Advances in Intelligent Manufacturing and Mechatronics
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Dr. Muhammad Amirul Abdullah is Researcher at the Innovative Manufacturing, Mechatronics & Sports (iMAMS), Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology in Universiti Malaysia Pahang (UMP). He was awarded a Master's Degree and received his Bachelor's Degree, both in Mechatronics Engineering, from International Islamic University Malaysia (IIUM). He holds a Ph.D. in Mechatronics Engineering from UMP, focussing on the employment of deep learning architectures in sports application. His research interest includes machine learning, robotics, control and automation, and sports engineering.
Dr. Ismail Mohd Khairuddin is Senior Lecturer at Universiti Malaysia Pahang. He received his Bachelor's Degree in Mechatronics Engineering from Universiti Teknikal Malaysia Melaka (UTeM) in 2010 and was awarded with a Master's Degree in Mechatronics and Automatic Control from Universiti Teknologi Malaysia in 2012. He then obtained his Ph.D. in Biomechatronics Engineering at the International Islamic University Malaysia. His research interests include rehabilitation robotics, mechanical and mechatronics design, mechanisms, control and automation, bio-signal processing as well as machine learning.
Dr. Ahmad Fakhri bin Ab. Nasir received his Bachelor's Degree in Information Technology from Universiti Malaya. He enrolled as Full-Time Master Student at the Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, and received his Master's Degree in Engineering (Manufacturing). He pursued his Ph.D. specializing in Pattern Recognition at the Universiti Sultan Zainal Abidin. He joined Universiti Malaysia Pahang as Senior Lecturer in 2016. His research interests are in the areas of computer vision, pattern recognition, image processing, machine learning, as well as parallel computing.
Wan Hasbullah Bin Mohd Isa graduated with a B.Eng. in Mechatronics (Precision Engineering) from the Hochschule Furtwangen University, Germany, in 2009. Hethen completed his M.Sc. in Mechatronics at Fachhochschule Aachen, Germany, and graduated in 2012. Currently, he is pursuing his Ph.D. Degree at the Delft University of Technology, Netherlands, in the field of smart materials/transducers. Since 2012, he is Lecturer at Faculty of Manufacturing and Mechatronic Engineering Technology of UMP, focusing his research on miniaturized energy harvesting systems, artificial intelligence, and smart material-based actuation/sensing applications.
Dr. Mohd Azraai Mohd Razman graduated from the University of Sheffield, UK, in Mechatronics Engineering. He then obtained his M.Eng. from Universiti Malaysia Pahang (UMP) in Mechatronics Engineering. He then completed his Ph.D. at UMP in the application of machine learning in aquaculture. His research interest includes optimization techniques, control systems, signal processing, instrumentation in aquaculture, sports engineering as well as machine learning.
Dr. Mohd Azri Hizami Rasid received his Ph.D. in Mechatronics from the Université de Technologie de Compiègne, Compiègne, France, in 2016, specializing in electrical machines. He is currently Senior Lecturer with the Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Malaysia. His research interests concentrate on electromechanical systems with multiphysics approach, focusing on thermal, and vibroacoustic behaviour.
Dr. Sheikh Muhammad Hafiz Fahami earned his first degree in Mechatronics Engineering Technology from University Kuala Lumpur-Malaysia France Institute. He obtained his Doctoral Degree in Mechatronics Engineering specializing in the Development of Autonomous Vehicle Systems at the University Technology Malaysia in Kuala Lumpur. His research interests include intelligent vehicle stability control, control system modelling, and mechatronics system.
Dr. Barry Bentley is Senior Lecturer at the Cardiff School of Technologies. He completed his Ph.D. at the University of Cambridge, where he worked at the MRC Laboratory of Molecular Biology in the areas of computational neuroscience and bioinformatics. He has conducted research and consultancy work for multiple organizations including the Open University, the University of Oxford, the European Space Agency, and ARM Holdings.
Dr. Pengcheng Liu is Assistant Professor in the Department of Computer Science, University of York, UK. Before joining York, he held several academic positions including Senior Lecturer (Associate Professor) at the Cardiff School of Technologies, Cardiff Metropolitan University, UK, Joint Research Fellowship at the Lincoln Centre for Autonomous Systems (LCAS) and Lincoln Institute of Agri-Food Technology (LIAT), University of Lincoln, UK, Research Assistant and Teaching Assistant at Bournemouth University, UK. He also held academic positions as Visiting Fellow at the Institute of Automation, Chinese Academy of Sciences, China, and Shanghai Jiao Tong University, China.
Content
A Computational Time Analysis of Discrete Simulated Kalman Filter Optimizer.- A Real-Time Social Distancing and Face Mask Detection System using Deep Learning.- A Systematic Review for Robotic for Cognitive Speech Therapy for Rehabilitation Patient.- An Estimation Steering Feedback Torque in Vehicle Steer by Wire System.- An Implementation of Sliding Mode Voltage Control Controlled Buck-Boost Converter for Solar Application.- An Optimized Deep Learning Model for Automatic Diagnosis of COVID-19 Using Chest X-Ray Images.- Automatic Vehicle Location (AVL): Evaluation on The Punctuality Index of City Public Bus Service.- Bearing Fault Diagnosis using Extreme Learning Machine based on Artificial Gorilla Troops Optimizer.- Classifying Ethnicity of the Pedestrian using Skin Colour Palette.- Cluster Analysis based on Image Feature Extraction for Automated Operational Modal Analysis.- Detection of Lead with IoT Water Monitoring System using Microstrip Antenna-Based Sensor.- Emotion Recognition using Ultra Short-term ECG Signals with a Hybrid Convolutional Neural Network and Long Short-Term Memory Network.- Enhancement of Morlet Mother Wavelet in Time-Frequency Domain in Electroencephalogram (EEG) Signals.- Fabrication of Aneurysm Biomodel using 3D Printing Technology.- Feature Selection of Medical Dataset Using African Vultures Optimization Algorithm.