Mekatronika: Journal of Intelligent Manufacturing and Mechatronics accepts original research paper, as well as review papers in fields related to Robotics and Automation, Artificial Intelligence and Computational Intelligence, Intelligent Control, Instrumentation and System Integration, Signal and Image Processing, Modelling and Simulation. All submission must be done through this online submission system. Mekatronika: Journal of Intelligent Manufacturing and Mechatronics is currently indexed in MyJurnal, MyCite and Google Scholar.
Associations of Physical Inactivity with Health-Related Markers in Adults: A Discriminant Analysis from a Fitness Promotion Campaign
The World Health Organization has initiated a strategic plan to curve the prevalence of physical inactivity across the globe by 10% and 30% by the years 2025 and 2030, respectively. However, a recent survey by the Ministry of Health Malaysia demonstrated that one in three Malaysian adults were physically inactive. Although survey data could provide useful information, it is prone to subjectivity. The current study is aimed at objectively evaluating the physical activity levels of Malaysian adults with respect to health-related markers, i.e., height, weight, body fat percentage (BFP), muscle mass, body mass index (BMI), basal metabolic rate (BMR), resting heart rate (RHR), blood pressure (BP), muscle endurance, and maximum oxygen consumption (VO2max). A total of 232 adults were recruited for this study. The physical activity levels of the participants was categorized into high, moderate and low by using the International Physical Activity Questionnaire and the above-mentioned health-related markers were measured via standard health-related marker measurements. A discriminant analysis was used as a statistical technique to distinguish the physical activity levels and the corresponding health-related markers of the participants. The results showed that there were no differences between the physical activity levels in terms of BMI, muscle mass, visceral fat, BMR, and BP (p>0.05). However, there were significant differences in percentile of BFP, RHR, muscle endurance, and VO2max (p<0.05). A high level of physical activity is attributed to lower RHR and BFP, muscular as well as cardiovascular endurance, which are non-trivial in helping individuals meet the demands of daily tasks.
Copyright (c) 2025 The Author(s) http://creativecommons.org/licenses/by-nc/4.0
Milling-Induced Surface Roughness in Carbon Fiber Reinforced Polymer: A Diagnostic Study between Ra and Sa
Carbon Fiber Reinforced Polymer or CFRP is among the composite with high utilisation especially in the high-end industrial sectors such as automotive, aviation and architecture. This is due to its superior characteristics such as excellent strength-to-weight ratio, high stiffness and corrosion, and can be manufactured near its end shape. However, the abrasive nature originated from the carbon fiber impregnated in the epoxy resin creates an anisotropic and inhomogeneous material. It is a challenge to machine the CFRP as the carbon fiber abraded the cutting edges. This implicated in the tool wear progression, thus affecting the surface quality of the CFRP. Since the CFRP is impregnated in the epoxy resin, machining must be conducted below the glass transitional temperature (Tg), to avoid degradation of the polymer to occur. This study investigates the influence of different cutting environments (dry, coolant and chilled air) on the surface evaluation of the CFRP during the milling process with a constant Vc = 150 m/min and Vf = 2100 mm/min. The surface roughness was evaluated by both Ra and Sa. There is a minimal contrast in the Ra values along the milling process, whereas the Sa value shows significant increases across the machining process for all the conditions of dry, coolant, and chilled air. It is indicated that milling in dry conditions produced Sa values that were 28.23 % and 14.48 % higher compared to coolant and chilled air, respectively. These values highlight the efficiency of coolant and chilled air in reducing the heat generation. This reduction minimizes the thermal effects thus improving the surface quality. Flooding the cutting zone with coolant prominently helps in reducing the heat produced from the friction between the CFRP and the mill tool. It is evidently supported and visualised by the pseudo-color depth maps of the areal surface roughness analysis. The areal surface roughness, Sa proven to be beneficial in understanding the surface topography and condition for inhomogeneous materials like CFRP. It justifies the occurrences of surface damages such as delamination, matrix smearing, and fiber pull out when observed under the scanning electron microscope. This study emphasizes the importance of suitable cooling method to maintain the integrity of the CFRP surfaces.
