Wan Khairunizam Wan Ahmad
Thumbnail Image
Preferred name
Wan Khairunizam Wan Ahmad
Official Name
Wan Khairunizam, Wan Ahmad
Alternative Name
Wan, Khairunizam
Ahmad, Wan Khairunizam Wan
Khairunizam, W. A. N.
Main Affiliation
Scopus Author ID
57200576499
Researcher ID
E-6072-2011

Research Output

Filters

14 3
31
Reset filters

Settings
Now showing 1 - 10 of 31
  • Publication
    Niblack algorithm modification using maximum-minimum (Max-min) intensity approaches on low contrast document images
    ( 2020-01-01)
    Wan Azani Wan Mustafa
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mat Yusoff A.S.
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Mohamad Nur Khairul Hafizi Rohani
    In recent decades, detection or segmentation has been one of the major interesting research subjects due to the analysis of the information. However, most of the historical document has degraded and low contrast problem. Recently, many binarization methods were proposed in order to segment the text region from the background region in the low-quality image. In this paper, an improved binarization method was inspired by Niblack method was presented. The modification focuses to find the optimum threshold value by using the Maximum-Minimum intensity technique. The main target is to reduce the unwanted detection image and increase the resultant performance compared to the original Niblack method. The proposed method was applied to the document images from H-DIBCO 2012 and H-DIBCO 2014 dataset. The results of the numerical simulation indicate that the target was achieved by the F-Measure by F-measure (58.706), PSNR (10.778) and Accuracy (86.876). This finding will give a new benchmark to other researchers to propose an advance binarization method.
  • Publication
    Blood vessel detection monitoring system and mobile notification for diabetic retinopathy diagnosis
    ( 2020-01-01)
    Mahmud A.S.
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Aminudin Jamlos
    ;
    Syed Zulkarnain Syed Idrus
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohd Al-Hafiz Mohd Nawi
    Disease diagnosis based on retinal image analysis is very popular in order to detect a few critical diseases such as diabetic retinopathy, high blood pressure, cancer and glaucoma. The important part of the retinal is a blood vessel. Besides, the blood vessel study plays an important part in different medical areas such as ophthalmology, oncology, and neurosurgery. The significance of the vessel analysis was helped by the continuous overview in clinical studies of new medical technologies intended for improving the visualization of vessels. In this paper, a new blood vessel detection based on a combination of Kirsch’s templates and Fuzzy C-Means (FCM) was proposed. The main objective of this study is to improve the detection result of FCM and achieved more effective performance compared to the Kirsch’s templates result. The proposed method experimented on 20 images is utilized namely from Digital Retina Images for Vessel Extraction (DRIVE) dataset. The resulting images are compared with the benchmark images based on a few image quality assessment (IQA) such as accuracy, sensitivity and specificity. The total average of accuracy is 92.64%, while sensitivity and specificity obtained was 95.73% and 60.45% respectively. The three parameters of the IQA will then be displayed in a column on the GUI. The second part of the system is for the mobile notification system to send SMS to a mobile phone. In order for the user to obtain the image analysis results, there must be a notification system on the mobile phone. By using the GSM module integrated with Arduino Uno, notification regarding image analysis will be sent to the mobile phone.
  • Publication
    Effect of different filtering techniques on medical and document image
    ( 2021-01-01)
    Wan Azani Wan Mustafa
    ;
    Sam S.
    ;
    Mohd Aminudin Jamlos
    ;
    Wan Khairunizam Wan Ahmad
    Image enhancement is very important stages used in image processing. A normal image enhancement process is using the filtering technique. Filtering helps the problems of the image display and can improvise the quality of the image. The problems that always happened in the image is illumination, noise and under-light images. In addition, these problems also caused a few troubles for image recognition for the daily life of certain people for their work. The objective of this study is to explore and compare a few starts of art filtering techniques based on the mathematical algorithm of the filters and then identifying the best method of the filters. There were a few methods that were selected in this project such as a high pass filter, low pass filter, high boost filter and others. All the selected filter experimented on the medical images and document images. The resulting images were evaluated using the Image Quality Assessments (IQA) which is a global contrast factor (GCF) and signal to noise ratio (SNR). Based on the numerical result, homomorphic low pas filter (HLF) provides a better performance among the other filters in terms of GCF (2.066) and SNR (8.907) value of the selected images.
  • Publication
    Time Domain Analysis for Emotional EEG Signals of Stroke Patient and Normal Subject
    ( 2023-01-01)
    Vincen E.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Yean C.W.
