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Interaction of microwave energy (900MHz – 2.2GHz) withhuman brain tissue

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INTERACTION OF MICROWAVE ENERGY (900MHz – 2.2GHz)
WITH HUMAN BRAIN TISSUE
APPROVED BY SUPERVISING COMMITTEE:
________________________________________
Ruyan Guo, Ph.D., Co-Chair
________________________________________
Bhalla Amar, Ph.D., Co-Chair
________________________________________
Buo Liu, Ph.D.
Accepted: _________________________________________
Dean, Graduate School
DEDICATION
This thesis is dedicated to my loving parents and sister. I am indebted to them for providing me
with constant inspiration.
INTERACTION OF MICROWAVE ENERGY (900MHz – 2.2GHz)
WITH HUMAN BRAIN TISSUE
by
LALITHKALYAN BONAM, B.E.
THESIS
Presented to the Graduate Faculty of
The University of Texas at San Antonio
In Partial Fulfillment
Of the Requirements
For the Degree of
MASTER OF SCIENCE IN ELECTRICAL ENGINEERING
THE UNIVERSITY OF TEXAS AT SAN ANTONIO
College of Engineering
Department of Electrical and Computer Engineering
December 2010
UMI Number: 1483703
All rights reserved
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a note will indicate the deletion.
UMI 1483703
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ACKNOWLEDGEMENTS
I would like to thank my supervising professor Dr. Ruyan Guo for their continual moral
support and guidance without which I would have been unable to accomplish this innovative
project. I would like to thank to Dr. Amar Bhalla and everybody who encouraged me and helped
me in my thesis project. I would like to thank to my friends and colleagues at UTSA, as they
constantly supported me in my efforts with their valuable advice.
December 2010
iv
INTERACTION OF MICROWAVE ENERGY (900MHz – 2.2GHz)
WITH HUMAN BRAIN TISSUE
Lalithkalyan Bonam, M.S.
The University of Texas at San Antonio, 2010
Supervising Professor: Dr Ruyan Guo, Ph.D.
In recent years, bio-science concerns on the public health risk from microwave energy
emitted from various sources. Despite much research efforts over few decades on the interaction
of microwave energy with bio-tissues are still unclear. Some research results show that there is
an elevated risk of causing the cancer in bio-tissues while other studies lead to the curing of
cancer tissues with microwave energy, but either of these studies leads to inconclusive results. In
this study an effective computational model has been developed to represent the interaction of
microwave electromagnetic field with human brain tissue. In order to simulate the Specific
Absorption mRate (SAR) a finite-difference time-domain (FDTD) numeric technique was used.
The results show that the Specific Absorption Rate (SAR) is a function of the input power and it
varies with distance. In this study a realistic human brain tissue and IEEE brain phantom models
have been analyzed. It has found that at 900MHz to 2.4GHz frequencies range, the SAR
increases above safety standard ( > 0.5 W/Kg) which can cause tissue damage . The distance
between the mobile phone antenna and brain is found to be inversely proportional to specific
absorption rate.
v
TABLE OF CONTENTS
Acknowledgements ........................................................................................................................ iv
Abstract ............................................................................................................................................v
List of Tables .............................................................................................................................. viii
List of Figures ................................................................................................................................ ix
CHAPTER I: INTRODUCTION
1.1 Overview ........................................................................................................................1
1.2 Scope & Application of microwave energy ...................................................................3
1.3 Aim of thesis ..................................................................................................................5
1.4 Thesis overview .............................................................................................................6
CHAPTER II: BACKGROUND STUDY OF THE RESEARCH
2.1 Interaction of microwave with human brain tissues ......................................................7
2.2 Finite Difference Time Domain (FDTD) .......................................................................9
2.3 Biological brain tissue background ..............................................................................12
2.4 Physical properties of brain .........................................................................................16
2.5 Effects of microwave interaction with brain tissue......................................................17
CHAPTER III: EXPERIMENTAL SETUP
3.1 Numerical Methods ......................................................................................................19
3.2 Boundary conditions ....................................................................................................21
3.2.1 Absorption Related Quantity ........................................................................21
3.2.2 Specific Absorption Rate (SAR)...................................................................22
3.2.2 Spatial Average SAR ....................................................................................22
3.3 Computational Modeling .............................................................................................23
vi
3.3.1 Segmentation tools & Image processing.......................................................23
3.4 Computational Simulation Tool ...................................................................................27
3.4.1 Design of generic GSM phone......................................................................28
CHAPTER IV: RESULTS AND DISCUSSION
4.1 Simulation Results .......................................................................................................30
4.2 Generic phone source ...................................................................................................32
4.3 Discussion ....................................................................................................................41
CHAPTER V: CONCLUSION & FUTURE WORK
5.1 Conclusion & Future work ..........................................................................................42
BIBLIOGRAPHY ........................................................................................................................44
VITA
vii
LIST OF TABLES
Table 2.1
Physical properties of the brain based on frequency…………………….16
Table 3.2
Parameters used for the generic phone .....................................................28
Table 4.1
SAR Values with maximum output power at different frequencies ........33
Table 4.2
Values while the user is communicating at 200mW................................33
Table 4.3
SAR Values while the user is communicating at 20mW .........................34
Table 4.4
SAR values for SAM phantom at Maximum output power ....................37
Table 4.5
SAR values for SAM phantom at 200mW ...............................................37
Table 4.6
SAR values for SAM phantom at 20mW .................................................38
Table 4.7
Distance between the brain and source is 106.395(mm)…………………40
Table 4.8
Distance between the brain and source is 106.395(mm)………………....40
viii
LIST OF FIGURES
Figure -2.1 Development of cancer from mutation produced by microwave interaction……...…8
Figure 2.2 Yee cell used for FDTD, about which E & H components are distributed………….10
Figure 2.3 Division of Brain…………………………………………………………………….13
Figure 2.4 2-D Detail Sketch of Four Lobes ……………………………………………………14
Figure 3.1 Segmentation of the human brain in ITK-Snap……………………………………...25
Figure 3.2 Segmented brain in different parts …………………………………………………..26
Figure 3.3 Shrinking of the number of elements in Scan IP software…………………………..27
Figure 4.1 Electric Field E(x,y,z,f0) at 900 MHz ………………………………………………31
Figure 4.2 Radiation of Electric field E(x,y,z,f0) near antenna at 900 MHz …………………...31
Figure 4.3 Radiation intensity of realistic brain in occipital, parietal, frontal lobes…………….34
Figure 4.4 Radiation intensity of phantom model in occipital, parietal, frontal lobes…………..35
Figure 4.5 Spherical Radition distribution while in comunnicating mode for phantom model....35
Figure 4.6 Spherical Radition distribution while in comunnicating mode for real tissue model.36
Figure 4.7 The generic phone at a distance of 13 cm from the brain……………………………36
Figure 4.8 Energy density radiations at in occipital lobes………………………………………38
Figure 4.9 Energy density radiations at in parietal lobes…..……………………………………39
Figure 4.10 Energy density radiations at in frontal lobes….……………………………………39
ix
CHAPTER I: INTRODUCTION
1.1 Overview
In recent years, biological science concerns on the public health risk from microwave
energy emitted from various sources. Despite much research efforts over few decades on the
interaction of microwave energy with biological tissues are still unclear. Some research results
show that there is an elevated risk of causing the cancer in biological tissues while other studies
lead to the curing of cancer tissues with microwave energy, but either of these studies leads to
inconclusive results. The aim of this study is to develop an effective computational model to
investigate the long term interaction of microwave energy with the biological tissues. We
characterize the spatial distribution of microwave energy absorbed by the biological tissues at
different frequencies. In order to study the interactions we consider the usage of mobile phone by
humans. Brain tumors are possible outcomes due to long-term exposure under RF radiation by
mobile phones. The electrical properties of the brain tumor at different frequencies are different
from the healthy brain tissues. The tumor tissue contains more water, and it reflects most of the
microwaves.
