Download - ABUBAKAR MOHAMMED NDOTTIWA
THE DESIGN OF 16 X 1 ARRAY PHOTON COUNTING CIRCUIT WITH
BRENT-KUNG ADDER OPTIMZATION
ABUBAKAR MOHAMMED NDOTTIWA
A project reportsubmitted in partial fulfilment of the
Requirements for the award of the degree of
Master of Engineering (Computer and Microelectronics System)
School of Electrical Engineering
Faculty of Engineering
Universiti Teknologi Malaysia
JANUARY 2020
iv
DEDICATION
This thesis is dedicated to my father Ndottiwa Ngari, who taught me that the
best kind of knowledge to have is that which is learned for its own sake. It is also
dedicated to my mother Adji Abba, who taught me that even the largest task can be
accomplished if it is done one step at a time.
v
ACKNOWLEDGEMENT
In preparing this thesis, I was in contact with many people, researchers,
academicians, and practitioners. They have contributed towards my understanding
and thoughts. In particular, I wish to express my sincere appreciation to my main
thesis supervisor, Dr. Suhaila Binti Isaak, for the encouragement, guidance, critics,
advice and motivation that are given to me during the course of my study. I am also
very grateful to all lecturers in the Department of Computer and Microelectronics
System and the entire staff of University Teknologi Malaysia for their guidance and
support are given to me. Without their continued support and interest, this thesis
would not have been the same as presented here.
I am also express my indebted gratitude to the Management staff of
Adamawa State Polytechnic Yola (ASPY) particularly Prof. Ibrahim. (Rector).
Another that needs to be mentioned is Prof. Bobboi Umar (former Rector), for
making it possible to benefit from oversea sponsorship through tetfund.
My fellow postgraduate students should also be recognized for their support.
My sincere appreciation also extends to all my colleagues and all those who have
provided assistance at various occasions. Their views and tips are useful indeed.
Unfortunately, it is not possible to list all of them in this limited space. I am grateful
to all my family member especially; my father Ndottiwa Ngari, my mother Adji
Abba, my step mother Aisha Ibrahim, my uncles; Alh. Husseini Usman, Alh. Hamma
Usman, Alh. Bakura Usman, and Salihu Ngari, my aunties Goggore Ngari, Dudu
Usman, and Adda-manga Usman, my Brothers and Sisters Abba Ndottiwa, Aliyu A.
Siddiki, Hamma Abba-adji, Engr. Abana Hamma, Arch. B. Bukura, Awwal Usman,
Saddik Husseini, and also to my wife Ummi Abubakar, and my children Aisha
Abubakar, Abdurrahman Abubakar.
vi
ABSTRACT
Farming involves sequence and processes of cultivating crops; among others
are bush clearing, irrigation, and soil testing. The soil testing carried out manually by
dissolving chemical reagent to form a color, then compared with a color chart is time
consuming and error prone without quantitative value. Subsequent systems comprise
spectrophotometer, general packet radio service (GPRS), artificial neural network
(ANN), and raspberry pi based are expensive and slow. Another system has been
developed based on field programmable gate array (FPGA) with capability to test
one sample at a time. FPGA supports parallel computing, but it depends on the
program written to synthesize the hardware. To tap this advantage, this study
developed a design of FPGA based photon counting system, using 16 bits parallel
adder (Brent Kung) algorithm. The developed system comprises 16 bits buffer, 16
bits adder, 16 bits parallel input serial output (PISO), and the clock divider circuit.
The incoming signal stored in buffer and passed to parallel adder. The parallel adder
passes the result to PISO and the clock divider was used to control the counting. The
main advantage of the method is speed, the algorithm allows parallel computation,
such that, it will be easily implemented in low cost FPGA module. The speed of the
adder was achieved by making FPGA operate in parallel mode. To test the
effectiveness of the proposed method, Verilog hardware description language was
used to synthesize through Vivado suite by Xilinx and simulated under different
condition by changing the samples signal with various degree of frequency. It was
further verified with Synopsys. The superiority of the proposed method over the
conventional is high frequency of 625 MHz in FPGA, 1190 MHz for Brent Kung
integrated circuit (IC) and 1639 MHz for two stage pipeline Brent Kung integrated
circuit with low power of 2 mW in gate levels. It is envisioning that the proposed
method will be very useful in the implementation of high performance, low cost for
not only soil nutrient measurement, but also for transforming other serial system to
parallel system.
