling Frequency

Fstop: Stop band Frequency

GS: Government Sponsor

Hz: Hertz

12

I/P: input

IBM: International Business Machines

IEEE: Institute of Electrical and Electronic

Engineers

IIF: Infinite Impulse response

ISA: Industry Standard Architecture

K: Kilo (103)

KIST: Kigali Institute of Science and

Technology

m: meter

m: milli (10-3

)

O/P: Output

PC: Personal Computer

PCI: Peripheral Component Interconnect

RADAR: Radio Detection And Ranging

RMS: Root Mean Square

SFAR: Student Financing Agency for

Rwanda

SONAR: Sound Navigation And Ranging

V: Volt

1

CHAPTER 1: INTRODUCTION

1.1. General introduction

An oscilloscope is one of the equipments needed to perform this research. Therefore, an

understanding of its working principle is required. To better understand the oscilloscope

controls, one need to know a little more about how oscilloscopes display a signal .Although there

are two types of oscilloscopes: analog and digital, in this work only the analog will be used.

Analog oscilloscopes work somewhat differently than digital oscilloscopes. However, several of

the internal systems are similar. Analog oscilloscopes are somewhat simpler in concept and are

described first, followed by a description of digital oscilloscopes. The working principle of these

two types of oscilloscope and the difference between them will be shown clearly in the following

chapters. Computers are used at the heat of almost every electronic system due to the fact that

their ability to quickly process and store large amount of data makes system more versatile and

perform many functions. The filters used here are digital because computers understand only

digital data. The design of these digital filters was done using Matlab software.

1.2. Structure of project report

This project report contains five chapters. The first chapter is introduction which discusses about

overview and motivation of this work .The second chapter is literature review which provides

details about what others have done concerning this research and set a benchmark for the current

as well as justifying the specific solution techniques. The third chapter deals with the system

design. The fourth chapter discusses the obtained result. Finally, the fifth chapter is all about the

conclusion and recommendations giving summary of the main findings statement of the

encountered problem as well as limitations.

1. 3. Statement of the problem

It has been observed that all over the world today’s electronics equipments are all almost of

digital in nature. Before the development of the digital equipments, the use of early devices in

some applications did not give the accurate output especially in Telecommunication due to many

factors including easy attraction of random disturbances or variations introduced in system

leading to the delay and losses for the signal. This problem will be recovered by the use of digital

equipments associated by some software. But nowadays; the arrival of digital equipments makes

the system very suitable and fast. Even due to the flexibility, digital system will lead the low cost

equipments. Due to these advantages of digital system, today and future’s life will become

digitalized.

1.4. Significance and justification

The work to be done helps in understanding and practices the concept of Digital Signal

Processing (DSP) techniques. These achieved by using computer to learn data acquisition

2

applications and Matlab software for designing Digital Filters which will be used to denoise the

acquired signal . Digital filtering is exceptionally flexible and can easily incorporate non-lineal

operations such as clipping or removal of data samples that appear to the incompatible with

neighboring samples, and therefore erroneous indicate the source. Moreover, it is much easier to

determine the response of a digital system than analog system. This is because the digital system

can be described by its difference equation and this is directly amenable to a solution using

computer.

1.5. Objectives

1.5.1. Main objective

Designing a computer-based system to process an analog signal generated from front

ended devices such as function generator.

1.5.2. Specific objectives

Understanding how to install and use Zelscope application software.

Acquiring analog signals using MATLAB Data acquisition toolbox.

Designing digital filters using MATLAB Program.

Denoising the acquired signals using digital filter.

Displaying the processed signals on PC and appreciating the comparison with the signal

displayed on oscilloscope.

1.6. Scope and limitation of the project

This study is about the design of a computer based system to process an analog signal. The study

is concentrated only the analysis of signals generated from end devices such as signal generator

and mobile phone with the frequencies which are audible (i.e. frequencies of the ranges from

20Hz to 20 kHz). The filters used to denoise the acquired signal are FIR low and band-pass filter

with equiripple methods.

1.7. Methodology

The methodologies used to achieve the objectives of this work are books bellowed in different

Libraries including KIST Library and internet documentations. Besides experiment, using

programming languages such as MATLAB carried out in KIST laboratory building (KIST4) in

electronics lab (Second Floor- room 33).

Equipments:

The following are electronic equipments that were used:

Signal generator.

Analog oscilloscope.

Personal Computer.

3

Mobile phone

Buffer circuit used as front end interfacing device(for signal generator).

4

CHAPTER 2: LITERATURE REVIEW

In order to get a clear understanding on the work to be done, it is necessary to make a review on

what was done by other researchers on the same field. This includes a review on some

applications, programs; components used to this research. The review of them is described

below.

2.1. Electrical instrumentation signals

2.1.1. Analog and digital signals

2.1.1.1. Definitions

A signal is any kind of physical quantity that conveys information. Audible speech is certainly a

kind of signal, as it conveys the thoughts (information) of one person to another through the

physical medium of sound. Hand gestures are signals, too, conveying information by means of

light.

2.1.1.2. Types of signal

There are two kind of signal: analog and digital signal. The difference between these signals is

described below. An analog signal is a kind of signal that is continuously variable whereas a

digital signal is one having discrete set of values [9]

. A well-known example of analog versus

digital is that of clocks: analog being the type with pointers that slowly rotate around a circular

scale, and digital being the type with decimal number displays or a "second-hand" that jerks

rather than smoothly rotates. The analog clock has no physical limit to how finely it can display

the time, as its "hands" move in a smooth, pauseless fashion. The digital clock, on the other

hand, cannot convey any unit of time smaller than what its display will allow for [9]

.

2.1.2 Basic analog signal measurements

To measure analog signal, it is better to know the instrumentation and instrument.

Instrumentation is a field of study and work centering on and control of physical processes.

These physical processes include pressure, temperature, flow rate, and chemical consistency. An

instrument is a device that measures and/or acts to control any kind of physical process. Due to

the fact that electrical quantities of voltage and current are easy to measure, manipulate, and

transmit over long distances, they are widely used to represent such physical variables and

transmit the information to remote locations [4][9]

.The most widely used instrument to measure

such electrical quantity is oscilloscope as it being described its working principle below in

fig.2.1.

5

2.1.2.1. Oscilloscope.

How does an oscilloscope work?

To better understand the oscilloscope controls, you need to know a little more about how

oscilloscopes display a signal. Analog oscilloscopes work somewhat differently than digital

oscilloscopes. However, several of the internal systems are similar. Analog oscilloscopes are

somewhat simpler in concept and are described first, followed by a description of digital

oscilloscopes.

2.1.2.1.1. Analog Oscilloscopes

When you connect an oscilloscope probe to a circuit, the voltage signal travels through the probe

to the vertical system of the oscilloscope. Following Figure is a simple block diagram that shows

how an analog oscilloscope displays a measured signal.

Figure2. 1: Analog Oscilloscope Block Diagram[9]

Depending on how you set the vertical scale (volts/div control), an attenuator reduces the signal

voltage or an amplifier increases the signal voltage.

Next, the signal travels directly to the vertical deflection plates of the cathode ray tube (CRT).

Voltage applied to these deflection plates causes a glowing dot to move. (An electron beam

hitting phosphor inside the CRT creates the glowing dot). A positive voltage causes the dot to

move up while a negative voltage causes the dot to move down.