    ;
    Wan Azani Wan Mustafa
    This paper aims to analyze the emotional Electroencephalogram (EEG) signals of different time windows. The time window of the signals is one of the variables that affect the efficiency of the EEG signal analysis. In this research, a total of 30 subjects are analyzed from three different groups namely 10 left brain damage (LBD), 10 right brain damage (RBD), and 10 normal control (NC) for six different emotional states. The 14-Channel Wireless Emotiv EPOC device with a sampling frequency of 128 Hz is used to extract EEG signal from the subjects. The 6th Order Butterworth Bandpass filter is used to extract the EEG signals with the frequency band of 8-49 Hz, which are alpha to gamma waves. The EEG signals are segmented in 2s, 4s, 6s, and 8s time windows for all frequency bands. In addition, the K-Nearest Neighbor (KNN) and Probabilistic Neural Network (PNN) classifiers are used to classify the six emotions in LBD, RBD and NC. The beta and gamma bands are the best performing EEG frequency band for emotion classification. In the investigation, 6s time windows have the highest classification accuracy for KNN with 81.90% and 8s time window for PNN classifier with 82.15%.
  • Publication
    An Aggressiveness Level Analysis Based on Buss Perry Questionnaire (BPQ) and Brain Signal (EEG)
    ( 2021-12-01)
    Munian L.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Xu T.K.
    ;
    Wan Azani Wan Mustafa
    ;
    Rahim M.A.
    Aggression is the most important human aspects that make daily things possible for individuals, to succeed and have a better level of behaviour. Aggression is feelings of anger or antipathy resulting in hostile or violent behaviour. The importance of aggression is to increase an individual dominance of the subject in their social environment. Traditionally, the subject's aggression is usually measured by using a survey through Buss-Perry Questionnaire (BPQ). Considering the variability of the aggressiveness level, this study proposes investigation of aggression by using BPQ and Electroencephalography (EEG) to evaluate the aggressiveness level of the subjects. The results of the BPQ are analysed based on the final score that are responded by the subjects. In EEG experiment, the evaluation of subject's aggressiveness while playing a smart phone game “Subway Surfers”, a basic method has been employed, namely correlation coefficient method. The EEG signals are recorded while the subject playing the game.The number of subjects involves in the experiment is 9 and they are the UniMAP's male students at the age of 21-25 years old. In the analysis, the induced aggression is compared between BPQ with Net Aggressiveness Index (NAI), which is obtained from brain signals (EEG). The BPQ obtains the subjects #2 and subject #8 are the highest Buss-Perry Aggresinenes Index (BPAI) scores, which are 0.32244 and 0.32223 respectively. Meanwhile in EEG analysis the subject #8 only achieves the highest score of 0.34713. From the results of the investigation, it could be concludedthat the use of EEG to identify the aggressiveness level will overcome the disadvantage of the conventional methods.
  • Publication
    Progress Monitoring in Upper Limb Stroke Rehabilitation by Using Muscle Activation and Hand Speed
    ( 2020-06-17)
    Lee H.L.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Cahyadi B.N.
    ;
    Wan Azani Wan Mustafa
    ;
    Syed Zulkarnain Syed Idrus
    Nowadays, Virtual Reality (VR) technology is commonly used in the rehabilitation to increase the motivation of the stroke patients do the exercises, however, very few researches to monitor the rehabilitation progress has reported. VR based rehabilitation is interesting because could motivate stroke patients in their long-term rehabilitation process. This research is to evaluate the progress monitoring of the subject after conducting three sessions of rehabilitation exercise. Five male and female healthy subjects were selected to do the rehabilitation game. Three VR games are designed for the subject to perform three different movement sequences. The selected upper limb characteristics are muscle activation and hand speed. An Electromyography (EMG) is used to measure the muscle activation through an electrical activity of the muscle, while a Kinect sensor is used to measure the hand speed. The experimental results show that the proposed upper limb characteristics are able to be used for monitoring progress in the rehabilitation.
  • Publication
    Temperature control using fuzzy controller for variable speed vapor compression refrigerator system
    ( 2022-01-01)
    Siti Qurrata Ain
    ;
    Mohd Saifizi Saidon
    ;
    Siti Marhainis Othman
    ;
    Aziz A.A.
    ;
    Wan Azani Wan Mustafa
    ;
    Wan Khairunizam Wan Ahmad
    Keeping the cold chain vaccine is crucial to a stable immunisation programme; however, faulty processes may occur more frequently than are often thought in developing nations. This paper discusses the quick and accurate control process for designing fuzzy controllers for variable speed vapor compression refrigerator system. The suggested controller is based on the fuzzy logic intended to improve performance while keeping the cooler’s constant internal temperature and increasing the refrigerator efficiency. Despite the external changes such as the outside temperature change or the volume change in the refrigerator vaccine, the fuzzy logic controller is utilised to maintain the interior temperature. However, a variable speed compressor (VSC) must be used to control the thermophysical characteristics, which dramatically alter the temperature with a small pressure change. In this case, fuzzy rules of the sort developed by Mamdani are used to build up the system. The programming platforms utilised to implement the model include MATLAB, SIMULINK, and Fuzzy Logic Toolbox (FLT). The efficiency of fuzzy logic controller design membership will be compared to ensure that the refrigerator temperature is more accurate and until it achieves the best performance, maintains a temperature of 5°C, and adapts to its surroundings. From the research done, the membership 2 with load shows the near accurate temperature of 5°C with steady-state error ±1.97°C.
