Ammar Zakaria
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Preferred name
Ammar Zakaria
Official Name
Ammar, Zakaria
Alternative Name
Zakaria, Ammar
Zakaria, A.
Main Affiliation
Scopus Author ID
36560557700
Researcher ID
D-2902-2015

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  • Publication
    Measurement of rice moisture content based on quantitative analysis from radio tomography images
    ( 2024-05-01)
    Nurul Amira Mohd Ramli
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Ruslinda A. Rahim
    ;
    Latifah Munirah Kamarudin
    ;
    Latifah Mohamed
    ;
    Moqbel Abdullah M.S.
    ;
    Ammar Zakaria
    Inefficient storage of paddy and rice grains can lead to grain deterioration, resulting in post-harvest losses ranging from 10% to 30%. The quality of grains cannot be improved throughout the storage period. Therefore, following the mechanisation of agricultural industries, air dryers have been developed to control the crops’ moisture level by blowing ambient or heated air into the silo to improve the aeration and allow the grains to be preserved with minimal loss of quality until the appropriate time for managing and marketing processes. However, the conventional sampling method used to measure the moisture level is inefficient because it is very localised and only represents part of the moisture distribution inside the bulk grains. Additionally, incorporating advanced technologies can be a significant cost limitation for small-scale industries. Thus, to address the issue, this research study developed a radio tomographic imaging (RTI) system in a silo-scale prototype using 20 sensor nodes operating at 2.4 GHz to localise and monitor the moisture level constructively. The RTI system reconstructs the cross-sectional images across the rice silo by measuring radio frequency attenuation, in terms of received signal strength (RSS) quality, caused by the rice moisture phantoms within the wireless sensor network (WSN) area. A total of five phantoms’ profiles having a percentage of moisture content (MC)of 15%, 20% and 25% were reconstructed using four image reconstruction algorithms,Linear Back Projection (LBP), Filtered Back Projection (FBP), Newton’s One-step ErrorReconstruction (NOSER) and Tikhonov Regularisation. Then, an image quality assessment,Mean Structural Similarity Index (MSSIM), was utilised to evaluate the performance of thereconstructed images. Lastly, a numerical method based on the first-order linear regressionmodel was introduced as a preliminary approach toward the method’s establishment. In summary, the experimental results demonstrated average image quality scores for all MClevels (15%, 20% and 25%), where the range scores are 0.2776 – 0.4755. Based on thenumerical analysis, the results support the possibility of engaging the proposed techniqueto monitor the moisture level inside a rice silo with the highest and lowest correlationcoefficients of 0.7218 and 0.5442, respectively.
  • Publication
    A new method of rice moisture content determination using voxel weighting-based from radio tomography images
    ( 2021-06-01)
    Ramli N.A.M.
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Latifah Munirah Kamarudin
    ;
    Latifah Mohamed
    ;
    Ammar Zakaria
    ;
    Anita Ahmad
    ;
    Rahim R.A.
    This manuscript presents a new method to monitor and localize the moisture distribution in a rice silo based on tomography images. Because the rice grain is naturally hygroscopic, the stored grains’ quality depends on their level of moisture content. Higher moisture content leads to fibre degradation, making the grains too frail and possibly milled. If the moisture is too low, the grains become brittle and are susceptible to higher breakage. At present, the single-point measurement method is unreliable because the moisture build-up inside the silo might be distributed unevenly. In addition, this method mostly applies gravimetric analysis, which is destructive. Thus, we proposed a radio tomographic imaging (RTI) system to address these problems. Four simulated phantom profiles at different percentages of moisture content were reconstructed using Newton’s One-Step Error Reconstruction and Tikhonov Regularization algorithms. This simulation study utilized the relationship between the maximum voxel weighting of the reconstructed RTI image and the percentage of moisture content. The outcomes demonstrated promising results, in which the weighting voxel linearly increased with the percentage of moisture content, with a correlation coefficient higher than 0.95 was obtained. Therefore, the results support the possibility of using the RTI approach for monitoring and localizing the moisture distribution inside the rice silo.
  • Publication
    Non-Contact breathing monitoring using Sleep Breathing Detection Algorithm (SBDA) based on UWB radar sensors
    ( 2022)
    Muhammad Husaini
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Intan Kartika Kamarudin
    ;
    Muhammad Amin Ibrahim
    ;
    Hiromitsu Nishizaki
    ;
    Masahiro Toyoura
    ;
    Xiaoyang Mao
    Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.
  • Publication
    Human Location Classification for Outdoor Environment
    ( 2019-12-03)
    Talib M.T.M.
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Latifah Munirah Kamarudin
    ;
    Nishizaki H.
