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Prof. Dr. Latifah Munirah Kamarudin
Universiti Malaysia Perlis

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Research Keywords & Expertise

0 Wireless Sensor Networks (WSN)
0 Internet of Things - IoT
0 human detection
0 Moisture content determination
0 Radio Frequency Sensing

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Journal article
Published: 21 July 2021 in Sensors
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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 CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

ACS Style

Chew Goh; Latifah Kamarudin; Ammar Zakaria; Hiromitsu Nishizaki; Nuraminah Ramli; Xiaoyang Mao; Syed Syed Zakaria; Ericson Kanagaraj; Abdul Abdull Sukor; Fauzan Elham. Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm. Sensors 2021, 21, 4956 .

AMA Style

Chew Goh, Latifah Kamarudin, Ammar Zakaria, Hiromitsu Nishizaki, Nuraminah Ramli, Xiaoyang Mao, Syed Syed Zakaria, Ericson Kanagaraj, Abdul Abdull Sukor, Fauzan Elham. Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm. Sensors. 2021; 21 (15):4956.

Chicago/Turabian Style

Chew Goh; Latifah Kamarudin; Ammar Zakaria; Hiromitsu Nishizaki; Nuraminah Ramli; Xiaoyang Mao; Syed Syed Zakaria; Ericson Kanagaraj; Abdul Abdull Sukor; Fauzan Elham. 2021. "Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm." Sensors 21, no. 15: 4956.

Journal article
Published: 26 May 2021 in Sensors
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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.

ACS Style

Nurul Mohd Ramli; Mohd Fazalul Rahiman; Latifah Kamarudin; Latifah Mohamed; Ammar Zakaria; Anita Ahmad; Ruzairi Rahim. A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images. Sensors 2021, 21, 3686 .

AMA Style

Nurul Mohd Ramli, Mohd Fazalul Rahiman, Latifah Kamarudin, Latifah Mohamed, Ammar Zakaria, Anita Ahmad, Ruzairi Rahim. A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images. Sensors. 2021; 21 (11):3686.

Chicago/Turabian Style

Nurul Mohd Ramli; Mohd Fazalul Rahiman; Latifah Kamarudin; Latifah Mohamed; Ammar Zakaria; Anita Ahmad; Ruzairi Rahim. 2021. "A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images." Sensors 21, no. 11: 3686.

Journal article
Published: 08 March 2021 in Sensors
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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.

ACS Style

Noraini Azmi; Latifah Kamarudin; Ammar Zakaria; David Ndzi; Mohd Rahiman; Syed Zakaria; Latifah Mohamed. RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques. Sensors 2021, 21, 1875 .

AMA Style

Noraini Azmi, Latifah Kamarudin, Ammar Zakaria, David Ndzi, Mohd Rahiman, Syed Zakaria, Latifah Mohamed. RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques. Sensors. 2021; 21 (5):1875.

Chicago/Turabian Style

Noraini Azmi; Latifah Kamarudin; Ammar Zakaria; David Ndzi; Mohd Rahiman; Syed Zakaria; Latifah Mohamed. 2021. "RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques." Sensors 21, no. 5: 1875.

Journal article
Published: 01 June 2020 in Smart Cities
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Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

ACS Style

Abdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities 2020, 3, 444 -455.

AMA Style

Abdul Syafiq Abdull Sukor, Latifah Munirah Kamarudin, Ammar Zakaria, Norasmadi Abdul Rahim, Sukhairi Sudin, Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities. 2020; 3 (2):444-455.

Chicago/Turabian Style

Abdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. 2020. "RSSI-Based for Device-Free Localization Using Deep Learning Technique." Smart Cities 3, no. 2: 444-455.

Full paper
Published: 07 April 2020 in Advanced Robotics
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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.

