FKEE - Official Portal

FACULTY OF ELECTRICAL &
ELECTRONICS ENGINEERING

sharfi_webo.jpg

Dr. Muhammad Sharfi bin Najib

Expertise: Intelligent Signal Processing, Inteligent System, Sensors, Classification

Google Scholar   |   Scopus   |   ResearcherID

Senior Lecturer,
Instrumentation and Control Engineering Research Cluster,
Faculty of Electrical and Electronics Engineering
Universiti Malaysia Pahang,
26600, Pekan, Pahang, Malaysia

Phone: +609-424-6065
Fax:   +609-424-6000
email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Academic Qualification | Teaching Courses | Research Supervision Publications

PUBLICATION

(Journal Title)

(Date/Year)

(Publisher)

Classification Of Agarwood Using ANN

2012

IJEESR

Electrical Circuit Model Of A Vanadium
Redox flow Battery Using Extended Kalman
filter

2013

ELSEVIER

Intelligent Classification Hazardous Gas
Using Sensors Array

2015

ICACTE

Classification Of Odor Profile Ammonia In
Fertilizer

2015

IJASET

A Doa System of Ammonia Emmission In
The Agriculture Sector

2014

ELSEVIER

A Potential Development Of Breathing Gas
Sensor Using An Open Path fibre
Technique

2016

ELSEVIER

Gravitational Search Algorithm: R Is Better
Than R2?

2016

ARPN

A Novel Online Dual Slope Delta Modulated
PWM Inverter

2009

IJEPE

Fish Quality Study Using Odor-Profile Case
Based Reasoning (Cbr) Classification
Technique

2016

ARPN

Classification Of Honey Odor-Profile Using
Case-Based Reasoning Technique (Cbr)

2016

ARPN

Intelligent Odor-Profile Classification Of
Kelulut Honey Using Case-Based
Reasoning Technique (Cbr)

2016

ARPN

Gaharu Sensor: Classification Using Case
Based Reasoning (Cbr)

2016

JEECIE

Classification Of Ammonia In Water For Oil
And Gas Industry Using Case Based
Reasoning (CBR)

2016

JEECIE

Classification Of Ammonia Odor-Profile
Using K-NN Technique

2016

JEECIE

Classification of waxy crufe oil odor-profile using gas sensor array

2018 IOPSCIENCE

 

sharfi_webo.jpg

Dr. Muhammad Sharfi bin Najib

Expertise: Intelligent Signal Processing, Inteligent System, Sensors, Classification

Google Scholar   |   Scopus   |   ResearcherID

Senior Lecturer,
Instrumentation and Control Engineering Research Cluster,
Faculty of Electrical and Electronics Engineering
Universiti Malaysia Pahang,
26600, Pekan, Pahang, Malaysia

Phone: +609-424-6065
Fax:   +609-424-6000
email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Academic Qualification | Teaching Courses | Research Supervision Publications

PhD Supervision

  • Ongoing - DEVELOPMENT OF MASS SPECTRAL LIBRARY FOR SESQUITERPENOIDS PRESENTS IN AGARWOOD (AQUALARIA MALACCENSISI) ESSENTIAL OIL AND INCENSE SMOKE.

Master Supervision

  • Completed - CLASSIFICATION OF AMMONIA IN HAZARDOUS WATER
  • Completed - CLASSIFICATION OF AMMONIA for PALM OIL
  • Completed - INTELLIGENT ODOUR PROFILE CLASSIFICATION OF HONEY USING CASE BASED REASONING AND K-NEAREST NEIGHBOURS
  • Completed - CLASSIFICATION OF ENGINE LUBRICANT OIL DEGRADATION USING ELECTRONIC NOSE
  • Ongoing - AN ULTRA VIOLET HALITOSIS (BAD BREATH) DETECTION USING AN OPEN-PATH OPTICAL FIBRE BASED SENSOR

FInal Year Project Supervision

  • Completed - CLASSIFICATION OF MEAT FRESHNESS USING ELECTRONIC NOSE
  • Completed - CLASSIFICATION OF MINERAL WATER PROFILE USING ELECTRONIC NOSE
  • Completed - CLASSIFICATION OF RICE FRESHNESS ODOR PROFILES USING ELECTRONIC NOSE
  • Completed - CLASSIFICATION OF DIFFERENT SMOKE ODOR PROFILES USING ELECTRONIC NOSE

 sharfi_webo.jpg

Dr. Muhammad Sharfi bin Najib

Expertise: Intelligent Signal Processing, Inteligent System, Sensors, Classification