vii
ABSTRAK
Perladangan melibatkan urutan dan proses-proses menanam tanaman; antara
prosesnya adalah membersihkan belukar, pengairan, dan pengujian tanah. Pengujian
tanah yang dijalankan secara manual dengan melarutkan reagen kimia untuk
menghasilkan warna, yang kemudian dibandingkan dengan carta warna adalah
memakan masa dan cenderung berlaku ralat tanpa nilai kuantitatif. Sistem-sistem
seterusnya terdiri daripada spektrofotometer, perkhidmatan radio paket umum
(GPRS), rangkaian neural tiruan (ANN), dan pangkalan Pi raspberi adalah mahal dan
lambat. Sistem lain telah dibina berdasarkan tatasusunan get boleh atur lapangan
(FPGA) dengan keupayaan untuk menguji satu sampel pada satu masa. FGPA
menyokong pengkomputeran selari tetapi ia bergantung kepada program yang ditulis
untuk membina perkakasan. Bagi mencungkil kelebihannya, kajian ini
membangunkan reka bentuk sistem pengukuran foton berasaskan FGPA
menggunakan algoritma penambah selari 16 bit (Brent Kung). Sistem yang
dibangunkan mengandungi penimbal 16 bit, penambah 16 bit, output bersiri input
selari (PISO) 16 bit, dan litar pembahagi jam. Isyarat masuk disimpan dalam
penimbal dipindahkan ke penambah selari. Penambah selari memindahkan keputusan
ke PISO dan pembahagi jam digunakan untuk mengawal kiraan. Kelebihan utama
kaedah ini ialah kelajuan, algoritma membenarkan pengkomputeran selari, dengan
itu ia mudah dilaksanakan dalam modul FPGA berkos rendah. Kelajuan penambah
dicapai dengan membuatkan FGPA beroperasi dalam mod selari. Bagi menguji
keberkesanan kaedah yang dicadangkan, bahasa pemerian perkakas Verilog
digunakan untuk mensintesis melalui suit Vivado oleh Xilinx dan disimulasikan di
bawah beberapa keadaan dengan mengubah isyarat sampel dengan pelbagai darjah
frekuensi. Ia kemudian disahkan dengan Synopsys. Keunggulan kaedah dicadangkan
berbanding kaedah konvensional ialah frekuensi tinggi pada 625 MHz dalam FPGA
dan 1190 MHz bagi litar bersepadu (IC) Brent Kung dan 1639 MHz bagi talian paip
dua peringkat litar bersepadu (IC) Brent Kung dengan kuasa serendah 2 mW pada
tahap get. Kaedah dicadangkan ini dijangka menjadi sangat berguna dalam
pelaksanaan bukan hanya pengukuran nutrien tanah prestasi tinggi dan kos rendah
tetapi juga untuk mengubah sistem bersiri lain kepada sistem selari.
ix
TABLE OF CONTENTS
TITLE PAGE
DECLARATION iii
DEDICATION iv
ACKNOWLEDGEMENT v
ABSTRACT vi
ABSTRAK vii
TABLE OF CONTENTS ix
LIST OF TABLES xiv
LIST OF FIGURES xv
LIST OF ABBREVIATIONS xviii
LIST OF SYMBOLS xix
CHAPTER 1 INTRODUCTION 1
1.1 Introduction 1
1.2 Problem Statement 5
1.3 Research Objectives 6
1.4 Research Scope 6
1.4.1 Advantages of optical sensors over
conventional sensors 8
1.5 Project Report Outline 8
CHAPTER 2 LITERATURE REVIEW 11
2.1 Introduction 11
2.1 Brief Overview of soil Nutrients Measurements 11
2.2 Sensors 12
2.2.1 Location sensors 13
2.2.2 Airflow sensors 13
2.2.3 Electrochemical sensors 14
2.2.4 Mechanical sensors 15
x
2.2.5 Dielectric Soil Moisture Sensors 16
2.2.6 Optical sensors 17
2.2.6.1 A photodetector (PD) 17
2.2.7 Deadtime 18
2.3 FPGA 19
2.3.1 Architecture of FPGA 20
2.3.2 The following are the advantages and
disadvantages of Arduino and FPGA 22
2.3.3 Disadvantages of Arduino raspberry 22
2.3.4 Advantages of FPGA 22
2.3.5 Disadvantage of FPGA 23
2.3.6 Application of FPGA 23
2.4 Adders overview 23
2.4.1 Ripple carry adder 24
2.4.2 Carry select adder (CSA) 24
2.4.3 Carry Look ahead adder (CLA) 25
2.4.4 Parallel prefix Adder (PPA) 25
2.5 Research Gap 26
2.6 Brent Kung adder 28
2.6.1 Pre-processing computation step 28
2.6.2 Carry generation steps 28
2.6.3 Post-processing step 29
CHAPTER 3 RESEARCH METHODOLOGY 31
3.1 Introduction 31
3.1.1 Project research flow 31
3.2 Methods 32
3.2.1 16-bits buffer RTL circuit 33
3.2.2 Clock Divider RTL circuit 35
3.2.3 Brent Kung adder RTL circuit 36
3.2.3.1 Preprocessing step 36
3.2.3.2 Carry generation step 36
3.2.3.3 Post-processing step 37
xi
3.2.4 The complete system of Brent Kung adder 40
3.3 Chapter Summary 40
CHAPTER 4 RESULT AND DISCUSSION 41
4.1 Introduction 41
4.2 16 X 1 Array photon-counting circuit Vivado 2018-3
RTL Synthesis results 42
4.2.1 16-bits Buffer Vivado 2018-3 RTL Synthesis 42
4.2.1.1 16-bits Buffer Vivado 2018-3 RTL
Synthesis Results 43
4.2.1.2 16-bits buffer Vivado 2018-3 RTL
timing results 44
4.2.1.3 16-bits Buffer Vivado 2018-3 RTL
frequency results 45
4.2.1.4 16-bits Buffer Vivado 2018-3 RTL
Power Results 45
4.2.2 Brent Kung adder 46
4.2.2.1 16 -bits Brent Kung adder Vivado
2018-3 synthesis 46
4.2.2.2 16-bits Brent Kung adder Vivado
2018-3 RTL post-synthesis Results 47
4.2.2.3 16-bits Brent Kung adder Vivado
2018-3 RTL post-synthesis timing
results 48
4.2.2.4 16-bits Brent Kung adder Vivado
2018-3 RTL post-synthesis
frequency results 49