ACS Style

Retnam Visvanathan; Kamarulzaman Kamarudin; Syed Muhammad Mamduh; Masahiro Toyoura; Ahmad Shakaff Ali Yeon; Ammar Zakaria; Latifah Munirah Kamarudin; Xiaoyang Mao; Shazmin Aniza Abdul Shukor. Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment. Advanced Robotics 2020, 34, 637 -647.

AMA Style

Retnam Visvanathan, Kamarulzaman Kamarudin, Syed Muhammad Mamduh, Masahiro Toyoura, Ahmad Shakaff Ali Yeon, Ammar Zakaria, Latifah Munirah Kamarudin, Xiaoyang Mao, Shazmin Aniza Abdul Shukor. Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment. Advanced Robotics. 2020; 34 (10):637-647.

Chicago/Turabian Style

Retnam Visvanathan; Kamarulzaman Kamarudin; Syed Muhammad Mamduh; Masahiro Toyoura; Ahmad Shakaff Ali Yeon; Ammar Zakaria; Latifah Munirah Kamarudin; Xiaoyang Mao; Shazmin Aniza Abdul Shukor. 2020. "Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment." Advanced Robotics 34, no. 10: 637-647.

Journal article
Published: 03 September 2018 in International Journal of Performance Analysis in Sport
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ACS Style

Sukhairi Sudin; Ali Yeon Md Shakaff; Ammar Zakaria; Ahmad Faizal Salleh; Latifah Munirah Kamarudin; Noraini Azmi; Fathinul Syahir Ahmad Saad. Real-time track cycling performance prediction using ANFIS system. International Journal of Performance Analysis in Sport 2018, 18, 1 -17.

AMA Style

Sukhairi Sudin, Ali Yeon Md Shakaff, Ammar Zakaria, Ahmad Faizal Salleh, Latifah Munirah Kamarudin, Noraini Azmi, Fathinul Syahir Ahmad Saad. Real-time track cycling performance prediction using ANFIS system. International Journal of Performance Analysis in Sport. 2018; 18 (5):1-17.

Chicago/Turabian Style

Sukhairi Sudin; Ali Yeon Md Shakaff; Ammar Zakaria; Ahmad Faizal Salleh; Latifah Munirah Kamarudin; Noraini Azmi; Fathinul Syahir Ahmad Saad. 2018. "Real-time track cycling performance prediction using ANFIS system." International Journal of Performance Analysis in Sport 18, no. 5: 1-17.

Journal article
Published: 02 September 2018 in Advanced Robotics
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ACS Style

Kamarulzaman Kamarudin; Ali Yeon Md Shakaff; Victor Hernandez Bennetts; Syed Muhammad Mamduh; Ammar Zakaria; Retnam Visvanathan; Ahmad Shakaff Ali Yeon; Latifah Munirah Kamarudin. Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization. Advanced Robotics 2018, 32, 903 -917.

AMA Style

Kamarulzaman Kamarudin, Ali Yeon Md Shakaff, Victor Hernandez Bennetts, Syed Muhammad Mamduh, Ammar Zakaria, Retnam Visvanathan, Ahmad Shakaff Ali Yeon, Latifah Munirah Kamarudin. Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization. Advanced Robotics. 2018; 32 (17):903-917.

Chicago/Turabian Style

Kamarulzaman Kamarudin; Ali Yeon Md Shakaff; Victor Hernandez Bennetts; Syed Muhammad Mamduh; Ammar Zakaria; Retnam Visvanathan; Ahmad Shakaff Ali Yeon; Latifah Munirah Kamarudin. 2018. "Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization." Advanced Robotics 32, no. 17: 903-917.

Journal article
Published: 11 June 2018 in Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)
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ACS Style

Reena Thriumani; Ammar Zakaria; Ye Htut @ Mohammad Iqbal Omar; Nur Farhanah Ab Halim; Amanina Iymia Jeffree; Ali Yeon Md Shakaff; Latifah Munirah Kamarudin; Abdul Hamid Adom. An Initial Study on Oxidized Graphene-Coated QCM Based Gas Sensor for Cancer Related Volatile Sensing Application. Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) 2018, 11, 29 -39.