Google Scholar   |   Scopus   |   ResearcherID

Senior Lecturer,
Instrumentation and Control Engineering Research Cluster,
Faculty of Electrical and Electronics Engineering
Universiti Malaysia Pahang,
26600, Pekan, Pahang, Malaysia

Phone: +609-424-6065
Fax:   +609-424-6000
email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Academic Qualification | Teaching Courses | Research Supervision Publications

Academic Qualification

  • 2014, Ph.D. in Electrical Engineering, Universiti Teknologi MARA, Malaysia
  • 2005, MSc. in Automation & Control, University of Newcastle Upon Tyne, United Kingdom
  • 2001, Eng (Hons) in Mechatronics, International Islamic University Malaysia, Malaysia with Honours

sharfi_webo.jpg

Dr. Muhammad Sharfi bin Najib

Expertise: Intelligent Signal Processing, Inteligent System, Sensors, Classification

Google Scholar   |   Scopus   |   ResearcherID

Senior Lecturer,
Instrumentation and Control Engineering Research Cluster,
Faculty of Electrical and Electronics Engineering
Universiti Malaysia Pahang,
26600, Pekan, Pahang, Malaysia

Phone: +609-424-6065
Fax:   +609-424-6000
email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Academic Qualification | Teaching Courses | Research Supervision Publications

Research Interest:

E-Nose

  • Development Of Dedicated E-Nose For Various Field And Application (Controller, Selection Of Siginificant Sensors, Mechanical Design Related To E-Nose).
  • Related Application & Field: Food Security, Environtment, Agriculture, Automotive

 Intelligent Signal Processing

  • Statistical Analysis Using Various Method, Pattern Recognition, Feature Selection, Feature Extraction

Classification

  • Artificial Neural Network, K-Nearest Neighbor, Case-Based Reasoning

National Research Grant:

  • Leader, Rdu130606 (Rm32,000), “Intelligent Classification System Using Case-Based Reasaning Technique To Technique To Detect Ammonia Gas Using K-Nn And Ann As Retrieval Method Based On E-Nose Sensor Centroid Feature Extraction Approach”, 2013 – 2015

Ump Research Grant:

  • Leader, Uic160904 (Rm116,000), “A Method Of Determining Grade Of Agarwood At Ambient Temperature”, 2016 – Current
  • Leader, Uic160301 (Rm116,000), “A Commercialized Prototype Unit Based On Odor Detection Using Cbr Intelligent Technique”, 2016 – Current
  • Leader, Prgs160380 (Rm3,000), “Intelligent Odor Profile Classification Of Different Honey Using E-Nose”, 2016 – 2018
  • Leader, Prgs160380 (Rm3,000), “Intelligent Odor Profile Classification Of Different Honey Using E-Nose”, 2016 – 2018
  • Leader, Rdu150374 (Rm32,000), “A Prototype Of Intelligent System Using Chemical Sensor Array For Engine Lubricating Oil Classification”, 2016 – 2018
  • Leader, Rdu150374 (Rm32,000), “A Prototype Of Intelligent System Using Chemical Sensor Array For Engine Lubricating Oil Classification”, 2016 – 2018
  • Leader, Rdu130370 (Rm32,000), “Development Of An Intelligent System For Agarwood Detection Using Case-Based Reasoning Technique”, 2013 – 2016
  • Leader, Rdu130370 (Rm32,000), Development Of An Intelligent System For Agarwood Detection Using Case-Based Reasoning”, 2013 – 2014

MAIN PROFILE

Bakri Hassan was born in Malaysia, in 1974. He received the B.Sc. in electrical engineering from Evansville University, USA in 1996, and the M.E.E in Power Electronics from Universiti Teknologi Malaysia, Johor in 2002, and the Ph.D. from Newcastle University, UK in 2016. Prior to joining academia, he was an engineer in an electrical-supply company involving in electrical installations, designs and testing.

He is currently a Lecturer in Electrical and Electronic Engineering Faculty at the Universiti Malaysia Pahang, Malaysia since 2002. His research interest includes power converters & drives systems, multilevel power converters: topology and control, and also their applications in renewable energy and electric vehicles.

Main Profile | Educational Background | Teaching Courses | Supervision | Research | Publication

Senior Lecturer,
Sustainable Energy & Power Electronics Research (SuPER) Cluster
Faculty of Electrical and Electronics Engineering
Universiti Malaysia Pahang,
26600, Pekan, Pahang, Malaysia
Phone: +609-424-6016
Fax: +609-424-6000
email: bakri[at]ump.edu.my