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Article ; Online: Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging.

Almaleeh, Abd Alazeez / Zakaria, Ammar / Kamarudin, Latifah Munirah / Rahiman, Mohd Hafiz Fazalul / Ndzi, David Lorater / Ismail, Ismahadi

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 1

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Edible Grain ; Machine Learning ; Oryza
Language	English
Publishing date	2022-01-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22010405
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Non-Contact Breathing Monitoring Using Sleep Breathing Detection Algorithm (SBDA) Based on UWB Radar Sensors.

Husaini, Muhammad / Kamarudin, Latifah Munirah / Zakaria, Ammar / Kamarudin, Intan Kartika / Ibrahim, Muhammad Amin / Nishizaki, Hiromitsu / Toyoura, Masahiro / Mao, Xiaoyang

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 14

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Algorithms ; Heart Rate ; Humans ; Polysomnography ; Radar ; Signal Processing, Computer-Assisted ; Sleep
Language	English
Publishing date	2022-07-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22145249
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity.

Abdullah, Abdulnasser Nabil / Kamarudin, Kamarulzaman / Kamarudin, Latifah Munirah / Adom, Abdul Hamid / Mamduh, Syed Muhammad / Mohd Juffry, Zaffry Hadi / Bennetts, Victor Hernandez

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 9

Abstract: For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and ... ...

Abstract	For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.
MeSH term(s)	Humidity ; Oxides ; Temperature
Chemical Substances	Oxides
Language	English
Publishing date	2022-04-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22093301
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques.

Azmi, Noraini / Kamarudin, Latifah Munirah / Zakaria, Ammar / Ndzi, David Lorater / Rahiman, Mohd Hafiz Fazalul / Zakaria, Syed Muhammad Mamduh Syed / Mohamed, Latifah

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 5

Abstract: 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 ...

Abstract	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.
Language	English
Publishing date	2021-03-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21051875
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images.

Mohd Ramli, Nurul Amira / Fazalul Rahiman, Mohd Hafiz / Kamarudin, Latifah Munirah / Mohamed, Latifah / Zakaria, Ammar / Ahmad, Anita / Rahim, Ruzairi Abdul

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 11

Abstract: 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. ... ...

Abstract	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.
Language	English
Publishing date	2021-05-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21113686
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm.

Goh, Chew Cheik / Kamarudin, Latifah Munirah / Zakaria, Ammar / Nishizaki, Hiromitsu / Ramli, Nuraminah / Mao, Xiaoyang / Syed Zakaria, Syed Muhammad Mamduh / Kanagaraj, Ericson / Abdull Sukor, Abdul Syafiq / Elham, Md Fauzan

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 15

Abstract: This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning ... ...

Abstract	This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers' drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO
MeSH term(s)	Air Pollution ; Algorithms ; Machine Learning ; Neural Networks, Computer ; Particulate Matter
Chemical Substances	Particulate Matter
Language	English
Publishing date	2021-07-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21154956
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS.

Thriumani, Reena / Zakaria, Ammar / Hashim, Yumi Zuhanis Has-Yun / Jeffree, Amanina Iymia / Helmy, Khaled Mohamed / Kamarudin, Latifah Munirah / Omar, Mohammad Iqbal / Shakaff, Ali Yeon Md / Adom, Abdul Hamid / Persaud, Krishna C

BMC cancer

2018 Volume 18, Issue 1, Page(s) 362

Abstract: Background: Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin ...

Abstract	Background: Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells. Method: The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium. Results: This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells. Conclusion: The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.
MeSH term(s)	Algorithms ; Biomarkers ; Biosensing Techniques ; Cell Line, Tumor ; Cells, Cultured ; Gas Chromatography-Mass Spectrometry ; Humans ; Lung Neoplasms/metabolism ; Reproducibility of Results ; Solid Phase Microextraction ; Support Vector Machine ; Volatile Organic Compounds/analysis ; Volatile Organic Compounds/metabolism
Chemical Substances	Biomarkers ; Volatile Organic Compounds
Language	English
Publishing date	2018-04-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041352-X
ISSN	1471-2407 ; 1471-2407
ISSN (online)	1471-2407
ISSN	1471-2407
DOI	10.1186/s12885-018-4235-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development of a Scalable Testbed for Mobile Olfaction Verification.