Development of a Solvent-cast Direct-write 3D Printer System for Polymeric Stent Fabrication
Polymeric stents can be produced using three-dimensional (3D) printing methods. The most commonly used 3D printer is fused deposition modeling (FDM), as it is cost-effective and easy to use. However, the solvent-cast printing (SCP) method has more advantages as it offers material versatility and can print products with complex geometries. Combining these two techniques to build a solvent-cast direct-write 3D printer will enable better production of polymeric stents. This study proposes a concept for creating a solvent-cast direct-write 3D printer by combining FDM and SCP 3D printing systems, aiming to enhance the production of polymeric stents. However, some challenges need to be addressed. Firstly, stents have complex network-like structures that require support during the printing process. Secondly, the stent will have a better quality if the polymeric material is printed from its solvent form. Therefore, three design concepts for the printing axis and printer nozzle are proposed and carefully selected to tackle the challenges. Then, to test the 3D printer, a sample stent is fabricated using thermoplastic polyurethane (TPU). Even though the 3D printer has several limitations, it is the initial step for designing an affordable way to fabricate in-house medical stents.
YOLOv12-based Detection for Early Breast Cancer Screening with a Portable Ultrasound System
This study explores the application of deep learning for breast lesion detection in ultrasound images using the YOLOv12 object detection model. Leveraging a compact wireless ultrasound probe and an Android-based inference pipeline, the system was developed to enable portable, AI-assisted screening in resource-limited settings. The model was trained and evaluated on an annotated ultrasound dataset and compared against the previous YOLOv11 version. YOLOv12 achieved a mean average precision (mAP) of 90.6% and an F1 score of 88.65%, outperforming YOLOv11 in both accuracy and inference speed. Processing time was also reduced, with YOLOv12 achieving image detection times between 1.34 to 4.42 seconds, compared to YOLOv11’s slower range. These results confirm YOLOv12’s suitability for real-time deployment on mobile platforms. Visual analyses across several test images show that YOLOv12 offers more consistent detection across varying lesion sizes and positions. The system's lightweight design, combined with its robust performance, makes it a promising tool for expanding diagnostic access in rural and underserved regions. Future work will focus on multiclass lesion classification, expanded datasets, and clinical usability studies to further validate its application in real-world healthcare environments.
Investigate The Performance of the ABSA-PD Controller on a DC Motor Based Pendulum System
The position of the pendulum system must be precisely controlled and maintained by a direct current (DC) motor. This project demonstrates using an ABSA-PD controller to keep the DC motor in its desired position. The prototype of a pendulum system powered by a DC motor must have a mathematical model developed to reproduce that. Matlab/Simulink is used to create and test the mathematical model. The mathematical model is tested in two scenarios: one without a controller and one with a PD controller. The first method of the Ziegler-Nichols approach is used to obtain the gain value of the controller. The created model will undergo further positional verifications. The Simulink toolbox in Matlab is used for the simulation. The controller's effectiveness in regulating the pendulum system was taken into account. The transient response parameters of rise time (Tr), settling time (Ts), percentage overshoot (%OS), and steady-state error of the system response will be used for comparison. The findings will demonstrate that the best projection to mimic the functionality of the pendulum system comes from a sound mathematical model.
Comparative Analysis of Single-Objective and Multi-Objective Genetic Algorithms for Blood Pressure Estimation using Cardiovascular Lumped-Parameter Model
Monitoring blood pressure is vital in diagnosing and managing cardiovascular conditions. To support this, computational models that can accurately estimate both systolic (SBP) and diastolic blood pressure (DBP) are becoming increasingly important. However, many optimization methods tend to focus on one value at the expense of the other. This study explores the use of genetic algorithms to fine-tune parameters in a cardiovascular lumped-parameter model. Two approaches are examined: a single-objective genetic algorithm (SOGA) that prioritizes SBP, and a multi-objective genetic algorithm (MOGA) designed to optimize both SBP and DBP simultaneously. Using clinical data from healthy individuals, we assess how well each method replicates real-world measurements. The findings reveal that while SOGA delivers highly accurate SBP estimates, it does so by sacrificing DBP accuracy. MOGA, on the other hand, achieves a more balanced fit, offering reasonable accuracy for both pressure values. The choice between these optimization strategies should therefore be guided by the specific demands of the application. Continuous refinement of multi‐objective formulations and validation on larger datasets will be essential to fully harness the potential of patient‐specific lumped‐parameter models in precision cardiovascular medicine.
