Ahmad Nasrul Norali
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Preferred name
Ahmad Nasrul Norali
Official Name
Ahmad Nasrul, Norali
Alternative Name
Norali, Ahmad Nasrul
Norali, A. N.
Norali, A. N.
Norali, Ahmad Nasrul Bin
Main Affiliation
Scopus Author ID
57210551450
Researcher ID
FQQ-2549-2022

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  • Publication
    Human breathing classification using electromyography signal with features based on mel-frequency cepstral coefficients
    ( 2017-01-01)
    Ahmad Nasrul Norali
    ;
    Abdullah A.H.
    ;
    Zulkifli Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Nataraj S.K.
    Typical method on assessing the human breathing characteristics is based on measurements of breathing air parameters. Another possible method for human breathing assessment is through the analysis of respiratory muscles electromyography (EMG) signal. The EMG signal from different breathing task will be analyzed in order to determine the characteristics of the EMG signal pattern. Thus, feature extraction need to be done on the EMG signals. This paper will look into the use of Mel-Frequency Cepstral Coefficients (MFCC) in providing the features for EMG signal. Analysis is done using different data analysis frame sizes. EMG signal classification is done using K-Nearest Neighbour. Results shows that MFCC is a good feature extraction method for this purpose with classification accuracy exceeds more than 90% for data analysis frame size of 2000 ms, 4000 ms, 5000 ms and 10000 ms.
  • Publication
    Simulation of Single Channel Magnetic Induction Tomography for Meningitis Detection by Using COMSOL Multiphysics
    ( 2021-11-25)
    Aiman Abdulrahman Ahmed
    ;
    Zulkarnay Zakaria
    ;
    Ali M.H.
    ;
    Anas Mohd Noor
    ;
    Siti Fatimah Abdul Halim
    ;
    Ahmad Nasrul Norali
    ;
    Pusppanathan J.
    ;
    Rahim R.A.
    Meningitis is a inflammation of the meninges and the most common central nervous system (CNS) due to bacterial infection. Numbers of children who have bacterial meningitis are still high in recent 15 years regardless of the availability of newer antibiotics and preventive strategies. This research focuses on simulation using COMSOL Multiphysics on the design and development of magnetic induction tomography (MIT) system that emphasizes on a single channel rotatable of brain tissue imaging. The purpose of this simulation is to test the capability of the developed MIT system in detecting the change in conductivity and to identify the suitable transmitter-receiver pair and the optimum frequency based on phase shift measurement technique for detecting the conductivity property distribution of brain tissues. The obtained result verified that the performance of the square coil with 12 number of turns (5Tx-12Rx) with 10MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.
      1
  • Publication
    An Open-Source, Miniature UV to NIR Spectrophotometer for measuring the transmittance of liquid materials
    ( 2022-01-01)
    Anas Mohd Noor
    ;
    Ahmad Nasrul Norali
    ;
    Zulkarnay Zakaria
    ;
    Fook Chong Yen
    ;
    Basri Noor Cahyadi
    The primary disadvantages of commercial spectrophotometers are expensive, heavy, and not portable. Furthermore, conventional instruments are only suitable to be used in a specialized laboratory. Even though some commercially available small-size instruments or devices are available, the price is still high. Therefore, a low-cost device is necessary without sacrificing accuracy and sensitivity. In this work, a low-cost, configurable, open-source and accurate portable spectrophotometer device was developed for education and laboratory analytical use. Commercially available photodetector is utilized as main component of the device due to broad spectral range from ultraviolet to near infra-red. The device performs well over a wide range of spectral wavelengths with small errors. We presume that the use of this work can offer a alternative for affordable and accurate device that is comparable to the commercially available products which also suitable for many applications.
