Ammar Zakaria
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Ammar Zakaria
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Ammar, Zakaria
Alternative Name
Zakaria, Ammar
Zakaria, A.
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Scopus Author ID
36560557700
Researcher ID
D-2902-2015

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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.

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Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging

2022-01-01 , Almaleeh Abd Alazeez , 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.

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A Design and Development of a Wireless Sensor Network for Potential Monitoring and Localization

2020-11-01 , Ramli N.A.M. , Mohd Hafiz Fazalul Rahiman , Malik M.F.A. , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Abdullah M.S.M.

This paper presents an analysis of the received signal strength indicator (RSSI) from the radio frequency signals for human identification in an indoor wireless sensor network (WSN). Instead of using closed-circuit television as the existing security platform, this indoor safety system was improved with a convenient, cheap, and low-power solution. The system was developed using 20 of ESP8266-12F Wi-Fi modules transmitters and another 2 of ESP8266-12F as the access points located in 3 m × 3 m area of interest. With a suitable coordinate of sensor nodes, a WSN telemetry could be established to minimize the blind spot area and limit the movement of the intruder with a minimum area of 0.2 m2. The RSSI measurement was repeatedly conducted for three different conditions, in an empty room, with the presence of a single intruder and the presence of multiple intruders. Based on the RSSI values, we found that there are distinctive features of data that can be utilized as flags for classifying the three above conditions. Besides that, to justify the efficiency of system performance, we also examined the sensitivity of RSSI values towards the variation of temperature. Our results show that the RSSI average values for both morning and night were practically the same. However, during the afternoon, the RSSI signal strength fluctuated by − 1.0 dBm. These results motivate the development of an alarm system that only uses the RSSI statistics to detect human presence.

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Method for RSSI Heat Map Estimation using Radial Basis Functions for Fingerprinting Localization

2021-12-01 , Wye K.F.P. , Syed Muhammad Mamduh Syed Zakaria , Latifah Munirah Kamarudin , Ammar Zakaria , Ahmad N.

Radio Frequency (RF) based indoor localization is a challenging task due to the dynamic and multipath factors although many research proposed different improvement strategies in their indoor localization works. This paper discusses an alternative method for RF-based mapping estimation for Received Signal Strength Indicator (RSSI) fingerprinting indoor localization. The RSSI fingerprinting mappings are estimated by different Radial Basis Function (RBF) interpolation methods. The RBF interpolation methods may reduce the number of fingerprint locations required for fingerprinting localization. The predicted RSSI and actual RSSI radio mappings are compared to determine the accuracy of each RBF. The best mean error of 2.008dBm in Inverse Multiquadric RBF is achieved between the actual and predicted mappings in a complex laboratory environment. The RF-based mapping estimation techniques presented in this paper have shown that the effectiveness to fulfil the perquisite of fingerprinting localization with lower time and labour requirement.

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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.

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Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment

2020-05-18 , Visvanathan R. , Kamarulzaman Kamarudin , Syed Muhammad Mamduh Syed Zakaria , Toyoura M. , Ali Yeon A.S. , Ammar Zakaria , Latifah Munirah Kamarudin , Mao X. , Shazmin Aniza Abdul Shukor

Mobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered.

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Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique

2022-10-01 , Abdul Syafiq Abdull Sukor , Cheik Goh Chew , Latifah Munirah Kamarudin , Mao X. , Nishizaki H. , 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.

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Pattern Clustering Approach for Activity Recognition in Smart Homes

2022-01-01 , Abdul Syafiq Abdull Sukor , Ammar Zakaria , Latifah Munirah Kamarudin , Wahab M.N.A.

In recent years, studies in activity recognition have shown an increasing amount of attention among other researchers. Activity recognition is usually performed through two steps: activity pattern clustering and classification processes. Clustering allows similar activity patterns to be grouped together while classification provides a decision-making process to infer the right activity. Although many related works have been suggested in these areas, there is some limitation as most of them are focused only on one part of these two processes. This paper presents a work that combines pattern clustering and classification into one single framework. The former uses the Self Organizing Map (SOM) to cluster activity data into groups while the latter utilizes semantic activity modelling to infer the right type of activity. Experimental results show that the combined method provides higher recognition accuracy compared to the traditional method of machine learning. Furthermore, it is more appropriate for a dynamic environment of human living.

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Non-Contact Breathing Monitoring Using Sleep Breathing Detection Algorithm (SBDA) Based on UWB Radar Sensors

2022-07-01 , Muhammad Husaini , Latifah Munirah Kamarudin , Ammar Zakaria , Kamarudin I.K. , Ibrahim M.A. , Nishizaki H. , Toyoura M. , Mao X.

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.

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Adaptive threshold determination for efficient channel sensing in cognitive radio network using mobile sensors

2017-03-13 , Mohammad Nayeem Morshed , Sabira Khatun , Latifah Munirah Kamarudin , Syed Alwee Aljunid Syed Junid , R Badlishah Ahmad , Ammar Zakaria , Moslem Fakir

Spectrum saturation problem is a major issue in wireless communication systems all over the world. Huge number of users is joining each day to the existing fixed band frequency but the bandwidth is not increasing. These requirements demand for efficient and intelligent use of spectrum. To solve this issue, the Cognitive Radio (CR) is the best choice. Spectrum sensing of a wireless heterogeneous network is a fundamental issue to detect the presence of primary users' signals in CR networks. In order to protect primary users (PUs) from harmful interference, the spectrum sensing scheme is required to perform well even in low signal-to-noise ratio (SNR) environments. Meanwhile, the sensing period is usually required to be short enough so that secondary (unlicensed) users (SUs) can fully utilize the available spectrum. CR networks can be designed to manage the radio spectrum more efficiently by utilizing the spectrum holes in primary user's licensed frequency bands. In this paper, we have proposed an adaptive threshold detection method to detect presence of PU signal using free space path loss (FSPL) model in 2.4 GHz WLAN network. The model is designed for mobile sensors embedded in smartphones. The mobile sensors acts as SU while the existing WLAN network (channels) works as PU. The theoretical results show that the desired threshold range detection of mobile sensors mainly depends on the noise floor level of the location in consideration.

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