River Segmentation Using Deep Neural Networks on Aerial Orthophoto
This study investigates the use of deep learning for automated river segmentation from UAV-captured aerial orthophotos, addressing the limitations of traditional and labor-intensive river monitoring techniques. We introduced, annotated dataset of high-resolution river imagery and evaluated multiple semantic deep neural network architectures, including U-Net, FPN, PSPNet, and LinkNet, using ResNet50 as backbone models. To optimize performance, various image patch sizes were tested, with 768×768 pixels providing the best trade-off between segmentation accuracy (88.76%) and computational efficiency. Among the tested models, U-Net with a ResNet50 backbone achieved the highest segmentation performance, with an average Intersection over Union (IoU) of 61%, an F1-score of 73%, a precision of 74%, and a recall of 77%. These findings demonstrate the potential of UAV-based remote sensing and deep learning for enhancing the accuracy and efficiency of river monitoring.
A Two-Wheel Mobile Robot Platform for Simulating and Understanding Spacecraft Attitude Dynamics and Control
This study investigates the design and simulation of spacecraft attitude control using a two-wheeled robot as a model. Spacecraft attitude control is a complex system requiring precision in maintaining the orientation of a spacecraft relative to a desired reference frame. However, due to its complexity, it is challenging to understand the system dynamics directly. Thus, this research simulates spacecraft attitude control by mimicking the system behavior using a two-wheeled, Arduino-based mobile robot. The primary objective is to develop a control system that reads gyroscope data and adjusts the motor speeds to replicate spacecraft stabilization through reaction wheels. Additionally, a PID controller is implemented to control the robot wheel's speed to simulate the operation of reaction wheels. The platform and model were designed using Arduino and tested with system identification techniques. The motor's RPM and transfer function were modeled to analyze and fine-tune the PID controller, which significantly enhanced the system's performance. A Particle Swarm Optimization (PSO) method was used to determine the optimal PID parameters, improving system stability and precision in the movement. The results indicated that the system achieved stability with less than 10% steady-state error, minimal overshoot, and fast settling times, thus demonstrating effective attitude control through PID regulation. The system exhibited the ability to adjust the motor speed based on the yaw angle, thereby simulating the torque-free motion typical of spacecraft. This study demonstrates how combining theoretical modeling, simulation, and hardware implementation can simplify the understanding of spacecraft attitude control.
Plate Number Recognition for International Islamic University Malaysia Gate Security using Machine Learning
The increasing number of vehicles at the International Islamic University Malaysia (IIUM) has led to challenges in traffic and parking management, posing significant enforcement difficulties for the Office of Security Management (OSeM). Therefore, a system needs to be designed to assist the university's policy enforcement to impose these rules and help OSeM to check for unregistered vehicles. To address these issues, this study proposes a robust and efficient vehicle license plate recognition system based on the state-of-the-art YOLOv5 object detection model. The proposed model was trained on a custom Malaysian vehicle dataset comprising 1,026 images. The proposed model detects and localizes the license plate, extracts the region of interest, and performs character recognition using EasyOCR. The YOLOv5 model achieved a mean average precision (mAP) of 99.5% for mAP@0.5-0.95, 87.7% at mAP@0.5, and a processing speed of 30 frames per second. The findings indicate that optimizing image-capturing techniques can further enhance detection accuracy, contributing to a more reliable real-time license plate recognition system for effective policy enforcement.
Machine Learning-Based Classification of Badminton Strokes Using IMU-Integrated Racket
Badminton, a fast-paced racket sport, demands precision, power, and strategic execution. While previous research has explored using inertial measurement units (IMUs) for stroke classification, this study presents an integration of sensor-embedded rackets with IMUs to provide a more accurate and automated approach. The collected motion data was processed and classified using machine learning models such as logistic regression, k-nearest neighbors (k-NN), and support vector machines (SVM). Of these, k-NN achieved the highest accuracy at 75 percent, with backhand strokes showing better classification precision. However, challenges in classifying forehand strokes suggest a need for further refinement in feature extraction. The study contributes a framework for improving stroke classification accuracy in badminton and offers insights into optimizing ML-driven motion analysis for more precise performance assessment in badminton.