    ;
    Ammar Zakaria
    Outdoor localisation can offer great capabilities in security and perimeter surveillance applications. The localisation of people become more challenges when involving with the nonlinear environment. GPS and CCTV are two localisation techniques usually use to localise human in an outdoor environment. However, they have weaknesses which result in low localisation accuracy. Therefore, the application of Device-free localisation (DFL), together with the Internet of things (IoT) is more appropriate due to their capability to detect the human body in all environmental conditions, and there is no problem losing signals as faced by GPS. This system offers excellent potential in humans localisation because humans can be detected wirelessly without any tracking device attached. In developing the DFL system, the main concern is the localisation accuracy. Although the existing DFL system gives significant result to the localisation, the accuracy is still low due to the large variation in RSSI values. Hence, a Radio Tomographic Imaging-based ANN classification (RTI-ANN) approach is proposed to increase the localisation accuracy. This Artificial Neural Network (ANN) is designed to learn the Radio Tomography imaging (RTI) input for classification purpose. Even though the RTI gives a good result to the localisation, however, it suffers from smearing effect. To eliminates this smearing area and background noise, pre-processing of the RTI image is required. Thus, extracting the valuable information technique from the RTI image has been proposed. By extracting the valuable information data from the RTI image, about 61% to 66% of the smearing noise is removed depending on the size of the RTI image. Only data directly associated with human attenuation used for training and learning of ANN. The experimental results show ANN system can localise human in the right zone for a given dataset.
  • Publication
    Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging
    ( 2022-01-01)
    Almaleeh A.A.
    ;
    Ammar Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Ndzi D.L.
    ;
    Ismail I.
    The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors’ knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos.
  • Publication
    Non-Contact Vital Sign Monitoring During Sleep Through UWB Radar
    ( 2022-01-01)
    Muhammad Husaini
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Kamarudin I.K.
    ;
    Ibrahim M.A.
    To date, ultra-wideband (UWB) radar is one of the leading technologies applied in the field of non-contact vital sign monitoring. A number of studies have focused on processing reflected UWB pulse signals into breathing and heart activities; however, most have emphasised the use of stationary subjects in their data collection processes. Therefore, this paper presents a feasible study conducted to extract the human vital signs of a non-stationary subject during sleep and the optimum position of the UWB radar. The proposed algorithm could measure the respiration rate (RR) and heart rate (HR) recorded regardless of any random body movements during sleep. An analysis of the entire slow time region for the signal was performed to remove random movement signals from the subject by implementing a sinusoidal fitting algorithm to monitor the periodic movement of the chest wall. Next, the value of R-squared was used to find the fit between the algorithm and output signals, and then, the signal was transformed into a frequency domain via Fourier transform (FT). This allowed the determination of the dominant peak from the breathing and heart rates before changing to the rate per minute. An experiment was also done to monitor three different UWB radar positions (i.e. top, side, and bottom of the bed) to identify its optimum location during sleep. All results were then compared with the polysomnography signal. The result found that the top position has the lowest error rate percentage for both RR and HR with only 0.72% and 3.71%, respectively.
      3
  • Publication
    Predictive analysis of in-vehicle air quality monitoring system using Deep Learning technique
    ( 2022)
    Abdul Syafiq Abdull Sukor
    ;
    Goh Chew Cheik
    ;
    Latifah Munirah Kamarudin
    ;
    Xiaoyang Mao
    ;
    Hiromitsu Nishizaki
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.
      1  20
  • Publication
    Feasibility analysis of indoor 3D localization system with UWB using least squares trilateration
    (Iran University of Science and Technology, 2025-06)
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Ammar Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Ali Yeon Md Shakaff
    ;
    Muhamad Naqib Mohd Shukri
    Accurate 3D Localization is very important for a wide range of applications, such as indoor navigation, industrial robotics, and motion tracking. This research focuses on indoor 3D positioning systems using ultra-wideband (UWB) devices. Two localization experiments were conducted using the Least Squares Trilateration method. In the first experiment, anchors were at the same height, while in the second, they were at varying heights. The lowest percentage errors in the first experiment were 0% at the x-axis, 0.21% at the y-axis, and 19.75% at the z-axis. In the second experiment, the lowest percentage errors in the experiment were 1.98% at the x-axis, 0.68% at the y-axis, and 17.86% at the z-axis, demonstrating improved accuracy with varied anchor heights at the axis. This work shows the z-axis measurements are unreliable and noisy due to the limited intersection of signal waves of each anchor in a same height anchors setup.
  • Publication
    A hybrid sensing approach for pure and adulterated honey classification
    ( 2012)
    Norazian Subari
    ;
    Junita Mohamad Saleh
    ;
    Ali Yeon Md Shakaff
    ;
    Ammar Zakaria
    This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.
      2  8
  • Publication
    Classification of Football Player Actions Using Sensing Data
    ( 2024-01-01)
    Hirasawa Y.
    ;
    Kinoshita Y.
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    This study classifies the actions of football players using sensing data acquired from wearable sensors attached to players and the ball. More than 800 sensing data with the labels of five types of player actions were created as a dataset. The neural networks were trained using 19 input items created by considering time-series variations in player and ball locations. The trained neural network model demonstrated a classification accuracy of 84.0%. The model successfully obtained sufficient accuracy for all types of actions.These results demonstrate that the sensing data and created input items can be effectively utilized for classifying the actions of football player.
      7  12