Muhammad Juhairi Aziz Safar
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Preferred name
Muhammad Juhairi Aziz Safar
Official Name
Muhammad Juhairi Aziz, Safar
Alternative Name
Safar, Muhammad Juhairi Aziz
Safar, M. Juhairi Aziz
Aziz Safar, M. J.
Juhairi Aziz Safar, Muhammad
Safar, Muhamad Juhairi Aziz
Safar, M. J.A.
Main Affiliation
Scopus Author ID
57212344599
Researcher ID
R-1349-2019

Research Output

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Performance analysis of multi-level thresholding for microaneurysm detection

2022-09-01 , Choong K.H. , Shafriza Nisha Basah , Haniza Yazid , Muhammad Juhairi Aziz Safar , Fathinul Syahir Ahmad Sa'ad , Lim Chee Chin

Diabetic retinopathy (DR) – one of the diabetes complications – is the leading cause of blindness among the age group of 20–74 years old. Fortunately, 90% of these cases (blindness due to DR) could be prevented by early detection and treatment via manual and regular screening by qualified physicians. The screening of DR is tedious, which can be subjective, time-consuming, and sometimes prone to misclassification. In terms of accuracy and time, many automated screening systems based on image processing have been developed to improve diagnostic performance. However, the accuracy and consistency of the developed systems are largely unaddressed, where a manual screening process is still the most preferred option. The main contribution of this paper is to analyse the accuracy and consistency of microaneurysm (MA) detection via image processing by focusing on Otsu’s multi-thresholding as it has been shown to work very well in many applications. The analysis was based on Monte Carlo statistical analysis using synthetic retinal images of retinal images under variation of all stages of DR, retinal, and image parameters – intensity difference between MAs and blood vessels (BVs), MA size, and measurement noise. Then, the conditions – in terms of obtainable retinal and image parameters – that guarantee accurate and consistent MA detection via image processing were extracted. Finally, the validity of the conditions to guarantee accurate and consistent MA detection was verified using real retinal images. The results showed that MA detection via image processing is guaranteed to be accurate and consistent when the intensity difference between MAs and BVs is at least 50% and the sizes of MAs are from 5 to 20 pixels depending on measurement noise values. These conditions are very important as a guideline of MA detection for DR.

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Design and Development of a Service Robot for Wi-Fi RSSI Fingerprint Data Collection

2020-09-18 , Bakri M.Q. , Abdul Halim Ismail , Mohd Sani Mohamad Hashim , Muhamad Safwan Muhamad Azmi , Muhammad Juhairi Aziz Safar , Marhaban M.H.

We have designed a service robot that can be used for Wi-Fi RSSI Fingerprint database construction for indoor positioning system. This work aims to aid and ease the signal fingerprint database construction process which currently conducted manually by carrying the data acquisition tools around the experimental field. The robot architecture design considered the values and constraint in performance, aesthetic, cost, and expandability. Analysis of the robot's mobile specification was made in order to choose the optimum hardware components. The robot has three main sections which are mobile platform, storage compartment, and user interactive screen that is capable to display facial expression and other useful information.

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Homogenized properties of porous microstructure: effect of void shape and arrangement

2017-10-29 , M. Thomas , Khairul Salleh Basaruddin , Muhammad Juhairi Aziz Safar , Shah Fenner Khan Mohamad Khan , Ishak Ibrahim

This paper aims to investigate the effect of void shape and arrangement on the effective elastic properties of porous microstructure. The characteristics of the voids are in different shapes, sizes and arrangement. The porous microstructure models were developed using CATIA. Then, Voxelcon was employed to analyse the multiscale finite element model and determine the homogenized properties. Based on the results, void shape, size, and arrangement of porous microstructure were found sensitive to the elastic (homogenized) properties. Ellipsoidal shape having the highest Young's modulus, whereas the spherical shape has the highest Poisson's ratio and shear modulus. Cubical shape was the lowest for all the elastic properties. Moreover, the formation arrangement in void cubical shape produced the highest Young's modulus and shear modulus.

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Comparison Between K-Nearest Neighbor (KNN) and Decision Tree (DT) Classifier for Glandular Components

2022-01-01 , Hun C.C. , Haniza Yazid , Muhammad Juhairi Aziz Safar , Ab Rahman K.S.

