International Conference on Multidisciplinary, Science,
Engineering and Technology (IMESET’19 Malaysia)
Sept 20-21, 2019,
Malaysia
IMESET’19 MALAYSIA
International Conference on
Multidisciplinary, Engineering,
Science, Education and Technology
(IMESET’19 Kuala Lumpur, Malaysia)
Hosted by University Malaysia Sabah,
September 20-21, 2019, Kuala Lumpur,
Malaysia
Imeset’19 Malaysia Book Of Abstracts
e - ISBN: 978-605-82480-6-9
Kuala Lumpur, Malaysia, 2019
International Conference on Multidisciplinary, Science,
Engineering and Technology (IMESET’19 Malaysia)
Sept 20-21, 2019,
Malaysia
IMESET’19 MALAYSIA
International Conference on Multidisciplinary, Engineering, Science,
Education and Technology (IMESET’19 Malaysia)
Hosted by Universiti Malaysia Sabah
September 20-21, 2019, Kuala Lumpur, Malaysia
BOOK OF PROCEEDINGS
IMESET’19 Malaysia
International Conference on Multidisciplinary, Science,
Engineering and Technology (IMESET’19 Malaysia)
Sept 20-21, 2019,
Malaysia
IMESET’19 MALAYSIA
International Conference on Multidisciplinary, Science,
Engineering and Technology (IMESET’19 Malaysia)
Sept 20-21, 2019,
Malaysia
IMESET’19 MALAYSIA
IV. INTERNATIONAL CONFERENCE ON MULTIDISCIPLINARY,
ENGINEERING, SCIENCE, EDUCATION AND TECHNOLOGY
(IMESET’19 MALAYSIA)
Hosted by Universiti Malaysia Sabah
Septemberber 20 -21, 2019, Kuala Lumpur, Malaysia
e - ISBN: 978-605-82480-6-9
EDITOR
Dr. Ali Farzamnia (University Maleysia Sabah University, Maleysia)
All papers have been peer reviewed.
International Conference on Multidisciplinary, Science,
Engineering and Technology (IMESET’19 Malaysia)
Sept 20-21, 2019,
Malaysia
IMESET’19 MALAYSIA
COMMITTEES
CONFEAdvisory Board / Danışma kurulu
• Prof. Dr. Murat Arı (Çankırı Karatekin University,Vice Rector)
• Prof. Dr. Abdul Karim Bin Mirasa(Universiti Malaysia Sabah,Dean of Engineering Faculty)
• Prof. Dr. M. Cengiz Taplamacıoğlu(Gazi University, Turkey)
Conference Chairs / Konferans Başkanları
• Dr. Javad Rahebi (Chair,THK University, Turkey)
(Responsible for Acceptence Letter and Attendance Certificate)
• Dr. Ali Farzamnia(Co Chair, Universiti Malaysia Sabah ,Malaysia)
(Responsible for organisaiton of malaysia side.)
• Dr. Mehmet Sait Cengiz(Co Chair, Bitlis Eren University,Turkey)
Organizing Board / Organizasyon kurulu
• Dr. Abdulrezzak Bakis (Bitlis Eren University)
• Dr. Cihan Onen (Bitlis Eren University)
Assoc.Prof. Dr. Mirvari Agayeva (Azerbaijan State University, Azerbaijan)
International Conference on Multidisciplinary, Science,
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IMESET’19 MALAYSIA
SCIENTIFIC BOARD
Prof. Dr. Vanita Jain
Professor and Head of Information Technology Department
Bharati Vidyapeeth's College of Engineering
New Delhi, India.
K.Premkumar
Associate Professor,
Department of EEE
Rajalakshmi Engineering College
Chennai, India
P.Sivakumar
Associate Professor
Department of EEE
Rajalakshmi Engineering College
Chennai, India
S. B. Ron Carter
Assistant Professor,
Department of Electrical and Electronics Engineering,
Rajalakshmi Engineering College
Chennai, India.
Kusum Tharani
Associate Professor & Head of EEE Department
Bharati Vidyapeeth's College of Engineering
Delhi, India
Dr Toufique Ahmed Soomro
Assistant Professor
Electronics Engineering Department
Quaid-e-Awam University of Engineering and Science Technology Sindh Pakistan
Ahmed J. Afifi
Computer Vision and Remote Sensing
Technische University Berlin
Berlin, Germany
Uçman Ergün, Associated Professor
Biomedical Engineering
Afyon Kocatepe University
Mustafa Poyraz, Professor Doctor
Firat University
International Conference on Multidisciplinary, Science,
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Hüseyin Polat, Assistant Professor
Technology Faculty of Gazi University
Firat Hardalac Professor
Gazi University
Ali Farzamnia
Assistant Professor
Electrical & Electronic Engineering Program
Faculty of Engineering,
Universiti Malaysia Sabah
Dr. Ayhan Akbaş
THK university
Computer engineering department
Dr. Yuriy Alyeksyeyenkov
THK university
THK university
Electrical & Electronics department
Associated Professor İbrahim Mahariq
THK university
Dr. Esra Karaoğlu
THK university
Dr. Tayfun Okan
Mechanic Engineering
Dr. Mohammad Rafighi
THK university
Dr. Reza Aghazadeh
International Conference on Multidisciplinary, Science,
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Sept 20-21, 2019,
Malaysia
IMESET’19 MALAYSIA
Dear colleagues and young researchers!
First of all, I would like welcome and greet you for taking part in the second ‘International
Conference Engineering, Science, Education and Technology (IMESET’19 Malaysia. I believe that
this event, which on Multidisciplinary, Engineering, Science, Education and Technology (IMESET’19
Malaysia)’ held by Universiti Malaysia Sabah.
On behalf of the Organizing Committee, I am very happy to open International Conference on
Multidisciplinary, is the fruit of an intensive and devoted teamwork, will have an invaluable
contribution to the scientific world.
Today, universities need to get their power of existence from their own studies by setting strong
relationships with economic, social and cultural resources of their territory as access to information
has been simplified, education has become a lifelong activity and rivalry has become dominant. One
of the basic features of universities is to produce information, science and technology to serve to next
generation and to the people of the region as well as the country, since the responsibilities of
universities are not restricted to equip their students with occupation.
We wish everyone a fruitful conference and pleasant memories in Kuala Lumpur, Malaysia.
Assoc. Prof. Dr. Mirvari AGAYEVA
Chair of IMESET’19 MALAYSIA
International Conference on Multidisciplinary, Science,
Engineering and Technology (IMESET’19 Malaysia)
Sept 20-21, 2019,
Malaysia
IMESET’19 MALAYSIA
Dear Friends and Colleagues,
Welcome to the in the first ‘International Conference Engineering, Science, Education and
Technology (IMESET’19 Malaysia).
This book containts all abstracts presented in the second ‘International Conference Engineering,
Science, Education and Technology (IMESET’19 Malaysia. 18 papers will be presented at the
Conference. Peer reviewed papers will be considered for publication in a spesial issue.
The program for this Conference required the dedicated efforts are here with recorded. Secondly, we
thank the members of the Program Committe and additional rewiewers for their dligent and
professional rewiewing. Last but not least, we thank the invited speakers for their invaluable
contribution. We would also like to take this opportunity to thank Universiti Malaysia Sabah for
supporting and motivating us in every respect. Special thanks are also due to the organizing committee
members, for completing all preparations that are necessary to organize this conference. I express my
gratitude to the members of technical committee of the conference for the design and proofreading of
the articles.
A successful conference involves more than paper presentations; it is also a meeting place, where
ideas about new research projets and other ventures are discussed and debated. Therefore some social
events have been arranged in order to promote this kind of social networking.
We wish you have an exciting Conference and an unforgettable stay in the city of Kuala Lumpur.
On behalf of Organizing Committee
International Conference on Multidisciplinary, Science,
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IMESET’19 MALAYSIA
Contents Mean Time To Repair Determinations For Subsystems Of A Military Vehicle According
To Availability Destination ............................................................................................................ 1 BER Perfomance Analysis on Modulation Techniques of WCDMA .................................. 2
A Revision in Tower Earthing Design for High-Voltage Transmission Network during
Lightning Condition ....................................................................................................................... 9 Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns
and Gaussian Low-Pass Filter ...................................................................................................... 22 New approaches for breast cancer nuclei detection in histological images ........................ 28 DESIGN OF IOT-BASED VERTICAL SYSTEM AQUAPONIC WITH FUZZY LOGIC
METHOD ..................................................................................................................................... 29
Equation Chapter 1 Section 1 Architecture and future prospect of 5G Technology .......... 42 Improved Dynamic Performance of Wind Energy Conversion System by STATCOM .... 47 Globalization and Employee Mobility: Interfusion for Customer Satisfaction in Selected
Multi-national Companies in Nigeria. Lagos Reviews ................................................................ 48
PROMOTING RENEWABLE ENERGY TECHNOLOGIES FOR RURAL
DEVELOPMENT IN NIGERIA .................................................................................................. 52 Increasing Efficiency of Gas Injection into Gas Cap of a Sandstone Oil Reservoir to Improve
Oil Recovery ................................................................................................................................ 53
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MEAN TIME TO REPAIR
DETERMINATIONS FOR
SUBSYSTEMS OF A MILITARY
VEHICLE ACCORDING TO
AVAILABILITY DESTINATION
Bayram KALENDER*
Gazi University Graduate School of Natural and
Applied Sciences, Mechanical Engineering Department,
Ankara, Turkey
Abstract
It is aimed to eliminate the need to develop a
method to ensure that the availability of the military
vehicles in the contracting stage, which is determined
for the needs of the army, can be provided in a cost-
effective manner at the beginning of the project.
A prototype fabricated military vehicle will be
used as a subject. The average Mean Time To Repair
(MTTR) for the vehicle systems will be determined
according to the expected availability target at the
contracting stage. In this study, real MTTR values and
availability values will be calculated by working on the
prototype vehicle manufactured and comparison will be
made. Since the actual readiness value is not suitable, a
histogram graph will be created with MTTR data to
determine which systems should be intervened.
Since a military vehicle with prototype
manufacturing was used in the study, due to
confidentiality, the numerical data cannot be shared
one-to-one and will be shared over a certain amount of
modified data to accurately reflect the results of the
calculations. The vehicle consists of 60 main systems.
The availability of the vehicle under the contract is
expected to be 80%. This value is distributed according
to previous experience, according to the main systems,
the number of subsystems and the mission critical
levels. Mean time between failures (MTBF) and MTTR
values of the systems used in the projects implemented
are taken into consideration. Although MTTR values of
the systems on the prototype vehicle were calculated at
this stage, MTBF values were obtained from past
projects using the same or very similar systems. The
availability of the prototype vehicle increased to
approximately 82%.
As a result of the studies, it was seen that the
availability of a vehicle in the preliminary design stage
was achieved thanks to its practical knowledge.
However, one of the major factors in the consistency of
calculations is the ability to make accurate estimates due
to experience. Considering this situation, it is aimed to
develop a theoretical calculation method.
Keywords: ILS, Maintainability, Availability, MTTR,
MTBF
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BER PERFOMANCE ANALYSIS ON
MODULATION TECHNIQUES OF
WCDMA
Ali Farzamnia
Faculty of Engineering
Universiti Malaysia Sabah
Kota Kinabalu, Sabah, Malaysia
Navinn A/L Mohanaraj
Faculty of Engineering
Universiti Malaysia SabahKota Kinabalu, Sabah, Malaysia
Ngu War Hlaing
Faculty of Engineering
Universiti Malaysia Sabah
Kota Kinabalu, Sabah, Malaysia
Abstract
Wideband Code Division Multiple Access (W-
CDMA) is a 3rd Generation or known as 3G
standard that provides a high speed and also high
capacity services in the most of the area nowadays.
It uses the Direct Sequence-Code Division
Multiple Access (DS-CDMA), channel access
method and also the Frequency Division Duplex
(FDD). In this paper, the analysis is done on the W-
DCMA modulation technique which was subjected
to Additive White Gaussian Noise (AWGN) and
Rayleigh fading channel. The performance analysis
of Bit Error Rate (BER) has been carried out by the
help of MATLAB. The simulation of BER and
Signal to Noise Ratio (SNR) was done for W-
CDMA systems in this paper. By doing this, we
could find the most suitable modulation technique
to deliver the efficient and optimum data rate to
mobile terminal.
Keywords: Wideband Code Division Multiple Access,
Bit Error Rate, Signal to Nosie ratio, Direct Sequence-
Code Division Multiple Access, Additive White
Gaussian Noise.
INTRODUCTION
It is fascinating that high rates of the data signal
can be transmitted effectively through the air by the
use of W-CDMA system. It also allows the
transferring of applications that are rich in
multimedia such as videos, documents and pictures
which have a significantly high resolution with a
speed data rate. Therefore, this article emphasizes
the importance of an appropriate modulation
method and mechanism for error correction for the
use in Wideband Code Division Multiple Channel
(W-CDMA). It is also clear that the 2G networks,
specifically the Gaussian Minimum Shift Keying
modulation scheme is broadly used in Global
System for Mobile GSM communication.