AMA Style

Reena Thriumani, Ammar Zakaria, Ye Htut @ Mohammad Iqbal Omar, Nur Farhanah Ab Halim, Amanina Iymia Jeffree, Ali Yeon Md Shakaff, Latifah Munirah Kamarudin, Abdul Hamid Adom. An Initial Study on Oxidized Graphene-Coated QCM Based Gas Sensor for Cancer Related Volatile Sensing Application. Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering). 2018; 11 (1):29-39.

Chicago/Turabian Style

Reena Thriumani; Ammar Zakaria; Ye Htut @ Mohammad Iqbal Omar; Nur Farhanah Ab Halim; Amanina Iymia Jeffree; Ali Yeon Md Shakaff; Latifah Munirah Kamarudin; Abdul Hamid Adom. 2018. "An Initial Study on Oxidized Graphene-Coated QCM Based Gas Sensor for Cancer Related Volatile Sensing Application." Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) 11, no. 1: 29-39.

Conference paper
Published: 19 March 2018 in IOP Conference Series: Materials Science and Engineering
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This work is concerned with the localization of gas source in dynamic indoor environment using a single mobile robot system. Algorithms such as Braitenberg, Zig-Zag and the combination of the two were implemented on the mobile robot as gas plume searching and tracing behaviours. To calculate the gas source location, a weighted arithmetic mean strategy was used. All experiments were done on an experimental testbed consisting of a large gas sensor array (LGSA) to monitor real-time gas concentration within the testbed. Ethanol gas was released within the testbed and the source location was marked using a pattern that can be tracked by a pattern tracking system. A pattern template was also mounted on the mobile robot to track the trajectory of the mobile robot. Measurements taken by the mobile robot and the LGSA were then compared to verify the experiments. A combined total of 36.5 hours of real time experimental runs were done and the typical results from such experiments were presented in this paper. From the results, we obtained gas source localization errors between 0.4m to 1.2m from the real source location.

ACS Style

Ahmad Shakaff Ali Yeon; Kamarulzaman Kamarudin; Retnam Visvanathan; Syed Muhammad Mamduh Syed Zakaria; Ammar Zakaria; Latifah Munirah Kamarudin. Gas Source Localization via Behaviour Based Mobile Robot and Weighted Arithmetic Mean. IOP Conference Series: Materials Science and Engineering 2018, 318, 012049 .

AMA Style

Ahmad Shakaff Ali Yeon, Kamarulzaman Kamarudin, Retnam Visvanathan, Syed Muhammad Mamduh Syed Zakaria, Ammar Zakaria, Latifah Munirah Kamarudin. Gas Source Localization via Behaviour Based Mobile Robot and Weighted Arithmetic Mean. IOP Conference Series: Materials Science and Engineering. 2018; 318 (1):012049.

Chicago/Turabian Style

Ahmad Shakaff Ali Yeon; Kamarulzaman Kamarudin; Retnam Visvanathan; Syed Muhammad Mamduh Syed Zakaria; Ammar Zakaria; Latifah Munirah Kamarudin. 2018. "Gas Source Localization via Behaviour Based Mobile Robot and Weighted Arithmetic Mean." IOP Conference Series: Materials Science and Engineering 318, no. 1: 012049.

Conference paper
Published: 31 December 2017 in Econometrics for Financial Applications
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This paper proposes and analyses the applicability of integrating Fuzzy C-Means (FCM) and artificial neural network (ANN) in rainfall forecasting. The algorithm of ANN and FCM clustering are integrated and applied to forecast short-term localized rainfall in tropical weather. Rainfall forecasting in this paper is divided into state forecast (raining or not raining) and rainfall rate forecast. Various type of back propagation extended network with hidden layers of ANN structured were trained. Training algorithm of Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used and trained. Transfer function in each neuron uses linear, logistic sigmoid and hyperbolic tangent sigmoid. Initial statistical analysis of weather parameter, data pre-processing approach and FCM clustering method were used to organize input data for the ANN forecast model. Input parameters such as atmospheric pressure, temperature, dew point, humidity and wind speed have been used. One to six hour predicted rainfall forecast are compared and analyzed. The result indicates that the integrated of FCM-ANN forecast model yield 80% for 1 h forecast.