A study of epidemiologists found that the increase in glioma is not because of use of
mobile phone it due to long term exposure of RF radiation. For more than 10 years of mobile
phone use reported on the side of the head where the tumor was located, an increased OR of
borderline statistical significance (OR = 1.39, 95% CI 1.01, 1.92, p trend 0.04) was found,
whereas a similar use on the opposite side of the head resulted in an OR of 0.98 (95% CI 0.71,
1.37). The overall results do not indicate an increased risk of glioma in relation to mobile phone
[1].
1
Another study reported that the “near- field” electromagnetic plume of seven and eight
inches around the antenna of the mobile phone caused leakage in the blood brain barrier
[2].Other studies distinguish the thermal effects and non-thermal effects of mobile telephony
through both electromagnetic radiation and pulsed microwave radiation [3] whereas the brain is
considered as an electrical organ. The statistics of brain tumors in U.S. indicated that there are
nearly 183,000 new cases a year. It implied that brain cancer is up to 25% since mobile phones
became popular. There are reports that show tumors are likely to be located on the same side of
the brain when the phone is placed [4]. In the same reference, it reported that an increase in the
occurrence of lymphoma in mice when exposed to type of radiation that comes from digital
phones. This may be the first evidence relating mobile phone radiation and brain tumor.
From biophysical point of view, the effect of mobile phone can be described by the
average RF energy absorption by the brain. It is found that the most likely regions to be affected
are in the temporal lobe with respective of the frequency, on the ipsilateral side, generally
absorbs at least a half of all the energy absorbed in the brain. The absorption in the outermost
layers of the brain on the ipsilateral side is also very high, which is besides true in the cerebellum
[5]. In a recent study who surveyed a large population of mobile phone users in Europe, it was
found that people’s main concern was about radiation from mobile phones, more so than about
other environmental hazards [6]. Given the needs to address this health concern, our aim is to
construct a computational methodology and a tool that enables further study of the cancer risk
associated with mobile phone use.
A mathematical model of time domain simulation is used to analyze the effect of microwave
radiation in the cerebellum. An external source of microwave radiation is developed by using a
generic phone model. One digital brain model is enhanced by using MRI images and these are
2
compared to the simulation of IEEE phantom model. In this thesis, the optimum utilization of
microwave energy over a focused region is described. To evaluate the electromagnetic radiation
Finite-difference time-domain (FDTD) technique is used. Finite-difference time-domain (FDTD)
is a popular computational modeling technique used to solve the Maxwell’s equations that
govern the biophysical response in the brain tissue. The amount of Specific Absorption Rate
(SAR) is characterized by the microwave behavior on brain tissue. Using the SAR distribution
we can speculate (stressful) the region that may be affected by the quantitative measure of
microwave energy deposited in a particular location. The study of biologic experiments explains
the stressful nature in the brain tissues resulting in alteration or mutation of the DNA sequence
which leads to the abnormal behavior of brain tissue.
1.2 Scope and Application of Microwave energy:
Every day, human begin is interacting with the microwave energy in the nature. The interaction
of microwave energy is being balanced by the physical nature of the human system. The adverse
effects of microwave energy come into the picture when there is a long term exposure of
microwave energy or at the short term of radiation with high intensities. Microwave energy has a
wide range of application. Here some applications are discussed that relates to a communication
and bioscience field.
•
The rate of enzymatic reactions is controlled by using insoluble substrates with the
controlled microwave radiation.[7]
•
Microwave energy is used for the treatment of patients with cancer.
•
Using a microwave oven equipped with stirring facility and both temperature and
pressure controls the speed of the organic reactions of naphthalene and anthraquinon.
3
•
Microwave signals travel by line of sight are not bent by the ionosphere as are lower
frequency signals and thus satellite and terrestrial communication links with very high
capacities are possible.
•
Effective reflection area (radar cross section) of a radar target is proportional to the
target’s electrical size. Thus generally microwave frequencies are preferred for radar
systems.
•
Various molecular, atomic, and nuclear resonances occur at microwave frequencies,
creating a variety of unique applications in the areas of basic science, remote sensing,
medical diagnostics and treatment, and heating methods.
•
Today, the majority of applications of microwaves are related to radar and
communication systems. Radar systems are used for detecting and locating targets and
for air traffic control systems, missile tracking radars, automobile collision avoidance
systems, weather prediction, motion detectors, and a wide variety of remote sensing
systems.
•
Microwave communication systems handle a large fraction of the world’s international
and other long haul telephone, data and television transmissions.
•
Most of the currently developing wireless telecommunications systems, such as direct
broadcast satellite (DBS) television, personal communication systems (PCSs), wireless
local area networks (WLANS), cellular video (CV) systems, and global positioning
satellite (GPS) systems rely heavily on microwave technology.
4
1.3 Aim of the Thesis
The aim of this study is to develop an effective computational model to investigate the
long term interaction of microwave energy with the biological tissue like a cerebellum and
characterize the spatial distribution of microwave energy absorbed by the cerebellum, which is
caused using a mobile phone at close distance to the brain. A mathematical model of time
domain simulation is used to analyze the effect of microwave energy distribution in the
cerebellum. An external source of microwave energy is developed by using a generic phone
model. One digital brain model is enhanced by using MRI images and the results are compared
to the simulation of IEEE phantom model. In this thesis, we studied the optimum utilization of
EM field distribution when applied on cancerous tissue.
