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TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)

Hardware Design of Equilateral Triangular

Microstrip Antenna Using Artificial

Neural Network

1Praveen Kumar Malik, 2Vineet Vishnoi, 3Shekhar Pundir 1Radha Govind Eng. College, Meerut UP, India,

2R.K.G.I.T, Ghaziabad UP, India 3JP Inst.of Eng, Meerut UP, India

[email protected], [email protected], [email protected]

Abstract— In Bio-Medical applications and wireless

communication systems, the microstrip antennas are now

days extensively used due to their low-profile features and

ease of fabrication. Artificial neural network computation is

especially used for designing equilateral triangular

microstrip antenna in wireless communication. In this article,

Back propagation multilayered-perceptron network is used.

The results obtained using ANN are being compared with the

experiment result which are in good agreement with the

experiment results. Further, keeping the same ANN design in

mind, hardware is designed using AVR microcontroller to

generate and display the obtained resonant frequency on

LCD.

Keywords- Microstrip, Resonance Frequency, Threshold

function, 8535 AVR, ICC AVR.

I. INTRODUCTION

Microstrip antennas due to their many features have drawn popularity among researchers and for industrial application. Design of microstrip antennas must be determined accurately due to their narrow bandwidth. Microstrip antennas have been used in different configurations like: square, rectangular, circular, and triangular etc. ANN, Genetic algorithm, etc. are the soft computing techniques [1] for calculating different parameters of microstrip patch antenna [2]. The formula based expression is explained to calculating resonant frequency of equilateral triangular microstrip antenna [3-4]. However, the majority of the studies proposed in this area have concentrated on rectangular and circular microstrip antennas. Triangular microstrip antenna design of patch must be very accurately determined to make antenna to resonate at particular band of frequency. Since it is a narrow band antenna which operates effectively in the vicinity of the resonant frequency. 8535 AVR microcontroller is used here and it has been programmed accordingly to display the calculated resonance frequency on the LCD as per the calculated frequency (Theoretically & as per MATLAB programmed).

II. EQUILATERAL TRIANGULAR MICROSTRIP PATCH

ANTENNA PARAMETERS

Equilateral triangular microstrip antenna comprises an

equilateral triangular conductor on a dielectric substrate

backed by a ground plane. The resonant frequency of such

antenna is a function of side length of patch, permittivity

of the substrate and its thickness. Design and experiment

study of this antenna was reported by many methods. The

resonant frequency obtained from the cavity model with

perfect magnetic walls is given by the formula [5].

fmn = (2c/3a(εr)1/2)(m2+n2+mn)1/2

Where “c” is the velocity of the electromagnetic

waves in free space, “εr” is the relative dielectric constant

of the dielectric constant of the substrate, m, n and the

integers that are never zero. Their is a difference between

theoretical and measure values of resonant frequency, in

order to obtain accurate values we consider measured

values for our design.

III. ARTIFICIAL NEURAL NETWORK

Artificial neural network is the result of academic investigation that uses mathematical formulation to model nervous system operation. A neural network is used to learn patterns & relationship in data. Neural network do not require explicit coding of the problems. In fact they require raw data to be processed. ANN are the signal processing system whose basic concept has been taken from the concept of the human brain with the basic element Neuron. There are three major techniques to be used to learn the neuron. (Supervised Learning, Unsupervised Learning, and Reinforcement Training). To take the decision of the output there are many activation functions. These activation functions can be used according to our requirements. Major activation functions are:

Identity function, Binary Step function, Signum function, Signoidal function, Tan Hyperbolic function. All the inputs coming to the neuron are multiplied with their synapse weights & added together. This output is compared with the threshold set by the activation function & the decision is taken accordingly either 0 or 1 OR -1 or +1. If we smoothen the threshold function, so that it more or less turns on or off as before, but has a sloping region in the middle that will give us some information on the inputs, we will be able to determine when we need to strengthen or weaken the relevant weights.