ACS Style

Noor Zuraidin Mohd-Safar; David Ndzi; David Sanders; Hassanuddin Mohamed Noor; Latifah Munirah Kamarudin. Integration of Fuzzy C-Means and Artificial Neural Network for Short-Term Localized Rainfall Forecasting in Tropical Climate. Econometrics for Financial Applications 2017, 325 -348.

AMA Style

Noor Zuraidin Mohd-Safar, David Ndzi, David Sanders, Hassanuddin Mohamed Noor, Latifah Munirah Kamarudin. Integration of Fuzzy C-Means and Artificial Neural Network for Short-Term Localized Rainfall Forecasting in Tropical Climate. Econometrics for Financial Applications. 2017; ():325-348.

Chicago/Turabian Style

Noor Zuraidin Mohd-Safar; David Ndzi; David Sanders; Hassanuddin Mohamed Noor; Latifah Munirah Kamarudin. 2017. "Integration of Fuzzy C-Means and Artificial Neural Network for Short-Term Localized Rainfall Forecasting in Tropical Climate." Econometrics for Financial Applications , no. : 325-348.

Conference paper
Published: 01 January 2017 in AIP Conference Proceedings
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The number of patients that require medical assistance is increasing each day while staff-patient ratio is not balanced causing issues such as treatment delay and often leads to patient dissatisfaction. Besides that, healthcare devices are getting complex and challenging for it to be handled and interpreted personally by patient. Lack of staff and challenges in operating the medical devices not only affect patient in hospital but also caused problem to home care patients that require full attention and constant monitoring. This urges for a development of new method or technology. At present, Wireless Sensor Network (WSN) is gaining interest as one of the major components in enabling Internet of Things (IoT) since it offers low cost, low power monitoring besides reducing devices dependency on wires or cable. Although, WSN is initially developed for military application, nowadays, it is being integrated into various applications such as environmental monitoring, smart monitoring and agricultural monitoring. The idea of wireless monitoring with low power consumption motivates researchers to discover the possibility of deploying wireless sensor network for mission critical application such as in healthcare applications. This paper presents the details on the design and development of wireless sensor network using Waspmote from Libelium Inc. for mission critical applications such as healthcare applications.

ACS Style

Noraini Azmi; Latifah Munirah Kamarudin. Enabling IoT: Integration of wireless sensor network for healthcare application using Waspmote. AIP Conference Proceedings 2017, 1 .

AMA Style

Noraini Azmi, Latifah Munirah Kamarudin. Enabling IoT: Integration of wireless sensor network for healthcare application using Waspmote. AIP Conference Proceedings. 2017; ():1.

Chicago/Turabian Style

Noraini Azmi; Latifah Munirah Kamarudin. 2017. "Enabling IoT: Integration of wireless sensor network for healthcare application using Waspmote." AIP Conference Proceedings , no. : 1.

Journal article
Published: 01 August 2016 in Journal of Visual Communication and Image Representation
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This paper presents an adaptive crowd counting system for video surveillance applications. The proposed method is composed of a pair of collaborative Gaussian process models (GP) with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, the proposed method dynamically identifies the best combination of features for people counting. The Mall and UCSD datasets are used to evaluate the proposed method. The results show that the proposed method offers a higher accuracy when compared against state of the art methods reported in open literature. The mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) for the proposed algorithm are 2.90, 13.70 and 0.095, respectively, for the Mall dataset and 1.63, 4.32 and 0.066, respectively, for UCSD dataset.

ACS Style

Zeyad Q.H. Al-Zaydi; David L. Ndzi; Yanyan Yang; Munirah L. Kamarudin. An adaptive people counting system with dynamic features selection and occlusion handling. Journal of Visual Communication and Image Representation 2016, 39, 218 -225.