The transmission of data through this 2G
modulation is just 1 bit per symbol thus it is without
a doubt that this modulation scheme is not suited
for the future generations of communication
systems. This paper provides useful insight about
the differences between the two modulations of
Quadrature Phase Shift Keying (QPSK) and 16-
Quadrature Amplitude Modulation (16-QAM) are
wide apart in the speed of transmission of data.
Notably, there is an emphasis that the
demodulators, modulators, transmission path, and
filter need to be perfect; a situation which is not
easy to attain [1].
Enhance Data Rate (EDGE) which linked to
the GSM evolution, is thought to be a good way of
switching to 3G which is a New Time Division
Multiple Access. It makes use of the frequency
bands namely 800, 900, 1800, and also 1900 MHz.
In this paper, a study is conducted to examine data
rate with a high speed and its effect on the
modulations of Direct-sequence Spread Spectrum
W-CDMA and how they affect the channels. The
two QPSK and QAM techniques of modulation are
selected since they are the best choices for the
delivery of higher rates of data for High Speed
Packet Access (HSDPA), which is a component of
the 3G network. The distribution of data is assumed
to be taking place through Bernoulli Effect [2].
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Simulations that depend on computers are the best
option that available now for the real-time
scenarios of radio systems on mobiles. The study
made use of 3 W-CDMA models each of them is
wireless. They included Multipath Rayleigh
Fading, W-CDMA system present within AWGN,
and W-CDMA system present within the AWGN
channel. The Cross-Sectional Function (CCF) and
Auto-Correlation Function (ACF) are the most
intuitive ways of characterizing CDMA codes [3].
In cell framework, diverse clients have diverse
channel characteristics in terms of SNR because of
the distance from the base station. The data
protection helps to control the quality of connection
according to the quality of channel so that an
excellent bit rate is obtained. By using the 8-Phase
Shift Keying (8PSK) and M-ary Quadrature
Amplitude Modulation (M-QAM) in W-CDMA,
the rate of data transmission can be higher. The
sequence that generated by Data that has undergone
modulation is spread using the code, known as M-
sequence. The transmission of data to users is done
simultaneously. There is a detection of each user’s
information data by the mobile user through a
correlation between the allocated code sequence
and the received signal.
The study of the W-CDMA’s system
performance is founded on 16-QAM and QPSK
techniques of modulation. From the generation of
data through the simulated W-CDMA codes on the
computer, we can obtain the link between models
QAM and QPSK modulations between the Bit
Error Rate [4]. In [5], BPSK and QPSK
modulations are investigated in terms of capacity
with Nakagami-m fading channel with the known
channel state information at the receiver.
Modulation techniques are discussed with the
knowledge of fiber communication [6]. Orthogonal
Frequency Division Multiplexing (OFDM)
systems are investigated with different arrays of
modulation techniques under the Rayleigh AWGN
channels [7].
The Multi-Input Multi-Output (MIMO)
architecture shown that the performance can be
better than Multi-Input Single-Output (MISO) used
with 32-PSK modulation, and it can be better with
efficient modulation technique like QAM instead
of PSK [8]. The finding of the study is that 16-
QAM and QPSK modulations in AWGN perform
better in comparison to the Multipath Rayleigh
channel. Additionally, there is a reduced
performance of the 16-QAM and QPSK after a
subjection to the Multipath fading accompanied by
an increased value of Doppler shift (Hz). In
comparison with the QPSK, it is clear that 16-QAM
has a poor performance in each of the AWGN
channels [9].
M-QAM modulation has the advantageous of
effectiveness for band limited channels because of
ability to transmit more than one bit at a time [10].
The QAM is a technique of adding two amplitude
modulated signals or known as AM signals into a
channel. Thus it can double up the bandwidth. The
other modulation technique is QPSK.
It transmits 2 bits per symbol such as 00, 10, 01
or 11. So the four quadrature phase shift keying’s
phase are 315, 225, 135, and also 45 degrees. If we
compare, the modulation schemes that can transmit
1 bit per symbol, this quadrature phase shift keying
is an advantageous in term of bandwidth. This
system can use same frequency for a baseband
signal, and thus the efficiency of bandwidth is
higher compare to BPSK and others.
In this paper, the performance
comparison carried out by differentiating the rate
of chip of pseudo-noise or known as pseudo noise
generator. The fading channel’s performance in
wideband code division multiple accesses is based
on the BER and also SNR. This paper mainly
focused on the Bit Error Rate performance analysis
of the modulation of WCDMA which is applied to
Rayleigh Fading Channels and also AWGN. The
modulation that has been used in this paper is 16-
ary QAM and also QPSK.
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SYSTEM MODEL
The most powerful, efficient and also
fitting method for this paper has to support for the
real time situations of mobile radio function. The
simulation was done for wideband code division
multiple access models by using the theoretical
formulae and parameters. In this paper, three
wideband code division multiple access (W-
DCMA) system was used. The WCDMA systems
models are in AWGN channel, Multipath Rayleigh
Fading and AWGN channels.
Modulation Schemes that will be studied are 16-ary
QAM and QPSK in AWGN. AWGN is produced due to thermal
motion of electrons in electrical materials. Thermal noise can be
expressed with a zero-mean Gaussian random process. The
transmitted signal can be obtained by summation of the random
signal and Gaussian noise random variable which is expressed in
equation (1).
Z a n= + (1)
Probability distribution function (pdf) for Gaussian
noise can be represented in equation (2) where σ2 is the variance of
AWGN.
( )2
1
212
aZ
P Z e
− −
= (2)
This noise can cause distortion on the
received signal which can give the multipath
fading.
QPSK is M-ray PSK modulation technique where
the value of M is four. QPSK can transmit 2 bits per symbol. The
phase match on one of four equally spaced values which is 0, π/2, π
and 3π/2. Each phase follows a unique pair of message bits. The signal of QPSK can be described in equation (3).
( ) ( ) ( ) ( ) ( )1 1cos 1 sin 12 2
QPSK s sS t E i t E i t
= − −
(
3)
Where; 1,2,3,4.i = The advantage of
QPSK over BPSK is that QPSK can send the data
twice than BPSK in the same bandwidth and QPSK
has same bit error probability with BPSK. Moreover, QPSK can do
differential encoding for detecting non-coherent method.
For QAM, the amplitude varies with the phase. It is
the unification of Amplitude Shift Keying (ASK) and PSK. The
general form of M-ary signal can be expressed in equation (4).
( ) ( ) ( )min min2 2cos 2 sin 2i c c
s s
E ES t f t f t
T T = + (
4)
0 , 1,2,...t T i M =
Where; Emin means the minimum amplitude of the
signal. The chip rate of pseudo noise (PN) generator will be varied
to investigate the performance of the system. Although there are so
many methods to differentiate CDMA codes, the most effective
method is the auto-correlation function (ACF) and cross-correlation
function (CCF).
The process of chip-wise convolution, correlation or
matched-filtering give ACF. ACF is a measure of correlation
between two time shifting in the same code. If two consecutive bits
are the same, it can be called periodic and if they are not the same,
it can be called aperiodic. The binary data of ‘+1’ and ‘-1’ with equal
probability comes out equally likely. The convolution of two
functions can be called CCF.
The operation of a chip-wise convolution between
two different spreading codes in a same family of codes can result
CCF. CCF also has periodic and aperiodic CCF. To measure the
value of CCF between two certain codes such as X(t) and Y(t), the
argument of the function is shown in equation (5).
( ) ( ) ( )0
1T
xxR t X t Y t dT
= + (5)
In this paper, for producing sequences of the code in
W-CDMA, linear feedback shift registers (LFSR) will be used in
which a number of cells (1 to r) are included in each shift register.
Each cell acts like a storage unit. By controlling a clock pulse,
each cell moves the contents to its output while reading its new contents from the input. Fig. 1 shows a single LFSR, which can generate a sequence from generation polynomial h(x) = x5 + x2 + 1.
Fig. 1. Three-Stage M-Sequences
A generator polynomial can represent with ‘n’
number of linear binary shift register. It means ‘n’ degree of binary
polynomial.
( ) 1 1
1 1... 1; 0,1n n
n n iH x h X h X h X h−
−= + + + +
(
6)
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In this paper, three-stage M-sequence and a random
sequence with a code length of 7 will be applied. A linear-feedback
shift register produces M-sequence with periodic auto-correlation.
According to the synchronous W-CDMA system, the data is spread
by each user, then they are modulated with M-sequence. Then, it will
be transmitted to the network users simultaneously. The mobile user
can detect the data with the correlating the received signal with a
code sequence corresponds to each user. The flowchart for the
simulation of W-CDMA system in this paper is shown in Fig. 2.
Fig. 2. Simulation Flowchart for WCDMA System
RESULTS AND DISCUSSION
The relationship of QPSK and QAM between
BER as a function are obtained by using the
generated data from a simulation of W-CDMA
models. The table 1 below shows the result for BER
versus SNR evaluation for the bit rate of 100Kbps
and table 2 shows for the 384Kbps. In this
simulation the value that obtained for BER are by
differentiating the SNR values from the range of 0
to 10.
TABLE 1. BER VERSUS SNR EVALUATION RESULT FOR
THE BIT RATE OF 100KBPS
SNR (dB) Errors BER
0 18392 0.18392
1 14307 0.14307
2 10349 0.10349
3 6820 0.06820
4 4069 0.04069
5 2154 0.02154
6 934 0.0093402
7 306 0.0030601
8 93 0.00093002
9 15 0.00015
TABLE 2. BER VERSUS SNR EVALUATION RESULT FOR
THE BIT RATE OF 384KBPS
SNR (dB) Errors BER
0 70673 0.18405
1 54670 0.14237
2 39605 0.10314
3 26332 0.068573
4 15573 0.040555
5 7909 0.020596
6 3611 0.0094097
7 1296 0.003375
8 326 0.00084896
9 66 0.00017188
The performances of QPSK and QAM
were degrading when it was subjected to the
Rayleigh multipath fading channels meanwhile, the
QPSK and QAM performed very well in AWGN
channel.
Fig. 3 shows the performance analysis
of W-CDMA with different channel using the
varied modulation techniques. In this simulation,
we can see that the performance of the connection
works very badly when the mobile terminal’s speed
increases.
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Fig. 3. Additive White Gaussian Noise channels in Wideband Code Division Multiple Access
The simulated graph is decreasing in
the form of exponentially. It shows that when the
users are increased the system is working poorly.
Then, the 16-QAM and QPSK compared. From this
simulation, we could see that the quadrature
amplitude modulation gives a bad performance in
both of the AWGN channel and also in Multipath
Fading channel. Fig. 4 and Fig. 5 show the
simulation results for the W-CDMA performance
under Rayleigh Fading and comparison results
between the Empirical and Theoretical Symbol
Error Rate (SER) respectively.
Fig. 4. Binary DPSK over Rayleigh Fading channel.
Fig. 5. The comparison between the empirical and theoretical
value of SER.
Next, the simulation results for 16-
QAM of WCDMA system is obtained. The
performance of it was poor and the reason was due
to the obstruction between the carriers’ phase of 16
QAM. The technique of modulation cannot be done
and it is suspected because the differences of
amplitude in the constellation of the signal. Fig. 6
shows the graph for the performance of BER and
SER for the QAM. Fig. 7 shows the graph of semi
analytic BER compared with the Theoretical BER
for 16-QAM.
Fig. 8 shows the comparison graph
between the QPSK, 16-QAM and also with 8-PSK
modulation. For PSK symbols the detection was
done by comparing with the phase of the received
code. Meanwhile for the QAM, it detects by
comparing both the amplitude and also the phase of
the code. If we compare the QPSK and QAM, we
can see that the QPSK’s symbols all lies on the unit
circle meanwhile the QAM’s symbols all lies on a
straight line that form a square.
Fig. 6. Symbol and Bit Error probability for the 16-QAM
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Fig. 7. Semi analytic BER compared with Theoretical BER for 16
Quadrature Amplitude Modulation
Fig. 8. Graph of comparing the 8-PSK,16-QAM and also with the
QPSK modulation techniques.
From the simulation, we can see that
the number of multiuser are limited to several
users. Thus the performance of the WCDMA is a
bit poor. The system needs to be improved so that
it can be simulate more users and it may help the
studies of the W-CDMA performances.
CONCLUSION
Overall, the main challenge encountered in
the field of telecommunication is the transmission
of information in the most efficient manner using
limited bandwidth. This is despite the fact that bits
of information get lost in the process and there will
be fading of the signal which had been sent
initially. This paper helps us to identify which
modulation technique is the best way to solve the
loss rate of information. The two modulation
methods that have been used in this paper are
QPSK and also the 16 QAM under the Rayleigh
Fading channels in the AWGN. The study has
provided enough indication that 16-QAM
technique is inferior in performance to QPSK when
considering W-CDMA system’s performance in
the AWGN channel. Lastly, we can see that as the
amount of mobile user increases, the modulation of
QPSK performs badly. Meanwhile, the 16-QAM
modulation technique failed to portrait the results
that expected as theoretical. This can be improved
by increasing the user for the simulation for QAM
modulation technique.