Developing a Sustainability Assessment Model for Coolant Impacts on Surface Quality in Ball End Milling
Cutting fluids play a critical role in machining operations, yet excessive or inefficient use poses environmental challenges and affects workers' health, highlighting the need for optimised and sustainable practices. This study addresses the challenge of balancing machining performance and sustainability by experimentally investigating ball end milling of AISI 1040 steel using uncoated HSS tools under dry, mist, 4% coolant, and 8% coolant conditions with constant cutting parameters. Machining performance was evaluated based on surface roughness, with mist coolant in down milling achieving the best results (average roughness of 0.462 μm), followed by mist coolant in up milling, 8% coolant, and 4% coolant in up milling. The research highlights the significant impact of coolant conditions on machining performance and surface quality while integrating sustainability principles. A regression-based model was developed to predict interactions between sustainability parameters and machining attributes, offering insights to optimise processes with environmental and societal considerations, thereby supporting sustainable manufacturing practices.
Development Algorithm and Assessing the Efficacy of Voice Control Robotic Prosthetic Hand
This research investigates the development and evaluation of a voice-controlled robotic prosthetic hand, offering a potentially more intuitive and user-friendly interface for individuals with upper limb differences. The system utilizes a Raspberry Pi as the central processing unit, leveraging the Python Speech Recognition library and the Google Cloud Speech API for speech-to-text conversion and command recognition. Five servo motors, controlled via a PCA9685 driver board, actuate the prosthetic hand’s fingers, mimicking essential grasping and individual finger movements. The performance of the system was assessed through rigorous testing with three participants, focusing on metrics such as word recognition accuracy, command success rate, and overall system latency. Results demonstrated high recognition accuracy, exceeding 97% across all participants, confirming the effectiveness of the chosen speech recognition engine. Command success rates were also consistently high, indicating reliable translation of spoken commands into the intended hand movements. However, the “grip” command presented challenges due to phonetic similarities with other words, highlighting the need for further optimization in speech recognition. Analysis of the system latency revealed that audio capture and processing time on the Raspberry Pi was the dominant contributor to overall delay, suggesting potential benefits from exploring local speech recognition methods. The servo motor performance was consistently fast and accurate, confirming the viability of the mechanical design and control strategy. This research successfully demonstrates the feasibility of voice control for robotic prosthetic hands, providing a foundation for future development and highlighting the importance of addressing pronunciation variability, optimizing latency, and incorporating user feedback for improved usability.
Automated Aircraft Structural Defect Detection Using Deep Learning and Computer Vision
Manual aircraft inspections are labor-intensive and susceptible to human error, potentially compromising safety and accuracy. This study presents an automated defect detection framework based on the Mask R-CNN instance segmentation model for identifying cracks and dents in aircraft structures. A dataset of 2,000 annotated images was generated using augmentation techniques and used to train a ResNet-101-based Mask R-CNN model. The system achieved high detection performance, with crack detection reaching a precision of 92.8%, recall of 88.7%, and F1 score of 90.75%; dent detection achieved 91.2% precision, 88.1% recall, and an F1 score of 89.62%. Evaluation using mean Intersection over Union (IoU) and Average Precision (AP@[IoU=0.50:0.95]) confirmed accurate defect localization and segmentation. These findings demonstrate the model's potential to improve inspection reliability and operational efficiency, contributing to safer, more consistent aircraft maintenance practices.
Reduced Sliding Mode Control Algorithm for Two-Link Planar Robot System
Robots have become popular these days due to their ability accuracy and precision. Areas of applications include welding, machining, moving items, storage, retrieval and other precise surgical operations. Controllers are continuously being improved in design simplicity, robustness, and performance accuracy. High-accuracy trajectory tracking is a challenging area in robot control due to nonlinearities and input couplings. To achieve high accuracy and precision, robots need accurate, fast-processing controllers. This study there focused on application of the sliding mode controller (SMC) but in the reduced or simplified algorithm form (SSMC) for the control of a two-link planar robotic system. The MATLAB/Simulink software was employed. Result was evaluated using the root mean square error (RMSE) for both links. The proposed SSMC was compared with the PID and SMC schemes. The RMSE values of the tracking errors with disturbance for the SSMC were 0.0751 and 0.0814 rad, for the SMC were 0.0755 and 0.0817 rad and for the PID were 0.2784 and 0.1062 rad. The RMSE values with no disturbance for SSMC they were 0.07435 and 0.0811 rad, for the SMC were 0.0752 and 0.08153 rad and finally with the PID were 0.2579 and 0.1021 rad. The length of algorithm as shown in the text was shorter for the SSMC compared to the SMC and close to that of the PID. It is clear that he proposed control scheme SSMC is simpler than the SMC. It also had the property of improved performance with robustness.