AMA Style

Zeyad Q.H. Al-Zaydi, David L. Ndzi, Yanyan Yang, Munirah L. Kamarudin. An adaptive people counting system with dynamic features selection and occlusion handling. Journal of Visual Communication and Image Representation. 2016; 39 ():218-225.

Chicago/Turabian Style

Zeyad Q.H. Al-Zaydi; David L. Ndzi; Yanyan Yang; Munirah L. Kamarudin. 2016. "An adaptive people counting system with dynamic features selection and occlusion handling." Journal of Visual Communication and Image Representation 39, no. : 218-225.

Journal article
Published: 09 December 2015 in Sensors
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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.

ACS Style

Syed Muhammad Mamduh Syed Zakaria; Retnam Visvanathan; Kamarulzaman Kamarudin; Ahmad Shakaff Ali Yeon; Ali Yeon Md. Shakaff; Ammar Zakaria; Latifah Munirah Kamarudin. Development of a Scalable Testbed for Mobile Olfaction Verification. Sensors 2015, 15, 30894 -30912.

AMA Style

Syed Muhammad Mamduh Syed Zakaria, Retnam Visvanathan, Kamarulzaman Kamarudin, Ahmad Shakaff Ali Yeon, Ali Yeon Md. Shakaff, Ammar Zakaria, Latifah Munirah Kamarudin. Development of a Scalable Testbed for Mobile Olfaction Verification. Sensors. 2015; 15 (12):30894-30912.

Chicago/Turabian Style

Syed Muhammad Mamduh Syed Zakaria; Retnam Visvanathan; Kamarulzaman Kamarudin; Ahmad Shakaff Ali Yeon; Ali Yeon Md. Shakaff; Ammar Zakaria; Latifah Munirah Kamarudin. 2015. "Development of a Scalable Testbed for Mobile Olfaction Verification." Sensors 15, no. 12: 30894-30912.

Journal article
Published: 14 May 2015 in BMC Bioinformatics
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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. 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. 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.

ACS Style

Nurlisa Yusuf; Ammar Zakaria; Mohammad Iqbal Omar; Ali Yeon Md Shakaff; Maz Jamilah Masnan; Latifah Munirah Kamarudin; Norasmadi Abdul Rahim; Nur Zawatil Isqi Zakaria; Azian Azamimi Abdullah; Amizah Othman; Mohd Sadek Yasin. In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology. BMC Bioinformatics 2015, 16, 1 -12.

AMA Style

Nurlisa Yusuf, Ammar Zakaria, Mohammad Iqbal Omar, Ali Yeon Md Shakaff, Maz Jamilah Masnan, Latifah Munirah Kamarudin, Norasmadi Abdul Rahim, Nur Zawatil Isqi Zakaria, Azian Azamimi Abdullah, Amizah Othman, Mohd Sadek Yasin. In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology. BMC Bioinformatics. 2015; 16 (1):1-12.

Chicago/Turabian Style

Nurlisa Yusuf; Ammar Zakaria; Mohammad Iqbal Omar; Ali Yeon Md Shakaff; Maz Jamilah Masnan; Latifah Munirah Kamarudin; Norasmadi Abdul Rahim; Nur Zawatil Isqi Zakaria; Azian Azamimi Abdullah; Amizah Othman; Mohd Sadek Yasin. 2015. "In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology." BMC Bioinformatics 16, no. 1: 1-12.

Journal article
Published: 31 December 2014 in Instrumentation Science & Technology
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ACS Style

Kamarulzaman Kamarudin; Syed Muhammad Mamduh; Ali Yeon Md Shakaff; Shaharil Mad Saad; Syed Muhammad Mamduh Syed Zakaria; Abu Hassan Abdullah; Latifah Munirah Kamarudin. Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment. Instrumentation Science & Technology 2014, 43, 74 -88.