REFERENCES
[1] Shih, Wen-Chan, Raja Jurdak, David Abbott, Pai H. Chou,
and Wen-Tsuen Chen. "Discovery Strategies for a
Directional Wake-Up Radio in Mobile Networks." Wireless
Network Performance Enhancement via Directional
Antennas: Models, Protocols, and Systems (2015): 65.
[2] Rawat, Shraddha, and Bhagwat Kakde. "A survey on BER
performance analysis in AWGN and Rayleigh Fading
Channel." International Journal of Advanced Technology
and Engineering Exploration 2, no. 7 (2015): 111.
[3] Rahman, M. A., M. M. Alam, Md Khalid Hossain, Md
Khairul Islam, Khan M. Nasir Uddin, and Md
Shahinuzzaman. "Performance Evaluation of a DS-CDMA
System in a Rayleigh Fading Environment." World Journal
of Engineering and Technology 4 (2016): 1-9.
[4] Kumaravel, Rasadurai, and Kumaratharan Narayanaswamy.
"Performance enhancement of MC-CDMA system through
novel sensitive bit algorithm aided turbo multi user
detection." PloS one 10, no. 2 (2015): e0115710.
[5] Yang, Pei, Yue Wu, and Hongwen Yang. "Capacity of
nakagami-$ m $ fading channel with BPSK/QPSK
modulations." IEEE Communications Letters 21, no. 3
(2016): 564-567.
[6] Sharma, Savita Rana Abhishek, and Savita Rana.
"Comprehensive study of radio over fiber with different
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modulation techniques–a review." International Journal of
Computer Applications 170, no. 4 (2017): 22-25.
[7] Farzamnia, Ali, Ngu War Hlaing, Muralindran Mariappan,
and Manas Kumar Haldar. "BER Comparison of OFDM
with M-QAM Modulation Scheme of AWGN and Rayleigh
Fading Channels." In 2018 9th IEEE Control and System
Graduate Research Colloquium (ICSGRC), pp. 54-58.
IEEE, 2018.
[8] Kumar, Rakesh, and Akhilesh Jain. "BER Evaluation of
Rayleigh Fading Channel with M-PSK
Modulation." International Journal of Computer
Applications 168, no. 2 (2017).
[9] Agrawal, Deepti, and J. S. Yadav. "Investigation of the
Effectiveness of Modulation Technique for Wireless
Communication with QPSK base Encoding." International
Journal of Scientific Research in Computer Sciences and
Engineering (2017).
[10] Raju, M., and K. Ashoka Reddy. "Evaluation of BER for
AWGN, Rayleigh fading channels under M-QAM
modulation scheme." In 2016 International Conference on
Electrical, Electronics, and Optimization Techniques
(ICEEOT), pp. 3081-3086. IEEE, 2016.
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A REVISION IN TOWER EARTHING DESIGN FOR HIGH-VOLTAGE
TRANSMISSION NETWORK DURING LIGHTNING CONDITION
Dr Azwadi Mohamad*
Lightning and Earthing Group, TNB Research Sdn Bhd, 43000 Kajang, Selangor, Malaysia.
ABSTRACT
The earth electrode of the transmission tower is designed in such a way to protect both the working personnel and
the equipment during a fault. The existing earthing system for transmission towers in TNB’s system is purposely
designed for low-frequency (normal power frequency) fault conditions that takes into account the step and touch
voltages. This earthing design is found to be inapt for lightning (transient) condition to a certain extent, which
involves a high-frequency domain. The current earthing practice of laying the electrodes radially in straight 60m
horizontal lines under the ground, in order to achieve the specified impedance value of less than 10 Ω, was deemed
ineffective in reducing the high-frequency impedance. This paper introduces a new earthing design that produces
low impedance value at the high-frequency domain, without compromising the performance of low-frequency
impedance. The performances of this new earthing design, as well as the existing design, were simulated for various
soil resistivity (SR) values at varying frequency. The simulated tower earthing design that gives small impedance
values at both low and high-frequencies was selected for actual field installation. From the simulation and field
measurement results, it is obvious that good earthing design should have a fine balance between compact and radial
electrodes under the ground.
KEYWORDS – Tower Earthing design, High-frequency, Lightning, TFR
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OPTIMIZATION OF QUICKLIME PRODUCTION FR
OM EGGSHELL USING RESPONSE SURFACE METHODOLOGY
Salisu Nuhu a Department of Polymer Technology, College of Science And Technology
Hussaini Adamu Federal Polytechnic Kazaure, Jigawa State
Abstract:- This study developed empirical response surface models for optimizing the quicklime characteristics.
The calcination process parameters evaluated were calcination temperature, calcination time, and eggshell particle
size. Two process models were successfully developed and validated for RSM models. The modeling validation
runs were within the 95% prediction interval of the developed models and their residual errors compared to the
predicted values were less than 5%. Results from this study shows that the significant parameters that influenced
the quicklime yield and reactivity are calcination temperature, calcination time and eggshell particle size. The RSM
approach shows that a compromised setting of calcination temperature of 945.91oC and calcination time of 180.82
min will produce quicklime of optimal yield of 99.6608 % and optimum level of calcination time of 210 min and
calcination temperature of 895.03oC produced optimum quicklime reactivity of 0.467835oC/s. The RSM models
developed in this study can be used in the quicklime production industries to find the settings of the calcination
process that will maximize quicklime quantity and quality. This will reduce the downtime encountered by industries
having problems caused by variation in the quality of purchased quicklime.
Key: Eggshell, 0ptimization, Calcination, Respones, Surface Methodology RSM
Introduction
Disposal and treatment of eggshell waste in order to free the environment and the society of the
menace constituted by accumulated solid waste have been issues of serious concern to individual
countries and the entire world (Gallala et al., 2007). The composition of the eggshells similar to that
of limestone lends the effects of it as an alternative to limestone for production of quicklime. It is
scientifically known that the Eggshell is mainly composed of compounds of calcium. Akande (2015)
presented eggshell as being composed of 93.70% calcium carbonate, 4.20% organic matter, 1.30%
magnesium carbonate, and 0.8% calcium phosphate. As previously mentioned, calcium carbonate
(CaCO3) is the major composition of the eggshell, accounting for 93.70% of the total composition of
the Eggshell. Akande (2015) also presented calcium carbonate, as an important constituent of eggshell.
Similarly, calcium carbonate is the primary raw material in the production of quicklime (Yusuf (2015);
Bello (2015), Akande (2015). It is, therefore, indicated that eggshell and limestone have the same
primary composition in calcium compounds. The calcium trioxocarbonate (IV) which has been
established to be common to eggshell and limestone is a naturally abundantly available mineral.
According to Akande (2015), calcium carbonate occurs abundantly in earth's crust as lime stone,
marble, and chalk, and as well as in natural ores like calcite, dolomite, and Iceland spar. Literature
search did not show any previous research on optimal production of quicklime from eggshell.
Aim and Objectives of the reseach
To applies response optimization to production of quicklime from eggshell using design of
experiment.
The objectives are:
to determine the settings of calcination temperature, time and particle size that will
maximize quicklime yield during quicklime production from eggshell in a laboratory kiln.
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to determine the settings of calcination temperature, time and particle size that will
maximize quicklime reactivity during quicklime production from eggshell in a laboratory
kiln.
Materials and methods
The major equipment used are briefly described, glasses ware such as measuring cylinder,
thermometer, thermocouple, beaker, mortar and pestle. Tyler mesh, Desiccators, Tong, crucible and
spatula were used for this study
Sample Collection
Eggshell used in this study was collected from dumpsite of Kaduna South metropolis located in
Kaduna state, Nigeria. The sample was carefully selected to avoid difference in characteristics.
1. Sample Preparation
Prior to the experiment, the eggshell sample collected was washed clean and dried to remove external
impurities i.e. silica. The eggshell sample was crushed using jaw crusher. The sample was collected and
grinded using Ball Mill grinding machine. The sample was collected and sieved using mechanical agitator
with different Tyler mesh arranged to obtain a particle sizes of 450microns.
REULT AND DISCUSSION
Response Surface Methodology Results
The RSM was implemented using Design Expert release 9.0. Two models were developed to
correlate the effect of the output response factors (quicklime yield and reactivity). The model result show
how the calcination process parameters (temperature, time and particle size) can be used to maximize the
output responses. The model result shows how the calcination process controlled parameters can be used
to maximize the calcination output responses (quicklime yield and reactivity) shown in table 1. Therefore,
the objective of the model is to determine the calcination time, temperature and particle size settings that
will maximize quicklime yield and reactivity.The quicklime yield empirical equations for the selected
model were obtained.
Table 1: Response Surface Methodology Experimental Run and Results of Quicklime Yield.
Exp
Run
Calcination
Temperature (oC)
Calcination
Time (min)
Eggshell
Particle Size
(mm) Quicklime
Yield [(%)]
Reactivity oC/s
1 890 210 0.3 87.4479 0.42444
2 980 210 0.575 91.6492 0.26444
3 980 165 0.3 95.2268 0.29111
4 890 165 0.575 90.83541 0.4200
5 890 165 0.575 89.1377 0.4200
6 890 210 0.85 94.2021 0.46222
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7 890 120 0.3 87.5556 0.415556
8 800 120 0.575 39.5429 0.108889
9 890 165 0.575 90.8728 0.415556
10 890 165 0.575 91.2621 0.42
11 800 165 0.3 42.6701 0.13111
12 980 165 0.85 93.0412 0.3
13 980 120 0.575 82.6721 0.29111
14 800 210 0.575 40.5121 0.197778
15 890 165 0.575 89.0268 0.422222
16 890 120 0.85 85.3757 0.402222
17 800 165 0.85 46.1234 0.193333
Response surface modelling of quicklime yield with respect to calcination process parameters
The results of the mean and variance models correlating calcination temperature and time to quicklime
yield and reactivity are presented in this section shown in table 2. Also presented are the results of model
selection and model terms significance. The mean and variance models developed provide the basis for
optimization to determine the settings of calcination temperature and time that will maximize quicklime
yield and reactivity.
Table 2: Sequential Model Sum of Squares (SMSS) Analysis for Quicklime Yield Model
Source
Sum of
Squares
Df
Mean
Square
F-
Value
p-
value Prob>
F
Mean vs Total
105175
.33 1
105175
.33 Linear vs
Mean
4739.7
5 3
1579.9
2 8.62
0.002
1
2FI vs Linear 43.94 3 14.65 0.06
0.978
4 Quadratic vs
2FI
2328.7
6 3 776.25
576.
13
<
0.0001
Sugges
ted
Cubic vs
Quadratic 4.95 3 1.65 1.47
0.349
4
Aliase
d
Residual 4.49 4 1.12
Total
112297
.21
1
7
6605.7
2
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Table 3: Lack of Fit Test for Quicklime Yield Model
Source p-value Prob> F Linear < 0.0001 2FI < 0.0001 Quadratic 0.3494 Suggested
Cubic Aliased
Pure Error
Table 4: Model Summary Statistics for Quicklime Yield Model
Sour
ce
Standard
Deviation R2
Adjust
ed R2
Predict
ed R2
PRES
S
Line
ar 13.5366
0.6
655 0.5883 0.3911
4336.
5243
2FI 15.2912
0.6
717 0.4747
-
0.3075
9311.
5157 Quad
ratic 1.1608
0.9
987 0.9970 0.9879
86.14
08
Sugg
ested
Cubi
c 1.0590
0.9
994 0.9975 +
Alias
ed
Quicklime yield model
The experimental data of Table 5. was used to obtain the second order empirical model coefficients
of Equations 1. The empirical model equations for the quicklime yield as a function of one noise variable
(eggshell particle size) and two controllable process variables (calcination temperature and calcination
time) was developed using experimental data of Table 5 and are represented as Equations 1 below. The
quadratic equation model terms excluding insignificant ones as shown in Table 6.
Table 5: Model Coefficient Table for Quicklime Yield Response Surface Reduced Quadratic
Model
Factor
Coefficient
Estimate
Intercept 90.2270
A-Temperature 24.2176
B-Time 2.3331
C-Particle Size 0.7303
AB 2.0020
AC -1.4097
BC 2.2335
A^2 -23.0064
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B^2 -3.6265
C^2 2.0448
The coded quicklime yield empirical model is shown in Equation 1.
4.1
.
Table 6: Analysis of Variance (ANOVA) Table for Quicklime Yield Response Surface Reduced
Quadratic Model
Source
p-value
Prob> F Model < 0.0001 Significant
A-Temperature < 0.0001 B-Time 0.0007 C-Particle Size 0.1184 AB 0.0107 AC 0.0455 BC 0.0063 A^2 < 0.0001 B^2 0.0004 C^2 0.0086 Residual Lack of Fit 0.3494 not significant
Pure Error Cor Total
Effects of process variables on quicklime yield using the optimized reactivity mean model
Figures 1 and 2 show the contour and surface plot of the quicklime yield model respectively. The
experimental levels of each factor and the relationship between them was graphically represented using
contour and response surface plots.The plot shows the effect of varying two process parameters while
holding the particle size at zero point. Figures 1 and 2 show influence of quadratic function of
calcination temperature and significant influence of calcination time on quicklime yield while neglecting
the particle size and keeping it constant at zero. The plots show that increased quicklime yield was
observed with increasing calcination time and temperature values. Figures 1 and 2 show increased
quicklime yield was observed with increasing calcination temperature and time values.