To evaluate the EM distribution and bio heat equation Finite-difference time-domain
(FDTD) technique is used. Finite-difference time-domain (FDTD) is a popular computational
modeling technique used to solve the Maxwell’s equations that govern the biophysical response
in the brain tissue. The amount of Specific Absorption Rate (SAR) is characterized by the
microwave behavior on brain tissue. Using the SAR distribution we can speculate (stressful) the
region that may be affected by the quantitative measure of microwave energy deposited in a
particular location. The study of biologic experiments explains the stressful nature in the brain
tissues resulting in alteration or mutation of the DNA sequence which leads to the abnormal
behavior of brain tissue.
The simulation results are performed over the range of frequency from 100MHz to 4GHz.
The simulation results are compared with realistic MRI image of brain tissue and IEEE phantom
model. From the results obtained, we can predict the chance of causing the tumor with SAR. The
SAR at 200mW while communicating at a frequency of 2.3 GHz is 0.986 W/Kg where the
5
antenna is at distance of 13cm away from the tissue. The SAR at 2.3 GHz is 0.986 W/Kg which
is above the safety standards and this leads to the stressful nature for cause tumor growth.
1.4 Thesis Overview
The first chapter briefly discusses the impact of the microwave energy over the biological
tissue. The impact is explained by considering the usage of cellular phones by human beings.
The scope and applications of microwave energy, how it is useful in curing disease like
cancerous tumor is discussed.
The second chapter we discuss the background of the research. We discuss the interaction
of microwave energy with the bio tissue. We discuss the approach that understands the
interaction microwave with brain tissue using mathematical model. We discuss the FDTD
approach for the microwave propagation in the media like tissues. We discuss the biological
background of human brain. We discuss the effect of microwave interaction on brain tissue.
The third chapter we perform the numerical method. We perform the computational and
mathematical model design of the phantom model and generic phone. We extracted the real
human brain model by using the MRI images and perform the computational simulation. We
calculated the numerical values for the setting up in the computational simulation.
The fourth chapters we discuss the results that are obtain at different range of frequency
i.e., 900MHz – 2.2GHz. We perform comparisons from realistic brain model with phantom
model. The maximum out power at the higher range of frequency with constant distance results
the high Specific Absorption Value (SAR). We computed the results by varying the distance of
the microwave source by targeted brain tissue.
6
The last chapter deals with conclusion that the SAR increases with increasing of
frequency. The high value of SAR on bio tissue will show the abnormal nature. The frequencies
above the 2.2GHz will alter the DNA structure result in tissue damage or tumor conversion. The
future improvement can make by design of protective shield in mobile phone which attenuates
the maximum output power. The computational model should be more accurate for
computational simulation of heavy model like human brain which contains lack of elements.
7
CHAPTER II: BASIC BACKGROUND
2.1 Interaction of microwave energy on brain tissue:
When a microwave propagates into the medium a proportion will be reflected and the rest will be
adsorbed or transmitted. The amount of the transmitted and absorbed depends on the complex
relative permittivity ‘e’ of medium. When microwave propagates in the medium tissue part of is
reflected and other is absorbed. The absorbed energy increases the temperature of the water
content there results in thermal vibration of the molecules [8]. The thermal impact of
microwaves energy results killing of tumor tissue. For the low level of radiation of the
microwave for long term exposure will results tissue damage [9]. In the atmospheric
environment we are interacting shortly with microwave but this interaction is balanced by
physical activity of the human body.
For long term exposure the bio tissue DNA structure alters. Then mechanism here, the
electric and magnetic components are perpendicular to each other when propagates into the
tissue the cell membrane blocks the electric component. The magnetic component will propagate
into the tissue. When magnetic field is present at two current carrying conductors the conductors
repel each other.
Figure 2.1: Development of cancer from mutation produced by microwave interaction [10].
8
Similarly as the DNA is in helix structure carrying current in closed loop and magnetic
field is present so it experiences repulsion. Hence the DNA structure alternates. Still there are
many adverse effect are studied for the long term of exposure of the microwave on the bio tissue.
As the study of microwave interaction on bio tissue is a wide area we narrow down the study to
human brain tissue. In order to study the microwave interaction on brain tissue it is need to under
in time domain. The mathematical model is used to do analysis in time domain i.e., Finite
Difference Time Domain (FDTD).
2.2 Finite Difference Time Domain (FDTD):
Finite Difference Time Domain (FDTD) is one of the most popular computational
electrodynamics modeling technique used to find the numerical solutions of complex structures.
FDTD solves the partial differential equations in both space and time domain so it is easy to
understand and easy to implement. K.S.Yee introduced the FDTD technique in 1996 [11]. As
FDTD is a time domain method it can use to solve the time dependent Maxwell’s curl equations.
In FDTD modeling technique the time dependent Maxwell’s curl equations are
discretized using central-difference approximations to the space and time partial derivatives.
When Maxwell's differential equations are examined, it can be seen that the change in the E-field
in time (the time derivative) is dependent on the change in the H-field across space (the curl).
This results in the basic FDTD time-stepping relation that, at any point in space, the updated
value of the E-field in time is dependent on the stored value of the E-field and the numerical curl
of the local distribution of the H-field in space. Similarly it relate to H-field. [11]
9
Figure 2.2 Yee cell used for FDTD, about which E & H components are distributed.[11]
Electromagnetics is governed by the time-dependent Maxwell’s curl equations for 1-D, which in
free space are
∂E 1
= ∇×H
∂t ε 0
(2.1 a)
∂H
1
= − ∇× E.
∂t
µ0
(2.1 b)
E and H are vectors in three dimensions, but if we consider only one dimension
∂E x
1 ∂Hy
=−
∂t
ε 0 ∂z
(2.2 a)
∂Hy
1 ∂Ex
=−
.
∂t
µ0 ∂z
(2.2 b)
To put these equations in a computer simulation, we approximate the derivatives with the
“finite-difference” approximations:
10
1 H yn (k + 1 / 2) − H y n (k − 1 / 2)
Ex n+1/ 2 (k) − E xn −1/ 2 (k )
=−
ε0
∆t
∆x
H y n+1 (k + 1 / 2) − H y n (k + 1/ 2)
=−
∆t
(2.3 a)
1 Ex n+1/ 2 (k + 1) − Ex n+1/ 2 (k)
.
µ0
∆x
(2.3 b)
In these two equations, time is specified by the superscripts, i. e., “n” actually means a time
t = ∆t ⋅n , and “k” actually means the distance z = ∆x ⋅ k . (It might seem more sensible to use ∆z
as the incremental step, since in this case we are going in the z direction. However, ∆x is so
commonly used for a spatial increment that I will use ∆x .)
