This thesis presents the classification of Agarwood from Malaysia and Indonesia regions based on signal processing technique. Signal processing for the Agarwood classification is a new area and has yet been actively implemented. In this thesis, the Agarwood has been pre-identified by experts using 32 sensor arrays to measure the Agarwood odor profile. General Agarwood pattern has been plot in 2D diagram. The odor profile from different samples have been normalized and pre-processed and visualized in 3D and 2D plot to find unique patterns. The variation of patterns that has been visualized has been marked as different group samples

Najib, Muhammad Sharfi

Universiti Teknologi MARA Institutional Repository


I n s t i t u t e  o f  G r a d u a t e  S t u d i e s  ( I G S )
* (MS) = Main Supervisor       (CS) = Co Supervisor
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Name :
Puteri Sarah Binti Mohamad Saad
Title :
Fabrication Of Hybrid Organic Solar 
Cell Using  MEH-PPV : I-MWCNT As 
An Active Layer
Supervisor : 
Prof. Engr. Dr. Mohamad Rusop 
Mahmood   (MS)
En Uzer Mohd Noor (CS)
Name :
Zakiah Mohd Yusoff
Title :
Self-Tuning Fuzzy Pid Control 
PF Hydro-Diffusion Essential Oil 
Extraction System
Supervisor : 
Dr. Mohd Hezri Fazalul Rahiman  (MS)
Prof. Dr. Hj. Mohd Nasir Taib (CS)
Name :
Zuhaila Mat Yasin
Title :
Performance Improvement Through 
Optimal Location And Sizing Of 
Distributed Generation
Supervisor : 
Associate Prof. Dr. Zuhaina Hj. Zakaria 
(MS)
Prof. Dr. Hjh. Titik Khawa Abdul 
Rahman (CS)
Attention in incorporating inorganic nanostructures into organic optoelectronic 
devices has been growing in the past few years. These so-called hybrid organic-
inorganic nanocomposites systems are being studied more closely due to its 
possibility in combining the advantageous characteristics of inorganic and 
organic components. Within a single composite, the properties can easily be 
changed or tuned by varying the size of nanoparticle, material composition, and 
concentration in the composites to match the device requirement. The most 
important thing, they can maintain the fabrication advantages of organic device 
such as easy processing, low production and material cost, and manufacturing 
the devices on large and flexible substrates. These are important in device 
application and commercialization. Therefore, research on improving the 
performance of organic solar cells by incorporating inorganic nanostructures 
in the organic materials has become important topic of research. The 
nanocomposited photoactive layer thin film which is MEH-PPV:MWCNT were 
prepared and characterized. The parameters that involved in the optimization 
are different composition of MWCNT in tetrahydrofuran (THF) and toluene, 
different composition of Iodine doped Multiwalled Carbon nanotubes (I-MWCNT) 
with low and high concentration of I-MWCNT. The characterizations involved 
are current-voltage (I-V), absorbance, transmittance and photoluminescence 
Essential oils (EO) is a substance extracted from a botanical material and 
always in high demanded. Steam distillation is a widespread method to isolate 
the essential oil from aromatic plants. The steam distillation method is most 
preferable due to factors of operational cost, cleanliness, system cost, high 
productivity and maintenance cost. However, some disadvantages of this method 
is loss of some volatile compounds, which will be diluted with boiling water within 
the distillation tank. The issue of steam distillation has not been given sufficient 
treatment in literature. The hydro-diffusion system was implemented as a viable 
alternative to overcome these setback. In the extraction process, the temperature 
will influence the final product of the extraction. The extraction temperature 
gives large effect on the percentage yield and quality of the oils. Almost all 
This thesis presents a new technique to determine the optimal locations and 
sizing of multiple DG units in a distribution system based on the concepts and 
principles of quantum mechanics in the Evolutionary Programming (EP) namely 
Quantum-Inspired Evolutionary Programming (QIEP). The concept of Quantum-
Inspired is implemented according to three levels namely quantum individuals, 
quantum groups and quantum global in order to accelerate the convergence time 
of the EP. To enhance the robustness of the algorithm, the QIEP technique is 
constructed based on multiobjective model in which the multiobjective functions 
consist of reducing power losses, increasing maximum loadability and cost 
minimisation. All simulations in this study were carried out using IEEE 69-bus 
distribution test system and 141-bus distribution test system. The performances 
of the multiobjective QIEP optimisation technique were compared with those 
obtained from EP optimisation technique in terms of fitness values, consistency 
Faculty of 
Electrical Engineering
Name :
Muhammad Sharfi Najib
Title :
Agarwood Classification Based 
On Odor Profile Using Intelligent 
Signal Processing Technique
Supervisor : 
Prof. Dr. Hj. Mohd Nasir Taib  (MS)
Dr. Nor Azah Mohd Ali (FRIM)  (CS)
This thesis presents the classification of Agarwood from Malaysia and Indonesia 
regions based on signal processing technique. Signal processing for the 
Agarwood classification is a new area and has yet been actively implemented. 
In this thesis, the Agarwood has been pre-identified by experts using 32 sensor 
arrays to measure the Agarwood odor profile. General Agarwood pattern has 
been plot in 2D diagram. The odor profile from different samples have been 
normalized and pre-processed and visualized in 3D and 2D plot to find unique 
patterns. The variation of patterns that has been visualized has been marked 