IV. MODEL FOR MULTILAYER BACK PROPAGATION

PERCEPTRON

Perceptrons are arranged in layers and so the model is

termed as multilayer perceptron. This model has three

layers; an input layer, output layer and a layer in between

which is not connected directly to the input or the output

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and hence called hidden layer. For the perceptron in the

input layer, we use linear transfer function, and for the

perceptron in the hidden layer and the output layer we use

the sigmoidal function. The input layer serves to distribute

the value it receives to the next layer and so, does not

perform a weighted sum or threshold. Because, we have

modified the single layer perceptron by changing the

nonlinearity from a step function to a sigmoidal function

and added a hidden layer, we are forced to alter the

learning rule as well. The input output mapping of

multilayer perceptron is given by: -

O=N3[N2[N1[I]]]

N1, N2, N3 represent nonlinear mapping provided by

input, hidden and output layer respectively. In this article

three layer network shows that the activity of neurons in

the input layers represents the raw information that is fed

in to the network. The activity of the neurons in the hidden

layer is determined by the activities of the neurons in the

input layer and the connecting weights between input and

hidden layers. Similarly, the activity of the output units

depends on the activity of the neurons in the hidden layer

and the weights between the hidden and the output layers.

V. TRAINING DATA SET

TABLE I: RESONANCE FREQUENCY FOR DIFFERENT VALUES OF H, ΕR, L,

M AND N.

H (in cm) εr L (in cm) m n Freq. in GHz

1 0.07 10.5 4.1 1 1 2.637

2 0.07 10.5 4.1 2 0 2.995

3 0.07 10.5 4.1 2 1 3.973

4 0.07 10.5 4.1 3 0 4.439

5 0.078 2.32 8.7 1 1 2.596

6 0.078 2.32 8.7 2 0 2.969

7 0.078 2.32 8.7 2 1 3.968

8 0.078 2.32 8.7 3 0 4.443

9 0.159 2.32 10 1 0 1.280

10 0.159 2.32 10 1 1 2.242

11 0.159 2.32 10 2 0 2.550

12 0.159 2.32 10 2 1 3.400

These data for resonance frequency are obtained from [6].

A computer program for the neural network method is

written in MATLAB. The program is written by asking the

five parameters as an input to for testing; using these

parameters the frequency is calculated by the neural

network model. Program is written as: -

clc;

A (1,1)= input ('Enter L (cm):');

A (1,2)= input ('Enter h (cm):');

A (1,3) =input('Enter εr:');

A (1,4)= input ('Enter m:');

A (1,5)= input ('Enter n:');

% For Example A= [4.1 0.0700 10.5 1 0]

B= [-0.2276 0.4733 -0.1294 -0.0961 -0.1294

-0.0719 -0.2930 -0.0375 0.3170 -0.0375

-0.1288 -0.2057 0.0256 0.6339 0.0256

-0.3088 -1.3080 0.5442 -1.6220 0.5442

0.5837 -0.8369 0.3751 -1.0582 0.3751];

C=A*B;

D= [0.0970 0.5817 0.0546 -0.5542 0.0546];

E=C+D;

F=1./(1+exp(-E));

G= [0.0110 0.6885 0.0110 -0.0110 0.0110

-0.7438 -0.8279 -0.7438 0.7438 -0.7438

0.1243 0.5890 0.1243 -0.1243 0.1243

-0.5686 -1.0892 -0.5686 0.5686 -0.5686

0.1243 0.5890 0.1243 -0.1243 0.1243];

H=F*G;

I= [0.1038 0.5852 0.1038 -0.1038 0.1038];

J=H+I;

K=2./(1+exp (-2*J))-1;

L= [0.9169

2.5635

0.9169

-0.9169

0.9169];

M=K*L;

N=3.1949;

O=M+N %O is the final output.

All the entries of table I can be verified using the

above program. Even more other data entries of (H, L, εr,

m and n) can also be done using the above program.

Note: All weight matrixes are calculated using neural

network training module in MATLAB.

VI. HARDWARE DESIGN

Main objective of the paper is to design an electronics

hardware using microcontroller, which satisfies the above

specified table data for different frequencies. This has

been done with the help of AVR 8535 microcontroller.

AVR 8535 is an 8 bit RISC microcontroller having

following features.

40 pin RISC Architecture

8 K Byte flash memory

512 byte RAM

512 byte EEPROM

04 input output ports.

8535 AVR is used in the following pin configuration.

Port C is used to provide the input data (as per Table

I). Port A and B are used to interface the 16x2 LCD. 8535

is operating at an external crystal of 12 MHz. 8535 and

LCD is operating on +5v DC supply. An important theme

of this design is the programming concept of the above

requirements. Here we have used a simulator ICCAVR

6.31 for programming platform and ISP of 8535

microcontroller.