4.2.2.5 16-bits Brent Kung adder Vivado
2018-3 RTL post-synthesis power
results 50
4.2.3 16-bits PISO Vivado 2018-3 RTL post-
synthesis Results 51
4.2.3.1 16-bits PISO Vivado 2018-3 RTL
synthesis results 51
4.2.3.2 16-bits PISO Vivado 2018-3 RTL
timing results 53
4.2.3.3 16-bits PISO Vivado 2018-3 RTL
frequency results 53
xii
4.2.3.4 16-bits PISO Vivado 2018-3 RTL
power results 54
4.2.4 Clock Divider Vivado 2018-3 RTL post-
synthesis results 55
4.2.4.1 Clock divider Vivado 2018-3 RTL
synthesis result 55
4.2.4.2 Clock divider Vivado 2018-3 RTL
Timing result 56
4.2.4.3 Clock divider Vivado 2018-3 RTL
frequency result 56
4.2.4.4 Clock Divider Vivado 2018-3 RTL
Power result 57
4.2.5 A full system of Brent Kung adder photon-
counting Vivado 2018-3 RTL post-synthesis
result 58
4.2.5.1 A full system of photon counting
Vivado 2018-3 RTL synthesis result 58
4.2.5.2 A full system of Brent Kung adder
photon-counting Vivado 2018-3
RTL timing result 60
4.2.5.3 The full system of photon counting
Vivado 2018-3 RTL frequency
result 60
4.2.5.4 The full system of photon counting
Vivado 2018-3 RTL power result 61
4.2.5.5 The full system of Brent photon-
counting Vivado 2018-3 RTL
resource utilization result 62
4.3 Result Analysis 63
4.3.1 Analysis of Brent Kung system photon-
counting design in Vivado 2013-3 63
4.3.2 Analysis of integrated circuits system design
in Synopsys 66
4.3.3 Comparison between FPGA and Integrated
Circuit of the proposed design 69
4.3.4 Comparison between FPGA and IC of the
proposed and previous design 71
4.4 Summary of the chapter 73
xiii
CHAPTER 5 CONCLUSION AND RECOMMENDATIONS 75
5.1 Research Outcomes 75
5.2 Contributions to Knowledge 75
5.3 Future Works 75
REFERENCES 77
xiv
LIST OF TABLES
TABLE NO. TITLE PAGE
Table 2.1 Research gap analysis for adders 27
Table 4.1 Parameters for photon counting System using Vivado 62
Table 4.2 FPGA Brent Kung adder system Parameters 64
Table 4.3 Brent Kung system gate level parameters 66
Table 4.4 Two stage pipelined Brent Kung system gate level
parameters I 66
Table 4.5 Two stage pipelined Brent Kung system gate level
parameters II 66
xv
LIST OF FIGURES
FIGURE NO. TITLE PAGE
Figure 1.1 Block diagram of the proposed system 5
Figure 1.2 Block diagram of the proposed system 7
Figure 2.1 Three distances based sensor for Position estimation [9] 13
Figure 2.2 Airflow sensor [11] 14
Figure 2.3 Electrochemical sensor [16] 15
Figure 2.4 Mechanical sensors and actuators [18] 16
Figure 2.5 Dielectric soil moisture measurement using TDR [19] 16
Figure 2.6 Basic FPGA [25] 19
Figure 2.7 FPGA Architecture [25] 21
Figure 2.8 FPGA logic block [25] 21
Figure 2.9 block diagram of 4-bits RCA [6]. 24
Figure 2.10 4-bits CSA adder block diagram [27] 24
Figure 2.11 4-bits CLA adder block diagram 25
Figure 2.12 8-bits basic PPA block diagram [30] 26
Figure 2.13 Pre-processing step computation. 28
Figure 2.14 Carry Generation step components [29]. 29
Figure 2.15 16-bits Brent Kung adder block diagram [30]. 30
Figure 3.1 Research flow diagram 32
Figure 3.2 Block diagram of photon counting using FPGA. 33
Figure 3.3 16-bits buffer RTL Schematic diagram 34
Figure 3.4 16-bits buffer RTL Schematic diagram 34
Figure 3.5 Clock Divider RTL schematic 35
Figure 3.6 16-bits Brent Kung adder RTL schematic diagram. 38
Figure 3.7 RTL schematic of CPD OF Brent Kung adder 38
Figure 3.8 Two-stage pipeline 39
xvi
Figure 3.9 16-bits PISO RTL schematic diagram 39
Figure 3.10 the complete system of Brent Kung adder for photon
counting 40
Figure 4.1 16-bits buffer post-synthesis Vivado 2018-3 RTL result 43
Figure 4.2 16-bits buffer gate-level results 43
Figure 4.3 16-bits buffer post-synthesis rtl timing report 44
Figure 4.4 16-bits Buffer Vivado 2018-3 RTL timing results 44
Figure 4.5 16-bits Buffer Vivado 2018-3 RTL frequency results 45
Figure 4.6 16-bits Buffer Vivado 2018-3 RTL power results 46
Figure 4.7 Brent Kung adder post-synthesis RTL result 47
Figure 4.8 Brent Kung adder gate-level Synopsys simulation result 48
Figure 4.9 Brent Kung adder post-synthesis timing result 48
Figure 4.10 Brent Kung adder post-synthesis frequency result 49
Figure 4.11 Brent Kung adder with two-stage pipeline frequency result 49
Figure 4.12 Brent Kung adder with power result 50
Figure 4.13 Brent Kung adder with two-stage pipeline power result 51
Figure 4.14 16-bits PISO Vivado 2018-3 RTL synthesis results 52
Figure 4.15 16-bits PISO Vivado 2018-3 RTL synthesis Results 52
Figure 4.16 16-bits PISO Synopsys gate-level simulation results 52
Figure 4.17 16-bits PISO Vivado 2018-3 RTL timing results 53
Figure 4.18 16-bits PISO Vivado 2018-3 RTL frequency Results 54
Figure 4.19 PISO Vivado 2018-3 RTL power results 54
Figure 4.20 Clock Divider Vivado 2018-3 RTL synthesis result 55