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Figure 1. Model Contour Plot Showing the Effect of Calcination Temperature and Time on
Quicklime yield for Response Optimization of Quicklime Yield Model.
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Figure 2. Model Response Surface Plot Showing the Effect of Calcination Temperature and Time
on Quicklime Yield for Response Optimization of Quicklime Yield Model.
Response Surface Modelling of Quicklime Reactivity with Respect to Calcination Process
Parameters
The results of the mean and variance models correlating calcination temperature and time to quicklime
reactivity are presented in this section. Also presented are the results of model selection and model terms
significance. The mean and variance models developed provide the basis for optimization to determine
the settings of calcination temperature and time that will maximize quicklime reactivity.
Table 7: Sequential Model Sum of Squares (SMSS) Analysis for Quicklime Reactivity Model
Source p-value Prob> F Model < 0.0001 Significant
A-Temperature < 0.0001 B-Time 0.0003 C-Particle Size 0.0019 AB < 0.0001 AC 0.0065 BC 0.0081 A^2 < 0.0001 B^2 0.3523 C^2 0.0222 Residual Lack of Fit 0.0088 Not significant
Table 8: Lack of Fit Test for Quicklime Reactivity Model
Source p-value Prob> F Linear < 0.0001
2FI < 0.0001 Quadratic 0.0088 Suggested
Cubic Aliased
Pure Error
Table 9: Model Summary Statistics for Quicklime Reactivity Model
Sour
ce
Standard
Deviation R2
Adjust
ed R2
Predict
ed R2
PRE
SS
Linear 0.116132
0.17
2369
-
0.01862
-
0.54252
0.32
6767
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2FI 0.130623
0.19
4568
-
0.28869
-
2.3486
0.70
9367 Quad
ratic 0.006979
0.99
839
0.9963
21
0.9758
6
0.00
5114
Sugg
ested
Cubic 0.002434
0.99
9888
0.9995
53 +
Alias
ed
Quicklime reactivity model
The experimental data of Table 10 was used to obtain the second order empirical model coefficients
of Equations 2. The empirical model equations for the quicklime reactivity as a function of one noise
variable (eggshell particle size) and two controllable process variables (calcination temperature and
calcination time) was developed using experimental data of Table 10 and are represented as Equations 2
below. The quadratic equation model terms excluding insignificant ones as shown in Table 11.
Table 10: Model Coefficient Table for Quicklime Reactivity Response Surface Reduced Quadratic
Model
Factor Coefficient Estimate
Intercept 0.4195556
A-Temperature 0.064444375
B-Time 0.01638875
C-Particle Size 0.011944375
AB -0.028889
AC -0.01333325
BC 0.012778
A^2 -0.200611175
B^2 -0.003388925
C^2 0.009944325
The coded quicklime reactivity empirical model is shown in Equation 2.
Table 11: Analysis of Variance (ANOVA) Table for Quicklime Reactivity Response Surface
Reduced Quadratic Model
Source
p-value
Prob> F Model < 0.0001 Significant
A-Temperature < 0.0001 B-Time 0.0003 C-Particle Size 0.0019 AB < 0.0001 AC 0.0065 BC 0.0081 A^2 < 0.0001
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B^2 0.3523 C^2 0.0222 Residual Lack of Fit 0.0088 not significant
Pure Error < 0.0001 Significant
Cor Total < 0.0001
Effects of process variables on quicklime reactivity using the optimized reactivity mean model
Figures 3 and 4 show the contour and surface plot of the quicklime reactivity model respectively. The
experimental levels of each factor and the relationship between them was graphically represented using
contour and response surface plots.The plot shows the effect of varying two process parameters while
holding the particle size at zero point.
Figure 3. Model Contour Plot Showing the Effect of Calcination Temperature and Time on
Quicklime reactivity for Response Optimization of Quicklime Reactivity Model.
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Figure 4. Model Response Surface Plot Showing the Effect of Calcination Temperature and Time
on Quicklime Reactivity for Response Optimization of Quicklime Reactivity Model.
CONCLUSION AND RECOMMENDATIONS
Conclusion
This study has demonstrated the application of single response optimization in seeking optimum
conditions for calcination experiments. Differently maximization of quicklime yield and reactivity was
investigated using single response optimization.
The empirical models developed were presented as contours and surface plots in order to explore the
model region of operability and have better understanding of the process.
The conclusions drawn from this study are summarized below.
• Calcination temperature of 945.91 oC, calcination time of 180.82 min and particle size of 0.84 mm
produced optimum quicklime yield of 99.6608% for single response optimization of quicklime
production from thermal decomposition of eggshell.
• Calcination temperature of 895.03 oC, calcination time of 210 min and particle size of 0.84 mm
produced optimum quicklime reactivityof 0.467835 oC/s for single response optimization of
quicklime production from thermal decomposition of eggshell.
Recommendations
This study has applied single response optimization to production of quicklime from eggshell in a
laboratory kiln. Through this study, it has been found that there are some areas that would benefit from
additional study. These areas include:
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• Application of multiple response optimization to production of quicklime from eggshell in a pilot
plant rotary kiln should be investigated.
• There are different categories eggs depending on the bird types and sample from each bird should
be used as the uncontrolled factor in the multiple response surface models.
REFERENCES
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Akande, H. F. (2015). Multiple Response Optimization of Quicklime Production from Limestone Using
Design of Experiment. Minna: PhD Thesis Federal University of Technology, Minna, Nigeria
Antonia, M., Bakolas, A., & Aggelakopoulou, E. (2000). The Effects Of Limestone Characteristics and
Calcination Temperature to The Reactivity of the Quicklime. Cement and Concrete Research, 3-
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Arias. J.L, Fink, D.J., Xiao, S. Q., Heuer, A.H., Caplan, A.I. (1993) Biomineralisation of eggshells: cell-
mediated acellular compartments of mineralised extracellular matrix. lnt.Rev.Cytol. Vol. 145: 217-
250 .
Baziotis, I., Leontakianakos, G., Ekonomou, G., Delagrammatikas, G., Galbenis, C. T., & Tsimas, S.
(2010). A Case Study of Different Limestones during Quicklime and Slaked-Lime Production.
Geological Society of Greece, 3(4), 2-5.
Bello, N. M. (2015). Calcination Kinetics of Okpella Limestone from Thermogravimetric Data for Limes
Production.Minna: Unpublished MEng Thesis Federal University of Technology, Minna, Nigeria.
Bhattacharya, T.K., Ghosh, A. & Das, S.K. (2001). Densification of Reactive Lime from Limestone.
CeramicsInternational, 27(3), 455-459.
Borgwardt, R. H. (1985). Calcination Kinetics and Surface Area of Dispersed Limestone Particles. AIChE
Journal, 31 (1), 103-111.
Borkowski, J. J., & Lucas, J. M. (1997). Designs of Mixed Resolution for Process Robustness Studies.
Technometrics, 39(4), 63-70.
Borror, C. M., & Montgomery, D. C. (2000). Mixed Resolution Designs as Alternatives to Taguchi
Inner/Outer Array Designs for Robust Design Problems. Quality and Reliability Engineering
International, 16(3), 117-127.
Box, G.E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of
the Royal Statistical Society, Series B,13(1), 1–45.
Boynton, R. S. (1980). Chemistry and Technology of Lime and Limestone. New York: John Wiley& Sons
Publishing, USA.
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Dogu, G. & Mine. (2002). Calcination Kinetics of High Purity Limestones. Chemical Engineering Journal,
5, 1-2.
Foraminifera (2012). Hydrated Lime Production from Limestone in Nigeria How Viable. Retrieved from
http://www.foramfera.com/index.php/investment-opportunities-in-nigeria/item/420.
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FACE RECOGNITION WITH SYMMETRICAL FACE TRAINING SAMPLES
BASED ON LOCAL BINARY PATTERNS AND GAUSSIAN LOW-PASS FILTER
Saad Omran Elhashmi Allagwail
Department of Electrical & Computer Engineering
Osman Serdar Gedik
Department of Electrical & Computer Engineering
University of Ankara Yildirim Beyazit
Ankara, Turkey
Abstract— In practical face recognition applications, the
human face can have a limited number of training images.
However, it is known that in general an increasing number of
training images also increases the performance of face
recognition system. In this case, a new set of training samples
can be generated from the original samples using the symmetry
property of the face. Although many face recognition methods
have been proposed, a robust face recognition system is still a
challenging task. In this paper, recognition performance is
improved by using the symmetry property of the face.
Moreover, the effects of illumination and pose variations are
reduced. A Local Binary Pattern based on Gaussian Low Pass
Filter, which is a new approach for face recognition using
symmetry, is presented. The method has three main stages,
Preprocessing, Feature Extraction and Classification. The
Gaussian Low Pass Filter is used for preprocessing. The Local
Binary Pattern is used for feature extraction, and the Euclidean
Distance is used for classification. The proposed method is
implemented and evaluated using the ORL dataset. This study
also examines the importance of the preprocessing stage in a
face recognition system. The experimental results show that the
proposed method has higher recognition rates than the methods
in the literature.
Keywords— Face recognition, Symmetry, Gaussian Low Pass
Filter, Local Binary Pattern
I. INTRODUCTION
Robust and accurate face recognition (FR) is
one of the most important problems in computer
vision applications. In the literature, there are
several methods used for FR, including holistic,
local and hybrid methods [1] [2]. On the other
hand, recent research has revealed that a symmetry-
based dataset for FR is a useful method to solve the
FR problem; thus, it is possible to realize FR using
the symmetry property of the face [3].
In this paper, a new method is proposed to
solve two main problems in FR that are still open.
First is the limited number of face training samples,
and second, the variations in poses and facial
expressions in addition to lighting conditions. The
proposed method uses the symmetry property of
the face to reduce the effect of those two problems.
In this paper, the Local Binary Pattern (LBP)
is used for feature extraction since this method
perform well for texture feature extraction that can
be used for FR [4] . The images of the face are
enhanced before extracting their features. This
enhancement operation is accomplished by a
preprocessing step using well-known techniques,
namely the Gaussian low-pass filter (GLPF) [5].
The proposed method is analyzed using a
benchmark facial dataset, namely the ORL [6].
There are many studies related to FR, such as
the authors of [7] presenting a robust method for
FR using sparse representation (SR). Although the
results are good, the method has a high
computational cost. FR with LBP is proposed by
Ahonen et al. [8] in which the algorithm is not
sensitive to light and accordingly this point is
considered the robustness of their study. The
authors of [9] use discriminative dictionary
learning and SR along with the Gabor filter bank
and the LBP for feature extraction and reduce the
influences of illumination changes. One of the
milestones for FR under variations can be stated as
the Fisherfaces and Eigenfaces [10] technique,
which is insensitive to illumination variations. Pose
variation is also studied in [11] by using view based
eigenfaces. The authors of [12] combine the
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generalized photometric stereo and eigen light field
concept to generate a generic method which is also
insensitive to illumination changes. The authors of
[13] present a method to arrange the variation of
poses and illumination, including shadows and
reflections; however, the computational cost in
their method decreases the efficiency of the
recognition system since they generate 3D models
from 2D images. Shashua et al. propose a method
in [14] based on the illumination invariant
signature image, since they show that it is possible,
even in bad conditions, to use a small dataset to
generate more images with varying illumination.
However, their method is not appropriate when the
images include some shadows. Georghiades et al.
show that in [15], when the pose is fixed, all
possibilities of illumination in the image space
have a convex cone. In addition, they use their
method to reconstruct the shape and albedo of the
face by training the system using only a few images
with different directions of light. The authors of
[16] introduce a modified asymmetric model to
overcome pose variations. However, the
performance of their method is affected by the
discretization resolution of the pose space.
Fig. 1- Face Recognition System
One of the most important properties in
nature, and particularly in human faces, is the
symmetry property. Many authors have noticed its
role [17]. It has been observed that the human face
is almost symmetrical, so the use of this property
for face detection (FD) and FR has been previously
studied, as in [18], where the authors develop a
technique to compute bilateral symmetry axis
automatically and use it in their research. Zhao and
Chellappa in [19] use the symmetry of the face to
reduce the effects of illumination in FD. It has been
shown that symmetry is also useful for extracting
the facial profile in facial recognition techniques,
as described in [20]. The authors of [21]
successfully apply the symmetry property to FD,
and they conclude that the expressions of the face
are also symmetrical. The authors of [3] improve
Classific
ation
Euclidean
Distance
Gaussian Low-Pass
filter
Prepr
ocessing
Local Binary Pattern Feature Extraction
Test Image
Original Dataset
Recognition
Result
Gene
rating more
images
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the rate of FR recognition accuracy by using the
symmetry property of the face to design a
collaborative representation-based classification
method (SCRC).