The signal also travels to the trigger system to start or trigger a "horizontal sweep." Horizontal

sweep is a term referring to the action of the horizontal system causing the glowing dot to move

across the screen. Triggering the horizontal system causes the horizontal time base to move the

glowing dot across the screen from left to right within a specific time interval. Many sweeps in

rapid sequence cause the movement of the glowing dot to blend into a solid line. At higher

speeds, the dot may sweep across the screen up to 500,000 times each second.

Together, the horizontal sweeping action and the vertical deflection action trace a graph of the

signal on the screen. The trigger is necessary to stabilize a repeating signal. It ensures that the

6

sweep begins at the same point of a repeating signal, resulting in a clear picture as shown in

following figure [9]

.

Figure2. 2: Triggering Stabilizes a Repeating Waveform[9]

In conclusion, to use an analog oscilloscope, you need to adjust three basic settings to

accommodate an incoming signal:

The attenuation or amplification of the signal. Use the volts/div control to adjust the

amplitude of the signal before it is applied to the vertical deflection plates.

The time base. Use the sec/div control to set the amount of time per division represented

horizontally across the screen.

The triggering of the oscilloscope. Use the trigger level to stabilize a repeating signal, as

well as triggering on a single event.

Also, adjusting the focus and intensity controls enables you to create a sharp, visible display.

2.1.2.1.2. Digital Oscilloscopes

Based on [9]

, some of the systems that make up digital oscilloscopes are the same as those in

analog oscilloscopes; however, digital oscilloscopes contain additional data processing systems.

With the added systems, the digital oscilloscope collects data for the entire waveform and then

displays it. When you attach a digital oscilloscope probe to a circuit, the vertical system adjusts

the amplitude of the signal, just as in the analog oscilloscope. Next, the analog-to-digital

converter (ADC) in the acquisition system samples the signal at discrete points in time and

converts the signal's voltage at these points to digital values called sample points. The horizontal

system's sample clock determines how often the ADC takes a sample. The rate at which the

clock "ticks" is called the sample rate and is measured in samples per second.

The sample points from the ADC are stored in memory as waveform points. More than one

sample point may make up one waveform point. Together, the waveform points make up one

waveform record. The number of waveform points used to make a waveform record is called the

record length. The trigger system determines the start and stop points of the record. The display

receives these record points after being stored in memory. Depending on the capabilities of your

oscilloscope, additional processing of the sample points may take place, enhancing the display.

7

Pretrigger may be available, allowing you to see events before the trigger point as shown in

fig.2.3.

Figure 2.3: Digital Oscilloscope Block Diagram[9]

Fundamentally, with a digital oscilloscope as with an analog oscilloscope, you need to adjust the

vertical, horizontal, and trigger settings to take a measurement.

Sampling Methods

The sampling method tells the digital oscilloscope how to collect sample points. For slowly

changing signals, a digital oscilloscope easily collects more than enough sample points to

construct an accurate picture. However, for faster signals, (how fast depends on the

oscilloscope's maximum sample rate) the oscilloscope cannot collect enough samples. The

digital oscilloscope can do two things:

It can collect a few sample points of the signal in a single pass (in real-time sampling

mode) and then use interpolation. Interpolation is a processing technique to estimate what

the waveform looks like based on a few points.

It can build a picture of the waveform over time, as long as the signal repeats itself

(equivalent-time sampling mode).

i. Real-Time Sampling with Interpolation

8

Digital oscilloscopes use real-time sampling as the standard sampling method. In real-time

sampling, the oscilloscope collects as many samples as it can as the signal occurs. See following

figure for single-shot or transient signals you must use real time sampling [9]

.

Figure 2.4: Real Time Sampling Diagram

[9]

Digital oscilloscopes use interpolation to display signals that are so fast that the oscilloscope can

only collect a few sample points. Interpolation "connects the dots." Linear interpolation simply

connects sample points with straight lines. Sine interpolation (or sin x over x interpolation)

connects sample points with curves. (See Following Figure2.5) Sin x over x interpolation is a

mathematical process similar to the "oversampling" used in compact disc players. With sine

interpolation, points are calculated to fill in the time between the real samples. Using this

process, a signal that is sampled only a few times in each cycle can be accurately displayed or, in

the case of the compact disc player, accurately played back.

Figure 2.5: Linear and Sine Interpolation Diagram

[9]

Equivalent-Time Sampling

Some digital oscilloscopes can use equivalent-time sampling to capture very fast repeating

signals. Equivalent-time sampling constructs a picture of a repetitive signal by capturing a little

bit of information from each repetition. (See Following Figure2.6) You see the waveform slowly

build up like a string of lights going on one-by-one. With sequential sampling the points appear

from left to right in sequence; with random sampling the points appear randomly along the

waveform [9]

.

9

Figure 2.6: Equivalent-time Sampling Diagram[9]

2.1.2.2. Zelscope review

2.1.2.2.1 What is Zelscope?

Zelscope is Windows software that converts PC into a dual-trace storage oscilloscope and

spectrum analyzer. It uses computer's sound card as analog-to-digital converter, presenting a

real-time waveform or spectrum of the signal - which can be music, speech, or output from an

electronic circuit. Zelscope features the interface of a traditional oscilloscope, with conventional

gain, offset, time base, and trigger controls. As a real-time spectrum analyzer, Zelscope can

display the amplitude and phase components of the spectrum.

Figure2. 7: Zelscope with two analyzing signal [14]

10

Figure 2.8: Normal mode, with both channels displayed[14]

2.1.2.2.2. What can Zelscope use for?

Zelscope is low-frequency oscilloscope and spectrum analyzer software. It can be useful in

tuning music instruments, adjusting audio circuits, or doing physics experiments. Acoustics is

the most evident area; Zelscope also allows for an easy measurement of short time intervals in

mechanics experiments.Zelscope has proven useful in debugging music and sound processing

software [14].

Note: All known sound cards contain a capacitor which provides AC coupling and prevents DC

from reaching the card's analog to digital converter. Low-frequency oscillations (below 15-

20Hz) usually get through, but may be distorted.

According to Nigel P. Cook, Digital Signal Processor (DSP) is concerned with the digital

representation of signals and the use of signal processors to analyze, modify, or extract

information from signals. Similarly, From Wikipedia, the free encyclopedia, DSP is concerned

with the representation of signal by a sequence of numbers or symbols and the processing of

these signals. In addition, we conclude that: Digital signal processing is the technique used to

analyze various digital signals and obtain information from the same. It is also used for transfer

of information from one place to another and also involves conversion in between analogue and

digital signals. Although, DSP represents signal digitally, the signal used in most popular form of

DSP is delivered from analog signals which have been sampled at rectangular intervals and

converted into digital form. The specific reason for processing a digital signal may be, for

example to remove interference or noise from signal, to obtain the spectrum of data, or to

transform the signal into a more suitable form. DSP is now used in many areas where analog

methods were previously used and in entirely new applications which were difficult or

impossible with analog methods for example, linear phase response can be achieved, and

complex adaptative filtering algorithms can be implemented using DSP [2]

.

In addition the applications of DSP and its basic operations such as convolution correlation,

filtering, transformation and modulation in detail are also very important [1]

.