Evaluating the Impact of Coolants on Tool Wear and Surface Quality in Ball End Milling: A Sustainability Performance Assessment
The role of cutting fluids in machining operations is crucial, impacting productivity, tool lifespan, and work quality. An experimental investigation was conducted on ball end milling of AISI 1040 steel using uncoated HSS tools under various coolant conditions and milling modes. This research encompassed four coolant conditions: dry, mist, 4% coolant concentration, and 8% coolant concentration, with constant cutting parameters. Machining performance was assessed based on tool wear and surface roughness. Results indicate a significant influence of coolant conditions on machining performance and surface quality. Mist coolant in down milling mode exhibited superior performance in terms of tool wear and average surface roughness (0.09mm and 0.462μm, respectively), followed closely by mist coolant in up milling mode, 8% coolant concentration, and lastly, 4% coolant concentration under up milling mode. This research not only evaluated the impact of different cooling conditions but also focused on integrating sustainability into the machining process. It is expected that the regression analysis will develop a model predicting how sustainability and machining attributes interact. This model will demonstrate the benefits of incorporating sustainability parameters into machining conditions, providing valuable insights for optimising processes with environmental and societal considerations. By creating a comprehensive interaction model, the research is anticipated to address practical aspects of coolant performance and support informed decision-making, ultimately enhancing both machining performance and sustainable manufacturing practices.
Copyright (c) 2024 The Author(s) http://creativecommons.org/licenses/by-nc/4.0
Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection
The development of deep neural networks for medical imaging applications, especially the diagnosis of intracranial hemorrhage (ICH) from CT scans, is greatly aided by machine learning frameworks such as Keras. This work investigates a pipeline that uses Keras' neural modules to distinguish between CT scans of the normal head and those with ICH. Transfer learning models are then used to categorize ICH subtypes. An extensive analysis of current research and techniques demonstrates the effectiveness of deep learning in medical imaging and emphasizes how AI may improve radiologists' diagnostic precision. Using windowing techniques to improve diagnostic features, the study preprocesses pictures from the RSNA Intracranial Hemorrhage Detection dataset. The study assesses performance indicators such classification accuracy using SVM, k-NN, and Random Forest classifiers combined with built-in models from Keras, such as Xception and DenseNet. Findings show that the Xception-SVM pipeline performs exceptionally well in binary classification tasks, achieving 76.33% accuracy, while DenseNet201-SVM performs well in multiclass classification, achieving 60% accuracy. These results highlight how crucial it is to choose the right pipelines for certain classification jobs in order to achieve the best results possible when using medical image analysis. In order to improve diagnostic precision in identifying cerebral hemorrhages, future research directions include increasing classifier performance, investigating sophisticated preprocessing techniques, and fine-tuning models.
Tracking Humans and Objects in Video Surveillance System Using Feature-Based Method
In recent years, video surveillance system has emerged as one of the active research area in machine vision community. This research intends to integrate machine vision into video surveillance system in order to enhance the accurateness and robustness of video surveillance system. To realize more robust and secure video surveillance system, an automated system is needed which can detect, classify and track human and objects even when the occlusion occurs. Object tracking is one of the most crucial parts of a automated surveillance system Hence, we proposed a tracking system which includes tracking of human and vehicles in real-time surveillance system and also in solving the problem of partially occluded human by utilizing fast-computation techniques without compromising the accuracy and performance of that particular surveillance system. In this research, we track the classified human and objects using feature-based tracking for five states, which are: entering, leaving, normal, merging, and splitting. The developed system can track the human even if occlusion occurs since we used merging and splitting cases in our tracking algorithm. The overall accuracy for our proposed system in tracking human and car is fine which is at 94.74%.
Machine Learning-Based Change Detection for Land Use Land Cover in Malaysia
Remote sensing has gained widespread attention due to its applications and technological advancements. This study aims to explore the use of remote sensing for change detection for land use land cover (LULC). The study begins by focusing on pre-processing, including radiometric, geometric, and atmospheric correction as well as image enhancement to produce quality images for further classification analysis. Two classification methods were explored: supervised and unsupervised. For supervised classification, Support Vector Machine (SVM), Classification and Regression Tree (CART), and Random Forest classifiers were tested. After thorough evaluation, it was determined that the Random Forest algorithm was the optimal choice, yielding a training accuracy of 99.6% and a test accuracy of 80% for LULC classification. For unsupervised classification, a cluster classifier was used. Change detection is then conducted through image subtraction of two different timelines. Supervised classification of LULC images resulted in a total change of 94.74 km² across three locations: Wang Kelian, Sungai Golok and Pengerang while unsupervised classification resulted in change of 23.56 km² for Lahad Datu and 20.51 km² for Sungai Golok.