      6  30
  • Publication
    Electromyography Signal Pattern Recognition for Movement of Shoulder
    ( 2021-11-25)
    Ahmad Nasrul Norali
    ;
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Muhammad Asymawi Mohd Reffin
    ;
    Asyraf Hakimi Abu Bakar
    ;
    Chong Yen Fook
    Pectoralis major and deltoid are two muscles that are associated with the movement of the shoulder. Electromyography (EMG) signal acquired from these two muscles can be used to classify the movement of the shoulder based on pattern recognition. In this paper, an experiment for EMG data collection involves eight healthy male subjects who perform four shoulder movements which are flexion, extension, internal rotation and external rotation. Feature extraction of EMG data is done using root mean square (RMS), variance (VAR) and zero crossing (ZC). For pattern recognition, the classifiers that are used are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Classification results shows highest accuracy on ZC feature using an SVM classifier with cubic kernel. The study on shoulder movement using EMG of pectoralis and deltoid muscles could be extended on arm amputees based on hypothesis that the EMG signal could be utilized for control of robotic prosthetic arm.
      1  36
  • Publication
    Classification of White Blood Cells Based on Surf Feature
    ( 2021-01-01)
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Aishah Mohd Noor
    ;
    Ahmad Nasrul Norali
    Conventional blood analysis using blood smear image were performed manually by experts in hematology is tedious and highly depending on the level of experience. Currently, computer-assist technology is developed to reduce the time-consuming process and improved accuracy. As an example, various image processing techniques used to quantify such as white blood cells (WBCs) morphological conditions or classification in the blood smear image, which assist experts in developing confidence decision making in the analysis of cells conditions linked to the specific diseases. However, the WBCs shape features are arbitrary than the red blood cells (RBCs) because of the maturation state, cell orientations or positions, cell color variations, and the quality of the image captured influences the performance of classification accuracy. Therefore, we proposed a scale and rotation invariance feature for WBCs classification using speed up robust feature (SURF). SURF is suitable to be applied in identifying objects even though the orientation, scale, and position are varying, such as WBCs in microscopic blood smear images. We analyzed the classification performances using a support vector machine (SVM) and an artificial neural network (ANN) of WBCs types in the microscopic image based on the cell nucleus. The results show that the purposed SURF feature method has an excellent performance of accuracy for both methods and suitable to be utilized for the application of cell types classification.
      1
  • Publication
    Muscle Fatigue Assessment Using Multi-sensing Based on Electrical, Mechanical and Acoustic Properties
    ( 2021-01-01)
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Ahmad Nasrul Norali
    This paper shows that a multi-sensing technique using electromyogram (EMG), mechanomyogram (MMG), and acousticmyogram (AMG) used to monitor the status of rectus femoris muscle over three states; minimal stress, moderate fatigue, and severe muscle fatigue. Test subjects need to do the designed exercise protocol to simulate these state conditions. The sensors are located at the rectus femoris muscle, and signals were recorded simultaneously. Analysis of signals is based on root mean square (RMS), mean power frequency (MPF), and power spectral density (PSD) plot were compared between the muscle state conditions. Results show that the RMS values of the muscle are increased as the contraction occurs, and the MPF signal is decreased for all sensing properties. On the other hand, the frequency signal is shifted to the left in the PSD plot as the muscle undergoes fatigued for all sensors. In conclusion, multi-sensing using EMG, MMG, and AMG are useful for assessing muscle fatigue condition. It also provide advantages over the single-measurement muscle assessment method.