Figure 4.21 Clock Divider Synopsys simulation result 55
Figure 4.22 Clock divider Vivado 2018-3 RTL timing result 56
Figure 4.23 Clock divider Vivido 2018-3 RTL frequency result 57
Figure 4.24 Clock Divider Vivado 2018-3 RTL power result 57
Figure 4.25 16-bits Brent Kung adder photon-counting system report 58
Figure 4.26 Full system of photon-counting Vivido 2018-3 RTL
Synthesis result 59
xvii
Figure 4.27 Full system of photon-counting Synopsys gate-level result 59
Figure 4.28 Full system of photon counting Vivado 2018-3 RTL
timing result 60
Figure 4.29 Full system of photon counting Vivado 2018-3 RTL
frequency result 61
Figure 4.30 Full photon counting system Vivado 2018-3 RTL power
result 61
Figure 4.31 Resources utilization 63
Figure 4.32 16-bits Brent Kung adder average fan-out 63
Figure 4.33 FPGA WNS versus frequency 65
Figure 4.34 FPGA power versus frequency 65
Figure 4.35 Integrated Circuit gate-level WNS versus frequency 67
Figure 4.36 Integrated Circuit Power versus frequency 67
Figure 4.37 Integrated Circuit WNS versus frequency of PBK 68
Figure 4.38 Integrated Circuit power versus frequency PBK 69
Figure 4.39 Comparison between of WNS of FPGA , Integrated circuit
Brent Kung and Integrated Circuit pipeline Brent Kung
design 70
Figure 4.40 Comparison between of Power of FPGA, Integrated circuit
Brent Kung and Integrated Circuit pipeline Brent Kung
design I 70
Figure 4.41 Comparison of Power Between FPGA, Integrated circuit
Brent Kung and Integrated Circuit pipeline Brent Kung
design II 71
Figure 4.42 Comparison of WNS Between Integrated circuit Brent
Kung and Integrated Circuit pipeline Brent Kung and
previous design [6] 72
Figure 4.43 Comparison of power between Integrated circuit Brent
Kung and Integrated Circuit pipeline Brent Kung and
previous design [6] 72
Figure 4.44 Comparison of Area between Integrated circuit Brent
Kung and Integrated Circuit pipeline Brent Kung and
previous design [6] 73
xviii
LIST OF ABBREVIATIONS
BK - Brent-Kung
PPA - Parallel Prefix Adder
PISO - Parallel input Serial output
PBK - Pipeline Brent-kung
WNS - Worse Negative Slack
FPGA - Field Programmable Gate Array
UTM - Universiti Teknologi Malaysia
RTL - Register Transfer Level
FA - Full Adder
CLA - Carry Look Ahead
CSA - Carry Select Adder
KSA - Koggey Stone Adder
TDR - Time Domain Reflectometer
FDR - Frequency Domain reflectometer
ANN - Artificial Neural Network
GPS - Global Positioning System
VCS - Verilog Compiler and Simulator
xix
LIST OF SYMBOLS
tD - Death time
f - frequency
TS - Slack Time
TC - Circuit Time
TR - Required Time
FMAX - Maximum Frequency
λ - Wavelength
hc - Photon energy
ΔΕ - Energy Transition
Δtq - Quenching Time
Δtr - Recharging Time
CHAPTER 1
INTRODUCTION
1.1 Introduction
Farming involves sequences of steps of preparing the land and the process of
cultivating crops. The farmers do these processes manually, which are very difficult
to carry out. The processes consist of bush clearing, planting, soil testing, fertilizer
application among others. The soil testing is being carried out basically in two broad
categories: the traditional method and the other is by using knowledge of
Engineering, such as Verilog Hardware Description Language (VHDL), which many
agricultural projects are successfully carried out with this knowledge, like irrigation
and many more [1]. Furthermore, there is a need for a soil analysis to determine the
nutrients and their equivalent quantities present in the soil, which aide expert to
recommend the needed amount of fertilizer to complement the deficiency of the
existing amount of nutrients in the soil, for efficient and economical production of
agricultural produce. Even though most soils have a huge amount of nutrients, when
soils are repeatedly used for growing and harvesting crops, nutrients level will
subsequently decrease in the soil. Low nutrients level in the soil leads to several
crops’ disorders and low yield. For a better yield of crop, there is a need to restore
the nutrients to the soil by supplementing it. Therefore, the farmers must add
macronutrients to the soil in the right proportion. Soil macronutrients are essential
nutrients that enhance plant growth in proportion to its level of presence and the
required amount. Macronutrients comprise nitrogen (N), phosphorous (P) and
potassium (K). N aid leaf and stem development and maintain its green colour, P
enhances the root system of the crop for better absorption of water and other
beneficial nutrients, and K helps the crops in producing flowers and fruits. The use of
fertilizer to meet the demand of the crop by utilizing the advantage of the existing
nutrients in the soil is ensured by an appropriate soil test [2]. Additionally, to meet up
with the increasing requirement of the rising population over the years, there must be