Zakaria, Syed Muhammad Mamduh Syed / Visvanathan, Retnam / Kamarudin, Kamarulzaman / Yeon, Ahmad Shakaff Ali / Md Shakaff, Ali Yeon / Zakaria, Ammar / Kamarudin, Latifah Munirah

Sensors (Basel, Switzerland)

2015 Volume 15, Issue 12, Page(s) 30894–30912

Abstract: The lack of information on ground truth gas dispersion and experiment verification information has impeded the development of mobile olfaction systems, especially for real-world conditions. In this paper, an integrated testbed for mobile gas sensing ... ...

Abstract	The lack of information on ground truth gas dispersion and experiment verification information has impeded the development of mobile olfaction systems, especially for real-world conditions. In this paper, an integrated testbed for mobile gas sensing experiments is presented. The integrated 3 m × 6 m testbed was built to provide real-time ground truth information for mobile olfaction system development. The testbed consists of a 72-gas-sensor array, namely Large Gas Sensor Array (LGSA), a localization system based on cameras and a wireless communication backbone for robot communication and integration into the testbed system. Furthermore, the data collected from the testbed may be streamed into a simulation environment to expedite development. Calibration results using ethanol have shown that using a large number of gas sensor in the LGSA is feasible and can produce coherent signals when exposed to the same concentrations. The results have shown that the testbed was able to capture the time varying characteristics and the variability of gas plume in a 2 h experiment thus providing time dependent ground truth concentration maps. The authors have demonstrated the ability of the mobile olfaction testbed to monitor, verify and thus, provide insight to gas distribution mapping experiment.
Language	English
Publishing date	2015-12-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s151229834
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology.

Yusuf, Nurlisa / Zakaria, Ammar / Omar, Mohammad Iqbal / Shakaff, Ali Yeon Md / Masnan, Maz Jamilah / Kamarudin, Latifah Munirah / Abdul Rahim, Norasmadi / Zakaria, Nur Zawatil Isqi / Abdullah, Azian Azamimi / Othman, Amizah / Yasin, Mohd Sadek

BMC bioinformatics

2015 Volume 16, Page(s) 158

Abstract: Background: Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) ... ...

Abstract	Background: Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen. Results: This study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy. Conclusions: The results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.
MeSH term(s)	Algorithms ; Bacteria/classification ; Bacteria/genetics ; Bacteria/isolation & purification ; Biosensing Techniques ; Data Mining ; Diabetic Foot/diagnosis ; Diabetic Foot/microbiology ; Discriminant Analysis ; Electronic Nose ; Gas Chromatography-Mass Spectrometry ; Humans ; In Vitro Techniques ; Neural Networks (Computer) ; Odorants/analysis ; Support Vector Machine
Language	English
Publishing date	2015-05-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-015-0601-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Improved maturity and ripeness classifications of Magnifera Indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor.

Zakaria, Ammar / Shakaff, Ali Yeon Md / Masnan, Maz Jamilah / Saad, Fathinul Syahir Ahmad / Adom, Abdul Hamid / Ahmad, Mohd Noor / Jaafar, Mahmad Nor / Abdullah, Abu Hassan / Kamarudin, Latifah Munirah

Sensors (Basel, Switzerland)

2012 Volume 12, Issue 5, Page(s) 6023–6048

Abstract: In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing ... ...

Abstract	In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.
MeSH term(s)	Acoustics ; Anacardiaceae ; Odorants
Language	English
Publishing date	2012-05-10
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s120506023
Database	MEDical Literature Analysis and Retrieval System OnLINE

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