Fig. 1: Pin Configuration of 8535 AVR

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VII. SOFTWARE DESIGN

Complete programming code is being written and

explained here to demonstrate the above neural design.

#include<iom8535v.h>

#include<lcdavr.h>

#include<math.h>

#include<stdio.h>

void display(int x, char databyte[16]);

void display1(int x, char databyte[16]);

void send_number(float);

void delay(int );

int x=0x80;

//Declaration of all header file and function to be used

in the program.

void main()

{

int i=0,j=0,m=0, m4=0, m5=0;

int q1=0, q2=0, q3=0, q4=0, q5=0;

float A[5];

float wt[5][5]={ -0.2276, 0.4733, -0.1294, -0.0961, -

0.1294,

-0.0719,-0.2930, -0.0375, 0.3170, -0.0375,

-0.1288,-0.2057, 0.0256, 0.6339, 0.0256,

-0.3088,-1.3080, 0.5442, -1.6220, 0.5442,

0.5837,-0.8369, 0.3751, -1.0582, 0.3751};

float b1[5]={0.0970, 0.5817, 0.0546, -0.5542,

0.0546};

float b2[5]={0.1038,0.5852, 0.1038,-0.1038,0.1038};

float b3=3.1949;

double y[5]={0.0,0.0,0.0,0.0,0.0};

double v[5][5]={0.0110, 0.6885, 0.0110, -0.0110,

0.0110,

-0.7438,-0.8279,-0.7438, 0.7438, -0.7438,

0.1243, 0.5890, 0.1243, -0.1243, 0.1243,

-0.5686,-1.0892,-0.5686, 0.5686, -0.5686,

0.1243, 0.5890, 0.1243, -0.1243, 0.1243};

Double u[5]={0.0,0.0,0.0,0.0,0.0};

Float z[1][5]={0.9169,2.5635,0.9169,-

0.9169,0.9169};

Float result=00.0000;

float m1=0, m2=0, m3=0;

//Declaration of all weight matrix to be used in

between input, hidden and output layer the neural

network.

DDRB=0xff; DDRC=0x00; DDRD=0xff;

lcd_init(); display(x, "Enter The value"); delay(5);

lcd_init();

start:

//Declaration of direction data register and lcd

initialized function.

while(1)

{

//if first key is pressed (Value of H to be inputted)

if ((PINC & 0x01) == 0) // first

{ delay(2); x=0x80;

if(q1==0){ display(x, "04.1000"); m1=4.100; q1++;

goto start; }

if(q1==1){ display(x, "08.7000"); m1=8.700; q1++;

goto start; }

if(q1==2){ display(x, "10.0000"); m1=10.000; q1=0;

goto start; }

}

//if second key is pressed (Value of εr to be inputted)

if ((PINC & 0x02) == 0) // second

{

delay(2); x=0x88;

if(q2==0){ display(x, "0.0700");m2=0.0700; q2++;

goto start; }

if(q2==1){ display(x, "0.0780"); m2=0.0780; q2++;

goto start; }

if(q2==2){ display(x, "0.1590"); m2=0.1590;

q2=0; goto start; }

}

//if third key is pressed (Value of L to be inputted)

if ((PINC & 0x04) == 0) // third

{

delay(2); x=0xc0;

if(q3==0){ display(x, "10.5000"); m3=10.5000; q3++;

goto start; }

if(q3==1){ display(x, "02.3200"); m3=2.3200; q3=0;

goto start; }

}

//if fourth key is pressed (Value of m to be inputted)

if ((PINC & 0x08) == 0) // fourth

{

delay(2); x=0xc8;

if(q4==0){ send_command(x); send_data('0'); m4=0;

q4++; goto start; }


q4++; goto start; }


q4=0; goto start; }

}

//if fifth key is pressed (Value of n to be inputted)

if ((PINC & 0x10) == 0) // fifth

{

delay(2); x=0xcc;


q5++; goto start; }


q5++; goto start; }


q5=0; goto start; }

}

/////////////////////////////////////////////////////////

//if sixth key is pressed (Calculation of the neural net

begain)

if ((PINC & 0x20) == 0) // execute

{

A[m]=m1; m++;

A[m]=m2; m++;