2
machinery put in place to improve the production of food. There are various sensors
used in the field of agriculture for this purpose, some of the sensors are; global
positioning system (GPS): for Location sensors, optical sensors: can be measure
almost every physical, chemical quantities of interest and electrochemical sensors:
pH and soil nutrient levels, Mechanical sensors: use to measure resistive forces in the
soil, dielectric sensors, Airflow sensors among others.
To increase crop production, fertilizers containing N, P, and K are crucial.
Inappropriate utilization of fertilizers will result in low quality in fruits, vegetables
lagging in colour, size, taste and even amount of the fruits. Moreover, the amount of
recommended N, P, K is related to the type of crop type and status of plant growth.
In addition, the amount of fertilizer to be used is further related to the existing
substances of N, P, K macronutrients in the soil. Therefore, Investigators are looking
for ways to enhance plant productivity while reducing fertilizer intake.
Macronutrients are uniformly varied on a small scale all over a cultivated land;
several researchers are involved in finding ways of evolving sensors to map these
nutrient contents. The spatial and temporal behavior of N, P, K is checked by
integrated crop management systems that were designed for this purpose. To
automate agricultural practice, which will improve drop production, monitoring N, P,
K values alongside the ph. should be continued [2].
Furthermore, farmers used to send the soil sample to the laboratory for
testing, analyzing and get the report back, this type of analysis takes a long time and
the sample is analyzed when it is not fresh. To analyze soil nutrients, specific colours
are produced from the reaction of the chemical reagents with the soil sample for a
particular nutrient. The degree of the presence of nutrient is measured by comparing
against a color chart since this is done manually, it is very slow to take days to
complete the testing, without quantitative values and lead to errors in the report [3,4]
Therefore, there is need to investigate the developed color solution. A
spectrophotometer is applied for this purpose, which is faster. However, the system
of spectrophotometer became complicated and expensive and needs an operator for
proper functioning. The developed of a sensing unit comprises; light emitting diode
(LED), photodiode (PD), microcontroller, analog to digital converter (ADC) and
3
general packet radio service (GPRS) modem is reported by Adhikary et. al [3]. The
PD received the light beam, which passes through the soil sample that is generated
from the light source LED; the received light can be used to compute the presence of
the nutrients in the sample. The result from the system shows a little deviation of
about 5 % from the Laboratory result, which is according to the researcher is not
costly to compare to spectrophotometer and does not requires operator [3]. However,
the system is very slow, operates sequentially and requires a network since is a
GPRS based which makes it expensive due to data subscription, the color detected is
in form of red, green and blue (RGB) which gives less accuracy. To solve this
problem, a design was proposed with an architecture of sensing unit based on field
programmable gate array (FPGA) system [4], the system is standalone device which
has similar procedure with another proposed system, with the exception of GPRS,
and instead of computing RGB values directly, the RGB were converted to hue (H)
intensity (I) saturation (S) values [3]. The system gives accurate result compared to
that RGB, but does not operate in real-time and process data serially. Even though,
the macronutrients are the most needed for the purpose of fertilizer application
recommendation, zinc (Zn), magnesium (Mg) and calcium (Ca) are also essential
nutrients for growth of plants and should be involved in the soil nutrients analysis, so
that adequate information on how much specific fertilizer are recommended in a
particular section of crop. A solution to this situation was proposed, by developing a
program that will give soil pH and six different nutrients level. The nutrients are
nitrogen, phosphorous, potassium, zinc, calcium, and magnesium by means of image
processing and artificial neural network through MATLAB. After following the same
procedure of preparing the soil sample. A camera is used to captures the pictures of
the sample for each nutrient, the images are processed through MATLAB, and the
model of training is done with an artificial neural network. The program
implementation was carried out successfully, and a qualitative result was obtained
[5].