11

2.2.1. DSP applications

Digital signal processing and analog signal processing are subfields of processing. But DSP

includes subfields like: audio and speech signal processing, sonar and radar signal processing,

sensor array processing, spectral estimation, statistical signal processing, digital image

processing, signal processing for communications, control of systems, biomedical signal

processing, seismic data processing, etc .The goal of DSP is usually to measure, filter and/or

compress continuous real-world analog signals. The first step is usually to convert the signal

from an analog to a digital form, by sampling it using an analog-to-digital converter (ADC),

which turns the analog signal into a stream of numbers as has seen in sampling method when the

working principle of digital oscilloscope was explained. However, often, the required output

signal is another analog output signal, which requires a digital-to-analog converter (DAC). Even

if this process is more complex than analog processing and has a discrete value range, the

application of computational power to digital signal processing allows for many advantages over

analog processing in many applications, such as error detection and correction in transmission as

well as data compression [6].

It finds its application in various areas ranging from broadcasting to

medicine. Let us have a look at some of the applications of the same.

Biomedical Applications: DSP is used extensively in the field of biomedicine. In it, the

various signals that are generated by the different organs in the human body are measured

in order to find information regarding the health of the same. For example, in case of

electrocardiograms, the electric signals generated by the heart are measured. Similarly, the

activity of the brain is monitored by electroencephalograms.

Automatic Control: These days, many gadgets are available that can perform their tasks

automatically. These devices contain various components that can take inputs depending

on the surrounding conditions. These are conveyed to the control unit of the device where

they are processed and the necessary action is taken. For example, a device like the

thermostat increases its resistance in proportion to temperature. This can be used to stem

the current in a machine whenever the temperature rises.

Broadcasting: DSP is used on a large scale for the broadcast of both television and radio

programs. In the process of recording the audio itself, a large amount of processing of the sound

waves takes place in order to enhance the same. Then, the signals are converted into digital format

and are broadcasted and are received at the respective receivers where they are again converted

into the analogous format and then, are filtered to remove the noise etc. Thus, the output of the

radio, TV etc. is generated.

12

Figure 2.9: Broadcasting via satellite[11]

Telecommunication: DSP is used to the greatest extent in this field. The various

conversations that one carried out these days are through the means of DSP which is used

in the transfer of the signals from one point to the other. Various methods are available to

transfer these audio signals. For example, if satellites are used then, the audio waves are

first converted into electromagnetic waves and then transferred over a wireless medium.

On the other hand, in case of optical fibers, the waves are converted into light waves and

are then transferred through these fibers [11]

.

Navigation: DSP is used to a great extent in navigation. Devices or systems such as

SONAR or Radar work primarily on the basis of DSP. For example, SONAR makes

use of sound waves (signals) in order to calculate the depth. On the other hand, radars

make use of radio waves in order to communicate the locations of various objects in a

particular radius [11]

.

13

Figure 2.10: Dish antenna used for helping in navigation [11]

.

Apart from those mentioned above, digital signal processing has various other applications.

For example, it is used in cars, remote controls, seismic analysis etc. Thus, DSP proves to be one

of the most useful techniques developed in the modern times [11]

Processing of audio signals is one of the most important and widely used applications of digital

signals processing. It is being used in many fields such as communication, broadcasting of audio

signals for radios, television etc. It primarily includes analysis of audio signals that fall in the

human hearing frequency by mathematical. The audio signals that fall in the human auditory

range depends both on physical and psychological factors. A separate branch has been

introduced to study the same and is called psychoacoustics. Wherever signals are concerned, one

has to deal with two different types viz. digital and analogue. The techniques that are used in

order to deal with these two types of audio signals are different. In case of analogue audio

signals, the pressure transformations are usually represented electrically in the form of voltage levels [11]

14

Figure 2.11: Processing of audio signal [11]

The digital representation of audio signals is usually in the form of binary digits that are used to

represent the pressure variations. One should note that the digital representation of the audio

signals is used only to facilitate the use of computers in the analysis of the same. In the real

world, these signals are continuous or analogous in nature. However, as the capacity of human

ears is limited, digital representation of the same is possible provided that the sampling rate of

the audio signals is high. Also, noise is always present with the audio signals that are required.

Thus, quantization of the audio signals does not result in the loss of large amount of actual

information. The generated audio is often converted into other forms in order to transfer it at

greater rates and with less amount of loss. For example, in case of optical fibers, the sound is

converted into light energy and is then transferred to the desired location. Such conversion of

audio into other forms is a part of audio processing as well. Other applications of audio

processing include broadcasting of sound, enhancement of audio etc.

Before an audio signal is broadcasted, a large amount of processing is done on it. This includes

mixing, different steps in recording, noise reduction etc. From the processing that is carried out

later on, various audio formats are generated depending on the method that is used for audio

encoding, the amount of original audio that is retained. Then, when the audio signal is received

on devices at your homes or at other places, some amount of processing is done on it once again.

This may include amplification of the audio in order to increase its loudness, noise reduction

once again, and adding various effects to it such as surround sound. In order to obtain the

surround sound effect, two audio signals are generated from the original one and are made out of

phase with each other [11]

.

Apart from these, the other applications of audio processing are innumerable. Thus, it will

always be among the most popular fields in which signal processing is applied as it finds its use

in some of the most popular devices such as television, cell phones etc.

15

2.2.2. The Basic DSP Operations

The basic DSP operations are:

-Filtering: Convolution of the signal x(n-k) and filter’s impulse response h(k) in time domain.

i.e. y(n)= ………………….. (2.1)

The common filtering objective is to remove or reduce noise from a wanted signal. This

operation will be explained clearly when designing this project.

-Discrete transformations: allow transformation of discrete-time signals in the frequency

domain or the conversion between time and frequency domain representations .The spectrum of

signal is obtained by decomposing it its constituent frequency components using a discrete

transformation. Conversion between analog time and frequency domain is necessary in many

DSP applications. For example, it allows for more efficiency implementation of DSP algorithms

such as those for digital filtering, convolution and correlation [2]

.

-Modulation: As digital are rarely transmitted over long distance or stored in large quantities in

their raw form, therefore modulation is required in order to modulate signals for matching their

frequency characteristics to those of transmission /or stored media to minimize signal

distortions, to utilize signal bandwidth efficiently, or to ensure that the signals have some desired

properties. The two applications are where modulation is extensively used (employed) are

telecommunication and digital audio Engineering [2].

2.3. Data Acquisition Toolbox

2.3.1. Basic Steps for Acquiring Data

The matlab Tutorial version 7.0, 6.8 and 7.8 illustrates how to perform basic data acquisition

using analog input subsystems and the Data Acquisition Toolbox software. It tells that for most

data acquisition applications, the following basic steps are required:

Install and connect the components of your data acquisition hardware. At a minimum, this

involves connecting a sensor to a plug-in or external data acquisition device.

Configure your data acquisition session. This involves creating a device object, adding channels,

setting property values, and using specific functions to acquire data.

Analyze the acquired data using MATLAB.

In this experiment data acquisition applications can be explained using a sound card.

2.3.2. Acquiring Data with a Sound Card

16

Suppose you must verify that the fundamental (lowest) frequency of a tuning fork is 440 Hz. To

perform this task, you will use a microphone and a sound card to collect sound level data. You

will then perform a fast Fourier transform (FFT) on the acquired data to find the frequency

components of the tuning fork. The setup for this task is shown below.