A[m]=m3; m++;

A[m]=m4; m++;

A[m]=m5;

for(i=0;i<5;i++)

{

for(j=0;j<5;j++)

{

y[i]=y[i]+(wt[j][i]*A[j]);

}

y[i]=y[i]+b1[i];

y[i]=1/(1+exp(-y[i]));

}

644


for(i=0;i<5;i++)

{

for(j=0;j<5;j++)

u[i]=u[i]+v[j][i]*y[j];

u[i]=u[i]+b2[i];

u[i]=2/(1+exp(-2*u[i]))-1;

}

for(i=0;i<5;i++)

result=result+u[i]*z[0][i];

result=result+b3;

send_number(result);

} }

}

//////////////////////////////////////

//finally calculated value is to be displayed on the

LCD.

void send_number(float x)

{

char d, i = 1,t=0;

lcd_init();

send_command(0xC3);

if(x < 0)

{

x = fabs(x);

send_data('-');

}

while(x >= 10)

{

x /= 10;

i++;

}

}

////////////////////////////////////////

void display(int x, char databyte[])

{

char y=0;

send_command(x);

while(databyte[y]!='\0')

{

send_data(databyte[y]);

y++;

}

}

///////////////////////////////////////////////////

//Delay of few moments.

void delay(int d)

{

int i,j;

for(j=0;j<=d;j++)

{

for(i=0;i<=32000;i++);

}

}

//End of the program.

VIII. CONCLUSION

The results obtained by neural network method are

compared with the experiment result for the different

modes of equilateral triangular patch antenna in Table II

which are in very good agreement with the experiment

results.

TABLE II: COMPARISON OF NEURAL NET CALCULATED AND HARDWARE

CALCULATED FREQUENCY

S. No Neural Network

Calculated Freq. in

GHz (as Per Table I)

Hardware Computed

Calculated Freq. in GHz

1 2.637 2.6361

2 2.995 2.9948

3 3.973 3.9726

4 4.439 4.4392

5 2.596 2.5978

6 2.969 2.9691

7 3.968 3.9659

8 4.443 4.4431

9 1.280 1.2803

10 2.242 2.2419

11 2.550 2.5500

12 3.400 3.3999

ACKNOWLEDGMENT

I would like to thanks Prof. Harish Parthasarthy,

NSIT, New Delhi for all his support. I am always grateful

for the encouragement of Prof. M P Tripathi, Agarsen

College of Eng, New Delhi that made me write and publish

paper. The author will also like to express sincere

appreciation and gratitude to R.G.E.C, Meerut which has

provided tremendous assistance throughout the project.

REFERENCES

[1] D.K. Neog, S.S. Pattnaik, D.C. Panda, S. Devi, B. Khuntia and M.

Dutta, "Design of a Wide Band Microstrip Antenna and use of

Artificial Neural Networks in the Parameter Calculation," IEEE

Antenna and Propagation Magazine, 47(3), June 2005,pp. 60–65.

[2] I. J. Bahl and P.Bartia, Microstrip antennas (Artech House, MA,

1980).

[3] J.S. Dahele and K.F. Lee, "On the resonant frequencies of the

triangular patch antenna," IEEE Trans. Antennas Propagat., vol.

AP-35, pp. 1oo–101, Jan.1987.

[4] Y. Suzuki and T. Chiba, "Computer analysis method for arbitrarily

shaped microstrip antenna with multi terminals," IEEE Trans.

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[5] K. Guney.” Resonant Frequency of a triangular Microstrip

Antenna.” Microwave opt.Technol.Lett.Vol6.No9.1993.PP 555–

557.

[6] R. Gopalakrishnan, N. Gunasekaran, “Design Of Equilateral

Triangular Microstrip Antenna Using Artifical Neural Networks”,

0-7803-8842-9/05, pp 246–249.

[7] W.Chen, K.F. Lee, and J.S. Dahele. ”Theoretical and Experimental

studies of the resonant frequencies of the Equilateral Triangular

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[8] J.S. Dahele and K.F.Lee.” On the Resonant Frequencies of the

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[9] K. Guney.” Resonant Frequency of a triangular Microstrip

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[10] S. Sagiroglu, K. Guney.” Calculation of Resonant Frequency for an

Equilateral Triangular Microstrip Antenna with the use of Artifical

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