AMA Style

Kamarulzaman Kamarudin, Syed Muhammad Mamduh, Ali Yeon Md Shakaff, Shaharil Mad Saad, Syed Muhammad Mamduh Syed Zakaria, Abu Hassan Abdullah, Latifah Munirah Kamarudin. Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment. Instrumentation Science & Technology. 2014; 43 (1):74-88.

Chicago/Turabian Style

Kamarulzaman Kamarudin; Syed Muhammad Mamduh; Ali Yeon Md Shakaff; Shaharil Mad Saad; Syed Muhammad Mamduh Syed Zakaria; Abu Hassan Abdullah; Latifah Munirah Kamarudin. 2014. "Flexible and Autonomous Integrated System for Characterizing Metal Oxide Gas Sensor Response in Dynamic Environment." Instrumentation Science & Technology 43, no. 1: 74-88.

Conference paper
Published: 01 December 2014 in 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)
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Lack of effective tools to diagnose lung cancer at an early stage has caused high mortality in cancer patients especially in lung cancer patients. Electronic nose (E-Nose) technology is believed to offer non-invasive, rapid and reliable analytic approach by measuring the odour released from cancer to assist medical diagnosis. In this work, using a commercial E-nose (Cyranose-320), we aimed to detect the volatile organic compounds (VOCs) emitted by different types of cancerous cells. The lung cancer cell (A549) and breast cancer cell (MCF-7) were used for this study. Both cells were cultured using Dulbecco's Modified Eagle's Medium (DMEM) with 10% of Fetal Bovine Serum (FBS) and incubated for three days. The static headspace of cell cultures and blank medium were directly sniffed by Cyranose-320. The preliminary results from this study showed that, the E-nose is able to detect and distinguish the presence of VOCs in cancerous cells with accuracy of 100% using LDA. To this end, the VOCs emitted from cancerous cells can potentially used as biomarker.

ACS Style

R. Thriumani; Ammar Zakaria; A.I. Jeffree; N.A. Hishamuddin; Ye Htut @ Mohammad Iqbal Omar; A.H. Adom; A.Y.M. Shakaff; Latifah Munirah Kamarudin; N. Yusuf; Yumi Zuhanis Has-Yun Hashim; K.M. Helmy. Cancer detection using an electronic nose: A preliminary study on detection and discrimination of cancerous cells. 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES) 2014, 752 -756.

AMA Style

R. Thriumani, Ammar Zakaria, A.I. Jeffree, N.A. Hishamuddin, Ye Htut @ Mohammad Iqbal Omar, A.H. Adom, A.Y.M. Shakaff, Latifah Munirah Kamarudin, N. Yusuf, Yumi Zuhanis Has-Yun Hashim, K.M. Helmy. Cancer detection using an electronic nose: A preliminary study on detection and discrimination of cancerous cells. 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). 2014; ():752-756.

Chicago/Turabian Style

R. Thriumani; Ammar Zakaria; A.I. Jeffree; N.A. Hishamuddin; Ye Htut @ Mohammad Iqbal Omar; A.H. Adom; A.Y.M. Shakaff; Latifah Munirah Kamarudin; N. Yusuf; Yumi Zuhanis Has-Yun Hashim; K.M. Helmy. 2014. "Cancer detection using an electronic nose: A preliminary study on detection and discrimination of cancerous cells." 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES) , no. : 752-756.

Conference paper
Published: 01 August 2014 in 2014 2nd International Conference on Electronic Design (ICED)
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This paper models the signal strength measurements at 2.4 GHz in indoor environment. The received signal strength indicator (RSSI) measurement is used to investigate the wireless network coverage in a real office environment where obstacles such as furniture are present. From this experiment, a mapping is created to determine the suitable positions for a short range sensor nodes deployment for sensing humidity, temperature, human movement, etc. The purpose is to evaluate the suitable area for WSN deployments using RF signal and to minimize the number of sensor nodes required for data gathering and monitoring applications. The result shows that through adequate planning of WSN nodes, good radio coverage and efficient monitoring can be achieved for greener building.