Figure 2.12: Acquisition of data with sound card[16]

2.3.2.1. Configuring the Data Acquisition Session

For this, you will acquire 1 second of sound level data on one sound card channel. Because the

tuning fork vibrates at a nominal frequency of 440 Hz, you can configure the sound card to its

lowest sampling rate of 8000 Hz. Even at this lowest rate, you should not experience any aliasing

effects because the tuning fork will not have significant spectral content above 4000 Hz, which is

the Nyquist frequency. After you set the tuning fork vibrating and place it near the microphone,

you will trigger the acquisition one time using a manual trigger.

You can run the above by typing daqdoc4_1 at the MATLAB Command Window.

Create a device object — create the analog input object AI for a sound card. The installed

adaptors and hardware IDs are found with daqhwinfo.

AI = analoginput('winsound');

Add channels — Add one channel to AI.

Chan=addchannel(AI,1);

Configure property values — Assign values to the basic setup properties, and create the variables

blocksize and Fs, which are used for subsequent analysis. The actual sampling rate is retrieved

because it might be set by the engine to a value that differs from the specified value.

duration = 1; %1 second acquisition

set(AI,'SampleRate',8000)

ActualRate = get(AI,'SampleRate');

set(AI,'SamplesPerTrigger',duration*ActualRate)

set(AI,'TriggerType','Manual')

17

blocksize = get(AI,'SamplesPerTrigger');

Fs = ActualRate;

Acquire data — Start AI, issue a manual trigger, and extract all data from the engine. Before

trigger is issued, you should begin inputting data from the tuning fork to the sound card.

start(AI)

trigger(AI)

wait(AI,duration + 1)

The wait function pauses MATLAB until either the acquisition completes or the time-out elapses

(whichever comes first). If the time-out elapses, an error occurs. Adding 1 second to the duration

allows some margin for the time-out.

data = getdata(AI);

Clean up — When you no longer need AI, you should remove it from memory and from the

MATLAB workspace.

delete(AI)

clear AI

2.3.2.2.Analyzing the Data

For this, analysis consists of finding the frequency components of the tuning fork and plotting

the results. To do so, the function daqdocfft was created. This function calculates the FFT of

data, and requires the values of SampleRate and SamplesPerTrigger as well as data as inputs.

[f,mag] = daqdocfft(data,Fs,blocksize);

daqdocfft outputs the frequency and magnitude of data, which you can then plot. daqdocfft is

shown below.

function [f,mag] = daqdocfft(data,Fs,blocksize)

% [F,MAG]=DAQDOCFFT(X,FS,BLOCKSIZE) calculates the FFT of X

% using sampling frequency FS and the SamplesPerTrigger

% provided in BLOCKSIZE

xfft = abs(fft(data));

18

% Avoid taking the log of 0.

index = find(xfft == 0);

xfft(index) = 1e-17;

mag = 20*log10(xfft);

mag = mag(1:floor(blocksize/2));

f = (0:length(mag)-1)*Fs/blocksize;

f = f(:);

The results are given below.

plot(f,mag)

grid on

ylabel('Magnitude (dB)')

xlabel('Frequency (Hz)')

title('Frequency Components of Tuning Fork')

Figure 2.13: Frequency component of Tuning Fork[16]

The plot shows the fundamental frequency around 440 Hz and the first overtone around 880 Hz.

A simple way to find actual fundamental frequency is

19

[ymax,maxindex]= max(mag);

maxfreq = f(maxindex)

maxfreq =441

The answer is 441 Hz.

2.3.2.3. Power spectral Density of data

Power spectral density (PSD) describes how the power (or variance) of time series is distributed

with frequency. Power spectral density function (PSD) shows the strength of the variations

(energy) as a function of frequency. In other words, it shows at which frequencies variations are

strong and at which frequencies variations are weak. Mathematically, it is defined as the Fourier

transform of autocorrelation sequence of time series. Being power per unit of frequency, the

dimension are those of power divided by Hertz[15]

.

2.4. Computer sound cards

Before talking any word about sound card it is better to make a little summary for some

computer applications and its function. Larry Long and Nancy Long in their eight edition

computer book describe computer as an entertainment center with hundred of interactive games.

He adds that it is virtual university providing interactive instruction and testing, a painter’s

canvas, video telephone, a CD player, a home or office library, a television, the biggest

marketing place in the world, the family photo album, the print shop, a wind tunnel that can test

experimental airplane designs, a recorder, an alarm clock that can remind somebody to keep an

appointment, an encyclopedia, It can also perform thousands of specialty function that require

specialized skills such as preparing taxes drafting legal documents counseling suicidal patients

and much more[5][7]

.

2.4.1. Introduction to computer sound cards

The sound card (also called an audio card) is the part of a computer which manages its audio

input and output.

Figure 2.14: Computer sound card [13]

20

Sound card is used to record data and then playing back the recorded data. The recorded data

uses the sound card’s analog input subsystem playing back data uses the card’s analog output

subsystem [14]

. It is usually a controller which can be inserted into an ISA (Industry Standard

Architecture) slot or PCI (Peripheral Component Interconnect) for more recent ones, but more

and more motherboards include their own sound card [13]

. Therefore sound card translates digital

sound into the electric current that is sent, to the speakers .As it have mentioned that Sounds are

defined as air pressure varying over time. To digitize sound, the waves are converted to an

electric current measured thousands of times per second and recorded as a number. When the

sound is played back, the sound card reverses this process, translating the series of numbers into

electric current that is sent to the speakers [3]

. A sound card allows a computer to create and

record real, high-quality sound.

2.4.2. Analog versus Digital object related to sound card

Sounds and computer data are fundamentally different. Sounds are analog - they are made of

waves that travel through matter. People hear sounds when these waves physically vibrate their

eardrums. Computers, however, communicate digitally, using electrical impulses that represent

0s and 1s. Like a graphics card, a sound card translates between a computer's digital information

and the outside world's analog information. Sound is made of waves that travel through a

medium, such as air or water. The most basic sound card is a printed circuit board that uses four

components to translate analogue and digital information:

An analog-to-digital converter (ADC)

A digital-to-analog converter (DAC)

An ISA or PCI interface to connect the card to the motherboard

Input and output connections for a microphone and speakers

Instead of separate ADCs and DACs, some sound cards use a coder/decoder chip, also called

a CODEC, which performs both functions.

Figure 2.15: Analog signal to be sampled by soundcard[12]

ADCs and DACs

21

Imagine using your computer to record yourself talking. First, you speak into a microphone that

you have plugged into your sound card. The ADC translates the analog waves of your voice into

digital data that the computer can understand. To do this, it samples, or digitizes, the sound by

taking precise measurements of the wave at frequent intervals [12]

.

Figure 2.16: An analog-to-digital converter measures sound

waves at frequent intervals [12]

.

The number of measurements per second, called the sampling rate, is measured in kHz. The

faster card's sampling rate the more accurate its reconstructed wave is. If you were to play your

recording back through the speakers, the DAC would perform the same basic steps in reverse.

With accurate measurements and a fast sampling rate, the restored analog signal can be nearly

identical to the original sound wave. Even high sampling rates, however, cause some reduction

in sound quality. The physical process of moving sound through wires can also cause distortion

[12].