Prostate cancer is one of the most common cancers in men, and the cases of this disease is increasing. Histopathological examination of prostate cancer is one of the main procedures for prostate cancer detection. The structural changes of the cytoplasm, stroma, lumen and nucleus in the glandular tissue will indicate the presence of cancerous or non-cancerous areas in the histopathology of prostate cancer. Therefore, a framework was developed to automatically segment and classify glandular tissue into cytoplasm, stroma, lumen, and nucleus, which can reduce the complexity of prostate cancer detection. The images underwent image enhancement using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, in segmentation phase, K-means clustering (KMC) and multi-level thresholding (MT) methods were implemented to segment the enhanced image into cytoplasm and stroma, lumen and nuclei regions. A total of 8 feature vectors are extracted from each segmented image. All these features were introduced into the classification system namely K nearest neighbor (KNN) and decision tree (DT). The overall results showed that the performance of KNN is better than DT with an accuracy of 86.67%, sensitivity and specificity are both 100% (the features of the KMC category). With the features of MT category, KNN achieved 84.44% in term of accuracy, 100% sensitivity and 96.67% specificity. Here, it can also be concluded that the features of the KMC category are more suitable for the classifiers. In addition, leave-one-out cross-validation has been implemented, which can improve the performance of the two classifiers.

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Performance analysis of Otsu thresholding for sign language segmentation

2021-06-01 , Tan Z.Y. , Shafriza Nisha Basah , Haniza Yazid , Muhammad Juhairi Aziz Safar

Sign language recognition system generally consists of three main processes, which are segmentation, modelling, and classification. Image segmentation plays a crucial role as the initial step in sign language recognition. Despite the many sign language recognition system algorithms proposed in the literature and their well-understood usage, their performance analyses are relatively limited. As such, the main motivation of this paper is to critically analyse the feasibility of successful sign language segmentation under variation of dynamic scene parameters such as noise, hand size, and intensity difference between hand and background. The focus is on image thresholding using Otsu technique, since it is the most commonly used in initial process of sign language segmentation. The analysis of this work was developed based on Monte Carlo statistical method, which showed that the success of sign language segmentation depends on hand size, hand background intensity difference, and noise measurement. The result showed that the sign alphabets with handheld shape like A, E, I, M, N, S, and T is easier to segment, while sign alphabets with finger-extend shape like C, D, F, G, H, K, L, P, R, U, V, W, and Y is harder to segment. Experiment using real images demonstrate the capability of the conditions to correctly predict the outcome of sign language segmentation using Otsu technique. In conclusion, the success of sign language segmentation could be predicted beforehand with obtainable scene parameters.

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Single Channel Magnetic Induction Measurement for Meningitis Detection

2021-01-01 , Aiman Abdulrahman Ahmed , Zulkarnay Zakaria , Ali M.H. , Pusppanathan J. , Rahim R.A. , Muji S.Z.M. , Anas Mohd Noor , Mohd Hafiz Fazalul Rahiman , Muhamad Khairul Ali Hassan , Muhammad Juhairi Aziz Safar , Ahmad Faizal Salleh

Bacterial meningitis is one of the most common and prominent infections which infects the central nervous system through the tissue layers and membranes that cover our brain and spinal cord. It is a staggering and fatal illness that kills patients within hours. The number of meningitis cases that has been recorded annually around the world are one million cases and 135,000 deaths. Early detection and start of sufficient treatment are considered as the main determinants for better result. MIT mechanism is noncontact electrodes of impedance measurement. This mechanism uses induction principle instead of contact electrodes to get the required information. This paper presents an overview on the potential of Magnetic induction tomography (MIT) in detecting meningitis disease. In MIT principle, single channel measurement process which consist of transmitter (Tx) and receiver (Rx) coil has been studied. In this field is disclosed about passive electrical field (PEP) which focuses on the three parameters which are dielectric permittivity, electrical conductivity, and magnetic permeability. In addition, this research project involves experimental setup. The applied frequency is between 1–10 MHz. Finally, in this project, the performance of the square coil with 12 number of turns (5Tx–12Rx) with 10 MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.

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Robot Face and Its Integration to the Mobile Robot for Wireless Signal Collection in the Fingerprinting-Based Indoor Positioning System

2021-12-01 , Sarhan M.A.H. , Abdul Halim Ismail , Mohd Nasir Ayob , Hashim M.S.M. , Muhamad Safwan Muhamad Azmi , Hassrizal Hassan Basri , Siti Marhainis Othman , Muhammad Juhairi Aziz Safar

The wireless data collection for instance the Received Signal Strength (RSS) of the Wireless Fidelity (Wi-Fi) remained unfavourable in the Indoor Positioning System utilizing the signal fingerprinting approach. This is because the enormous sampling time and routines works making it tedious human labour. To alleviate this issue, we propose to use a robot for wireless data collection. The robot, named 'ICSiBOT' is a service robot with multiple purpose such as assisting human in daily lives, guest or hospitality robot and man others. This paper mainly describes the ICSiBOT robot face with speech recognition technology and the integration of the robot face to the motion controller. The experimental was conducted to see the correlation between the synthesized instructions from the speech in terms of distance need to be travelled i.e., the location for wireless signal collection and translate them into actual distance travelled. The results showed that the robot is able to travel to the specific distance as instructed to the robot face.