II. METHOD STAGES
A) Preprocessing
The Gaussian Low-Pass filter (GLPF) is used
for the preprocessing stage. The GLPF is the result
of smoothing an image using a Gaussian function.
It is used to filter images and reduce image noise
[22].
Fig. 2- Gaussian Low-Pass Filter
This unit allows only the lower-frequency
components of the image to pass [5]. The equation
of a Gaussian function in two dimensions is given
by the following formula:
𝐺(𝑥, 𝑦) =1
2𝜋𝜎2𝑒
−𝑥2+𝑦2
2𝜎2 (1)
Where, x and y refers to a two-dimensional
coordinate, and 𝜎 represents the standard deviation
of the Gaussian distribution in the spatial domain.
B) Feature Extraction
The Local Binary Pattern (LBP) method is
used for the feature extraction stage since it is one
of the most widely used methods to analyze and
model texture [4]. It can be basically described as
a 3 × 3 square operator. In this square, the
8-neighborhood pixels are compared with the one
in the center. If the pixel values of the neighbors
are greater than or equal to the pixel value at the
center, they are replaced by 1. If not, then their
values are replaced by 0. Then the new binary
values of the neighbors are concatenated to
produce one decimal value that is considered a new
value for the pixel in the center. The window is
passed to the next pixel and the same operation is
repeated. These new decimal values represent the
histogram of the input texture. Equation (2)
describes the algorithm of the LBP operation:
𝐿𝐵𝑃𝑃,𝑅(𝑥,𝑦) = ∑ 𝑠(𝑔𝑝 − 𝑔𝑐)2𝑃𝑃−1𝑃=0 (2)
Where 𝑠 is the sign function, 𝑔𝑝 represents the gray level
value of the neighboring pixels, and 𝑔𝑐 represents the gray
level value of the central pixels. 2𝑃is required to produce
decimal values.
The traditional LBP [8] analyzes the texture of the image
and thresholds a 3 × 3 square neighborhood with the center
pixel value. It only uses the sign information to produce the
LBP, as illustrated in Fig. 3 [4]
Fig. 3- LBP architecture
In a newer implementation, the LBP operation is upgraded
to deal with any size of neighborhood by replacing the square
with a circle [23]. This can be described by (P, R), where P is
the number of interested pixels on the circle, while R is the
radius of the used circle. Fig. 4 illustrates an (8, 2)
neighborhood. Additionally, there are a number of other
modifications to LBP [4].
Fig. 4- Circular (8, 2) neighborhood
The term 𝐿𝐵𝑃𝑃,𝑅𝑢2
is used to describe the LBP operation,
where u2 denotes the use of a uniform pattern. The resulting
histogram results in the necessary information distributed in
the image, such as edges, corners, uniform areas, etc. The
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effective operation must take care of the spatial information in
the image during the representation. One strategy to
accomplish this is to partition the image into a number of small
areas 𝑅0, 𝑅1, … , 𝑅𝑚−1 [8], where 𝑚 is the number of areas. If
the size of the histogram is B, then the length of the feature
vector is mB. It is obvious from this relation that the number
of areas m determines the length of the feature vector, which
means selecting small areas resulting in long feature vectors
leading to extreme use of memory and slow classification
processing. Selecting large areas causes loss of spatial
information. An example of a preprocessed face image
partitioned into 36 windows and the resulting face feature
histogram are illustrated in Fig. 5 [24].
Fig. 5- Example of a preprocessed face image partitioned into 36
windows and a feature histogram
C) Classification
The Euclidean Minimum Distance classifier is used for the
classification stage since it is considered to be one of the most
popular classifiers that can be easily designed and widely
used [25]. In general, it is used to examine the similarities
between objects.
The Euclidean distance d between two points i and j, where
i = (i1, i2,..., in) and j = (j1, j2,..., jn), in Cartesian coordinates, is
the length of the straightest line between them. This distance
is given by the formula:
𝑑(𝑖, 𝑗) = √(𝑖1 − 𝑗1)2 + (𝑖2 − 𝑗2)2 + ⋯ + (𝑖𝑛 − 𝑗𝑛)2 (3)
Therefore, if the two points are close to each other, then
the value of d is small; otherwise, it is large. The Euclidean
vector is the location of a point in a Euclidean n-space, where
the length of this vector is measured by the formula of the
Euclidean norm, given by:
‖𝐼‖ = √𝑖12 + 𝑖2
2+. . . +𝑖𝑛2 (4)
This tool is used to test how similar one object (face) is to
another by testing the similarities between their respective
feature vectors.
D) Dataset
The ORL (Olivetti Research Laboratory) is a well-known
face dataset that is used to test FR algorithms. It has 400
images of 40 distinct persons, 10 images for each person. The
dataset is variational in many aspects. First, the images were
taken at different times during the lives of the people. Second,
the images include different variations and different facial
expressions, such as closed or open eyes. Some of the people
are smiling, others are not. In addition, there are a number of
people wearing spectacles while others are not wearing
spectacles. Furthermore, a number of the images include up to
twenty degrees of tilting and rotation of the face [3]. The size
of each image is 112x92 pixel. A number of face images from
the ORL dataset are illustrated in Fig. 6.
Fig. 6- Sample images from the ORL Dataset
III. EXPEREMENTS AND RESULTS
This section shows some results obtained using
MATLAB. The experiments were implemented on images
from the ORL dataset.
The FR system consists of three stages. The first stage is
the Preprocessing Stage, in which the GLPF is used. The
second stage is the Feature Extraction Stage, where the LBP
is examined. In the final stage (the Classification Stage), the
Euclidean distance is used as a classifier.
The procedure is carried out and tested, first, using only
the Original Training Samples (OTS) and the second time
using the Original with Symmetrical Training Samples
(OSTS). Also, the experiment is implemented one time
without a preprocessing stage and the second time using a
preprocessing stage. The performance is compared in the four
experiments along with the performance of SCRC [3].
A) Generating new images
In order to increase the size of the training data, new
training images are generated using the symmetry property of
the face, since those images reflect some appearance of the
face that is not shown by the original images as illustrated in
Fig. 7.
Face features
histogram
LBP Transformation
g) f) e) d) c) b) a)
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Fig. 7- a) Original image, b) left side, c) right side, d) mirror of left side, e) mirror of right side, f) integrating left side with mirror, g) integrating right
side with mirror
B) Experiment on original ORL face dataset
In the experiment, one, two up to nine face images of each
person with size 112x92 were taken respectively as the
training samples and the rest of images were taken as testing
samples. The features of the training and testing images are
extracted using the LBP method. Each image has one feature
vector, 𝑓 = [𝑓1, 𝑓2 ⋯ 𝑓𝑚], where 𝑚 is the number of one image
features.
The feature vector of the test image is compared with the
feature vectors of the training images using the Euclidean
distance classifier. The person who has a training image
feature vector with a minimum Euclidean distance is
considered as the recognition result.
The recognition rate is calculated as the average of the
recognition results for each number of training samples.
C) Experiment on symmetrical ORL face dataset
The procedure is repeated as in B, the only difference is
that the original and the generated symmetrical images are
used together to train the system, the results show that the use
of symmetrical images improves the recognition rate from
92.5% to 95% for 9-training samples as shown in Table 1.
D) Experiment using a preprocessing stage
The procedure is repeated as in C and D but in this case
using the GLPF as a preprocessing stage, the GLPF being used
with a standard deviation of σ = 1 and a window size of
5 pixels. With 9-training images, the results show that adding
GLPF as a preprocessing stage improves the recognition rate
from 92.5% to 95% for OTS and from 95% to 100% for
OSTS. The results of the experiments for training samples
6,7,8 and 9 are summarized in Table 1.
TABLE I. The recognition rates of different methods on ORL dataset
P
reprocessi
ng
Method
F
eature
Extract
ion
Metho
d
No. of Training Images
6 7 8 9
Recognition Rate %
N
o
L
BP
OTS
90
92.5
92.5
92.5
O
STS
9
5
9
5
9
2.5
9
5
G
LPF
L
BP
OTS
92.5
97.5
95
95
O
STS
9
5
9
7.5
1
00
1
00
N
o
S
CRC
O
STS
9
5
9
6
9
6
9
5
Fig. 8 shows the recognition rates versus the variation of
training sample size for OSTS. From the figure, we can see
the proposed method (using original and symmetrical images
for training along with a preprocessing stage) outperforms
other four methods.
Fig. 8- Recognition rates using LBP, proposed method and SCRC
methods versus size of training set
IV. CONCLUSION
This paper presents an effective method to
overcome the restricted number of the training sets
using the symmetry property of the face. The use
of this property also reduces the effect of
illumination and pose variations. First, a new set of
face images is generated using the left and right
halves of each face. Second, the original and
generated samples are preprocessed using the
GLPF; then the features of these samples are
extracted using LBP method. Finally, the
Euclidean classifier is used to obtain the results of
the recognition. This paper also shows that adding
a preprocessing stage to the recognition system has
a significant impact on the improvement of
recognition rates since it increases the recognition
rate from 92.5% to 100% (for 8-training images)
and from 95% to 100% (for 9-training images).
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fisherfaces: Recognition using class specific linear projection," Pattern
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NEW APPROACHES FOR BREAST CANCER NUCLEI DETECTION IN
HISTOLOGICAL IMAGES
Faozia Ali ALSARORI*, Hilal Kaya 2* *2 Department of Computer Engineering, Ankara Yıldırım Beyazıt University; Ankara, Turkey.
Abstract
Cancer is one of the leading causes of death worldwide and is known to infect a specific member.
If it is not detected early, its spread in cells may be difficult to control and treat. Breast cancer is the
sixth most common cause of death among other cancers.
The ability to automatically detect breast cancer nuclei in microscopy images is of significant
interest to a wide range of biomedical research and clinical practices. Cell detection methods have
evolved from employing hand-crafted features to deep learning-based techniques. The work proposed
in this paper is divided into two stages to provide a concept system for detection. The first stage is
using Convolutional Neural Network (CNN) followed by Fuzzy C-Mean and then combine these two
methods to reach a satisfactory performance. Based on a comprehensive study of the most studies done
so far, using such technique hasn’t been used for breast cancer nuclei detection. Furthermore, this
study has been carried out on histological dataset contains100 microscopic slides of H & E stained
samples of breast biopsy. The proposed method registered the shortest median distance which is 2.14
comparing with the latest studies.
Keywords: Convolutional Neural Network (CNN), Fuzzy C-Mean, Cell Detection.
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DESIGN OF IOT-BASED VERTICAL SYSTEM AQUAPONIC WITH
FUZZY LOGIC METHOD
Gerhard Imanuel Simare Mare, Haris Rachmat, Denny Sukma Eka Atmaja
Telkom University, Telkom University, Telkom University
Abstract— There are opportunities in the use of urban farming because the level of urban farming
community participation in Indonesia is 10%. One of the uses of urban farming is aquaponic. A system is
needed that makes it easy for humans to monitor and control the variables of water pH, EC water and water
level on aquaponic. Aquaponic is an integration of aquaculture and hydroponics. The application of the
Internet of Things (IoT) requires a connection between everyday objects and devices using networks. Fuzzy
logic method functions to evaluate water quality before automatic system with after automatic system. This
research aims to design a monitoring system for water pH, EC, water temperature, water level and
controlling water pH, water level and IoT-based fish feeding on an aquaponic vertical system, designing a
system to determine water quality using the fuzzy logic method. Vertical aquaponic system testing by
planting plants in the vertical system aquaponic and measured for the first 15 days obtained average growth
of plant height of 0.361 cm compared to plant growth in conventional systems to get average growth of
plant height of 0.282 cm. Based on data processing using fuzzy logic Sugeno obtained water quality before
the automatic system is bad and the water quality after the automatic system is good.
Index Terms—fuzzy logic method, Internet of Things (IoT), Raspberry Pi 3 B, Vertical system aquaponic
I. INTRODUCTION
Industrial revolution 4.0 is the integration and relationship between physical and digital in the
industrial world that depends on, digitalization and integration of value chains horizontally and
vertically, digitization of products and services and introduction of innovation business models
(Gilchrist, 2016). According to Gilchrist (2018) the industrial revolution 4.0 can be applied to
several sectors namely, the health sector, logistics, manufacturing and agriculture. From each
sector in the utilization of the industrial revolution the health sector has the highest readiness level
of 30%, followed by the logistics sector by 25%, manufacturing by 25%, and finally is agriculture
and other sectors by 20%. From these data the agricultural sector still has a low level of readiness
compared to other sectors so that there are still opportunities to develop the utilization of the
industrial revolution 4.0 in the agricultural sector. The development of agriculture and animal
husbandry that uses technology is called smart farming with plants, animals and soil media
receiving the proper care needed (Huawei, 2017). The characteristic of smart farming is treating
plants and animals as accurately as possible which requires several core elements, such as
automatic detection to determine variations in the behavior of soils, plants and animals. This can
be achieved by sensors. In addition to detecting automatically, smart farming also requires a
decision system, and a model that will translate measured variances into actions taken by
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considering the environment accurately. The use of some of these core elements requires
customized technology (De Wilde, 2016).