Moreover, the artificial neural network requires adequate data training to
obtain an accurate result, the training process often takes longer time and operate
sequentially, since the system is based on microcontroller, which slows down the
operation. The fertilizer recommendation based on the qualitative result of soil
nutrients for specific crops will lead to under or over-application of the fertilizer,
4
which is not required. Under the application of fertilizer will lead to plant disorder,
and over-application also amounts to a waste of resources, which should be
discouraged. The processing speed should be enhanced to fast-track the
measurement. The measuring should be improved to handle high-speed parallel data
computation.
To solve this problem, this project is proposed to develop a parallel
processing system that can handle 16 incoming signals from spectroscopy. The 16
signals are represented by digit 0 to 15. To achieve the implementation of the parallel
system, an algorithm and platform that can support parallel processing must be
adapted. Parallel prefix (Brent Kung) adder algorithm and FPGA respectively is
proposed. 16-bit high speed-processing unit based on FPGA, which is incorporated
in spectroscopy. The spectroscopy system comprises a light source that generates
light, which is dispersed through monochrome, the optical fiber is used for
propagating the light source to a cuvette containing a solution. The solution absorbed
some of the light, the absorbance level depends on the concentration of ions in the
solution. In addition, some light passed through the solution to the detector where the
light energy will be detected and converted to current by photodetector. The weak
current is amplified, through the amplify and converted voltage. The analog voltage
is converted to a digital voltage by analog to digital converter, which sends the signal
to FPGA for processing and counting, the block diagram is presented in Figure 1.1.
The FPGA module consists 16-bits buffer that is used to synchronize the
unsynchronized incoming signal from the spectroscopy, which is asynchronous. The
Brent Kung adder is served as the counter of the signals. A clock divider is used to
maintain the appropriate timing for the circuit to operate correctly. The PISO is used
to convert the parallel signal to serial. Both buffer and PISO are registers, that is,
they are synchronous system, the clock divider is used to ascertain the data integrity
at the output of the PISO which is shown in Figure 1.2.
5
Spectroscopy
Optical
fiber
Display
FilterAmplifier
A/D
converter
Vin
GND
Vref
B1
B8
Sign
ENB
A/D Converter
FPGA
MODULE
A
12.45
Cuvette
Solution
Light
Source
Photodiode
Figure 1.1 Block diagram of the proposed system
1.2 Problem Statement
The overuse or underuse of fertilizer in the farmyard due to inadequate
information regarding the presence of nutrients and their corresponding levels should
be discouraged.
To analyze soil macronutrients, specific colors are produced from the reaction
of the chemical reagents with the soil sample for a particular nutrient. The degree of
the presence of nutrients is measured by comparing it against a color chart, which
will lead to error and time-consuming. A complicated spectrophotometer is applied
for this purpose. However, the system of spectrophotometer needs operator for
proper functioning. Another system was developed based on FPGA which can test
one sample at a time [5]. Furthermore, a photon counting system was developed
6
based on 16 bits Kogge Stone adder by applying FPGA [6]. The system has high
speed with a large area which leads to higher power consumption.
Therefore, there is a needs to utilize the advantages of parallel computing
supported by FPGA, to design a new system that will fully support parallel operation
and exploit those advantages, by applying Brent Kung adder which has a low area
and consequently will lead to lower power consumption.
1.3 Research Objectives
The aim of this project is to develop a high-speed real-time photon counting
using FPGA that will operate parallel. Besides this, the analysis of the performance
of photon counting will be conducted by adapting Brent Kung adder method. In
addition, to analyses the performance of real-time photon counting by using FPGA
with various dead time tD. In other words, the objective can be summarized as
follows:
i. To develop a real-time photon-counting simulation in Xilinx Vivado
Environment using FPGA
ii. To perform analysis of photon counting by adapting Brent Kung Adder
method.
iii. To analyse the performance of real-time soil spectroscopy by using FPGA
and Synopsys with various dead time tD.
