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165 results on '"Mokhtar, Norrima"'

12. A Diversity-Based Adaptive Synchronous-Asynchronous Switching Simulated Kalman Filter Optimizer

Author

Aziz, Nor Azlina Ab., Aziz, Nor Hidayati Abdul, Muhammad, Badaruddin, Ibrahim, Zuwairie, Mubin, Marizan, Mokhtar, Norrima, Mohamad, Mohd Saberi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Kasruddin Nasir, Ahmad Nor, editor, Ahmad, Mohd Ashraf, editor, Najib, Muhammad Sharfi, editor, Abdul Wahab, Yasmin, editor, Othman, Nur Aqilah, editor, Abd Ghani, Nor Maniha, editor, Irawan, Addie, editor, Khatun, Sabira, editor, Raja Ismail, Raja Mohd Taufika, editor, Saari, Mohd Mawardi, editor, Daud, Mohd Razali, editor, and Mohd Faudzi, Ahmad Afif, editor

Published
2020
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18. Selection and Pathways to Next Generation Innovations of Speed Over Ground (SOG) Measurement Technologies to Overcome Harsh Conditions and Technological Limitations: A Review

Author

Mahdi Imam, Jakaria, Mokhtar, Norrima, Wan Muhamad Hatta, Sharifah Fatmadiana, Rashdan, Mostafa, Salman, Mohammad, Khandaker, Mayeen Uddin, and Islam, Mohammad Aminul

Abstract

Determining speed over ground (SOG) with high precision is a critical challenge for marine and aerial vehicles due to various harsh conditions and technological limitations. Here, we review and evaluate current SOG measurement technologies, such as water speed sensor, air speed sensor, global navigation satellite system (GNSS), underwater acoustic positioning system (UAPS), Doppler velocity log (DVL), Doppler navigation system (DNS), simultaneous localization and mapping (SLAM), conventional inertial navigation system (INS), and their performance amidst harsh condition and technological limitations. It reveals the vulnerabilities, and noting their limitations when faced with stressors and technological challenges, thus highlighting the urgent need for resilient and innovative SOG measurement solutions. A substantial focus is placed on emerging sensor technologies, including cold atom inertial sensor (CAIS), micro-opto-electro-mechanical systems (MOEMS) inertial sensor, and particle imaging velocimetry (PIV) inertial sensor. Each of them presents a new frontier in navigational sciences, offering the potential for improved precision and robustness against harsh conditions that traditionally hamper SOG measurement accuracy. However, they still suffer from technological limitations like integration error over time. The Galilean projectile model-based SOG Sensor is spotlighted for its potential to provide a contactless, environment-independent SOG measurement, which is less prone to both harsh conditions and technological limitations like integration error over time. As such, this article outlines a pathway toward selecting the best SOG sensor based on harsh conditions and technological limitations for particular applications, as well as future research direction that could significantly enhance the reliability and accuracy of SOG measurements, ensuring safer and efficient navigation.

Published
2024
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20. Edu-Kit Arm Robot with Protocol Data for Robotics Course

Author

Sendari, Siti, primary, Zaeni, Ilham Ari Elbaith, additional, Rahmawati, Yuni, additional, Rahmawati, Hetti, additional, Shodiq, Muhammad Ahsan Fatwaddin, additional, Khoirurizka, Nurdin, additional, Wibowo, Danang, additional, Setumin, Samsul, additional, Mokhtar, Norrima, additional, Lin, Hsien-I, additional, and Hartono, Pitoyo, additional

Published
2023
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22. Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents.

Author

Husnain, Anees ul, Mokhtar, Norrima, Mohamed Shah, Noraisyah Binti, Dahari, Mahidzal Bin, Azmi, Amirul Asyhraff, and Iwahashi, Masahiro

Subjects
*ENVIRONMENTAL monitoring, *MACHINE learning, *REINFORCEMENT learning, *ORTHOGONAL arrays, *GASES, *SENSOR arrays
Abstract

There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi's orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Banana fruit classification using computer vision.