According to research conducted by Hallet et al (2017) it was found that starting from 10% the
level of community participation in urban farming activities in Indonesia and nearly 70% in
Vietnam. In the city of Bandung, urban farming is characterized by the existence of the Bandung
Berkebun community (Vidyana and Murad, 2016). The Bandung Berkebun community was
established in 2011, with activities in the form of gardening in some abandoned land in the city of
Bandung, increased by some collaboration with schools and other institutions (Vidyana and
Murad, 2016). Based on the application of urban farming in the city of Bnadung and data that the
level of participation of Indonesian people in urban farming by 10% can be concluded that there
are opportunities to develop urban farming in Indonesia. In addition, on average 15 to 20 percent
of world food production comes from urban farming (Hallet et al, 2017).
One of the uses of smart farming is aquaponic (Datta, 2018). As for the understanding of
aquaponic is the integration of aquaculture and hydroponics. This is considered to be sustainable
agriculture because there is a closed water circulation with fish droppings that will be useful for
plants as nutrients (Goddek et al, 2015). The aquaponic mechanism is based on the principle of
hydroponics in which chemical salts dissolved in water are a source of nutrition for plants grown
on growing media that grow above water. In aquaponic systems, organic aquaculture enriched
wastewater is a source of nutrients released and available to plants with a number of processes
carried out by a large number of microorganisms that live in water (Datta, 2018). In the application
of aquaponic, smart farming can be applied because there are several variables that must be
monitored and controlled regularly, namely, pH and EC levels in pond water (Nayyar and Vikram,
2016).
Research on the application of smart farming in aquaponic conducted by Kyaw, and Ng (2017)
that is, designed aquaponic can be monitored remotely using the web or mobile app. Components
used in the form of sensors, temperature sensors, pH sensors, ultrasonic sensors and light sensors
and other components used are fish feeders. This study uses Arduino as a microcontroller (Kyaw
and Ng, 2017). In addition, the research uses the goodness-of-fit-measure method to process the
results of the design. In this study the results in the form of plant growth data and fish weight were
measured by the relationship between the two. But in that study there was no monitoring and
controlling of water levels. Although the circulation of water in aquaponic can minimize the
reduction in water. But the water in aquaponic can still be reduced due to evaporation or
evaporation, and plant transpiration. The water level must be maintained (Goddek, 2015).
Whereas the research conducted by Shout and Scott (2017) designed the aquaponic control
system. In this study using the fuzzy logic method used to evaluate inputs and provide the right
output. In this study the use of fuzzy logic was chosen because the variables used namely pH and
water temperature have blurred values. In this study the variables monitored were water
temperature, and water pH. The research still has not monitored the variables needed for an
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aquaponic namely pH, EC, and water level and the variables that must be controlled are pH and
water level (Datta, 2018). Therefore we need a smart farming system in the form of an integrated
aquaponic vertical system IoT. In this study, aquaponics will be controlled and monitored for pH
and water level, EC (Electrical Conductivity) and feeding, and will be monitored for pH and EC.
As for the fuzzy logic method in this study to issue an output in the form of an assessment based
on existing variables, namely pH and EC.
II. LITERATURE REVIEW
A. Vertical System Aquaponic
The aquaponic mechanism is based on the principle of hydroponics in which chemical salts
dissolved in water are a source of nutrition for plants grown on growing media that grow above
water.
In this system, the place for the tank where the fish live and the reservoir is placed at the bottom.
Then the vegetables are planted in a hole in a pipe arranged vertically. Water that is rich in nutrients
is pulled to the top level with the help of a pump. Water is allowed to drip down through the roots
of plants. Because most systems are closed, little or no waste is produced, and there is no need for
fertilizer or pesticides. This is a system that will save water and storage space. Figure 2.1 is a
picture of the vertical aquaponic system.
Figure 2.1 Vertical System Aquaponic
B. Internet of Things (IoT)
The application of the Internet of Things (IoT) requires the connection between everyday objects
and devices with all types of networks such as corporate intranets, peer-to-peer networks and even
global internet networks (ITU, 2005). The application of the Internet of Things (IoT) depends on
technical innovations in a number of fields. First, in connecting everyday objects and devices to
the database and to the internet, it requires a system that is cost-effective and uncomplicated.
Second, data collection can have the ability to detect changes in the physical status of objects,
namely through sensor technology. Furthermore, progress in minaturation and nanotechnology
means that little things have the ability to interact and connect. The combination of all these
developments will form the Internet of Things (IoT) that connects objects through detection and
intelligence. The application of the Internet of Things (IoT) occurs in developments in several
fields, namely, smart materials, smart homes, smart vehicles, robots and smart
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C. Fuzzy Logic Sugeno
Mechanisms In general, in fuzzy logic systems there are four basic elements (Bede B, 2013),
namely:
1. Rule base (rule base), which contains linguistic rules that are sourced from existing
reference models in the Sugeno method using rules in the form of: IF x1 is A1 AND ...
AND xn is An THEN y = k
2. A decision making mechanism (inference engine), which demonstrates how experts make
decisions by applying knowledge;
3. Fuzzification process (fuzzyfication), which changes the firm quantity (crisp) to the
amount of fuzzy;Proses defuzzifikasi ( defuzzyfication ), yang mengubah besaran fuzzy
hasil dari inference engine , menjadi besaran tegas ( crisp )
Fuzzyfication. Input value is definite (crisp input) which is converted to fuzzy input form in the
form of linguistic value which is determined based on its membership function (Gottwald S, 1993).
Inference. The fuzzy rule is written as: IF antecendent THEN consequent. In a fuzzy rule-based
system, the inference process takes into account all the rules that have been made before. At this
stage there are two stages that must be carried out sequentially, namely, the impilcation process
and the composition process of rules (Gottwald S, 1993). According to Nguyen and Sugeno (1998),
the Sugeno method used in this process uses fuzzy set operations as in Table 2.1.
Table 2.1 Fuzzy set operations
OR (Union) AND
(intersection)
MAX 𝐌𝐚𝐱𝛍𝐀(𝐱), 𝛍𝐁(𝐱) MIN 𝐌𝐢𝐧𝛍𝐀(𝐱), 𝛍𝐁(𝐱)
After doing the implication process, the rule composition process will be carried out. According
to Nguyen and Sugeno (1998) the composition of the rules in the sugeno method uses the Max
function, the composition of the rules constitutes an overall conclusion by taking the maximum
membership level of each consequent function implication and combining all the conclusions of
each rule, so as to obtain a fuzzy solution area as follows:
μ(z) = maxμc(z), μc(z)
Deffuzification. At this stage the output value obtained is changed in the non-fuzzy form. The
Sugeno deffuzification method uses the center of single-ton method.
z∗ =∑ μ(z). z
∑ μ(z)
III. DATA PROCESSING
A. Design of Vertical System Aquaponic
In designing a vertical aquaponic system, the design is made first using software design. In the
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vertical aquaponic system, water is pumped from the fish pond at the bottom of the aquaponic to
the top through the inside of the pipe. After that the water will flow down from the pipe above.
The flow of water from above will flow down and hit the plants in the existing plant pots. Figure
3.1 is a vertical aquaponic system.
Figure 3.1 Vertical system aquaponic
B. Design of Automation System
At this stage the design of the automation system is carried out in the form of installing parts in the form of sensors,
ADS1115, water pumps, mini pumps, relays that lead to the output carried out, with Raspberry Pi. Figure 3.2 is a
schematic diagram.
Figure 3.2 Schematic diagram
According to Blinder (2009) block diagram is a picture of the whole system that is connected by an arrow line
consisting of input, process, and output parts. Figure 3.3 is a block diagram of an existing system
Relay
untuk
pH up
Relay
untuk
pH
down
Relay
untuk
pompa
air
Sensor
pH
Sensor
EC
ADS1115
Sensor
Ultrasonik
Raspberry Pi 3 B
DS18B20
(Sensor
Temperatur
e)
Relay
untuk
fish
feeder
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Figure 3.3 Block diagram
C. Design of Internet of Things System
In making the application in this study there are several features available to support the android
application that will be used. In the monitoring feature, the user can monitor the state of water
temperature, water pH, EC water and water level in aquaponic. In this feature there is a report
function that will send data on temperature, pH, EC and water level to the user's email every day.
In controlling feature, the user can monitor the existing output by looking at the indicators of each
output. In this feature the user can turn on the fish feeder. In the graphic data feature, users can see
the state of water temperature, water pH, EC and water level directly every minute, every 15
minutes and every hour. Figure 3.4 is a feature in the application
Figure 3.4 Application Feature
D. Fuzzy Logic
The membership function will be used at the fuzzyfication and deffuzzyfication stages. The
membership function functions to change the linguistic term on variables to quantitative. Figure
3.5 is a graph of the pH variable membership function.
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Figure 3.5 pH Membership Function graphic
The pH variable uses trapezoidal curves in acidic, normal and basic membership. This model
is used because the peak point value of each variable has a value of more than one particular range
of values. The EC variable uses trapezoidal curves in low, normal and high membership. This
model is used because the peak point value of each variable has a value of more than one particular
range of values. Figure 3.6 is a graph of the EC variable membership function.
Figure 3.6 EC Membership Function graphic
In the variable height of the water using the quadratic trapes at low and high membership.
This model is used because the peak point value of each variable has a value of more than one
particular range of values. Whereas the normal category uses a triangular curve because the peak
point value of the variable has a value of just one value. Figure 3.7 is a graph of the membership
function of the water level variable.
Figure 3.7 Water Level Membership Function graphic
At this stage, linguistic rules are derived from existing reference models in the Sugeno
method using rules in the form: IF x1 is A1 AND ... AND xn is An THEN y = k
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In this research there are 27 rules because there are 3 variables that become input variables.
In Table 3.3, the fuzzy rules are used . Table 3.3 fuzzy rules
Rules number
Input Output
pH EC Water
Level Water
quality
1 Acid Low Low Bad
2 Acid Low Normal Bad
3 Acid Low High Bad
4 Normal Low Low Bad
5 Normal Low Normal Bad
6 Normal Low High Bad
7 Base Low Low Bad
8 Base Low Normal Bad
9 Base Low High Bad
10 Acid Normal Low Bad
11 Acid Normal Normal Bad
12 Acid Normal High Bad
13 Normal Normal Low Bad
14 Normal Normal Normal Good
15 Normal Normal High Bad
16 Base Normal Low Bad
17 Base Normal Normal Bad
18 Base Normal High Bad
19 Acid High Low Bad
20 Acid High Normal Bad
21 Acid High High Bad
22 Normal High Low Bad
23 Normal High Normal Bad
24 Normal High High Bad
25 Base High Low Bad
26 Base High Normal Bad
27 Base High High Bad In the Sugeno output method that will be produced in the form of constants so for this study
there are two water quality categories namely Bad and Good. Whereas the assessment is as follows. Bad = 0 ≤ x ≤ 50
Good = 50 <x <100
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At this stage the calculation is done using the fuzzy method. Table 3.4 is the result of fuzzy
logic before the automatic system is applied. Table 3.5 shows the fuzzy results after the automatic
system was applied.
Table 3.4 result of fuzzy logic before the automatic system is applied
Sample Input Output
pH EC Water
level Defuzzyfication
result Category
1 8,55 2,81 14,97 0 Bad
2 8,58 2,76 15,06 0 Bad
3 8,56 2,82 15,07 0 Bad
4 8,51 2,79 14,96 0 Bad
5 8,50 2,82 15,06 0 Bad
6 8,47 2,82 14,92 0 Bad
7 8,49 2,81 14,95 0 Bad
8 8,46 2,85 14,96 0 Bad
9 8,50 2,76 14,96 0 Bad
10 8,49 2,79 14,91 0 Bad
11 8,47 2,83 14,97 0 Bad
Sample Input Output
pH EC Water
level Defuzzyfication
result Category
12 8,50 2,77 15,03 0 Bad
13 8,46 2,81 14,97 0 Bad
14 8,47 2,82 15,07 0 Bad
15 8,47 2,79 15,01 0 Bad
16 8,45 2,79 14,90 0 Bad
17 8,46 2,78 14,90 0 Bad
18 8,49 2,75 15,06 0 Bad
19 8,46 2,76 14,90 0 Bad
20 8,45 2,85 14,95 0 Bad
Average 0 Bad
Table 3.5 result of fuzzy logic after the automatic system is applied
Sample
Input Output
pH EC Water
level
Defuzzyfication
result
Category
1 6,90 2,84 18,19 81 Good
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2 6,95 2,82 17,82 82 Good
3 6,93 2,78 18,17 83 Good
4 6,93 2,81 18,04 96 Good
5 6,89 2,75 17,81 81 Good
6 6,91 2,8 18,07 93 Good
7 7,00 2,83 17,81 81 Good
8 6,99 2,79 17,82 82 Good
9 6,90 2,85 18,10 90 Good
10 6,96 2,81 18,14 86 Good
11 6,86 2,75 17,91 91 Good
12 6,87 2,81 18,12 88 Good
13 6,86 2,82 17,98 98 Good
14 6,99 2,78 18,20 80 Good
15 6,89 2,85 17,84 84 Good
16 6,90 2,78 17,93 93 Good
17 6,90 2,83 17,96 96 Good
18 6,91 2,83 17,88 88 Good
19 6,95 2,83 17,82 82 Good
20 6,96 2,82 17,99 99 Good
Average 87,7 Good
E. Calculation of Plant Growth
In processing plant growth data obtained from the average growth of 25 plants from conventional systems and vertical aquaponic systems in the first 15 days. After collecting the data, the processing of plant growth data for both systems is processed. Data processing of plant growth is done by calculating the average plant growth of both systems on each day. Table 3.6 is the data processing of the two systems.