We rearrange the above equations to:
Ex n+1/ 2 (k) = E x n−1/2 (k ) −
∆t
H yn (k + 1 / 2) − H y n (k − 1 / 2)
ε 0 ⋅ ∆x
[
H y n+1 (k + 1 / 2) = H y n (k + 1/ 2) −
]
∆t
[E n+1/ 2 (k + 1) − Ex n +1/ 2 (k)].
µ0 ⋅ ∆x x
(2.4 a)
(2.4 b)
Notice that the calculations are interleaved in both space and time. In Eq. (1.4 a), for example,
the new value of Ex is calculated from the previous value of Ex and the most recent values
of H y . This is the fundamental paradigm of the finite-difference time-domain (FDTD) method
Fig. 2.2) [11].
The matlab computation reduced equation is below:
Ex(1,k) = ex(1,k) + cb(1,k)*( hy(1,k-1) – hy(1,k) )
(2.5 a)
hy(1,k) = hy(1,k) + 0.5*( ex(1,k) – ex(1,k+1) )
(2.6 b)
When the medium like tissue is considered the computation équations are below for 1-D [12],
11
Ex(1,k) = ca(1,k)*ex(1,k) + cb(1,k) *( hy(1,k-1) – hy(1,k) )
(1.13 a)
Hy(1,k) = hy(1,k) + 0.5*( ex(1,k) – ex(1,k+1) ),
(1.13 b)
where
eaf = dt*sigma/(2*epsz*epsilon)
(1.14 a)
ca(1,k) = (1. - eaf)/(1. + eaf)
(1.14 b)
cb(1,k) =0.5/(epsilon*(1. + eaf)).
(1.14 c)
For the 3-D Maxwell’s equation to simplify when propagating in the medium like tissue it
required high level of computational model. The brain tissue contains millions of elements i.e., it
contains millions of small Yee cells to compute hence reduces the computation time. To
overcome the situation a software vendor tool SEMCAD-X is used. SEMCAD-X can able to
solve the Maxwell’s equations in shorter computation time.
2.3 Biological brain tissue background:
The highly complex organ of the human nervous system and the most sensitive part of the body
is considered as brain. It is the central part of the human nervous system and holds various
activities of human body such as:
(i) Collecting signals from different organs of the body,
(ii) Categorize various functions in the body,
(iii) And send of the information to different organs of the body.
12
It also controls main activities of the body such as perception, cognition, concentration,
sensation, memory and action. According to approximation, it is made up of 50-100 billions of
nerve and other type of cells. Many ways are there of dividing the brain anatomically. Therefore,
it is conservatively divided into three segments: (a) the forebrain, (b) the midbrain, and (c) the
hindbrain.
Each segment consists of mutually dependent parts.
Figure 2.3 Division of Brain [13].
Forebrain is the biggest part of the brain. It is located in the topmost part of the brain. It
consists of the cerebrum, the thalamus and the hypothalamus. The Cerebrum is also called as the
cortex, which is the biggest part of the brain. Major roles of the cerebrum are thinking,
reasoning, and remembering. The cerebrum is again sub-divided into the left hemisphere and the
right hemisphere. The left hemisphere pedals the left side of the body the right hemisphere
pedals the right side of the body. These connections between the two hemispheres are supplied
by the corpus callosum which is a string band of nerves. The cortex of each hemisphere is
13
completely divided into four lobes or sections. The external layer of the brain is made up of large
mass of gray matter which typically consists of nerve cells and controls the behavior of brain.
The internal layer is called white matter and is made up of nerve fibers called axons. Axons are
liable for conveying information between individual nerve cells. The four lobes of the brain are
occipital lobe, Parietal Lobe, Frontal Lobe and Temporal Lobe.
Figure 2.4 2-D Detail Sketch of Four Lobes [14].
The Occipital lobe is the smallest lobe of the brain and it is situated at the backside of the skull.
The existence of the overlying of the occipital bone gave its name for the lobe. It partitions the
cerebrum from the cerebellum called the tentorium cerebella, which is a method of the dura
mater. The most important function of the occipital lobe involves elucidation of visual impulses
which is done by a visual pathway. This lobe is located advanced to the occipital lobe and later
to the frontal lobe.
14
Partition of the parietal lobe from that of the frontal lobe is completed by the central
sulcus which on the other hand lateral sulcus separates the parietal lobe from the temporal lobe.
The functions of the parietal lobe are: elucidation of pain is controlled by the parietal lobe and is
its main function. Liable for basic ambiance such as touch, pain, pressure, temperature (both heat
and cold) and several joint activities. Discrimination of the intensity of various stimuli, like
distinctive warm from cold, is one of the purposes of parietal lobe. It is responsible for
accumulating data, which helps in later on fine tuning tactile sensation, i.e., it helps in distinguish
common familiar objects placed in our hand without looking at them.
The frontal lobe is liable for personality distinctiveness. This part of cerebrum is exactly
located at the rear the forehead. Main function of the frontal lobe is visualization. It directly
connects the frontal lobe with frontal bone. The functions of frontal lobe are: Seriously and
analytical thinking. Frontal lobe holds memory and cognition. Any breakdown in the frontal lobe
pilots to memory loss. Recognizing future penalty resulting from ongoing actions is the
capability and activity which mostly occurs in the pre-frontal area which is forward part of the
frontal lobe. Emotional aspects of a human are accumulated in frontal lobe. Motor activity is
conceded by the motor cortex that is situated in the frontal lobe. This is one of the regions which
frequently implicate chronic and progressive degenerative diseases of the brain that damages
motor control, speech, and other functions [15].
Temporal lobe is a section of the cerebral cortex that is present under the Sylvain fissure.
It is present on both sides of the brain. This lobe is home to the primary acoustic complex. The
functions of temporal lobe are: Temporal lobe is accountable for auditory sensation and is where
15
the Wernicke’s area, i.e., where the language identification center is located. The function of
speech is located in the left temporal lobe. Sensation of smell is also listed in the temporal lobe
Temporal lobe is partly liable for emotion, memory and speech.
2.4. Physical Properties of Brain:
Brain tissue is mainly self-possessed of gray matter and white matter. At diverse
frequencies diverse properties of the brain experience a change in relative permittivity and
conductivity. Permittivity (εr) and conductivity (σ) are the determining factors in the assimilation
of RF power since these parameters differ significantly with the frequency range (10MHz10GHz). And the values are taken based on IEEE standard 1528-2003 for determining Spatial
Peak Average over 1g and or 10 g of the head tissue similar material. In Table 2.1, the values of
both the relative permittivity and conductivity differ significantly in the frequency range from 10
MHz to 10 GHz.
Table 2.1. Physical properties of the brain based on frequency.