as different group samples. From 32 data sensor arrays, several significant 
data sensor array have been pre-processed using principal component analysis 
(PCA) as data reduction process. The selected data from PCA are applied as 
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L e a d i n g  Y o u  T o  G r e a t e r  H e i g h t s ,  D e g r e e  b y  D e g r e e 11
measurements and physical properties measurements which 
involved Field Emission Scanning Electron Microscopy 
(FESEM) and Atomic Force Microscopy (AFM). It was found that 
annealed MWCNT gave the best results in physical, electrical 
and optical properties. Meanwhile, comparing THF and 
toluene, THF convey the best results in all characterizations. 
The composition of I-MWCNT that was chosen to be used in 
organic solar cells was 60 wt% of I-MWCNT. In this work, bulk-
heterojunction solar cells based on poly (2-methoxy-5-(2’-ethyl-
hexyloxy)-p-phenylene vinylene) (MEH-PPV) and a highly 
conductive multiwalled carbon nanotubes (I-MWCNT) were 
fabricated and characterized by white light I-V and external 
quantum efficiency measurements. The influences of different 
temperature treatment of the nanocomposite layer, the various 
concentrations of Iodine and different metal contact used as 
cathode on the solar cell device performance were studied. 
It was found that the optimized temperature occurred at 75oC 
with optimized Iodine concentration of 1g. The best metal 
contact with high efficiency was given when Platinum (Pt) was 
compounds of essential oils are unstable at high temperature 
and should be regualted below the saturated temperture 
throughout the extraction process. In order to regulate the 
temperature, a suitable controller is required. Three controllers 
namely PID, HFPID and STFPID are proposed and integrated 
to hydro-diffusion system to control the steam temperature. 
All developed controllers are expected to improve system 
performance in both transient and steady state dynamics. The 
ARX structure has been used to represent the system dynamic 
and successfully implemented in the simulation studies. Real-
time implementation of the simulated controllers have been 
and computation time. In addition, the comparison also has 
been made between single objective and multiobjective 
optimisation. On top of that, the multiobjective QIEP is also 
applied to determine the optimal undervoltage load shedding 
(UVLS) in various loading conditions according to load profile 
with and without DG. From the analysis, it was found that the 
multiobjective QIEP had yielded better optimal solutions and 
more consistent with faster convergence time as compared 
to other techniques. In order to ensure that the proposed 
technique is suitable for on-line application, a novel intelligent 
based technique is presented to predict the optimal output of 
DG and optimal undervoltage load shedding at various loading 
conditions. At this stage, a classical Artificial Neural Network 
used. The achieved highest short circuit current density and 
energy conversion efficiency is 0.052mA/cm2 and 0.001%. 
Lastly, a new structure used Titanium dioxide (TiO2) as n-type 
layer in organic solar cells was prepared. This layer act as 
hole blocking layer that prevents a direct contact between 
MEH-PPV:I-MWCNT and Indium Tin Oxide (ITO) substrates. 
The nanocomposited MEH-PPV:I-MWCNT with 60 wt% of 
I-MWCNT was prepared on ITO using Gold (Au) as the anode. 
It was found that interfacial area between MEH-PPV and TiO2 
has slight improved. Therefore, there is tendency to adapt 
device efficiency, short-circuit current (JSC), open-circuit 
voltage (VOC) and the fill factor (FF). The initial values for both 
short circuit current density and power conversion efficiency 
are 0.115006 mA/cm2 and 0.414 x 10-3 % respectively.
carried out on the real extraction process. The performance 
of the proposed controllers were evaluated. All the controllers 
have shown their ability to track the set point change and 
curb the disturbance in real-time. However, the STFPID 
with 5 membership controller is the most preferable, and 
demonstrated better performance compared to the HFPID 
and PID controller. By applying the proper temperature control 
during extration process give better quality and preventing 
quality degradation of the essential oils. 
(ANN) is developed using systematic training and testing 
procedures. Next, a novel hybrid Artificial Neural Network - 
Quantum-Inspired Evolutionary Programming (QIEP-ANN) is 
developed for comparison. Later, a Least-Squares Support 
Vector Machine (LS-SVM) model was developed using cross-
validation technique. Finally, a novel hybrid Quantum-Inspired 
Evolutionary Programming - Least-Squares Support Vector 
Machine (QIEP-SVM) was presented. The results showed 
that the QIEP-SVM model had shown better prediction 
performance as compared to classical ANN, LS-SVM and 
QIEP-ANN.
input to compute sensor centroid for k-NN and ANN model 
design. To test the robustness of the classification techniques, 
the data sets are randomized for both k-NN classifier and 
ANN model. The classification results of the k-NN classifier 
and the ANN model utilizing significant sensor centroid new 
features for Agarwood grades and regions. It was found that 
the k-NN classifier and the ANN model is able to classify 100% 
of Agarwood grade and region.


Agarwood classification based on odor profile using intelligent signal processing technique / Muhammad Sharfi Najib

http://ir.uitm.edu.my/19494/1/ABS_MUHAMMAD%20SHARFI%20NAJIB%20TDRA%20VOL%206%20IGS_14.pdf

Agarwood classification based on odor profile using intelligent signal processing technique / Muhammad Sharfi Najib

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