1.4 Research Scope
Due to the interaction radiation of electromagnetic, light energy with matter,
spectroscopy is very important in identifying compound and its concentration in
solution. In absorbance spectroscopy, the system comprises a light source,
monochrometer, light detectors, and amplifier as presented in Figure 1.1. LED is
used to generate light that illuminates the sample in the cuvette. When the incidence
7
light hits the solution, some of the light is been absorbed by the solution, while the
remaining light can be transmitted to the detector (sensor). The photodetector detects
the transmitted light and converts it to current (weak). The current is proportional to
the intensity of light. The detected weak current signal is amplified and converts to
voltage, which then digitizes for the processing in FPGA. The main advantages of
spectroscopy are simple to use and accuracy [7].
Figure 1.2 Block diagram of the proposed system
The design implementation of the photon counting; consists of 16 bits buffer,
16 bits high speed adder, 16 bits Parallel input Serial output as shown in Figure 1.2.
The 16 bits buffer would be used to settle the incoming signal from Spectroscopy to
guarantee consistencies of data. 16 bits high-speed adder (Brent Kung adder) served
as a counter to the incoming signal from the buffer. The PISO is used to serially
arrange the data at the output. The programming language used is Verilog, through
Vivado Xilinx 2018 software version 3, for the synthesis and simulation of the
design. The analysis of the result was performed and benchmarked with the existing
system for the delay, frequency, power consumption and area of the system. The
details of the block diagram of the is illustrated in Chapter 3 Figure 3.2.
8
1.4.1 Advantages of optical sensors over conventional sensors
Optical sensors have significant advantages over the non-optical sensors,
based on their properties; some of the advantages are Higher sensitivity, Passiveness
of electric, free from interference of electromagnetic, dynamic range very wide, both
distributed and points configuration, Capabilities of multiplexing. Furthermore, one
can measure almost every physical quantities of interest and very large chemical
quantities with optical sensors. The physical quantities such as Temperature,
Pressure, flow, liquid level, Position (Displacement), vibration, Magnetic field,
Rotation, Radiation, Force, pH values, Acceleration, Acoustic field, Humidity,
Velocity, Strain, Electric field, etc. and chemical species [6]. Moreover, some
researchers used Arduino/ raspberry pi while other researchers used FPGA.
1.5 Project Report Outline
There are five chapters in this project report. Chapter 1 introduces the
objectives of the project and defines the scope of the project.
Chapter 2 then covers some basic sharing towards understanding soil
nutrients measurements system operations and their findings. It similarly looks in
brief, some of the implementations of different adders towards achieving high speed
with comparatively low power consumption implementation.
Chapter 3 then reports on the design implementation from the actual flow of
work to some details on the circuits that were implemented for this project.
Chapter 4 discuss the design synthesis and simulating the functionality of the
actual implementation of photon-counting circuit and evaluating the result obtained
from the simulations.
9
Chapter 5 focuses on summarizing and drawing a conclusion of the result
obtained from the photon counting circuit and briefly explain the future work that
can be an improvement from this project implementation. To estimate the parameters
REFERENCES
[1] Kadu A, Rode S V. VHDL based System for Sensing NPK and PH Values in
Soil and Suggesting Fertilizers for Different Crops. Certif Int J Eng Sci Innov
Technol 2008;9001:2319–5967.
[2] Shylaja, S N;Veena M. Real- T ime Monitoring of Soil Nutrient Analysis u
sing WSN. Int. Conf. Energy, Commun. Data Anal. Soft Comput., IEEE;
2017, p. 3059–62.
[3] Adhikary T, Das AK, Razzaque MA, Chowdhury MEH, Parvin S. Test
implementation of a sensor device for measuring soil macronutrients. Proc
2015 Int Conf Netw Syst Secur NSysS 2015 2015.
[4] Ingale V, Vaidya R, Phad A, Shingare P. A sensor device for measuring soil
macronutrient proportion using FPGA. Int. Conf. Commun. Signal Process.
ICCSP 2016, India: IEEE; 2016, p. 715–8.
[5] Puno JC, Sybingco E, Dadios E, Valenzuela I, Cuello J. Determination of soil
nutrients and pH level using image processing and artificial neural network.
HNICEM 2017 - 9th Int. Conf. Humanoid, Nanotechnology, Inf. Technol.
Commun. Control. Environ. Manag., vol. 2018- Janua, IEEE; 2017, p. 1–6.
[6] Kumar NKAR. 16 Bit Counter Data Acquisition Implementation With High
Speed Adder Design Using Verilog. University Teknologi Malysia, 2018.
[7] Farrukh MA. Functionalized Nanomaterials. 2016.
[8] elprocus. Know All About Light Sensor Circuit n.d.
[9] Global Positioning System - Google Search n.d.
https://www.google.com/search?q=Global+Positioning+System (accessed
December 29, 2019).
