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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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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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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Modelling of Retinal Images for Analysis of Diabetic Retinopathy Severity Levels

2021-11-25 , Qaid M. , Shafriza Nisha Basah , Haniza Yazid , Muhammad Juhairi Aziz Safar , Mohd Hanafi Mat Som , Lim C.C.

Synthetic data by various algorithms that resemble actual data in terms of statistical features. Computer-aided medical applications have been extensively applied to model specific scenarios, such as medical imaging of retinal images for diabetic retinopathy (DR) detection. The available data and annotated medical data are typically rare and costly due to the difficulties of conducting medical screening and rely on highly trained doctors to review and diagnose. The modelling of retinal images for DR analysis is essential since it will provide a model to guide and test DR detection algorithms. This paper aims to model normal retina and non-proliferative diabetic retinopathy (NPDR) stages (mild, moderate, and severe) data models with the variation of dynamic models. The Digital Retinal Images for Vessel Extraction (DRIVE), The Standard Diabetic Retinopathy Database, Calibration Level 1 (DIARETDB1), and E-OPHTHA datasets are analyzed to obtain the specification of the human retina and DR lesions. In the data modelling phases, the model includes the bright and dark retinal lesions with the variation of dynamic parameters. 4100 synthetic images are used where 200 normal images and 3900 NPDR images to test the performance of DR detection algorithms over the full range of parameters.

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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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A Review on Magnetic Induction Spectroscopy potential for fetal acidosis examination

2022-02-01 , Siti Fatimah Abdul Halim , Zulkarnay Zakaria , Pusppanathan J. , Anas Mohd Noor , Ahmad Nasrul Norali , Mohd Hafiz Fazalul Rahiman , Muji S.Z.M. , Rahim R.A. , Engku-Husna E.I. , Muhamad Khairul Ali Hassan , Muhammad Juhairi Aziz Safar , Ahmad Faizal Salleh , Mohd Hanafi Mat Som

Fetal acidosis is one of the main concerns during labor. Currently, fetal blood sampling (FBS) has become the most accurate measurement of acidosis detection. However, it is invasive and does not provide a real time measurement due to laboratory procedures. Delays in diagnosis of acidosis have caused serious injury to the fetus, especially for the brain and the heart. This paper reviews the new technique in diagnosis of acidosis non-invasively. Magnetic Induction Spectroscopy (MIS) has been proposed to be a new device for acidosis detection in recent years. This paper explains the basic principle of MIS and outlines the design specifications and design considerations for a MIS pH probe. It is expected that readers will gain a basic understanding of the development of a MIS pH probe from this review.

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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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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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