ACS Style

J. S. C. Turner; L. M. Kamarudin; D. L. Ndzi; A. Harun; Ammar Zakaria; A. Y. M Shakaff; A. R. M. Saad; S. M. Mamduh. Modelling indoor propagation for WSN deployment in smart building. 2014 2nd International Conference on Electronic Design (ICED) 2014, 398 -402.

AMA Style

J. S. C. Turner, L. M. Kamarudin, D. L. Ndzi, A. Harun, Ammar Zakaria, A. Y. M Shakaff, A. R. M. Saad, S. M. Mamduh. Modelling indoor propagation for WSN deployment in smart building. 2014 2nd International Conference on Electronic Design (ICED). 2014; ():398-402.

Chicago/Turabian Style

J. S. C. Turner; L. M. Kamarudin; D. L. Ndzi; A. Harun; Ammar Zakaria; A. Y. M Shakaff; A. R. M. Saad; S. M. Mamduh. 2014. "Modelling indoor propagation for WSN deployment in smart building." 2014 2nd International Conference on Electronic Design (ICED) , no. : 398-402.

Conference paper
Published: 01 August 2014 in 2014 2nd International Conference on Electronic Design (ICED)
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Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.

ACS Style

H. N. Maamor; F. N. A. Rashid; N. Z. I. Zakaria; Ammar Zakaria; Latifah Munirah Kamarudin; M. N. Jaafar; A. Y. M. Shakaff; N. Subari; N. Yusuf; S. W. M. Ismail; K. N. A. K. Adnan. Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique. 2014 2nd International Conference on Electronic Design (ICED) 2014, 270 -274.

AMA Style

H. N. Maamor, F. N. A. Rashid, N. Z. I. Zakaria, Ammar Zakaria, Latifah Munirah Kamarudin, M. N. Jaafar, A. Y. M. Shakaff, N. Subari, N. Yusuf, S. W. M. Ismail, K. N. A. K. Adnan. Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique. 2014 2nd International Conference on Electronic Design (ICED). 2014; ():270-274.

Chicago/Turabian Style

H. N. Maamor; F. N. A. Rashid; N. Z. I. Zakaria; Ammar Zakaria; Latifah Munirah Kamarudin; M. N. Jaafar; A. Y. M. Shakaff; N. Subari; N. Yusuf; S. W. M. Ismail; K. N. A. K. Adnan. 2014. "Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique." 2014 2nd International Conference on Electronic Design (ICED) , no. : 270-274.

Conference paper
Published: 01 August 2014 in 2014 2nd International Conference on Electronic Design (ICED)
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Ultrasonic sensor is one of the most cost-effective sensor used to obtain range information and obstacle avoidance. Due to its simplicity, this sensor is widely used in mobile robot applications to acquire environment features and mapping. Although the sensor can track a still or moving target, it does not provide information on the shape and pattern of the detected object. This paper proposes and highlights a low cost method using an array of ultrasonic sensors to be embedded on multiple robots for wall features extraction. Instead of using a single sensor, multiple sensors are used to increase the accuracy and improve coverage on the field of view of the sensor. More information can be extracted such as bearing angle of walls and possibly the shape of an object. A multiple pulse transmit and instantaneous multiple echo receive approach is implemented. The experimental results prove that this method is able to extract different type of wall features, accurately.

ACS Style

R. Visvanathan; S. M. Mamduh; K. Kamarudin; M. H. M Razali; A. S. A. Yeon; A. Zakaria; L. M. Kamarudin; S. A. A. Shukor; A. Y. M. Shakaff; F. S. A. Saad; N. A. Rahim; Visvanathan R.; Mamduh S.M.; Kamarudin K.; Razali M.H.M.; Yeon A.S.A.; Zakaria A.; Kamarudin L.M.; Shukor S.A.A.; Shakaff A.Y.M.; Saad F.S.A.; Rahim N.A.. Multi channel ultrasonic sensing system for wall features extraction. 2014 2nd International Conference on Electronic Design (ICED) 2014, 351 -355.