Frequency
(MHz)
Relative
Magnetic
Electrical
permittivity Conductivity conductivity
(er)
Relative
Density
ρ
permeability Heat
(kg/m3)
σ
µ
(S/m)
900
41
Specific
Thermal
conductivi
capacity
-ty
(J/Kg/K)
(W/m/K)
0.97
1
1030
1
3650
0.528
1800
40
1.40
1
1030
1
3650
0.528
2200
39
1.80
1
1030
1
3650
0.528
16
2.5. Effects of microwaves interaction with brain tissue:
There are more than 120 diverse types of brain cancer. Categorization system to identify
the brain tumors is based on the cell basis, activities of the cell, which is from least aggressive
(benevolent) to the most aggressive (malevolent). Most of the organizations use the
categorization system of World Health Organization. Brain cancer is a disease that initiates in the
brain well-known as primary brain cancer. Metastatic brain cancer initiates in any other part of
the body and then move about to the brain. These can be benign, with no cancer cells, or
malignant, with cancer cells that cultivate quickly. Tumors are primarily classified into two
forms: benign and malignant tumors.
Benign tumors consist of noncancerous cells are well-known to be benign tumors. These
tumors can be simply detached and are not hazardous. Benign brain tumors develop and press on
nearby regions of the brain [16]. They seldom spread into other tissues and probability of
reoccurring.
Malignant tumors consist of cancerous cells are well-known to be malignant tumors.
These tumors develop and extend assertively. Obstacles while deletion due to their scattering
nature and brain being the most perceptive part of the body. Malignant brain tumors are liable to
grow very rapidly and stretch on to other tissues in the brain. Malignant tumor as it grows,
affects the working of the body part.
17
Cancer which is liable to happen in the brain due to the mobile phone radiation [5] is:
Acoustic neuroma, Meningioma, and Glioma. These are the kinds of cancer where the studies are
paying attention in arising some of the tissues liable to be the most exposed to radiofrequency
fields (RF) from the mobile phone. Acoustic Neuromas also well-known as vestibular
schwannomas [17]. It is well thought-out to be nonmalignant. This consist of 6 percent of all
intracranial tumors, about 30 percent of brainstem tumors and about 85 percent of tumors in the
section of the cerebellopontine angle. Glioma is a kind of brain cancer that initiates in the brain
or spine. It is called glioma since it occurs from glial cells [18]. The most frequent site of glioma
is brain. They are diverse kinds of glioma based on the category of the cell. These are the second
mainly familiar primary cancer of the central nervous system, occurring from arachnoid “cap”
cells of the arachnoid villi in the meninges. These tumors can be together benign and malignant
[19].
18
CHAPTER III EXPERIMENTAL SETUP
3.1 Numerical Methods:
The common choices of numerical methods are: finite difference method, finite element
method, finite volume method, boundary element method, and spectral method. Although the
finite element method is more accurate and has the advantage to deal with irregular domains, it is
challenging to deal with the curl operator in Maxwell equations, especially in high frequency
range. On the other hand, finite difference is simple to implement with advance of computer
technology, the accuracy issue can be resolved by using a very fine grid. To solve Maxwell
equations, the Finite Difference Time Domain method (FDTD) is particularly useful. FDTD is a
numerical method for solving partial differential equations both in time and space. The FDTD
method also employs central difference representations of the continuous partial differential
equations to create iterative numerical models of wave propagation. Initially developed for
electromagnetic problems by [11], it is widely used in various scientific and engineering fields
today to solve wave equations. The crucial problem that effects FDTD accuracy is the imprecise
description of the geometry due to the stair casing effect resulting from rectangular grid in 2-D or
cubical grid in 3-D. The issue is resolved by using Conformal FDTD, which is similar to
conventional FDTD but, takes into account more geometrical details. The error caused by the
stair casing is subsequently reduced. In addition to improve accuracy in time integration, an
Alternating Direction Implicit ADI-FDTD is used which differs from traditional FDTD by its
time integration scheme. In the case of ADI-FDTD, the leap-frog time-integration scheme is
replaced by an approximation of the Crank-Nicholson scheme applied to Yee discretization.
Both numerical schemes are implemented in SEMCAD X software.
19
This FDTD method has been known for a long time, as a lot of computer memory is needed for
the implementation of the method it was not used commonly until the beginning of 1980’s. The
electric and magnetic field components are allocated in space on a staggered mesh of a Cartesian
coordinate system. The E- and H-field components are updated in a leap-frog scheme using the
finite-difference form of the curl which surrounds the component. The FDTD solver has
availability of computational power and straight forward implementation of algorithms which
makes FDTD method to currently be the leading method for numerical assessment of human
exposure to electromagnetic waves. Maxwell's curl equations are discretized using a 2nd order
finite-difference approximation both in space and in time in an equidistantly spaced mesh. The
first partial space and time derivatives are shown below.
∂F(i, j, k, n)/ ∂x = [Fn(i+1/2, j, k)-Fn(i-1/2, j, k)]/x + O[(x)2]
∂F(i, j, k ,n)/ ∂t = [Fn+1/2(i, j, k)-Fn-1/2(i, j, k)]/t + O[(t)2]
Where Fn = electric or magnetic fields at time n·t;
i, j and k are the lattice spatial indices;
O [(x) 2] and O [(t)2] represent high order terms;
and σE and σH are the electric and magnetic losses for the proposed allocation of the fields in
space and time leads to Ex component.
Ex (n+1:I, j, k) = [1-(t σi, j, k/2eI, j, k)/ 1+(t σi, j, k/2eI, j, k)] Ex (n:I, j, k) + [t/eI, j, k)/ 1+(t σi, j,
k/eI, j, k)]
{Hz(N+1/2:I, J+1/2, K) - Hz(N+1/2:I, J-1/2, K)}/y – {Hy(N+1/2:I, J, K+1/2) -
Hy(N+1/2:I, J, K-1/2)}/Z.
20
3.2 Boundary Conditions
There are two types of boundary conditions, one in time and one in space. The time
boundary conditions are example for initial values of the fields, and are not used here since the
field is initialized before the computational time has started. The source is turned on immediately
after the simulation starts. A voltage gap is introduced between two cells. The boundary
conditions are the conditions at the object surfaces of computational domain. SEMCAD-X
provides three types of boundary conditions for the objects to truncate the computational domain
[20]. The first boundary condition is ABC (Absorbing Boundary Condition), PEC (Perfectly
Conducting Condition) and PMC (Perfectly Magnetic Conduction). Perfectly Conductive
Boundary Conditions truncate the computational domain with perfectly conducting planes. The
tangential components of the E-fields on the outer boundaries are set to zero. FDTD treats
perfect electric conductors as boundary conditions for the electromagnetic field. This means that
the field components a PEC-solid contains are set to zero in FDTD simulations.
