KUMARAGURU COLLEGE OF

TECHNOLOGY

SIMULATION OF HUMAN NOSE

USING NEURAL NETWORKS

AUTHORS:

MANIVANNAN.P.S Manivannan.tn89@gmail.com 9894244556 GANESH RAJ.V Ganesh_stanite@yahoo.co.in 9943658100

ABSTRACT

ARTIFICIAL NEURAL NETWORK:

Artificial Neural Network (ANN) is an information processing technique that

Uses the biological nervous system , which includes the human brain to process some

data. It is composed of a large number of highly interconnected processing elements

(neurons) working in a very complex manner such that it is not noticed by either humans

or other computer techniques in unison, to solve specific problems. Neural networks have

the remarkable ability to obtain meaning from complicated data and can be used to

extract patterns and detect trends that are even unthinkable to the human mind.

ELECTRONIC NOSES:

In this paper we describe the various applications of neural networks and how

Neural networks are being implemented in designing Electronic/artificial noses. The

electronic nose is a small mechanical device that is capable of deciphering a wide

variety of smells, ranging anywhere from food to human breath to poisonous toxins.

An electronic nose is generally composed of a chemical sensing system (e.g., sensor

array or spectrometer) and a pattern recognition system (e.g., artificial neural network).

IMPLEMENTATION:

ANNs are systematically implemented in electronic noses. Electronic noses have several

applications in telemedicine. Telemedicine is the practice of medicine over long distances

via a communication link. The electronic nose would identify odours in the remote

surgical environment. These identified odours would then be electronically transmitted to

another site where an odour generation system would recreate them. Because the sense of

smell can be an important sense to the surgeon, telesmell would enhance telepresent

(surgery via communication links) surgery. Other applications of Electronic noses on

space & food industry are discussed. Who knows, maybe the future is just a sniff away!!

NEURAL NETWORKS: DEFINITION:

Neural Network (NN) is an

information processing paradigm that

is inspired by the way biological

nervous systems, such as the brain,

process information. It is a

sophisticated modeling technique

capable of modeling complex

functions. It is a nonlinear approach.

EASE OF USE:

Neural networks learn by example.

The user gathers up representative

data, invokes training algorithms to

auto learn the structure of data even

though user does not have heuristic

knowledge to select and prepare data

about neural networks, the level of

application of knowledge is much

lower than other nonlinear statistical

methods.

CHARACTERISTICS OF

NEURAL NETWORKS :

ROBUSTNESS AND FAULT

TOLERANCE:

The decay of nerve cells does not

seem to affect the performance

significantly.

FLEXIBILITY: The network

automatically adjust to a new

environment without using any

programmed instructions

ABILITY TO DEAL WTH

VARIETY OF DATA SITUATIONS:

The network can deal with

information that is fuzzy, noisy and

inconsistent.

COLLECTIVE COMPUTATION:

The network performs routinely many

operations in parallel and also a given

task in a distributed manner

HISTORICAL

BACKROUND: Neural network

simulations appear to be a recent

development. However, this field was

established before the advent of

computers, and has survived at least

one major setback and several eras.

Many important advances have been

boosted by the use of inexpensive

computer emulations. Following an

initial period of enthusiasm, the field

survived a period of frustration and

disrepute. During this period when

funding and professional support was

minimal, important advances were

made by relatively few researchers.

These pioneers were able to develop

convincing technology which

surpassed the limitations identified by

Minsky and Papert. Minsky and

Papert, published a book (in 1969) in

which they summed up a general

feeling of frustration (against neural

networks) among researchers, and was

thus accepted by most without further

analysis. Currently, the neural network

field enjoys a resurgence of interest

and a corresponding increase in

funding.

The first artificial neuron was

produced in 1943 by the

neurophysiologist Warren McCulloch

and the logician Walter Pits. But the

technology available at that time did

not allow them to do too much.

MERITS:

SPEED:

Biological Neural networks process

information in milliseconds range.

ANN in nanoseconds.

EFFICIENT: Able to detect more

chemicals than the number of sensors

used and allows less expensive

sensors.

DEMERITS:

FAULT TOLERANCE :

Information is distributed throughout

the network. Even if few connectors

are snapped information is still

preserved. But in computers if a chip

is corrupted information in the

memory cannot be retrieved

PROCESSING:

Neural networks can perform parallel

operations massively.

SIZE AND COMPLEXITY:

Number of neurons in brain is

estimated to be

around10^11.Computation is not

restricted to inside the soma whereas

in nn it is restricted

ARCHITECTURE:

NNs are composed of the

following elements:

Neuron (soma)

Inputs (dendrites)

Outputs of Neurons (axons)

Weights (synapses)

STRUCTURE OF BIOLIGICAL NEURON:

STRUCTURE OF ANN:

FUNCTIONING

All the weights of a NN comprise

its weight set, W = {w,v}.