22

Figure 2.17: A PCI sound card

[12]

2.4.3. Conditions required to interface any signal to computer sound card

Before interfacing any signal to your computer sound card, many precautions are required in

order to prevent the damage of that sound card. According to theory written by Luis Guerra

edited by Christopher Lyons and Gino Sigismondi, many users of computer sound cards

purchase a professional microphone to improve upon the performance of the microphone

included with the sound card. But because interconnection procedures in the computer world

differ from those used in professional audio, it is not always easy to make a professional

microphone work with a computer. To be successful in connecting a microphone to your

computer, you must know some things about both your microphone and your sound card.

2.4.4. Chip Chat Sound Card Technical Specifications

Before connecting any signal to computer you have to pear attention with computer sound card

specifications in order to prevent that external signal to damage this sound card. Below there are

computer sound card specification for any kind of PC.

Sound card Audio Characteristics:

Sampling resolution: 8 and 16 bit (Stereo or monaural)

Sample rate: 4.0 kHz - 44.1 kHz

Dynamic range: 16 bits resolution (65535 discrete levels)

Frequency response: 20 Hz - 20 kHz, 3dB

Signal to Noise ratio: 80 dB

Audio in Input Impedance: 30kΩ (30kilohms)

Audio in Input Signal Level: 1.41Volts (RMS)

23

Microphone Input Impedance: 20kΩ (20kilohms)

Audio Out Drive: 4Ω (4 ohms)

Note: Most sound cards support sample frequencies approximately 5-10 kHz to 44.1 kHz.

Sample frequency outside this rang can produce unexpected results.

Sound card connectors characteristics:

Audio In: Mini-phone jack (3.5 mm)

Microphone input : Mini-phone jack (3.5 mm)

Audio Out: Mini-phone jack (3.5 mm)

CD-Audio: SoundBlaster and IBM (shrouded pin header, 1x4, 2 mm)

Generic (shrouded pin header, 1x4 0.10 inch)

IBM Front Panel: Pin header 2x8 0.10 inches

Automatic gain for microphone

ADPCM compression/decompression reduces audio file size on hard disk.

Jack

The "jack" is without a doubt the most commonly used connector for small-scale audio

equipment. Jacks are normally divided into three different types, based on their diameter:

2.5 mm jack: The smallest jack;

3.5 mm jack: The traditional jack, which corresponds to a headphone jack;

6.35 mm jack: The jack used for semi-professional sound systems, in order to connect

speakers, amplifiers, or microphones.

There are two versions of each of these jacks:

Mono jacks, for sending monophonic sound. This kind of jack has two contacts: a

reference, found on the body of the cord, and the signal on the tip.

Stereo jacks, for sending stereophonic sound. This kind of jack has three contacts: The

same two as its mono counterpart, as well as an additional ring for sending another audio

channel.

Figure 2.18: mono connector[13]

24

Figure2. 19: Stereo connector[13]

In computer sound cards, the plugs for jacks are generally colour-coded so users can easily tell

which type of audio device each one connects to, and whether they are audio inputs or outputs.

Figure 2.20: Jack[13]

25

CHAPTER 3: SYSTEM DESIGN

This work consists of two experiments (Designs) as mentioned bellow:

The first one consists in designing a system with Zelscope application software.

The second design consists in developing a system whereby a signal generated by function

generator is acquired, processed and displayed on PC screen. The same signal is displayed

on the physical oscilloscope for comparison purpose.

3.1. Example of analyzing data by matlab

Before making the above designs it is better to lean how matlab software analyzes data

(signal).This is done with help of example. This example is obtained by recording the sound

using sound record of PC. After recording, recorded signal is saved on Desk top. The saved

signal is analyzed by Matlab.

The procedure to be discussed above is as follows:

Click on Start bottom menu All programs Accessories

Entertainments Sound Recorder

After recording this signal, save it by using file button on sound recorder menu. This saved

signal has to be copied to workspace in Matlab software in order to analyze it with this software.

3.1.1. Signal analysis

This signal to be analyzed is obtained when it has been generated by clamping hands through

PC microphone. Next, it is saved into Matlab workspace by giving it any file name. The behavior

of this signal after to be analyzed by matlab is shown by the fig.3.21 and its Power spectral

Density(Fig.3.22) .

26

Figure 3.21: Signal analyzed by matlab

27

Figure 3.22: Power Spectral density of data

3.2. Data Acquisition with MATLAB Programming

This part of the design illustrates how to perform basic data acquisition tasks using analog input

subsystems and the Data Acquisition Toolbox software. For most data acquisition applications,

these basic steps should be followed:

1. Create the analog input object AI for a sound card

2. Configure your data acquisition session. This involves creating a device object, adding

channels, setting property values, and using specific functions to acquire data.

3. Analyze the acquired data using MATLAB.

The experiment examines data acquisition applications using a sound card .Generally, this

part consists of taking analog signal using matlab code and analyzing it with matlab. The

result obtained of the signal after to be acquired by matlab codes is shown by two figures

bellow.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-55

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

Frequency

Pow

er

Spectr

um

Magnitude (

dB

)

Power Spectral Density of the data

28

Figure 3.23: Data Acquired using Matlab acquisition program

29

Figure 3.24: Power spectrum density of data acquired using Matlab acquisition program

The signals obtained above are created by clamping hands .Those three lines (on fig.

3.3.)Indicates the time intervals of the clamps . As it shows that they are number of three, means

that the times used to clamp hand is three.

Note: Although the above Figures (i.e. Fig 3.21&Fig 3.23) are analyzed by Matlab, they are not

exactly the desired signal because due the environment, this analog signal is mixed with random

noise which has to be eliminated. Therefore the design of digital filter is needed in order to

denoise this noisy signal.

3.3. Digital Filter Design

The Design process for this Filter begins with its characteristics .A digital filter is characterized

by its transfer function, or equivalently, its difference equation. Mathematical analysis of the

transfer function can describe how it will respond to any input. As such, designing a filter

consists of developing specifications appropriate to the problem (for example, a second-order

low pass filter with a specific cut-off frequency), and then producing a transfer function which

meets the specifications.The transfer function for a linear, time-invariant, digital filter can be

expressed as a transfer function in the Z-domain; if it is causal, then it has the form:

…………3.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-55

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

Frequency

Pow

er

Spectr

um

Magnitude (

dB

)

Power Spectral Density of the data

30

Where the order of the filter is the greater of N or M.

The above equation of transfer function originates from the solution of different equation of

digital filter:

M

k

N

l

l lnxbknyany1 0

k ][][][ …………………………………………………………………………………….3.3

Where:

x[n-k] is the input signal,

y[n-l] is the output signal,

ak and bl are the filter coefficients, also known as tap weights, and

Taking Z-transforms on both sides, applying the time shifting property, we get:

kY[z]z-k

+ kX[z]z-k

……………………………………………………….3.4

kY[z]z-k

= kX[z]z-k

…………………………………………………………….3.5

Y[z] k z

-k] = X[z] k z

-k…………………………………………………………….3.6

H[z]=

=

k z-k/

k z-k

]………………………………………………………….3.7

Expanding the numerator and denominator of equation (3.7) ,we get

H[z]=

= b0+b1z-1+b2z-2+b3z-3+….+bNz-N/1+a1z-1+a2z-2+a3z-N+…+aMz-M…………3.8

By comparing equations (3.1) and (3.8), it is clear that these two equations are the same with

Y[z] =B[z] and X[z] =A[z]

This form is for a recursive filter, which typically leads to infinite impulse response behavior, but

if the denominator is unity, then this is the form for a finite impulse response filter. For FIR

digital Filter the equation (3.8) will be converted to:

H[z]= b0+b1z-1+b2z-2+b3z-3+….+bNz-N………………………………………………………………….3.9

it why the denominator of equation(3.8) is 1. Although there are two type of Digital Filters: Finite Impulse Response(FIR) and Infinite

Impulse Response(IIR), only the FIR Filter has to be used due to its following advantages over

other kinds of filter:

Are inherently stable. This is due to the fact that all the poles are located at the origin and

thus are located within the unit circle.