3.2.1 Absorption Related Quantity
Power absorbed in a unit volume of a homogeneous tissue, for constant electric field intensity
(E) Within the volume is equal to [21].
P = σ (E2/2)
The initial rate of temperature increase resulting from the absorption of radio frequency power
(P) (above equation) (conduction process neglected) is equal to
∆T/∆t = kP
21
Where: ∆T is the temperature increase in time ∆τ and k is the proportionality factor dependent
on the tissue specific heat and density. This temperature is resulting from absorption of radio
frequency radiation.
3.2.2 Specific Absorption Rate (SAR)
The specific absorption rate (SAR) is a value describing how much power absorbed in biological
tissue when the body is exposed to electromagnetic radiation.
SAR = σE/2ρ|E|2 = c(dT/dt)
Where c is the specific heat capacity, σE the electric conductivity, ρ is the mass density of the
tissue, E induced electric field vector and dt/dT the temperature increase in the tissue. The
electromagnetic (EM) wave absorption in biological tissue is evaluated in terms of the Specific
Absorption Rate (SAR), measured in W/kg.
Frequently International standard and scientific organization in their studies refer to peak
spatial-averaged values of SAR. This is because of two reasons: Maximum SAR on a single
point (denoted as local SAR) is too sensitive for approximation in computational methods and
Energy deposited at point invariably smeared out due to heat conduction that implies that local
SAR is not thermally significant.
3.2.3 Spatial-Average SAR
Spatial-average SAR is computer at every point of the tissue by averaging the values enclosed in
a region R with a mass M in a finite volume V. More specifically, the spatial-average SAR is
computed as
22
Where r0 € R. The implemented algorithm is based on the IEEE Standard C95.3-2002.
3.3 Computational Modeling:
In computational modeling we consider the MRI data of the human tissue. From the MRI
data the head has to be segmented. The geometry of the brain tissue is depend on the volumetric
pixels know as voxel. Every voxel of the Cartesian representation of MRI data is well-known as
made (mainly) of one material. Extraction of MRI data is purely depending on the quality of the
MRI image. The good quality of the MRI data helps to identify the diverse tissues. Diverse
tissues have dissimilar electromagnetic properties. ‘Signal- to- Noise Ratio’ is a criterion for
image quality. Interface among tissues are meshed, preferably using triangles which are
shapeless and further used as inputs for the volume mesh generators.
3.3.1. Segmentation Tools and Image Processing:
The segmentation of MRI data is done by using various software vendors. ITK-SNAP
Software for segmentation is used to partition 3-D images. The images have to be consistent, i.e.,
having unique intensity rate per pixel. Snap can read a diversity of image layouts, MRI, CT, and
PET also including RAW, Analyze, GIPL. Snap symbolize segmentation of pixels (voxels) with
labels in the input image. It supplies a set of tools which partitions the essential volumetric data
23
quicker and easier [22]. Snap can be used in two diverse modes. The first mode is manual
segmentation and second is the semi-automatic segmentation. In semi-automatic segmentation
mode, a powerful ‘level set’ segmentation algorithm is used to segment anatomical structures in
three dimensions.
Brain is segmented in to four parts. This segmentation is prepared by bearing in mind
different 2-D views of brain, the axial view, sagittal view and coronal view. In ITK-SNAP tool
we can either by hand segment or mechanically segment the part. Segmenting the components is
completed using automatic segmentation.
In automatic segmentation is completed by snake evolution. Considering all the
observation of a part and adding bubble for snake evolution, one time it is complete we can get a
3-Dimensional image in the 3-D panel. The first three views are 2-D views and the left most
division is 3-D panel which illustrates up to the 3-D image of the segmented component.
24
Figure 3.1 Segmentation of the human brain in ITK-Snap.
Meshing is done in ITK-SNAP, after once the segmentation is completed we require to update
mesh. This is completed by automatic meshing. Each segmented fraction is meshed using
triangular elements. Cerebellum only is segmented into 400,000 elements. That illustrates that
the software itself updates refine mesh. More number of elements gives more exactness. But on
the whole has elements around 400,000 elements which are too many elements. We attempted
dropping the number of elements and could decrease utmost of half the number of total elements.
Finally it was 200,000 elements which are still too much for the limited Element Method
platform to handle them.
25
Figure 3.2 Segmented brain in different parts [23].
The segmented brain has more number of elements nearly 4 million elements. To create a
Yee cells for this many elements a high level ended computational model is required. The other
way of approach is to reduce the size of the number of elements. The reducing the size of the
elements depends on the step size. We used different software to reduce the number of elements
called as Scan IP [24]. The Scan IP processing software offers a broad range of the image
visualization and segmentation. This software overcomes the volumetric problem. The number
of elements in reduced to 20,000 elements and these elements are updated as mesh. Each mesh
represents a Yee cell with defined step size.
26
Figure 3.3 Shrinking of the number of elements in Scan IP software.
3.4 Computational Simulation Tool:
The segmented brain is updated as mesh and for this mesh a microwave source is applied.
To run simulation there are different tools like Matlab Simlink, COMSOL etc. To analyze the
simulation the simulation should run on time domain. The time domain simulation is tested in
SEMCAD-X. In the Semcad x we will import the updated mesh. The source of microwave is
Global System for Mobile Communications (GSM) phone and is designed in Semcad –x. The
design of the microwave source and the interaction of microwave with brain are explained as
fallows.
27
3.4.1 Design of Generic GSM phone:
The generic phone is designed by consider the phone case as brick type solid and a
monopole antenna is designed. The monopole antenna is designed by considering the cylinder
shape solid. The modulated signal is propagated in harmonic progression. The generic phone is
placed at a distance of 13 cm from the mobile to imported segmented brain. The simulation is
carried out with most using frequencies. The parameters used of the generic mobile phone.
Table 3.2 Parameters used for the generic phone.
Excitation
Phone Type
Harmonic
Generic Phone
Frequency
900 MHz,
1800MHz,
2200MHz
Simulation
Time
10 Periods
Solver
FDTD (Finite
Difference
Time Domain)
For the metallic solids surface impedance adds the ability in modeling the nm scale
problems. The impedance adding can be harmonic and broadband. This surface impedance can
be in terms of electrical conductivity, magnetic conductivity, Permittivity and Permeability. For
each simulation a source and sensor is defined.
The field data is recorded in the defined region called as sensor during the simulation.
The data recording is basically about the electric and magnetic components [20].
Sensors define regions in the model where field data is recorded during the simulation.