[10] Schriber S. Smart Agriculture Sensors: Helping Small Farmers and Positively
Impacting Global Issues, Too. Mouser Electron n.d.
https://www.mouser.ph/applications/smart-agriculture-sensors/.
[11] ResearchGate n.d. https://www.researchgate.net/figure/Block-diagram-for-
controlling-the-flow-of-drying-air_fig1_266501804/download (accessed
December 29, 2019).
[12] Cao S, Chen J, Sheng W, Wu W, Zhao Z, Long F. The Fabrication and
78
Development of Molecularly Imprinted Polymer-based Sensors for
Environmental Application. Elsevier B.V.; 2012.
[13] Tan J, Yan XP. Discrimination of Analytes with Fluorescent Molecular
Imprinting Sensor Arrays. Elsevier B.V.; 2012.
[14] Warudkar G, Dorle S. Review on sensing the fertility characteristics of
agriculture soils. 2016 Int. Conf. Inf. Commun. Embed. Syst. ICICES 2016,
IEEE; 2016.
[15] Klusáčková M, Nesměrák K. Significant electrochemical sensors for ethylene
and propylene: the state-of-the-art. Monatshefte Fur Chemie 2018;149:1503–
13.
[16] electrochemical sensor - Google Search n.d.
https://www.google.com/search?q=electrochemical+sensor (accessed
December 29, 2019).
[17] Allwood JM, Tekkaya AE. Writing a review paper. J Mater Process Technol
2012;212:1–2.
[18] Pasquale M. Mechanical sensors and actuators. Sensors Actuators, A Phys
2003;106:142–8.
[19] Susha Lekshmi SU, Singh DN, Shojaei Baghini M. A critical review of soil
moisture measurement. Meas J Int Meas Confed 2014;54:92–105.
[20] Hane K, Sasaki M. Optical Sensors and Their Applications 2002;1:60–8.
[21] S. M. Sze kwok k. ng. Physics of Semiconductor Devices Third Edition. n.d.
[22] Isaak S, Bahador SNB. A Linear Array passively quenched Single Photon
Avalanche Diode. 2015 IEEE Student Conf Res Dev SCOReD 2015
2015:441–6.
[23] Bahador N, Hamid FKA, Hamzah A, Isaak S, Ismail R. Improved dead time
response for Si Avalanche Photodiode. 2012 10th IEEE Int Conf Semicond
Electron ICSE 2012 - Proc 2012:396–8.
[24] Fpga altera Board Vs Micro Controllerarduino n.d.
https://www.reddit.com/r/FPGA/comments (accessed January 28, 2019).
[25] Basics of FPGA Architecture and Applications n.d.
https://www.elprocus.com/fpga-architecture-and-applications/ (accessed
January 28, 2019).
[26] Krishna, T Vamshi; s N mamatha GN. sunil MP. Simulation study of brent
kung adder using cadence tool. Int J Adv Res Ideas Innov Technol
79
2018;4:564–72.
[27] Rani MJ, Andal SSS, Kalyani P. Carry Select Adder design using Brent Kung
Adder. Int J Eng Trends Appl – 2017;4:50–3.
[28] Potdukhe PP, Jaiswal VD. Design of high speed carry select adder using brent
kung adder. Int Conf Electr Electron Optim Tech ICEEOT 2016 2016;4:652–
5.
[29] Abidin AZ, Junid SAM Al, Sharif KKM, Othman Z, Haron MA. 4-bit brent
kung parallel prefix adder simulation study using Silvaco EDA tools. Int J
Simul Syst Sci Technol 2012;13:47–55.
[30] Raju A, Patnaik R, Babu RK, Mahato P. Parallel prefix adders-A comparative
study for fastest response. Proc. Int. Conf. Commun. Electron. Syst. ICCES
2016, IEEE; 2016, p. 1–6.
[31] Taj M. Implementation of PPA-Brent Kung Adder For Computing
Application. Int J Res 2017;4:1479–86.
[32] March UG. RTL and Technology Schematic Viewers. vol. 685. 2011.
[33] Rahman AA-HA. Vivado_Timing_Analysis_Tutorial.Pdf 2017.
[34] Xilinx. UG899, I/O adn Clock Planning 2017;899:96.
[35] Krishna TV. Simulation study of brent kung adder using cadence tool. Int J
Adv Res Ideas Innov Technol 2018;4:564–73.
[36] Potdukhe PP, Jaiswal VD. Design of high speed carry select adder using brent
kung adder. Int Conf Electr Electron Optim Tech ICEEOT 2016 2016:652–5.
[37] Durga MV, Deepthi A. Design and implementation of Parallel Prefix Adders
using FPGAs. IOSR J Electron Commun Eng 2013;6:41–8.
[38] Anjana D, Jose J. Design and FPGA Implementation of Optimized Parallel
Prefix Adder. Int J Innov Res Sci Eng Technol 2016;5:12975–85.