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Holonomic Mobile Robot Planners: Performance Analysis

2022-01-01 , Aljamali Y.S. , Muhammad Juhairi Aziz Safar , Khairul Salleh Basaruddin , Yazid H. , Basha S.N. , Fathinul Syahir Ahmad Sa'ad , Hassan M.K.A.

Many algorithms have been proposed to tackle the path planning problem in mobile robots. Among the well-known and established algorithms are the Probabilistic Road Map (PRM) algorithm, A* algorithm, Genetic algorithm (GA), Rapidly-exploring random tree (RRT), and dual Rapidly-exploring random trees (RRT-connect). Hence, this paper will focus on the performance comparison between the aforementioned algorithms concerning computation time, path length, and fail and success rate for producing a path. For the sake of fair and conclusive results, simulation is conducted in two phases with four different environments, namely, free space environment, low cluttered environment, medium cluttered environment, and high cluttered environment. The results show that RRT-connect has a high success rate in producing a feasible path with the least computation time. Hence, RRTs-based sampling algorithms, in general, and RRT-connect, in specific, will be explored in-depth for possible optimization.

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An Overview of Medical Applications in Meningitis Detection

2020-07-09 , Abdulrahman Ahmed A. , Zulkarnay Zakaria , Hamood Ali M. , Pusppanathan J. , Zarina Mohd Mhji S. , Anas Mohd Noor , Mohd Hafiz Fazalul Rahiman , Muhamad Khairul Ali Hassan , Muhammad Juhairi Aziz Safar , Ahmad Faizal Salleh

Meningitis remains one of the common infections among young children with high morbidity and mortality rates. In Southeast Asian, only few studies were reported published which evaluated meningitis clinically in the last two decades. Similarly, few studies in Malaysia evaluated meningitis among adolescents and children. Globally, more than one million cases with 135,000 deaths has been recorded yearly, and in Malaysia, severe neurological complications occurs in 9-25% of cases which affirms the most serious risk manifests from bacterial meningitis. Therefore, early detection and effective treatment are required before the irreversible damages occur. This paper reviews the current states and perspectives of diagnostic techniques on meningitis detection. Currently, there are three diagnostic techniques available for meningitis detection, such as blood cultures, spinal tap (lumbar puncture), and imaging techniques (CT scan, MRI, EIT, Ultrasonography, Nuclear imaging and X-ray). However, these techniques have limitations that may limit the chances of carrying out the early detection of the disease. The essence of this review is that meningitis requires an effective technique that is capable of carrying out the early detection of the disease by differentiating normal people and Meningitis infected patients so as to promote longevity worldwide. In this review magnetic induction tomography (MIT) technique is proposed to diagnose meningitis earlier as it is non-intrusive, non-invasive, contactless, and electrode-less imaging technique which does not expose the patients to a harmful radiation.

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Effect of Sample Sizes in Fingerprinting Database for Wi-Fi System

2021-01-01 , Sa’ahiry A.H.A. , Abdul Halim Ismail , Muhammad Juhairi Aziz Safar , Latifah Munirah Kamarudin , Mohd Sani Mohamad Hashim , Muhamad Safwan Muhamad Azmi , Toyoura M.

Indoor positioning system has been an essential work to substitute the Global Positioning System (GPS). GPS utilizing Global Navigation Satellite Systems (GNSS) cannot provide an accurate positioning in the indoor due to the multipath effect and shadow fading. Fingerprinting method with Wi-Fi technology is a promising system to solve this issue. However, there are several problems with the fingerprinting method. The fingerprinting database collected has different sample sizes where the previous researcher does not indicate any standard for the sample size to be used. In this paper, the effect of the sample sizes in fingerprinting database for Wi-Fi technology has been discussed deeply. The statistical analyzation for different sample sizes has been analyzed. Furthermore, two methods which are K- Nearest Neighbor (KNN) and Deep Neural Network (DNN) are being used to examine the effect of the sample sizes in term of accuracy and distance error. The discussion in this paper will contribute to the better sample size selection depending on the method taken by the user. The result shows that sample sizes are an important metrics in developing the indoor positioning system as it effects the result of the location estimation.

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