31

Require no feedback. This means that any rounding errors are not compounded by

summed iterations. The same relative error occurs in each calculation. This also makes

implementation simpler.

They can easily be designed to be linear phase by making the coefficient sequence

symmetric; linear phase, or phase change proportional to frequency, corresponds to equal

delay at all frequencies.

3.3.1. FIR digital filter

Mathematically, it can be described as follows

x[n](input sequence) y[n](output sequence)

Figure 2.25: A conceptual Representation of digital filter

Where x[n] is the input signal

h[n] is the filter which is going to filter that input signal

y[n] is the output signal

y[n]= ………..................................................................................(3.10)

Note: The above equation is the output signal from FIR filter. It is clear that for this kind of

filter, the impulse response is of finite duration since h(k) have only N values and the

denominator is unit which are among the characteristics of it(FIR filter) . Since some input

signal to the system is audio signal(signal obtained in case of playing music ) with the

bandwidth of 20 Hz-20 KHz and as has mentioned in the scope and limitation of this project, the

special kind of filters are required to denoise this signal .These kinds of filter are nothing but FIR

band-pass and low pass filter. And Linear-phase equiripple filters are desirable because they

have the smallest maximum deviation from the ideal filter when compared to all other linear-

phase FIR filters of the same order. Equiripple filters are ideally suited for applications in which

a specific tolerance must be met, such as when designing a filter with a given minimum stopband

attenuation or a given maximum passband ripple. For designing this kind of filter, it is not

necessary to go deeply into detail for the design of this filter. This kind of filter has introduced as

tool which helps to get the desired signal. The procedure of designing the intended filter starts by

typing the MATLAB command fdatool.

H[k],k=0,1,……..

(Impulse response)

32

The specifications of Filters Designed are shown by Figures bellow

3.3.2. Band Pass filter Design Specifications

Figure 3.26: Band-pass filter design

From the above figure this band- pass filter (BPF) of finite impulse response (FIR) has two cutoff

frequencies: Fpass1 and Fpass2.

Regions between specification values like Fstop1 and Fpass1 are transition regions

where the filter response is not explicitly defined.

The region between Fpass1and Fpass2 the pass band zone where the desire frequencies

have to pass.

Fs specify the sampling frequency .Fs in Hz as a scalar trailing all other numeric input

arguments. If you provide a sampling frequency, all frequencies in the specification are in

Hz.

dB specify the magnitude in dB (decibels)

Ap — passband ripple in dB (the default units).

33

Astop1 — attenuation in the first stopband in dB (the default units).

Astop2— attenuation in the second stopband in dB (the default units).

BWpass — bandwidth of the filter passband. Specified in normalized frequency units by

default.

BWstop — bandwidth of the filter stopband. Specified in normalized frequency units by

default.

3.3.3.Low pass filter Design Specifications

To get the filtered data from unfiltered data of figure 3.3,we design FIR low-pass filter designed

is of equiripple methods with the following specifications:

Figure 3.27: Low pass filter design

Region between specification values like Fpass and Fstop is transition regions where

the filter response is not explicitly defined.

34

The region between 0 and Fpass is the pass band zone where the desire frequencies have

to pass.

Fs specify the sampling frequency Fs in Hz as a scalar trailing all other numeric input

arguments. If you provide a sampling frequency, all frequencies in the specification are in

Hz.

dB specify the magnitude in dB (decibels)

Apass — passband ripple in dB (the default units).

Astop — attenuation in the stopband in dB (the default units).

BWp — bandwidth of the filter passband. Specified in normalized frequency units by

default.

BWstop — bandwidth of the filter stopband. Specified in normalized frequency units by

default.

Note:The filtered sound signal ‘data’ from figure 3.23 looks as follows:

Figure 3.28: Filtrered Data Acquired using Matlab programming

3.4. The design using zelscope

This design consists of using the different equipments such as Mobile phone, some wires which

interface two audio connectors: from phone’s headphone jack to computer sound card’s Audio In

for out putting signal (audio signal) to Zelescope inside PC through sound card.

0 0.5 1 1.5 2 2.5 3 3.5 4

x 104

-1.5

-1

-0.5

0

0.5

1

1.5

35

The signal is obtained when music is played using mobile phone. Then it passes to computer’s

Audio In through the wire connecting two Audio Connectors. These audio connectors are stereo.

The first one on mobile phone’s microphone line out, the second one on the sound card line in.

As the music played by mobile phone is an audio signal which is of an analog in nature, the

computer sound card is there to do the job of Analog to Digital Convertor in order to get the

output digital signal from sound card which can be understood by computer as digital equipment.

This out signal from sound card is being to be analyzed by zelscope.

When you want to analyze signal on zelscope pear attention with calibration as follows:

1) make sure that the vertical offset for CH1 is zero (up-down slider in the middle

position);

2) apply a signal with a known amplitude;

3) using V/DIV buttons and slider, adjust gain so the visible amplitude of CH1 is 2 divisions

or higher;

4) click CAL button, answer OK to the message box;

5) click on the trace at the point of known voltage.

6) type the reference voltage in the dialog box, click OK. The vertical scale may abruptly

change, as the V/DIV setting will be now a real one.

After analyzing the signal, you can save it using settings –save menu on Zelscope display as

screenshot .Finally, this saved signal is being to be compared with that obtains using matlab

programming language. Let see how this looks like using matlab programming language.

Blok diagram summarizing this first design is as follows:

Audio connector on mobile PC’s audio I n

Phone line in

PC

Wire connecting two audio connectors

Figure 3.29: Block diagram showing the design using Zelscope

The following procedures used for achieving what have done using the above design:

1) Playing music with mobile phone.

2) Plug audio connector into mobile phone line out. This audio connector may be mono or

stereo. But for this experiment only stereo audio connectors have used.

3) Open zelscope software into PC

Plug other audio connector on PC’s Audio In.

Note: audio connector on mobile phone line out and audio connector on PC’s Audio In are

linked together in other to transfer signal from mobile phone to PC.

Zelscope

Mobile phone

36

4) Calibrate zelscope diagram into your PC.

5) Analyze the signal using zelscope software.

6) Save the analyzed signal on Zelscope display.

7)Acquire the sound signal played by mobile phone using matlab programming language

Acquisition, sampling, plotting, saving and loading this analogue signal "data" is manipulated

using the following matlab code shown in the appendices of this book.

3.5. Design using physical devices

3.5.1. Designing methodology and process

The design was verified based on the principle of operation of some analog components along

with their association with other components or circuits. After designing and collecting all the

required components, the design was carried out using different electronic equipments available

in electronic laboratory such as:

Signal generator

Figure 3.30: Photo of signal generator

37

Analog oscilloscope

Figure 3.31: Photo of analog oscilloscope

PC

Figure 3.32: Photo of PC

In addition the interface between PC and signal generator was also designed and implemented.