The field sensor is used to record the electric field (E) and the magnetic field (H). Based on the
requirement and necessity you can turn” ON” and “OFF” the sensors. In harmonic simulation
mode “steady-state” condition is tested, that means once the simulation reaches the steady state,
the simulation will be stopped. Edge sensors are automatically added at the terminals of the edge
28
sources. We have different types of sensors based on the requirement we can choose any one
more number of sensors for the simulation. Types of sensors are Field Sensor, Edge Sensor,
Voltage Sensor and Current sensor. The sensors used in the simulation of generic phone are Field
Sensor and Edge Sensor. Field sensor records electromagnetic field, i.e., records electromagnetic
(E, H, J etc) near field data [20]. Overall field sensor is one of the types of field sensor that
records the field thorough out the grid. One more sub classification of the field sensor is “FarField” sensor that records “nearfield” data first and then to applies “near-and-far-field”
transformation to extract the far field data. Edge sensor is used to record voltage, current and
power impedance etc.
Sources are placed to excite the fields. It defines regions in the model where
electromagnetic energy is injected into the model. SEMCAD-X software is best suitable to
determine fields induced in the living tissue by EM sources. The source used in the model is
“Edge Source.” Edge source represents a straight edge in the model, restricted to run parallel to
one of the grid axes. Edge source is created by entering two endpoints where it is supposed to be
created for excitation. Edge Sensors are automatically added at the terminals of all edge sources.
Carrier wave is a sinusoidal wave that carries information that can further modulated with
an input signal. Usually the carrier signal is of much higher frequency than the input signal.
Carrier wave can either transmit information through space as an electromagnetic wave or allow
several other carriers at different frequencies to share a common physical transmission medium.
Commonly used methods to modulate the carrier wave are frequency modulation (FM) and
amplitude modulation (AM).
29
CHAPTER IV: RESULTS AND DISSCUSSION
4.1 Simulation Results:
The Specific Absorption Rate (SAR) is amount of energy absorbed in unit mass of the
material. The value of the Specific Absorption Rate determines the tissue nature. Higher value of
the SAR results tissue damage or tumor conversion.
The mathematical definition of the SAR is:
1
SAR = σ E 2 max
2
Where E is the electric field, σ is the conductivity and ρ is the density is measured in W/kg. The
value of the SAR is expressed in two ways; one way is to compute an average value of SAR in a
cell of 1 gram and the other way is to compute an average value in a cell of 10 grams. SAR can
also be measured over a whole body, but the interesting area is head and the brain. The SAR
values which are recorded for are compared with the safety standards of the Federal
Communication Commission (FCC). The computation results which are obtain shows that as the
higher intensity frequency limit which shows the crossing the safety standard. The safety limit of
the SAR for 1g is 1.6 W/kg.
The SAR values are calculated at different frequency range from 900MHz to 2.2GHz.
These values are plotted at different modes which basically deal with in communicating mode
and stand alone mode. Radiation pattern of the microwave with human brain tissue is observed to
determine the energy intensity levels. The SAR values are compared with realistic brain tissue
and phantom model.
30
The source of the microwave is considering as a generic phone which transmits the modulated
the signal at the microwave range frequency. The modulated signal is propagated as a harmonic
propagation which represents the E and H fields. In SEMCAD-X, each field can be shown. Here
the E-field and H-filed components are shown in phone.
Figure 4.1. Electric Field E(x,y,z,f0) at 900 MHz.
Figure 4.2. Radition of Electric field E(x,y,z,f0) near antenna at 900 MHz.
31
4.2 Generic Phone Source:
The generic phone designed based on the Global system for mobile (GSM)
communications is the mobile technology presently used by 2 billion users. The case considered
by observing the phone in talking and stand alone mode. This two general possible case is
consider and at three different frequencies of phone are operated. The different situations of the
phone possible power calculations were made respectively. The different frequencies are
considered are 0.9GHz, 1.8GHz and 2.2 GHz.
In talking mode where the phone is in use, i.e., “TALKING” by the user. In this mode it
has considerably less power when compared to maximum power. It most consider situation
where the maximum amount of energy absorbed. The maximum power values depend on the
amount of the time spent in talking mode. In this study the talking mode time is consider as 30
min talking for regular intervals of time.
In standby mode the phone is not in use, but base station antenna and the mobile phone
antenna always try to communicate irrespective of usage during this time also the phone will
have a power that is comparatively very less power to that of the maximum power. When the
phone at the ringing mode it has high power levels. In Table 4.1, it shows computed SAR at
different frequencies in the GSM technology with maximum output power at a distance of 13cm
from the cerebellum to the mobile phone antenna (900 MHz-2W, 1800MHz-1W, 2200 MHz0.5W). Also observed that as the distance increases the amount SAR values decreases.
32
Table 4.1. SAR Values with maximum output power at different frequencies.
Frequency
Relative
Conductivity
SAR
Spatial Peak
Spatial peak
(MHz)
Permittivity
(S/m)
(W/kg)
Average (1g)
Average(10g)
(W/kg)
(W/kg)
900
41.5
0.896
2.45
0.0725
0.476
1800
40.0
1.340
2.74
0.977
0.603
2200
39.6
1.560
2.41
0.77
0.4529
In Table 4.2, it shows SAR at different frequencies in the GSM technology with output power
200mw (Talking Mode) at a distance of 13cm from cerebellum. (900 MHz-200mw,1800MHz200mw,2200 MHz-200mw).
Table 4.2. SAR Values while the user is communicating at 200mW.
Frequency
Relative
Conductivity
SAR
Spatial Peak
Spatial peak
(MHz)
Permittivity
(S/m)
(W/kg)
Average (1g)
Average(10g)
(W/kg)
(W/kg)
900
41.5
0.96
0.245
0.0825
0.0476
1800
40.0
1.40
0.548
0.177
0.141
2200
39.6
1.60
0.9630
0.311
0.229
33
In Table 4.3, it shows SAR at different frequencies in the GSM technology with output power
20mw (Standby Mode) at a distance of 13cm from cerebellum. (900 MHz-20mw, 1800MHz20mw, 2200 MHz-20mw).
Table 4.3. SAR Values while the user is communicating at 20mW
Frequency
Relative
Conductivity
SAR
Spatial Peak
Spatial peak
(MHz)
Permittivity
(S/m)
(W/kg)
Average (1g)
Average(10g)
(W/kg)
(W/kg)
900
41.5
0.096
0.0245
0.0825
0.00476
1800
40.0
0.140
0.0548
0.0177
0.0141
2200
39.6
0.160
0.09630
0.0311
0.0229
We consider the brain in sections wise. The sections are called as lobes and these lobes are
defined as Occipital, Frontal and Temporal lobes. The radiation intensity at each section is
shown below.
Figure 4.3 Radiation intensity of realistic brain in occipital, parietal, frontal lobes.
34
Figure 4.4 Radiation intensity of phantom model in occipital, parietal, frontal lobes.