Table 3.6 Data processing of plant growth
Day Conventional Vertical
system
1 0,5375 0,7
2 0,56 0,324
3 0,628 0,612
4 0,324 0,284
5 0,376 0,292
Day Conventional Vertical
system
6 0,136 0,332
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7 0,132 0,304
8 0,16 0,168
9 0,192 0,24
10 0,144 0,22
11 0,264 0,22
12 0,124 0,212
13 0,268 0,36
14 0,236 0,224
15 0,144 0,248
Average 0,282 0,316
IV. ANALYSIS
A. Analysis of Fuzzy Logic
According to Datta (2018), it was found that the variables affecting plant growth were pH,
and the level of water conductivity. As for the plants 'height in aquaponic can be reduced due to
the evaporation and absorption of water by plants so that the plants' height should be controlled to
avoid water that can run out (Datta, 2018). As for aquaponic EC levels are obtained from fish
droppings so that EC levels can be controlled from the amount of fish in the pond. In this study
the fuzzy logic method is used to evaluate inputs and provide the right output. In this study the use
of fuzzy logic was chosen because the variables used are pH and the level of water conductivity
has a blurred value. So to get the value of water quality in the form of constant use of the Sugeno
fuzzy logic method is considered appropriate because according to Nguyen and Sugeno (1998) the
Sugeno fuzzy logic method is a fuzzy inference method that gets the system output not in the form
of fuzzy sets, but in the form of constants.
After the Sugeno fuzzy logic process is done, the average result of defuzzyfication before
the automatic system is applied is Bad, because before the automatic system is applied, the monitor
system is carried out manually with a pH meter to measure pH, EC meter to measure the level of
water conductivity and a ruler to measure the height. water. As for controlling before the IoT
system there is no controlling system. Meanwhile, after the automatic system was applied the
average defuzzification was obtained Good because it was included in the Good range which was
50 <x ≤ 100, because after the IoT system was applied to monitor user variables, it could be
monitored through an android application. As for the controlling system after the automatic system
is applied to the pH when the pH is below the normal limit of 6.5, the pump will run the pH up
while for the pH level above the normal limit of 7.0, the pump will be turned down. As for
controlling the water level, when the water level is below the normal limit of 18 cm, it will turn on
the water pump. On the controlling system, the user can see the pump indicator that lights up
through the android application. From the two results the average value of water quality there is
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an increase in the value of water quality from 0 to 85 so that the application of the IoT system can
increase the value of water quality in ponds.
B. Analysis of Plant Growth
From processing data on plant height growth on both systems, namely conventional and
vertical aquaponic systems, the results obtained on conventional from the first 15 days amounted
to 0.2817 cm and plant height growth on vertical aquaponic systems amounted to 0.316 cm. The
data is obtained from 25 plants from the two planting systems. In conventional planting systems
plants are planted in polybags using planting media in the form of soil. As for the vertical system,
the aquaponic system is planted in a pot arranged vertically on a pipe with the planting medium
used is nutritious water.
Water nutrition in the vertical aquaponic system is obtained from fish sludge in the pond that
is flowed by a water pump. From the average plant growth of the two systems, the plant growth in
the vertical aquaponic system is better than the conventional system of 12.17%. In addition to
better plant growth in the vertical aquaponic system, it can save land because plants are planted in
potted plants and arranged vertically. Because it does not require a large amount of land to eat
vertical aquaponic systems suitable for agriculture in the city. In the vertical aquaponic system,
nutrients do not need to be added anymore because nutrients are obtained from fish droppings. If
the conventional system of providing nutrients needs to be given routinely in the form of fertilizer.
V. CONCLUSION
Based on the results of the IoT-based aquaponic vertical system design that is integrated between
Raspberry Pi 3 B and the Android application, the design system is already running with Good. Due to the
growth of plants in the first 15 days on the aquaponic vertical system get an average plant height of 0.316
cm gives a positive impact of plant height growth on conventional systems with an average plant height
growth of 0.282 cm. Besides testing the monitoring system of water pH variables, water health, water
temperature and water EC content in 10 times the overall trial experiment with Good running and
controlling system testing on water pH, water health and fish feeding in 10 times the overall trial trial
running with Good.
Determination of water quality with variables namely, water pH, EC water content, and water health
with a normal pH limit of 6.5-7, normal EC limit of 2.5 - 3 ms / cm and a normal limit of water height of
18 cm . Water quality data processing using the fuzzy logic method using Matlab R2018a software before
the automatic system is applied, after the automatic system is applied the fuzzy logic processing results are
in the range of 0 ≤ x ≤ 50 while for water quality after the automatic system is applied it gets the results of
fuzzy logic 50 processing <x ≤ 100. Therefore, the water quality after the automatic system is applied is
better than before the automatic system is applied.
REFERENCES
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Binder, M. D., Hirokawa, N. dan Windhorst, U. (2009) ‘Block Diagram’, in Encyclopedia
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ARCHITECTURE AND FUTURE PROSPECT OF 5G TECHNOLOGY
M. Sayrac1 M. Ari2 C. Taplamacioglu3 J. Rahebi4
1. Department of Physics, Cankiri Karatekin University, Cankiri, Turkey,
2. Electrical and Electronics Engineering Department, Cankiri Karatekin University, Cankiri, Turkey,
3. Electrical and Electronics Engineering Department, Gazi University, Ankara, Turkey, [email protected]
4. Electrical and Electronics Engineering Department, University of Turkish Aeronautical Association,
Ankara, Turkey, [email protected]
Abstract Fifth generation mobile communication technology that is freed of existing obstacles in the communication
system will be used in many countries after 2020. Researches are being conducted all over the world to develop new
technologies that have an important place for the widespread use of 5G technology. This new technological research
will bring solutions such as high speed, high capacity, spectral efficiency, energy efficiency to problems in data transfer
system. 5G technology offers the services in product engineering, documentation, electronic transactions, etc. The
main aim of 5G is to design seamless wireless communication world compared to earlier generations. This study
focuses the challenges, spectrum distribution and application areas of 5G technology. Also, it is mentioned about the
opportunities that will create usage areas of 5G technology in Turkey.
Keywords: Technology Route, 5G, 5G Spectrum, Wireless Communication, Data Transfer
I. INTRODUCTION
Generated and transmitted data worldwide are expected to be approximately zettabyte (1zettabay= 1021 bayt) size
after 2020. Currently used communication technologies would not support such large volumes of big data. For this
reason, next generation technology that can carry large amounts of data is required. This new technology is called the
5th generation mobile communication technology. Researches are being carried to prepare 5G wireless technology for
worldwide use. 5G technology is expected to be used commercially worldwide in the near future. There is also an
increasing demand from industries such as health and education sectors to take advantage of 5G technology. The
advantagement of 5G technology over current communication technologies can be given as follows [1].
5G technology provides:
• Thousands times higher data rate than 4G,
• Faster connections,
• Lower latency,
• Data rate greater than 1 Gb / s,
• Reduction of the delay time compared to 4G technology by a factor of five times,
• Devices with 5G enabled artificial intelligence (AI) capabilities,
One of the most challenging issues in the wireless signal communication is to provide a better wireless connection
in rural areas. This topic is still the most discussed topic among industries and researchers because of the unstable
environmental factor. Fast and reliable wireless data communication will be provided by overcoming the obstacles
causing wireless signal communication as a result of the improvement of 5G technology.
The fifth generation mobile communication system is one of the ambitious technologies that could change the
current communication technologies. For this reason, it is important to investigate the requirements, challenges,
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benefits, advantages, and disadvantages of 5G technology for successful implementation. The challenges of 5G
technology are described in related references [2-4]. Many innovation studies are being carried out to make this
technology widely used in the future. One of these studies is a millimeter wavelength equipment developed for 28GHz
and 38GHz operation frequencies [5]. Another study describes the role of cloud computing for achieving 5G network,
and that is expected to be difficult due to the increment in the number of wireless devices [6]. Important technologies
in 5G technology are explained in several articles. These important technologies are communication technologies such
as device-centric, architecture, millimeter wavelength and smart device, and they can have a big impact on design of
5G technology. Evolution of 1G to 5G technology is shown in Figure 1. This technological development started with
voice distribution with 1G technology. Then it was continued with multimedia services with 3G technology. 5G
technology will be used in many areas with the broadband of networks. 5G technology will find application in many
different areas in addition to high-speed data transfer. This new technology will be used in a wide range of applications
such as agricultural sensors, autonomous vehicles, and remote health screening [8].
II. FEATURES OF 5G
The data transfer speed of 4G technology is approximately 100 Mbps. This data transfer is fast, but it is not enough
to meet the data transfer rate in the near future. The data transfer rate for users after 2020 is expected to be 1Gbps or
even more [9].
In addition to massive data transfer speed, 5G technology will ensure safe data transfer in crowded and
electronically noisy areas. For example, such crowded areas as concert hall or stadium, users are exposed to delays or
interruptions in signal transmission due to overloading the network. The benefits of 5G technology are aimed at
providing better data transfer in crowded areas [9]. In addition, 5G technology will be definitely used in potential
Figure 1. The development of communication technology over the years, Ref [7].
Figure 2. Advantages of 5G technology, Ref.
[16].
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applications such reading invoices at long distances, e-health service, smart cities and video surveillance system. It is
aimed to develop such services and to facilitate daily life at long distances with the 5G technology [10]. Data transfer
traffic in indoor areas is greater than data transfer traffic in external areas. Also, the data transfer rate in the internal
areas is not very good for the speed of data transmission in the external areas compared to this excess data flow. Data
usage traffic is expected to increase by approximately 30% in the near future. For this reason, 5G technology is
intended to meet the large amount of data rate with the best service quality. High-quality communication technology
will find use in every aspect of life in the future. Therefore, it is very important that this communication technology
is to make efficient, safe and high data transfer. With the development of the communication technologies, it will find
a wide range of applications in radars, unmanned aircraft systems and security screening imaging and biological
detection. The advantage of this technology is that it offers a wide range of applications by providing significant
benefits to the community since it is user-friendly and safe to use.
III. MACHINE TO MACHINE COMMUNICATIION IN 5G TECHNOLOGY
Machine-Machine (M2M) communications will be used very widely in all areas of life with the development of
communication technologies. The main task of the M2M device is to collect and to send data such as temperature,
humidity level, and fuel consumption for other devices or infrastructure. Developed 5G technology networks are
expected to use and benefit in every aspect of life with features such as increased productivity of future M2M
communication and reduced data latency between devices. Internet usage in the world increased by approximately
250 million users and more than 4 billion people used the internet in 2018 reports [12]. The vast majority of internet
users believe that the internet does more than harm. This will lead to increase the number of internet and mobile device
users each year and so an increase the data traffic. Therefore, 5G technology is a must to meet the increasing data
flow. The increasing use of digital data stream and the penetration rate in Turkey are increasing every year. Thus, new
communication technology is an important requirement for Turkey for catching up the leader of this technologies.
According to the BTK report the use of mobile penetration rates in Turkey is in a good place among other countries.
Turkey as having the mobile penetration rate of 91.46% between the mobile device and other countries in the use of
digital data has a good position [13].
5G technology will bring in features and innovations that cannot be compared with previous communication
technologies. These innovations will enable broadband ranges to be used in areas such as automotive, health, and
security as well as rapid data transmission, increasing the need for broadband ranges and meeting the need for
communication of many devices. 5G technology will allow thousands of sensors to communicate with improved
mobile bandwidth and machine-machine communication. Moreover, 5G will have an important place for critical
machine-machine communication because of safe and fast communication service, and it will be used in autonomous
cars and health applications [14]. With the further development of 5G technology, Virtual Reality (VR) technology
can reduce the use of bank branches, and may even make it completely unnecessary in 10 years [14]. With the use of
internet of things and communication smart cities, smart cars, smart homes, information retrieval, billing and payment
would be possible [14, 15]. With the improvement of 5G technology, it will provide convenience in many areas, Figure
2.