38

Figure 3.33: Photo of interface circuit

3.5.2. Design process

3.5.2.1 Interface circuit design

As stated previously, it has found that it is dangerous to connect the sound card to anything other

than standard audio equipment. Therefore, a voltage divider or a buffer amplifier is highly

recommended for preventing the damage hardware and/or data that can potentially occur.

A schematic of this interface is available bellow:

Figure 3.34: Interfacing circuit between PC sound card and signal generator

This circuit design contains input resistance, variable resistance and diodes for protection issues.

From our design, we have been limiting only for audible signal which can contain a restricted

rang of frequency: from 20Hz to 20 KHz.

For simple electronic circuits, it may be sufficient to get a qualitative insight on dedicated

electrical signals. This interface circuitry allows using the line-in input of a standard PC

soundcard to be used as a 2-channel oscilloscope. In our circuit design, we have been selecting

only channel1.This setup does not allow for exact measurements, however for some applications,

a qualitative insight on dedicated timing or signal characteristics may be obtained. The interface

limits the signals from input channel 1 and 2 to maximum amplitude of 1.2 V (over-voltage

protection circuitry). At the left side of the schematic above, the two input channels for signal

39

measurement and the common ground are shown. At the right side, next to the protection diodes,

the connections for the 3.5 mm stereo jack to the soundcard (line-in) are drawn.

Note: The results and data obtained from this above design realized in laboratory have been

analyzed experimentally.

Block diagram of the design is illustrated below

Figure 3.35: Block diagram of the design using oscilloscope

General description

The signal generator in this block diagram is there to generate signal frequency. This signal

frequency is fed to oscilloscope through interfacing circuit. This interface between signal

generator and oscilloscope does a job as a buffer for attenuating the amplitude of voltage in order

to protect PC sound card which can be damaged by electrical signal from signal generator at the

same time the signal is fed to PC. The PC screen displays signal frequency analyzed by Matlab

software. The output to PC screen is one which has to be compared to oscilloscope’s output

display.

Figure 3.36: Photo of experiment for complete system

3.5.2. Procedure of experiment

As above figure shows, this experiment was designed by generating the frequency of 200 Hz.

This signal passes through interface circuit to oscilloscope. The amplitude (voltage) of this signal

reaches on oscilloscope display will have the value of 0.5volts due to resistors implement

interface circuit as described in interface design. This voltage of 0.5V has to be fed to PC sound

Oscilloscope

PC

Interface

Signal

generator

Oscilloscope

PC

Interface

Signal

generator

Oscilloscope

PC

Interface

Signal

generator

Oscilloscope

PC

Interface

Signal

generator

40

card as data which is being to be acquired with matlab code in order to be analyzed by matlab

software. As maximum amplitude to PC sound card is 1.2 V, we only choose 0.5V as amplitude

which is less than that maximum voltage in order to avoid the damage of sound card. But also,

any amplitude less than 1.2V can be used.

41

CHAPTER4. ANALYSIS OF RESULTS AND DISCUSSION

This chapter describes and discusses the results obtained during the experiment of this project.

4.1. Analysis of results

4.1.1. Results about Zelscope

1. Signal obtained on Zescope Display

This signal is obtained by connecting the two stereo connectors one for Mobile phone line out

and PC sound card line In,and tnen the signal is analysed on Zelscope Display accordingly.

Figure 4.37:Signal analyzed on Zelscope Display

2.Signal obtained by acquisition using Matlab Programming language

The two Stereo connectors are plugged into the two Audio In. One for mobile phone line out and

other for PC sound card line In.After plugging these stereo connectors ,they have to connected

together in other to pass signal from mobile phone to PC. The result obtained after acquisition of

signal (signal played by mobile) is described by the figures below:

42

Figure 4.38: Accquisition of signal “Data”

0 50 100 150 200 250 300 350 400 450 500-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1Acquired signal "data"

Frequency in Hz

Magnitude in v

olts

43

Figure 4.39: Power spectral density of data

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-50

-40

-30

-20

-10

0

10

Frequency

Pow

er

Spectr

um

Magnitude (

dB

)Power Spectral Density of the data

44

Figure 4.40: Filtered signal “data”

4.1.2. Description of interface circuit

By well done of amplitude adjustment, the experiment shows some calculated amplitudes that

our design interface circuit is allowed to pass. These are the current values obtained when doing

experiment in the laboratory using signal generator, oscilloscope display and calculations. From

mathematical formula (apply of ohm’s law), the interface output current from these values of

input voltage and resistors on the circuit are given in the table by:

Io=

........................................................... (4.11)

Where Io : output current

Vi: input voltage from signal generator

Ri: input resistance

Rv: variable resistance

0 50 100 150 200 250 300 350 400 450 500-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1M

agnitude in V

olts

Filtered Signal "Data"

Frequency in Hz

45

Interface I/P voltage

(amplitude) in mV

Input Resistance in Ω Variable Resistance in

Ω

Interface O/P current

(amplitude) in mA

20 10000 5000 0.00133

50 10000 5000 0.00333

100 10000 5000 0.00667

200 10000 5000 0.0133

500 10000 5000 0.0333

1000 10000 5000 0.0667

Table.1: Results for interface circuit.

4.1.3. Result of experiment about acquisition of signal

The input signal to be acquired must have the characteristics shown by the table 4.1 above. This

signal is a square as shown on oscilloscope display.

1. Acquisition of signal

Stereo is plugged into computer’s Audio In through sound card.

a) Acquired signal “data”:unfiltered signal

46

Figure 4.41: Acquired signal “data”

b) Power spectral density:

0 50 100 150 200 250 300 350 400 450 500-0.2

0

0.2

0.4

0.6

0.8

1

1.2Acquired signal "data"

Frequency in Hz

Magnitude in v

olts

47

Figure4. 42: Power spectral density of unfiltered signal “data”

c) Filtered signal “data”:

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-50

-40

-30

-20

-10

0

10

20

Frequency

Pow

er

Spectr

um

Magnitude (

dB

)Power Spectral Density of the data

48

Figure 4.43: Filtered signal “data”of figure 4.41

4.2. Discussion

4.2.1. Discussion about the use of Zelscope

In this part of experiment, the signal obtained from Zelscope display and that obtained using

matlab programming language are similar but not completely similar. The similarities come from

in sense that the original signal (song) used is the same for both cases. Dissimilarities found are

due to the fact that during acquiring of the signal using Matlab and analyzing with Zelscope

display, the song is not played simultaneously and yet the frequency is not for whole song (it is

harmonized).

4.2.2. Discussion about oscilloscope

4.2.2.1. Acquisition of signal

As shown by the fig4.41, this displayed signal is obtaining by generating a square wave of 0.5V

and a frequency of 200 Hz on oscilloscope display from signal generator. Thus, using matlab

software the distorted signal has been occurred .This distorted signal is due to the junction of

interface circuit with oscilloscope probes and the junction of interface circuit, oscilloscope

probes with the stereo connectors to the PC sound card line In, which generate the losses. In

addition, signal generator and PC sharing the same source of power supply may also cause the

0 50 100 150 200 250 300 350 400 450 500-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Frequency in Hz

Magnitude in V

olt

Filtered Signal"Data"

49

distortions on generated signal. Therefore that’s why the work of filtering is needed. Even the

filtering process has being done; the filtered signal is not exactly the same as that has displayed

on oscilloscope because of the problems mentioned early. But the similarity like 98% is obtained

as shown by fig. 4.43.