The number of neurons per layer ,

number of hidden layers, and the

specified connections for each

layer comprise the network

architecture.

In the preceding figure, all of the

zeroth inputs to either the hidden

our output layer are referred to as

thresholds and are typically set to -

1.

The weights of a neural network

can be any positive or negative

value.

The input values are multiplied by

the weights that connect them to a

particular neuron.

Neurons take this weighted sum as

input and use an activation

function to compute the neurons

output.

The output of one neuron becomes

the input to another neuron

multiplied by a different subset of

weights.

The input coming into a neuron,

Hj, can be calculated as:

Hj = xi wij

Where xi represents the ith input and

wij represents the weight

connecting the ith input to the

jth hidden unit

The activation of Hj, f(Hj), can be

computed using a variety of

functions:

Here a sigmoid function is used

FORWARD PASS: Feed-forward

ANNs allow signals to travel only

one way from input to output.

There is no feedback i.e. the output of

any layer does not affect the same

layer

Hj = xi wij

Ok = f(Hj) vik (where

f is a sigmoid function)

Outk = f(Ok)

IMPLEMENTATION OF

ANN IN E-NOSE

ELECTRONIC NOSE:

Electronic noses are generally made

up of two main parts: a sensing system

and a pattern recognition system. In

the past,

gas chromatography and mass

spectrometry have been used as the

sensing system although these are

usually expensive and time

consuming. Today, the use of

chemical sensors has been established

to analyze odours. Essentially, each

odour leaves a characteristic pattern or

fingerprint of compounds. Known

odors can be used to build up a

database to train a pattern recognition

system. One possibility is to have a

sensor for every chemical, though this

would be costly since there are so

many different chemicals. The answer

is in artificial neural networks

(ANNs). ANN are able to detect more

chemicals than number of sensors it is

H1

H2

H3

O1

H0 x0

x1

x2

x3

w01

w12 w13

w21

w22

w23

w31 w32

w33

w02 w03

w11

v01

v11

v21

v31

Neuron (Soma) Input (dendrite) Output (Axon) Weight (Synapse)

Out1

utilizing. ANNs also allow for less

selective and therefore less expensive

chemical sensors. The artificial neural

networks are trained to distinguish

certain odours from certain chemical

combinations. Pattern recognition is

gained through giving the network

known odours and classifying them

with a signature. Then the nose is

tested to see how well the ANN has

learned. The results can be adjusted

through experimentation. The sensors

basically measure the change in

voltage due to the presence of certain

chemicals. The chemicals in the air

change the oxygen content over the

sensors, which are electronic circuits.

By changing the oxygen content, the

resistance across the sensor is changed

which can be measured as a voltage

drop from the normal or standardized

conditions. This analog signal must

then be translated into a digital signal

by an A/D converter in order for the

computer to understand the

information. The number of odour

signatures the system can recognize

depends on the number of sensors

used and the number of grey levels in

the convertor. The maximum signature

number is given by gn, where n is the

number of sensors and g is the number

of grey levels. A 10-bit converter has

a grey level value of 1024, so an array

of three sensors could yield over a

billion different signatures.

Unfortunately, the actual number is far

below this value.

SENSING SYSTEM:

There are two types of sensors namely

>polymer based sensors

>metal oxide sensors.

POLYMER BASED SENSORS: The inside of a polymer-based

electronic nose is composed of various

types of polymer films. These

polymer films are made up of an

insulator and a filler. The polymer

insulator actually absorbs some of the

gas molecules. The substance that is

found inside of the insulator is a

conducting polymer with a certain

resistance. Each film in the electronic

nose is made of a different insulator

and a different conducting polymer.

The resistance of the polymer films

before exposure to a gas is initially

recorded. Then, as a gas passes

through the electronic nose, it is

actually absorbed into the polymer

films. Upon absorption of the gas, the

polymer insulators swell. This affects

the conducting polymer contained

inside of the insulator by limiting the

number of connected pathways

throughout the conductor . The larger

the insulator swells, the fewer the

number of connections in the polymer

conductor, which decreases the

resistance of each polymer film to a

degree.

Since each film in the electronic nose

is made of a different insulator and

conducting polymer, the final

resistance of each film is different.

The combination of the ultimate

resistances forms the fingerprint

needed for the identification of the

gas. The films are connected to an

artificial neural network, which

contains pre-programmed fingerprints

of odors. If the smell is not

programmed into the machine, it will

not be able to identify it. Using the

programmed fingerprints, the

polymer-based electronic nose is then

able to identify the gas. This data is

finally sent to a computer, which

displays the data determined by the

electronic nose.