IV. REQUİREMENTS FOR 5G TECHNOLOGY
The available frequency ranges that can be used in the electromagnetic spectrum have reached the saturation point
because of the high data transfer and increased number of devices connected to each other. The current mobile
communications systems use frequencies below 6GHz, and these frequencies have already used because these
frequencies have also been used in other wireless technologies. Therefore, new spectrum ranges should be defined for
next generation technology. High frequencies will be used to transport signals with the introduction of 5G technology,
and antenna structures will be designed to contain many antennas. This new antenna designs would bring a large
number of radio frequency (RF) bandwidth requirements. The possible frequency ranges that can be used for 5G
technology under 6GHz are proposed as 3.4-3.8GHz. The frequency ranges that can be used for frequencies above
6GHz operate in the 28GHz band in the US and in the Far East countries. This frequency range overlaps with satellite
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connections in Europe and the 26GHz band could be used as an alternative, Figure 3. It is thought that these frequency
ranges will satisfy the large amounts of data needed in the future [14]. There is signal loss in current cellular
communication networks. For preventing the signal loss, cells size will be reduced so that number of cells will be
increased. Larger frequency band will be required to provide data capacity in 5G technology. Products that can operate
at frequencies above 90GHz will be of great importance in this respect [14]. In addition, 5G technology is aimed to
reach the white spectrum that provides wide frequency band [11].
V. OPPORTUNITIES OF 5G TECHNOLOGY IN TURKEY
It is expected that the application areas of 5G technology will reach different levels as compared to previous
generation technologies. Researches about 5G technology conducted in Turkey in parallel with the researches carried
out in the world will provide great opportunities for Turkey. 5G technology will find applications in many areas such
as high data rate, low latency, more data carrying capacity, automotive industry, and health [14]. According to the
BTK report penetration rates in developed cities in Turkey are higher than in the developing cities in Turkey, and there
is a noticeable difference among both [13]. 5G technology in Turkey will create equal opportunity to reach the signal
and the data transfer rates. It is expected to create nearly 22 million jobs worldwide thanks to 5G technology. In
addition, it will have the opportunity to use 5G technology in the new industrial areas such as fast data transfer, e-
health and autonomous vehicles.
VI. CONCLUSIONS
In present days, wireless signal traffic is increasing due to increasing demand for communication areas. New
methods are being developed to meet increasing data rate. One of these methods is 5G technology. In this paper,
discussion and evaluation of 5G technology are studied. This study aims to emphasize the importance of 5G
technology. Advantages of 5G technology against 4G technology have been mentioned. Features, data transfer rate,
and data security of 5G technology have been mentioned. The frequency ranges required for the efficient use of 5G
technology are mentioned. The aims of 5G technology in machine-machine communication are discussed. Digital data
usage in the world and Turkey is mentioned. Benefits and opportunities of new generation technologies to Turkey are
specified. In general, the main purpose of 5G technology is to provide fast and reliable data transfer free of obstacles.
This will be possible by integrating current technologies into 5G technology. There will be a new era in the area of
wireless signal communication by widespread use of 5G technology after 2020.
REFERENES
[1] Pekka Pirinen, A brief overview of 5G research activity, ICST, 17–22, 2014.
[2] Shanzhi Chen, Jian Zhao, The requirements, challenges, and technologies for 5G of terrestrial mobile
telecommunication, IEEE Commun. Mag. 52, 36–43, 2014.
Figure 3. Spectrum of 5G technology, Ref.
[16].
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[3] J. Thompson, X. Ge, H.C. Wu, R. Irmer, H. Jiang, G. Fettweis, S. Alamouti, 5G wireless communication systems:
prospects and challenges, IEEE Commun. Mag. 52, 62–64, 2014.
[4] S. Patil, V. Patil, P. Bhat, A review on 5G technology, Int. J. Eng. Innovative Technol. 1, 1, 2012.
[5] T.S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, F. Gutierrez, Millimeter wave mobile
communications for 5G cellular: it will work!, IEEE Access 1, 335–349, 2013.
[6] P. Rost, C. Bernardos, A. Domenico, M. Girolamo, M. Lalam, A. Maeder, D. Sabella, Cloud technologies for
flexible 5G radio access networks, IEEE Commun. Mag. 52, 68–76, 2014.
[7] Evolution, converge, and innovation 5G white paper, White Paper, Datang Wireless Mobile Innovation Center,
2013.
[8] T. Nakamura, S. Nagata, 5G Standards Frontline Report – High Speeds and High Capacity or Ultra High
Confidence and Low Latency, Which Should Come First: Two Sides of a Debate at the 3GPP, Fifth Generation
Mobile Communication Promotion Forum, 2016.
[9] METIS Project, Scenarios, Requirement and KPIs for 5G Mobile and Wireless Communication System, 2013.
[10] 5G Radio Access Ericsson White Paper, 2013.
[11] METIS Project: Intermediate description of spectrum needs and usages principles, 2013.
[12] İmdat Türkay, “Digital in 2018" Raporunda Dünyada ve Türkiye’de Durum”, https://vergialgi.net/, Erişim, 10-
02-2019.
[13] BTK, Türkiye Elektronik Haberleşme Sektörü Penetrasyon Oranlarının Diğer Ülkelerle Kıyaslanması ve İl
Bazında Analizi, Bilgi Teknolojileri ve İletişim Kurumu Ankara, Nisan 2014.
[14] BTK, 5G ve Ötesi, Bilgi Teknolojileri ve İletişim Kurumu
[15] O. Dikmen, S. Kulaç, “5G Teknolojisine Genel Bir Bakış”, 4th International Symposium on Innovative
Technologies in Engineering and Science, 2016
[16] www.emfexplained.info/, “EMF Explained Series”, March, 2018
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IMPROVED DYNAMIC PERFORMANCE OF WIND ENERGY
CONVERSION SYSTEM BY STATCOM
ABDELOUAHED Touhami
Intelligent Control and Electrical Power System Laboratory, University of Sidi Bel-Abbes,
Algeria
Zidi Sid AHMED, FADI M. ALBATSHB
Electrical Engineering Section, University Kuala Lumpur, International College, Malaysia
Abstract
The renewable energy plays an important role to provide electrical energy other than
conventional sources. Wind power is one of the renewable energy sources used to minimize the
environmental impact on conventional plant; it is one of the fastest growing sources of energy in the world.
However, when the wind power is integrated to an electric grid may cause problems important in terms of
power quality such as active / reactive power variation, variation of voltage, flicker, harmonics, and
electrical behavior of switching operations. The paper study demonstrates the power quality problem
due to installation of wind turbine with the grid, and regarding to this problem the STATCOM is
connected at a point of common coupling to mitigate the power quality issues. Simulation studies are
carried out in the MATLAB/Simulink environment to examine the performance of the wind farm with and
without STATCOM for improving Power Quality of wind farms connected to electrical network.
Keywords: renewable energy, Wind farm, Power system, STATCOM, Power Quality
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GLOBALIZATION AND EMPLOYEE MOBILITY: INTERFUSION FOR
CUSTOMER SATISFACTION IN SELECTED MULTI-NATIONAL
COMPANIES IN NIGERIA. LAGOS REVIEWS
Haruna Abdullahi
Chrisland University Abeokuta, Nigeria
Abstract
Globalization is impacting positively and negatively on every aspects of human resources
administration, employee mobility is not an exception, in the event of daunting challenges of
satisfying customer with the state-of-the-earth products. Modern employees have to be globally
mobile in a bid to experience and acquire the necessary modes of operation in the overall interest
of their customers. Hence, this paper investigates globalization and employee mobility as
interfusion for customer satisfaction in selected multi-national companies in Nigeria. Lagos
Reviews. This paper utilizes modernization, scientific management by Taylor and contingency
theories. The study employed cross sectional survey design and was descriptive, combining both
qualitative and quantitative techniques of study; data for the study was sourced secondarily through
textbooks, online sources and journals, and primary data through administration of 550
questionnaires on employees of the three multinational companies; Nestle Nigeria Plc, Total
Nigeria and Mobil Nigeria Unlimited, the sample size was selected through purposive and simple
random. However, 533 questionnaires were returned from the field. The quantitative data was
analyzed with frequency table. The mean age of the respondents was 43.7 years, 70.2%(374) were
married,(67.3%)(358.7) had Bachelor Degree, a significant percentage(58.8%)(313) have
experience off and on shore business exposure and the mean number of years spent in service by
the respondents was 25.3 years. Sizeable number of the respondents (56.7%)(303) was of the views
that mobility exposed them to new experiences and better customer satisfaction. The paper
recommends global exposure of employees in order to improve their services and ensure
organizational sustainability and continual global training and retraining for operational resilience
in view of ever-increasing global business competitiveness. This study will be beneficial to human
resource practitioners and scholars in the field of human resource and allied disciplines would
initiate research from this study.
Keywords: Globalization, Employee Mobility, Customer Satisfaction, Multi-National
Companies Business Competitiveness, Nigeria, Lagos
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Solving the Combined Emission and Economic Dispatch of
Power System Including Solar Photo Voltaic Generation with
Optimization Method
Abdurazaq Elbaz, Muhammed Tahir Güneşer
Department of Electrical & Electronics Engineering, Karabuk University, Karabuk, Turkey
Abstract
The economic dispatch of power system is an integral part of power system and major role
and purpose of using it are for achieving a reliable and efficient operation out of power system
generation networks, and this operation should be obtained by minimizing the generator fuel cost.
Getting optimal solutions for economic dispatch problem requires efficient optimization
algorithms. Hybrid Bat-Crow Search Algorithm is one of the most recent methods and it is already
proved its efficiency and reliability for solving the economic dispatch problem. In this paper we
proposed Hybrid Bat-Crow Search Algorithm in order to solve the economic dispatch problem
based on large scale power system including solar photo voltaic generation. Hybrid Bat-Crow
Search Algorithm is one of the most recent methods and it is already proving its efficiency and
reliability for solving the economic dispatch problem. This study proposes Hybrid Bat-Crow
Search Algorithm in order to solve the economic dispatch problem based on the large scale power
system including solar photo voltaic generation. We proved that the Hybrid Bat-Crow Search
Algorithm for efficiency and take a perfect performance for small-scale systems comparing with
the Hybrid Bat-Crow Search Algorithm. To test the performance of Hybrid Bat-Crow Search
Algorithm during small and large scale power system, we applied it to Combined Economic
Emission Dispatch problem.
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Keywords: CEED, Solar Photo Voltaic, Optimization Method, BAT algorithm, Crow search
Algorithm
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[14] L. Singh and J. Dhillon, "Fuzzy satisfying multiobjective thermal power dispatch
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economic dispatch," IEEE Transactions on Power Systems, vol. 15, pp. 541-545, 2000.
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Generators Using Valve Point Effects and Multiple Fuel Options."
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PROMOTING RENEWABLE ENERGY TECHNOLOGIES FOR RURAL
DEVELOPMENT IN NIGERIA
BASHIR AHMED DANZOMO
Departmet Of Mechanical Engineering, Hussaini Adamu Federal Polytechnic, Kazaure,
Jigawa State
ABUBAKAR AHMED
Department Of Electrical Engineering, Hussaini Adamu Federal Polytechnic, Kazaure,
Jigawa State
ABSTRACT
Currently a high proportion of the world’s total energy output is generated from fossil fuels
such as oil and coal. In general, the quest for an option to conventional power schemes for
extension to remote and rural locations of developing countries like Nigeria arises from the high
costs associated with the extension, as well as maintenance, of the power grid system to rural areas.
It is universally accepted that fossil fuels are finite and it is only a matter of time before their
reserves become exhausted. The need for supplementary or even alternatives that ideally will be
non-depletable energy sources have since been recognized. These non-depletable energy sources
are replenishable and are also referred to as renewable energy sources as they are available in
cyclic or periodic basis. These include: Solar Energy which has estimated world wide average
power potentials of 24 W /m2 of the earth’s surface; Hydropower, major sources which are still
under developed, has an estimated potential of the range of 2-3 TW. Available also in limited areas
of the world are Wind energy and Biomass. This paper reviews the availabity of renewable
energies and their current level of usage in rural communities of Nigeria with a view to put forward
necessary policy measures that are essential in order to promote the use of these technologies.
Keywords: rural communities; fossil fuels; biomass; energy consumption; man-hour
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INCREASING EFFICIENCY OF GAS INJECTION INTO GAS CAP OF A
SANDSTONE OIL RESERVOIR TO IMPROVE OIL RECOVERY
Abdollah Esmaeili
Cyprus International University – Cyprus
Abstract
In this paper, according to actual condition of this oil field, we investigated about gas
injection to find an optimized gas injection process for this oil field. Recovery efficiency of this
EOR method was tested experimentally using several cores of this sandstone reservoir. Reservoir
rock and fluid properties changing during this process were investigated. For this purpose, a set of
experimental tests based on core flooding tests on sandstones was designed. Totally, this research
was done in two sections. In phase 1, called problem statement, we tried to get enough data and
information about this field to know the problems in this field related to this research topic. In
phase 2, called finding solution methods, solution methods for solving these problems were
investigated.
The author wishes to express his sincere thanks and gratitude to Cyprus International
University (CIU) for their helps and encouragements in connection with this study. I am grateful
for all their assistance.
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