Notice: Although the experiments were well done, some measured values were not the same as

the intended values due to the challenges mentioned above.

50

CHAPTER5. CONCLUSION AND RECOMMENDATIONS

5.1. Conclusion

To achieve and verify the hypothesis of design a computer based system to process an analog

signal, interfacing circuit between PC sound card and signal generator, different circuit sections

have been designed and their operating conditions have set out. This is illustrated by the

designed circuit with different sections. Zelscope software used as oscilloscope to display signal

from mobile phone. A signal generator plays role of setting a fixed frequency, voltage

(amplitude) required for the whole sound card by using interface circuit and oscilloscope for

displaying a signal. In this implementation 0.5v was used as input to the sound card jack.

According to the generated signal using signal generator, it has been found that the output

frequency and amplitude of generated signal increase or decrease by adjusting manually a

variable resistance in order to control the sound card input voltage (amplitude) .The 16KΩ input

resistor, 50KΩ variable resistor and 1N914 diodes also have been designed for protection issues

of the PC sound card to allow a small amplitude from signal generator to be used on PC sound

card Matlab software have played important role for data acquisition and filtering . For filtering

effect, the design of Digital filter have done using fdatool but noise in the signal did not

completely eliminated due to the unfixed frequency of original signal from harmonics of songs.

5.2. Recommendations

This project concerning the “DESIGN OF COMPUTER BASED SYSTEM TO PROCESS

ANALOG SIGNALS” is a low level method of interfacing exactly a signal generated from

signal generator and display it on oscilloscope, then analyze it on PC by using matlab

programming language. Therefore, it is recommended to those who need to improve this project,

to work on High level application with interface. We therefore recommend KIST to tell the

students to be efficiently and qualitatively familiarized to use software programming such as

Matlab programming language during preparing their final year projects. KIST is also

recommended to help final year students by allowing them to have access to computer,

electronics and electrical equipments. It is also recommended to improve the practical skills for

students especially in engineering faculty by modifying the program Curriculum and by adding

more laboratory courses as requested in the general extra academic life. Some important

implemented projects are being realized by the students during their final year projects, but after

that no follow up about them. Therefore, KIST is recommended to install a workshop rooms for

final year projects storage. This will help people who need to use or to improve them. In addition

as today’s world systems are digitalized, KIST as High institute of Technology is recommended

51

to encourage students to do the final year project based on digitalization and implementations

using software programs.

52

REFERENCES:

1. Practical Digital Electronics by Nigal P. Cook,2004

2. Digital Signal Processing . A Practical Approach . 2nd

Ed. by Emmanuel C.

Ifeachor & Barrie W. Jervis,2002

3. Introduction to computers by Peter Norton’s 4th

Ed.,2001

4. Jason Starck (June 2000): HTML document formatting, which led to a much

better-looking second edition

5. Computer by Larry Long and Nancy Long in their 8th

Ed.,1996

6. James D. Broesch, Dag Stranneby and William Walker. Digital Signal Processing:

Instant access. Butterworth-Heinemann. p. 3. )

7. http: //www.Prenhall.com/long, www. Prenhall.com/demo)

8. http: //www.allaboutcircuits.com/vol_1/chpt_9/1.html(on Monday 12 July 2010)

9. http://www.hobbyprojects.com/oscilloscope_tutorial/oscilloscope_working.html(11

June2010)

10. books.google.com/books(Apr.2010)

11. Wikipedia,free-encyclopedia:www.scienceprog.com/digital-signal-

processing(k.kumaresan: October 3rd, 2009 at 4:28 pm).

12. http://www.howstuffworks.com/sound-card.htm;

http://computer.howstuffworks.com/sound-card.htm(Copyright© 1998-

2010 HowStuffWorks, Inc)

13. http://en.kioskea.net/contents/pc/carte-son.php3(24Apr.2009), en.kioskea.net/.../affich-

209761-how-do-i-bypass-my-sound-jack (29 Nov 2009)

14. http://www.zelscope.com Copyright © 2001-2006 K.Zeldovich, N.Shusharina

15. http://www.cygres.com/OcnPageE/Glosry/Spec.html;

http://www.cbi.dongnocchi.it/glossary/PowerSpectralDensity.html

16. Matlab tutorial version 7.0

53

APPENDICES

54

Appendix1

MATHLAB CODE USED TO ACQUIRE, SAMPLE, PLOT, SAVE AND LOAD

ANALOGUE SIGNAL "DATA"

%CREATION OF THE OBJECT

clear all;

close all;

clc;

ai = analoginput('winsound');%Create the analog input object AI

for a soundcard

%ADDITION OF CHANNELS

Chan = addchannel(ai,1);%Add two hardware channels to ai

%CONFIGURATION OF PROPERTY VALUES

Duration = 5;%5 seconds acquisition

set(ai,'SampleRate',8000);%Specify the per-channel rate at which

analog data is converted to digital data

ActualRate = get(ai,'SampleRate');% define the device sample

rate. This might be different from the specified value

set(ai, 'SamplesPerTrigger', duration*ActualRate);%Specify the

number of samples to acquire for each channel group member for

each trigger that occurs.

set(ai,'TriggerType','manual');%set the manual trigger function.

The trigger will occur immediatly after the trigger function is

manually issued

blocksize=get(ai,'SamplesPerTrigger');

Fs=ActualRate;% define the sampling frequency

%DATA ACQUISITION

start(ai);

trigger(ai);% apply a manual trigger

wait (ai, duration+1);%pause MATLAB until either the acquisition

completes or the timeout elapses

%Adding 1 second to the duration allows some margin for the

timeout

[data,time] = getdata(ai);% Extract all data from the engine

size(data(1:500));%returns the number of rows and columns in

data as separate output variables with data scale of 1:500

55

save data.mat % save the acquired data into the .mat format

load data % load the acquired data into the workspace

input('press any key to view the original data')% View the graph

of original data

figure (1)

plot(data(1:500)); % Plot the acquired data with scale of 1:500

title('Acquired signal "data"')% This is text giving a title

for the graph of original data

xlabel('Frequency in Hz'), %This is text describing x-axis

ylabel('Magnitude in volts') %This is text describing y-axis

input('press any key to visualize the Power Spectral Density of

the data') % View the graph of power spectral density

figure(2)

psd(data(1:500))%plot of power spectral density for the data

title('Power Spectral Density of the data')% This is text giving

a title for the graph of power spectral density of the data

Appendix 2

MATHLAB CODE USED TO FILTER THE ACQUIRED DATA

load data;%call data from workspace

load VIERAGGAS % load VIERAGGAS as filter coefficients into

workspace

b=Num;%Numerator coefficients

a=1;% Denominator coefficient

y=filter(b,1,data);% To filter off the noisy signal using

designed filter

figure(3)

plot(data(1:500)) % Plot the acquired data with scale of 1:500

figure(4)

plot(y) % Plot the filtered data

input('press any key to visualize original data') % View the

graph of original data