  • Publication
    Automatic People Counting System Using Aerial Image Captured by Drone for Event Management
    ( 2021-01-01)
    Mohd Saifizi Saidon
    ;
    Wan Azani Wan Mustafa
    ;
    Rajasalavam V.R.
    ;
    Wan Khairunizam Wan Ahmad
    Event management refers to the ability to apply project management skills in order to initiate large scale social or business events. Hence, it requires the use of organizational as well as business management skills to envision, plan, and finally execute any such event. Therefore, to count or estimate the number of people who attend such events is one of important tool in event management. In common, counting number of people in events can be done by counting manually traditional headcount system. Nevertheless, this process or technique consumes much time and is also a difficult task to execute for a considerable number of people or a bigger crowd. Therefore, a modern counting system like automatic people counting system is developed to enhance the process of counting people. Thus, various method of counting has been proposed in the past decades. Consequently, automatic counting people using digital image processing technique is introduced to overcome this problem. Thus, to monitor or to count the number of people can be done by using Unmanned Aerial Vehicle (UAV) or drones. The use of drones can take a broader picture, saving time and becoming more efficient. For this research, the DJI Mavic Pro Drone is used to scout the areas. This paper is focusing on counting the number of people images. Thus, the images are firstly compared between RGB and HSV colour model. Then, the HSV colour model has been chosen for the thresholding process. Here, the images are compared between Otsu thresholding and manual thresholding. Both thresholding method gives a good segmentation result, but Otsu’s method is chosen because of its higher accuracy. Moreover, noise removal technique is employed in order to get good smoothing performance and produce better counting results. This paper is fully developed with MATLAB R2013a software. This technique has proven to be a good image processing technique with total accuracy of 91%. The hardware system is also developed to transmit the counting results.
      1
  • Publication
    Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal
    ( 2024-06-01)
    Abdul Hafiz Abd Halin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Muhajir Ab. Rahim
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Saidatul Ardeenawatie Awang
    The Brain-Computer Interface (BCI) is a field of research that studies the EEG signal in order to elevate our understanding of the human brain. The applications of BCI are not limited to the study of the brain wave but also include its applications. The studies of human emotions specific to the vehicle driver are limited and not vastly explored. The EEG signal is used in this study to classify the emotions of drivers. This research aims to study the emotion classifications (surprise, relax/neutral, focus, fear, and nervousness) while driving the simulated vehicle by analyse the EEG signals. The experiments were conducted in 2 conditions, autonomous and manual drive in the simulated environment. In autonomous driving, vehicle control is disabled. While in manual drive, the subjects are able to control the steering angle, acceleration, and brake pedal. During the experiments, the EEG data of the subjects is recorded and then analyzed.
      1  32
  • Publication
    An Experimental Framework for Assessing Emotions of Stroke Patients using Electroencephalogram (EEG)
    ( 2020-06-17)
    Wan Khairunizam Wan Ahmad
    ;
    Yean C.W.
    ;
    Murugappan M.
    ;
    Ahmad Kadri Junoh
    ;
    Zuradzman Mohamad Razlan
    ;
    Wan Azani Wan Mustafa
    ;
    Shahriman Abu Bakar
    ;
    Ibrahim Z.
    ;
    Nurhafizah S.
    This research aims to assess the emotional experiences of stroke patients using Electroencephalogram (EEG) signals. Since emotion and health are interrelated, thus it is important to analyse the emotional states of stroke patients for neurofeedback treatment. Moreover, the conventional methods for emotional assessment in stroke patients are based on observational approaches where the results can be fraud easily. The observational-based approaches are conducted by filling up the international standard questionnaires or face to face interview for symptom recognition from psychological reactions of patients and do not involve experimental study. This paper introduces an experimental framework for assessing emotions of the stroke patient. The experimental protocol is designed to induce six emotional states of the stroke patient in the form of video-audio clips. In the experiments, EEG data are collected from 3 groups of subjects, namely the stroke patients with left brain damage (LBD), the stroke patients with right brain damage (RBD), and the normal control (NC). The EEG signals exhibit nonlinear properties, hence the non-linear methods such as the Higher Order Spectra (HOS) could give more information on EEG in the signal's analysis. Furthermore, the EEG classification works with a large amount of complex data, a simple mathematical concept is almost impossible to classify the EEG signal. From the investigation, the proposed experimental framework able to induce the emotions of stroke patient and could be acquired through EEG.
      1  17