Author

Makain, Felice Maynelia, Shah, Noraisyah Mohamed, and Mokhtar, Norrima

Subjects
BANANAS, COMPUTER vision, FRUIT, CONVOLUTIONAL neural networks, DATA augmentation, GRAYSCALE model
Abstract

Automatic banana fruit classification system was developed utlising pre-existing convolutional neural networks (CNN) models via transfer learning. The proposed model has the ability to classify five types of local banana fruits; Awak, Berangan, Cavendish, Lemak Manis, and Mas bananas. Dataset of 627 images was created to train our computer vision-based model. Data augmentation involving grayscale conversion, image zooming, horizontal and vertical flip, 90-degree rotation, and brightness adjustment is used to increase the total number of images to 6270. The augmented images are separated into three datasets, namely train, validation, and test datasets, with 4389 training images, 1254 validation images, and 627 test images respectively. Three CNN architectures including VGG16, InceptionV3, and ResNet152V2 were trained, and their performances evaluated. All three of the models are optimized by conducting several experiments to find the best parameters for learning rate, batch size, input size, and number of epochs. The best CNN architecture is found to be ResNet152V2 with a test accuracy of 94%, training accuracy of 94%, training loss of 14%, precision of 95%, recall of 94%, and an F1-score of 94%. The ResNet152V2 model is then integrated into our automated banana fruit classification system to predict the class of an input banana image. Overall, the automated banana fruit classification system performed well during the testing process by using new local banana fruit images. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Fully automated unsupervised artefact removal in multichannel electroencephalogram using wavelet-independent component analysis with density-based spatial clustering of application with noise

Author

Sai, Chong Yeh, Mokhtar, Norrima, Iwahashi, Masahiro, Cumming, Paul, Arof, Hamzah, Sai, Chong Yeh, Mokhtar, Norrima, Iwahashi, Masahiro, Cumming, Paul, and Arof, Hamzah

Abstract

Electroencephalography (EEG) is a method for recording electrical activities arising from the cortical surface of the brain, which has found wide applications not just in clinical medicine, but also in neuroscience research and studies of Brain-Computer Interface (BCI). However, EEG recordings often suffer from distortions due to artefactual components that degrade the true EEG signals. Artefactual components are any unwanted signals recorded in the EEG spectrum that originate from sources other than the neurophysiological activity of the human brain. Examples of the origin of artefactual components include eye blinking, facial or scalp muscles activities, and electrode slippage. Techniques for automated artefact removal such as Wavelet Transform and Independent Component Analysis (ICA) have been used to remove or reduce the effect of artefactual components on the EEG signals. However, detecting or identifying the signal artefacts to be removed presents a great challenge, as EEG signal properties vary between individuals and age groups. Techniques that rely on some arbitrarily defined threshold often fail to identify accurately the signal artefacts in a given dataset. In this study, a method is proposed using unsupervised machine learning coupled with Wavelet-ICA to remove EEG artefacts. Using Density-Based Spatial Clustering of Application with Noise (DBSCAN), a classification accuracy of 97.9% is achieved in identifying artefactual components. DBSCAN achieved excellent and robust performance in identifying artefactual components during the Wavelet-ICA process, especially in consideration of the low-density nature of typical artefactual signals. This new hybrid method provides a scalable and unsupervised solution for automated artefact removal that should be applicable for a wide range of EEG data types.

Published
2021

49. A survey on applications of black hole algorithm

Author

Subari Norazian, Shapiai Mohd Ibrahim, Mohammed Suad Khairi, Ab Rahman Tasiransurini, Md. Yusof Zulkifli, Ibrahim Zuwairie, Ab. Aziz Nor Azlina, Abdul Aziz Nor Hidayati, Mokhtar Norrima, and Adam Asrul

Subjects
General Relativity and Quantum Cosmology, Optimization algorithm, Black hole algorithm, Computer science, General relativity, Astrophysics::High Energy Astrophysical Phenomena, General Medicine, Algorithm, Cosmology
Abstract

In recent years, there is a growing interest in the design and development of nature-inspired optimization algorithms. One of the algorithms is black hole algorithm (BHA), which is inspired by the black hole in general relativity and cosmology. This paper reports a review of the black hole algorithm (BHA). This survey emphasizes on the applications of BHA.

Published
2018
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50. A review on fundamental advancements of black hole algorithm

Author

Ab Rahman Tasiransurini, Ibrahim Zuwairie, Mohammed Suad Khairi, Shapiai Mohd Ibrahim, Ab. Aziz Nor Azlina, Subari Norazian, Abdul Aziz Nor Hidayati, Md. Yusof Zulkifli, Adam Asrul, and Mokhtar Norrima

Subjects
General Relativity and Quantum Cosmology, Theoretical physics, Development (topology), Optimization algorithm, General relativity, Computer science, Black hole algorithm, Astrophysics::High Energy Astrophysical Phenomena, General Medicine, Cosmology
Abstract

In recent years, there is a growing interest in the design and development of nature-inspired optimization algorithms. One of the algorithms is black hole algorithm (BHA), which is inspired by the black hole in general relativity and cosmology. This paper reports a review of the BHA. This review emphasizes on the fundamental advancements of BHA. A brief research gap is presented at the end of this paper.

Published
2018
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