AMA Style

R. Visvanathan, S. M. Mamduh, K. Kamarudin, M. H. M Razali, A. S. A. Yeon, A. Zakaria, L. M. Kamarudin, S. A. A. Shukor, A. Y. M. Shakaff, F. S. A. Saad, N. A. Rahim, Visvanathan R., Mamduh S.M., Kamarudin K., Razali M.H.M., Yeon A.S.A., Zakaria A., Kamarudin L.M., Shukor S.A.A., Shakaff A.Y.M., Saad F.S.A., Rahim N.A.. Multi channel ultrasonic sensing system for wall features extraction. 2014 2nd International Conference on Electronic Design (ICED). 2014; ():351-355.

Chicago/Turabian Style

R. Visvanathan; S. M. Mamduh; K. Kamarudin; M. H. M Razali; A. S. A. Yeon; A. Zakaria; L. M. Kamarudin; S. A. A. Shukor; A. Y. M. Shakaff; F. S. A. Saad; N. A. Rahim; Visvanathan R.; Mamduh S.M.; Kamarudin K.; Razali M.H.M.; Yeon A.S.A.; Zakaria A.; Kamarudin L.M.; Shukor S.A.A.; Shakaff A.Y.M.; Saad F.S.A.; Rahim N.A.. 2014. "Multi channel ultrasonic sensing system for wall features extraction." 2014 2nd International Conference on Electronic Design (ICED) , no. : 351-355.

Conference paper
Published: 01 August 2014 in 2014 2nd International Conference on Electronic Design (ICED)
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Aquaculture is an important to national food security. Productivity of aquaculture farms hinges on water quality. Lack of appropriate instrumentation for measurement of water quality is a hindrance to the industry. This experiment proposed and verify the application of e-nose and e-tongue for water quality parameters for shrimp farming. Results indicated it has the potential but required additional analytical techniques. Thus, by using sensor array technologies, e-nose and e-tongue has been employed in classification of different type of water that has been used in aquaculture farming. E-nose consists of 10 metal oxide sensors meanwhile e-tongue consists of 13 working electrodes and one reference electrode. Linear Discriminant Analysis (LDA) was used as data classifier. The e-nose and e-tongue was able to classify different type of water with the accuracy up to 95%. These results show the potential use of e-nose and e-tongue to classify the different type of water used in aquaculture industry.

ACS Style

K. N. A. K. Adnan; N. Yusuf; H. N. Maamor; F. N. A. Rashid; S. W. M. Ismail; R. Thriumani; Ammar Zakaria; L. M. Kamarudin; A. Y. M. Shakaff; M. N. Jaafar; M. N. Ahmad. Water quality classification and monitoring using e-nose and e-tongue in aquaculture farming. 2014 2nd International Conference on Electronic Design (ICED) 2014, 343 -346.

AMA Style

K. N. A. K. Adnan, N. Yusuf, H. N. Maamor, F. N. A. Rashid, S. W. M. Ismail, R. Thriumani, Ammar Zakaria, L. M. Kamarudin, A. Y. M. Shakaff, M. N. Jaafar, M. N. Ahmad. Water quality classification and monitoring using e-nose and e-tongue in aquaculture farming. 2014 2nd International Conference on Electronic Design (ICED). 2014; ():343-346.

Chicago/Turabian Style

K. N. A. K. Adnan; N. Yusuf; H. N. Maamor; F. N. A. Rashid; S. W. M. Ismail; R. Thriumani; Ammar Zakaria; L. M. Kamarudin; A. Y. M. Shakaff; M. N. Jaafar; M. N. Ahmad. 2014. "Water quality classification and monitoring using e-nose and e-tongue in aquaculture farming." 2014 2nd International Conference on Electronic Design (ICED) , no. : 343-346.