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.
Main Affiliation
Scopus Author ID
57192974774
Researcher ID
G-8267-2016

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Rf-based moisture content determination in rice using machine learning techniques

2021-03-01 , Azmi N. , Latifah Munirah Kamarudin , Ammar Zakaria , Ndzi D.L. , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Latifah Mohamed

Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

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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.

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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.

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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.

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Signal propagation analysis for low data rate wireless sensor network applications in sport grounds and on roads

2012 , David L. Ndzi , M. A. Mohd Arif , Ali Yeon Md Shakaff , Mohd Noor Ahmad , Azizi Harun , Latifah Munirah Kamarudin , Ammar Zakaria , Mohd F. Ramli , Mohammad Shahrazel Razalli

This paper presents results of a study to characterise wire- less point-to-point channel for wireless sensor networks applications in sport hard court arenas, grass fields and on roads. Antenna height and orientation effects on coverage are also studied and results show that for omni-directional patch antenna, node range is reduced by a factor of 2 when the antenna orientation is changed from vertical to horizontal. The maximum range for a wireless node on a hard court sport arena has been determined to be 70m for 0dBm transmission but this reduces to 60m on a road surface and to 50m on a grass field. For horizontal antenna orientation the range on the road is longer than on the sport court which shows that scattered signal components from the rougher road surface combine to extend the communication range. The channels investigated showed that packet error ratio (PER) is dominated by large-scale, rather than small-scale, channel fading with an abrupt transition from low PER to 100% PER. Results also show that large-scale received signal power can be modeled with a 2nd or der log-distance polynomial equation on the sport court and road, but a 1st order model is sufficient for the grassfield. Small-scale signal variations have been found to have a Rice distribution for signal to noise ratio levels greater than 10 dB but the Rice K-factor exhibits significant variations at short distances which can be attributed to the influence of strong ground reflections.

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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.

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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.

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Rice Grain Moisture Sensing Based on UHF RFID Tag

2022-06-24 , Radzi A.S.M. , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.

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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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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.

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