METAL OXIDE SENSORS:

Metal oxide-based electronic noses

also contain several main parts, but

they are mostly different than those of

the polymer-based electronic nose. It

must also contain a power supply in

order to run the machine. Instead of

an air pump, the metal oxide-based

electronic nose contains a sampling

chamber where the sample to be

analyzed is placed. A pump and fan

system then wafts air from the sample

through the electronic nose and over

its sensors.

These sensors are contained in

chambers located inside of the

electronic nose. In order to work

properly, the chambers must be kept at

an elevated temperature, so a heater is

also attached to the electronic nose.

Each chamber, constructed of a non-

reactive material, which does not

affect the metal oxide sensors,

contains a various number of metal

oxide sensors, as well as one

integrated sensor. This integrated

sensor is sensitive to changes in

humidity and temperature and records

the changes relative to the sample .

Therefore, if a drastic change occurs,

the experimenter will be able to

interpret the data accordingly.

The sensors contained in a metal

oxide-based electronic nose vary in

number and composition according to

the electronic nose’s purpose.

Determining the number and

composition of sensors needed comes

down to understanding exactly how

the sensors operate. Each sensor is

composed of a ceramic substance

coated with a different semi-

conductive metal oxide film. When

exposed to a gas, the surfaces of the

films chemically react with the gas,

and an electron transport takes place.

This translates into a change in the

sensors’ resistances. Since each

surface is covered in a different metal

oxide, each reacts differently to the

gas and therefore, has a different

change in resistance.

. An electronic nose designed to

detect a simple gas will only have a

few sensors, while one designed to

detect a more complex gas will require

many more sensors.

Once the sensors react to the

gas, a fingerprint of the odor is then

made up of the combination of the

sensors’ changes in resistance. This

fingerprint is sent to a data acquisition

device, which is hooked up to the

electronic nose. The device then

translates the fingerprint into the

actual identity of the gas and sends

this information to a computer.

WHICH IS BETTER?

It is hard to definitely say which type

of electronic nose is better because it

depends on how it is being applied

In general, though, the polymer-based

electronic nose seems to be superior to

the metal oxide-based electronic nose.

The metal oxide-based

electronic nose surpasses the polymer-

based in only a few limited areas. The

metal oxide-based electronic nose

shows a high repeatability due to the

fact that its sensors do not change

shape to record the electrical data.

They also show a very high sensitivity

to substances, especially organic

compounds.

Beyond these few areas, the

metal oxide-based electronic nose falls

short. Since it has to be kept at a high

temperature, it can be quite a hassle to

use. Its sensors also demonstrate a

very low selectivity, and because of

this, they must be used in arrays. The

sensors are not sensitive enough to be

used on their own.

Polymer-based electronic

noses greatly surpass the metal oxide-

based electronic noses. They are more

convenient because they can be used

at any temperature. They also

demonstrate lower power consumption

as well as a faster response time.

The polymer sensors are what

really make this electronic nose better.

The fact that they are made of

polymers offers unlimited possibilities

for the types of sensors as well as the

arrangement of arrays. Not only do

they make diverse arrays, the polymer

sensors have such a high selectivity

that they can be used individually as

well. Unlike the metal oxide sensors,

polymer sensors also show no

sensitivity to water, increasing the

environments in which this type of

electronic nose can be used. The

sensors are also very easy to make and

can be done so at a lower cost than the

metal oxide sensors. Finally, they

show a durability of at least six

months, which is long for this type of

new technology . Research

overwhelmingly suggests that

polymer-based electronic noses

surpass metal oxide-based electronic

noses in almost every category. While

some industries do prefer the metal-

oxide based electronic nose, most

choose the polymer-based for its

convenience, reliability, and accuracy.

PRE-TRAINING PROCESS:

Each chemical vapor presented to

the sensor array produces a

signature or pattern characteristic

of the vapor.

By presenting many different

chemicals to the sensor array, a

database of signatures is built up.

This database of labeled signatures

is used to train the pattern

recognition system.

The goal of this training process is

to configure the recognition

system to produce unique

classifications of each chemical so

that an automated identification

can be implemented

An Example to illustrate the above

training procedure:

Assume that we want a network to

recognise hand-written digits. We

might use an array of, say, 256

sensors, each recording the presence

or absence of ink in a small area of a

single digit. The network would

therefore need 256 input units (one for

each sensor), 10 output units (one for

each kind of digit) and a number of

hidden units. For each kind of digit

recorded by the sensors, the network

should produce high activity in the

appropriate output unit and low

activity in the other output units. To

train the network, we present an image

of a digit and compare the actual

activity of the 10 output units with the

desired

activity. We then calculate the error,

which is defined as the square of the

difference between the actual and the

desired activities. Next we change the

weight of each connection so as to

reduce the error. We repeat this

training process for many different

images of each different images of

each kind of digit until the network

classifies every image correctly.

