Latifah Munirah Kamarudin
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Latifah Munirah Kamarudin
Official Name
Kamarudin, Latifah Munirah
Alternative Name
Kamarudin, Latifah Munirah
Kamarudin, Latifah M.
Kamarudin, L. M.
Kamarudin, Munirah L.
Kamarudin, L.
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Scopus Author ID
57192974774
Researcher ID
G-8267-2016

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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
    Design and deployment of LoRaWAN smart streetlight for smart city
    ( 2024-02-08)
    Zakaria S.M.M.S.
    ;
    Ammar Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Visvanathan R.
    ;
    Rahim Y.A.
    ;
    Kamarudin K.
    ;
    Azmi N.
    ;
    Mohammad N.S.
    Streetlights are one of the major contributors of a city's energy usage, creating a large carbon footprint especially for highly populated areas. Current streetlights are turned on and off based on fixed schedule or by light sensors. Previous works have suggested the dimming of streetlights during zero traffic conditions to reduce energy consumption. This requires a reliable and economical communication backbone to ensure minimal service disruption. This work presents the design and performance evaluation of LoRa-based smart streetlight controllers in an urban environment. The deployment was designed to provide stress test, simulate communication connectivity, maintenance routine, firmware test and environmental conditions. The setup may also be used for staff training purposes and demonstration. The results of this work may be used achieve the effective control method for power saving, system stability, robustness and long-term performance. The deployed system includes test controllers, nodes, application server, database, gateway server and visualization dashboard. The system design demonstrated low packet error rates of approximately 1% and command response time of less than 3s in real world conditions.
  • 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
    Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors
    ( 2010)
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Abdul Hamid Adom
    ;
    Mohd Noor Ahmad
    ;
    Masnan, Maz Jamilah
    ;
    Abdul Hallis Abdul Aziz
    ;
    Nazifah Ahmad Fikri
    ;
    Abu Hassan Abdullah
    ;
    Latifah Munirah Kamarudin
    An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
  • 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
    2D LiDAR based reinforcement learning for Multi-Target path planning in unknown environment
    ( 2023)
    Nasr Abdalmanan
    ;
    Kamarulzaman Kamarudin
    ;
    Muhammad Aizat Abu Bakar
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Latifah Munirah Kamarudin
    Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.
      3  35
  • Publication
    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.
      22  1
  • Publication
    Novel energy effcient protocols with realistic radio propagation models for wireless sensor networks in agriculture
    ( 2012)
    Latifah Munirah Kamarudin
    A wireless sensor network (WSN) is an emerging technology that enables a variety of possible applications. The performance of a WSN depends on many factors such as the physical layer parameters (i.e. transmission power and frequency selection), and the communication protocols (MAC and routing protocols). Accounting for these factors, the technical challenges remain in designing and deploying a robust WSN with limited energy supplies in a harsh environmental condition, where the unreliability of wireless propagation channel restricts the performance of the sensor node. Researchers have invested a lot of time and e ort into developing high performance communication protocols to meet the growing challenges of WSN. However, there is still no approach that is able to meet the requirements and challenges of agriculture application, especially in realistic simulation design of WSN protocols. This thesis focuses on the simulation of the proposed novel energy efficient protocols that are robust to variations in the radio propagation environment. The proposed protocols ensure the connectivity between the cluster members and cluster head (CH), applicable for dense networks, low network overhead and most importantly, energy e cient. To address this, an actual measurement on vegetation attenuation is carried out to ascertain the best propagation model for WSN protocol design and evaluation in a simulation platform. Based on these models, a MAC layer algorithm designed for clustering protocols such as LEACH, called AgriMAC is proposed, and it is combined with dynamic transmit power control algorithm, DytCon. The performances of these algorithms are compared with LEACH in term of energy e ciency and network lifetime. Results show that the performance of these algorithms achieves a substantial improvement in network lifetime compared to LEACH. AgriMAC eliminates the penalty of having more cluster heads to the network lifetime, where a steady performance is achieved when the number of cluster is between 4 and 10 with approximately 4:8% improvements over the maximum net- work lifetime achieves by LEACH. In order to solve various issues of LEACH clustering protocol such as unbalanced network partitioning and variable number of cluster heads per round, a novel energy eficient clustering protocol, DynClust is proposed. The protocol combines a machine learning technique called k-means, where it groups the nodes into clusters with AgriMAC and DytCon to optimize the network lifetime. DynClust exhibits vital properties such as robustness against variations in the radio propagation environment, a very low control overhead, simple and yet e cient. The protocol improves LEACH in term of cluster distribution and cluster membership. From the simulation results, DynClust achieves approximately 318% improvements over LEACH in term of network lifetime in various prop- agation environments. These allow the possibility of WSN to be simulated accurately in dynamic and harsh agriculture applications.
      14  1