      1
  • Publication
    Classification of human breathing activity based on Electromyography signal of respiratory muscles
    ( 2020)
    Ahmad Nasrul Norali
    Electromyography (EMG) signal based pattern recognition have been applied for various applications especially in Human-Machine Interface (HMI). Most of previous research works focused on human muscles which related to movements of arms and fingers for controlling body parts through intention. Statistical analysis has been used in most research works on muscle assessment and performance measurement. There are only few literatures on EMG pattern recognition study that used other than arms or hands. Ths thesis will focus on the use of EMG pattern recognition to classify the human breathing task. Four respiratory muscles have been chosen for EMG data acquisition i.e. sternocleidomastoid, scalene, external intercostal and diaphragm. The selected human subjects are used to perform four breathing tasks which are quiet, deep, deep & hold and fast breathing. The feature extraction of EMG consists of basic features and its convolution. The basic features are Root Mean Square (RMS), Zero Crossing (ZC), Mean Frequency (MF) and Mean Power (MP). The convolution is performed between pairs of the bsic features. The EMG pattern recognition is performed by using K-Nearest Neighbor (K-NN), Multilayer Perceptron Artificial Neural Network (MLP-ANN) and Support Vector Machine (SVM). Segmentation window size is configured at 1000 ms. Results showed highest accuracy of 96.86% on convolution of RMS and MF features using SVM classifier. The convolution feature extraction will enhance the EMG classification accuracy for human breathing compare to basic features of time and frequency domain. Study on classification of human breathing activity based on EMG of respiratory muscles can be implemented in biofeedback rehabilitation. The EMG from respiratory muscles can be used in physical therapy for disabled person by controlling an assistive device such as robotic limb and electric wheelchair.
      16  2
  • Publication
    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.
      2  25
  • Publication
    Classification of electromyography signal from residual limb of hand amputees
    ( 2022-01-01)
    Ahmad Nasrul Norali
    ;
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Al-Mahdi Y.S.M.
    ;
    Fook Chong Yen
    ;
    Asyraf Hakimi Abu Bakar
    Several researchers had worked on collecting electromyography (EMG) signal from amputees and come out with dataset that could be utilized for study in EMG signal processing and classification for decoding of amputee movement intention. This paper presents the work on classification of EMG signal based on the residual limb of amputees with intuitive hand movement based on interactive exercises. Dataset is obtained from NINAPRO public database website where 11 amputee subjects performed intuitive exercise of 17 hand gestures and EMG signal is acquired from the residual arm. Eight feature extraction methods are performed to obtain the EMG feature which are Mean, Minimum, Median, Skewness, Kurtosis, Approximate Entropy, Fuzzy Entropy and Kolmogorov Complexity. Two classifiers are used for EMG classification which are k-Nearest Neighbour and Ensemble classifier. Results shows average accuracy of 87.65% with Ensemble classifier for classification of movement exercise with all features of EMG is used as input to classifier.
      1  34
  • Publication
    Modelling and Simulation using Finite Element Method of Surface Acoustic Wave Biosensor for Gas Detection Application
    ( 2021-11-25)
    Anas Mohd Noor
    ;
    Ahmad Nasrul Norali
    ;
    Zulkarnay Zakaria
    ;
    Mazlee Mazalan
    ;
    Yufridin Wahab
    A surface acoustic wave (SAW) sensor detects changes in physical properties such as mass and density on its surface. Compared to other types of sensors, SAW sensor have a good stability, high selectivity and sensitivity, fast response, and low-cost. On the other hand, to design and optimize a SAW biosensor requires a long process including time and cost using conventional methods. Therefore, numerical simulation and computational modelling are useful and efficiently conduct analysis for the SAW biosensor. In this paper, a numerical simulation technique is used to analyse the SAW device sensitivity for the application of gas detection. The SAW biosensor can detect very small mass loading by changing its sensor resonance frequency. The two-dimensional (2D) device model is based on a two-port SAW resonator with a gas sensing layer. We made two design of SAW biosensor device with frequency of 872 MHz and 1.74 GHz. A gas with vary concentration from 1 to 100 ppm were used to determine the change of the device resonance frequency. As a result, the high frequency (1.74 GHz) device, shows that the resonance frequency is shifted larger than to the low frequency (872 MHz) device. In addition, the high frequency device offers five times more sensitivity than the low frequency device. By changing the sensor design, the sensor characteristics such as sensitivity can be altered to meet certain sensing requirements. Numerical simulation provides advantages for sensor optimization and useful for nearly representing the real condition.
      3  26