PATTERN RECOGNITION

SYSTEM:

An important application of neural

networks is pattern recognition.

Pattern recognition can be

implemented by using a feed-forward

(figure 1) neural network that has been

trained accordingly. During training,

the network is trained to associate

outputs with input patterns. When the

network is used, it identifies the input

pattern and tries to output the

associated output

pattern. The power of neural networks

comes to life when a pattern that has

no output associated with it, is given

as an input. In this case, the network

gives the output that corresponds to a

taught input pattern that is least

different from the given pattern.

CONTROL ALGORITHM:

The Electronic nose consists of two

objects to be controlled namely the

Control of inhalation pump

Control of valve responsible

for phase measurement.

The control program of an Electronic

nose is implemented using these steps:

Turn on the pump

collect one sample from each

active sensor

send the samples to the

display

If all samples are collected

goto step5 else goto step 2.

Turn off the pump

Data acquisition from the nose

is finished.

The control program is basically

structured as an endless loop within a

main function . A switch statement tat

checks the variable “com” ,which is

used in the loop to direct the program

to the correct action decided by the

input character.

The variable “com” gets its character

from a receive – buffer with the help

of

an interrupt service routine. The basic

structure of program is as follows:

Void main(void)

{

While(1)

{

com = getchar(); // wait

for serial interface

switch(com)

{

Case’!’:

break;

case’L’:

default:

break;

} }}

MAJOR APPLICATIONS:

ELECTRONIC NOSE IN SPACE:

In the space station, astronauts are

surrounded by ammonia. It flows

through pipes, carrying heat generated

inside the station outside to space.

Ammonia helps keep the station

habitable.

But it's also a poison. And if it leaks,

the astronauts will need to know

quickly. Ammonia becomes

dangerous at a concentration of a few

parts per million (ppm).

Hense electronic noses comes into

application in space station. Here the

Enose consists of 16 different polymer

films.These films are capable of

conducting electricity and are used to

detect the gases.

ELECTRONIC NOSES IN TELEMEDICINE:

Because the sense of smell is an

important sense to the physician, an

electronic nose has applicability as a

diagnostic tool.

An electronic nose can examine

odours from the body (e.g., breath ,

wounds, body fluids, etc.) and identify

possible problems. Odours in the

breath can be indicative of

gastrointestinal problems, sinus

problems, infections, diabetes, and

liver problems.

Infected wounds and tissues emit

distinctive odors that can be detected

by an electronic nose. Odours coming

from body fluids can indicate liver and

bladder problems.

ELECTRONIC NOSES IN FOOD INDUSTRY:

Applications of electronic noses

in the food industry include

Quality assessment in food

production .

inspection of food quality by

odour.

control of food cooking processes.

inspection of fish.

monitoring the fermentation

process.

verifying if orange juice is natural.

Monitoring food and beverage

odours.

inspection of beverage containers.

E- NOSE IN SEWAGE:

Each stage of a sewage treatment

process emits odor causing

compounds and these compounds may

vary from one location in sewage

treatment works to another. In order to

determine the boundaries of legal

standards reliable and efficient odor

measurement methods need to be

measured. An E-NOSE equipped with

12 different poly pyrrole sensors is

used for characterizing sewage odour.

CONCLUSION:

The computing world has a lot to

gain from neural networks. Their

ability to learn by example makes

them very flexible and powerful.

Perhaps the most exciting aspect of

neural networks is the possibility that

some day 'concious' networks might

be produced. There is a number of

scientists arguing that conciousness is

a 'mechanical' property and that

'concious' neural networks are a

realistic possibility.

Judging by its popularity in

current trade and scientific magazines

and its various practical uses, the

future of ANN in electronic nose

looks very bright. It is being

introduced into the medical world as a

device that can actually identify a

disease from a sample of the patient’s

breath. So far, the electronic nose has

a wide variety of uses, and its

possibilities are literally endless. In

the near future, the electronic nose

could very well become a part of

everyday life.

Finally, we would like to state that

even though neural networks have a

huge potential we will only get the

best of them when they are integrated

with computing, AI, fuzzy logic and

related subjects.

BIBLIOGRAPHY:

“Articifial Neural

networks”,B.YEGNANARAYA

NA.

B.S. Hoffheins, Using Sensor

Arrays and Pattern Recognition

to Identify Organic Compounds.

MS-Thesis,

B.S. Hoffheins, Using Sensor

Arrays and Pattern Recognition

to Identify Organic Compounds.

MS-Thesis,