Figure 4.5 Spherical Radition distribution while in comunnicating mode for phantom model.
35
Figure 4.6 Spherical Radition distribution while in comunnicating mode for real tissue model
Figure 4.7 The generic phone at a distance of 13 cm from the brain.
In Table 4.4, it shows SAR at different frequencies in SAM phantom with maximum output
power (900MHz-2W, 1800MHz-1W, 2200MHz-0.5W).
36
Table 4.4. SAR values for SAM phantom at Maximum output power.
Frequency
Relative
Conductivity
SAR
Spatial Peak
Spatial peak
(MHz)
Permittivity
(S/m)
(W/kg)
Average (1g)
Average(10g)
(W/kg)
(W/kg)
900
41.5
0.96
33.1
18.6
10.4
1800
40.0
1.40
15.7
9.05
5.31
2200
39.6
1.60
7.27
4.0
2.3
In Table 4.5, it shows SAR at different frequencies in SAM phantom: “Talking Mode:” SAR at
different frequencies in SAM phantom with output power in “Talking Mode” (900MHz-200mw,
1800MHz-200mw, 2200MHz-200mW).
Table 4.5. SAR values for SAM phantom at 200mw.
Frequency
Relative
Conductivity
SAR
Spatial Peak
Spatial peak
(MHz)
Permittivity
(S/m)
(W/kg)
Average (1g)
Average(10g)
(W/kg)
(W/kg)
900
41.5
0.96
3.31
1.86
1.04
1800
40.0
1.40
3.15
1.81
1.06
2200
39.6
1.60
2.91
1.6
0.92
In Table 4.6, SAR at different frequencies in SAM phantom: “Standby Mode:” SAR at different
frequencies in SAM phantom with output power in “Standby Mode” (900MHz-20mw,
37
1800MHz-20mw, 2200MHz-20mW).
Table 4.6 SAR values for SAM phantom at 20mw.
Frequency
Relative
Conductivity
SAR
Spatial Peak
Spatial peak
(MHz)
Permittivity
(S/m)
(W/kg)
Average (1g)
Average(10g)
(W/kg)
(W/kg)
900
41.5
0.96
0.331
0.186
0.104
1800
40.0
1.40
0.315
0.181
0.106
2200
39.6
1.60
0.291
0.16
0.092
Figure 4.8 Energy density radiations at in occipital lobes
38
Figure 4.9 Energy density radiations at parietal lobe.
Figure 4.10 Energy density radiations at frontal lobe.
39
Table 4.7 Distance between the brain and source is 106.395(mm)
Distance Between source and
E field(V/m)
H field(A/m)
106.395
3.13423
0.0359751
132.5727
3.4871
0.0408459
101.71
6.31174
0.0368988
Brain(mm)
Table 4.8 Distance between the brain and source is 132.395(mm)
Distance Between source and
E field(V/m)
H field(A/m)
106.395
6.05066
0.0334596
132.5727
3.99609
0.0339326
101.71
9.4106
0.0484394
Brain(mm)
40
4.3. Discussion
There are different standard committees, which have defined how the SAR should be measured.
Examples of organizations are ICNIRP, ANSI-IEEE and FCC. Each has different standards and
different measurement methods. Note that the standards for the threshold value of SAR are not
the same published by different organizations. Some studies include the phantom with the hand
holding a phone and some do not, some consider it have homogenous others to be heterogeneous
including every tissue of the brain in the model. Complexity of heterogeneous model is larger
than the complexity of a homogeneous model. Many research results published recently their
research results concerning the effects of mobile phones depend on all these parameters which
considers position of the phone as an essential factor. The parameter of how the SAR value
varies with distance between the brain and the phone is also included. SAR is measured with
power. The power level depends on the distance to the base station and if there are any large
objects to penetrate. For example if a phone call is made inside a building or from a train. As a
result a more accurate way of measuring SAR would be both with lower power levels used when
communicating and with full power. The important thing to measure would be in the “talking
mode”; how much power that is used when talking on the phone. The technology used for the
mobile phones now a day is GSM technology, with different access method based on the
frequency. For the second generation (2G) the access technology is TDMA, CDMA with mobile
phone operating frequencies at 900MHZ and 1800 MHz. The third generation (3G) technology
has mobile operating frequency at 2200MHZ.
41
CHAPTER V: CONCLUSIONS
The increase of mobile phone became the part of the human life. As the mobile operates in
different frequencies ranges, at higher frequencies the risk factor of health should concern. The
risk factor mainly depends on the amount of energy spent per unit mass i.e., SAR and amount of
time spent microwave interaction on the bio tissue. When the microwave interaction is more than
output power will be high this leads to abnormal tissue behavior. Different cases have been
simulated by considering the various parameters with respect to frequency.
We observe that distance between the cellular phone and brain increases SAR values decreases.
At higher range of frequency the interaction of microwave energy with brain tissue is more when
the distance between the brain and source is less. In addition output power also affects the SAR,
i.e., which is high when the mobile phone is at peak power but an interesting fact is that it has
SAR even when the phone is in “Standby Mode”.
Each mobile phone manufacture industry give the SAR values of the device that is
basically depends on the type of the antenna they model. The output depends on the antenna
design. Internal antenna will have more impact when compared to external antenna. Internal
antenna radiates maximum power and it is directly radiated in the tissue. The external antenna
radiation partially absorbed by the hand during the usage time. The SAR depends on the
transmission stations also. As the transmission between the mobile and base station is higher so it
increases the output power of the mobile so it increases the SAR value. The higher range of
frequency results indicates the affect of the tissue nature by altering the DNA structure which
result converting into tumor and also help in treatment of tumor. It also affects the hsp level by
producing a delay in generating the hsp protein. This delays makes it repeated stressful nature
42
which leads to fatigue in cellular response and develop a stress tolerance which alters the
response pattern. GSM technology uses different frequencies to operate mobile phones. The
latest technology is 3G that includes iPhones and blackberries. A higher frequency is needed for
them to operate based on the applications and requires functioning such as Wi-Fi. Higher
frequency and higher output power will have a very large impact of specific absorption rate, it
increases very drastically and likely to have more chance of DNA mutation due to stressful
conditions induced within the brain cells.
43
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45
VITA
Lalith Kalyan Bonam was born in pulivendala, India on 15th of June 1986. He is the youngest
son of Shri Subramanyam Bonam and Manjula Devi Bonam. He completed his Bachelors (BE) in the
field of Electronics and Communications at T.K.R college of Engineering, India in the year 2008. In
the year 2008, he entered the graduate program in Electrical Engineering at The University of Texas
at San Antonio. In the same year, he joined as a research assistant in UTSA under the guidance of Dr.
Ruyan Guo and has been working on Microwave simulation on bio tissues.
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