Dynamic Target Grasping Strategy Of Industrial Robot Based On Machine Vision
Object grasping is the predominant application focus of robots, with machine vision technology playing a crucial role in enabling successful grasping. As research on machine vision technology advances, its initial application in capturing static targets has gradually expanded to include tracking and capturing moving targets. Detecting the target position is a common method for estimating and predicting its motion state, allowing for stable capture of moving targets. However, if the target's position changes unexpectedly due to interference, the original predicted position becomes invalid. Therefore, continuous localization and tracking of the target are necessary to assess deviations and approach the new target position in order to achieve successful capture. In this study, we utilize ROS as a platform to investigate dynamic grasping strategies based on visual feedback. We construct a dynamic grasping system using an UR5 industrial robot within ROS framework. The RealSense D435i camera is mounted at the end effector of the robot arm in an Eye-in-Hand configuration to obtain RGB-D image data representing the field of view at execution end point. By performing coordinate conversion, we acquire three-dimensional coordinates of objects in relation to base coordinate system of industrial robot. A visual feedback control strategy is designed to facilitate grasping operations on moving targets located on conveyor belts.When the target moved along the conveyor belt at a speed of 10mm/s, the camera realized accurate recognition of the object position, and the error was controlled within 1mm. At the same time, the industrial robot grasps and tracks the position error below 2mm to complete the target grasp.
Pill Recognition via Deep Learning Approaches
Deep learning significantly transforms pill imaging recognition in the healthcare and pharmaceutical industries by automating the identification and classification processes based on visual indicators. It is important to develop a robust deep learning framework to ensure the accurate dispensing of medications. The features such as size, color, shape, markings and text imprint are scrutinized by these methods. However, real-world matching is difficult due to factors like the similarity of pill forms and the scarcity of databases. The goal of this work is to improve deep learning models for better classification of pill images. A dataset of 994 images are utilized from a public pharmaceutical database which sorted by 20 common type of pills. These images were split into training, validation, and testing sets in a 70:15:15 ratio. There are three different models which are YOLOv3, YOLOv5, and YOLOv8 were employed to the system. These models use performance metrics like recall, mean Average Precision (mAP), and precision as results. According to our results, YOLOv8 did remarkably well, obtaining a precision and F1-score of 99.17% and 96.95%, respectively, while YOLOv5 great with mAP and recall of 94.83% and 95%, respectively, outperforming the YOLOv3 model. The success of YOLOv8 underscores its significance in reducing medical errors with its accurate, real-time capabilities for identifying pills. The use of artificial intelligence in pill recognition not only lowers the chance of incorrect medication use but also streamlines the duties of healthcare professionals. This shift allows them to prioritize crucial responsibilities and simplifies the process of pill identification.
A Comparative Analysis of Intelligent Control Approaches for the Ball-and-Plate Problem
This paper conducts a comprehensive comparative analysis of five intelligent control approaches applied to the Ball-and-Plate problem, evaluating their performance based on Step Response metrics and Trajectory Tracking Mean Absolute Error (MAE). The techniques examined include Proportional-Integral-Derivative (PID), Linear Quadratic Regulation (LQR), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Fuzzy Logic Control (FLC). Through rigorous experimentation and analysis, each technique’s strengths and weaknesses are identified, with MPC and SMC emerging as superior options in terms of response time and trajectory tracking accuracy, notably achieving zero overshoot and minimal errors. LQR exhibits exceptionally fast response times, while PID and FLC offer moderate performance. The study’s findings provide valuable insights for selecting appropriate control techniques tailored to specific application requirements and suggest avenues for future research, including the exploration of hybrid control approaches and adaptive control algorithms to enhance system robustness and reliability in addressing the Ball-and-Plate problem.
Smart Agriculture: Precision Farming Through Sensor-Based Crop Monitoring and Control System
The escalation of the global population and the depletion of natural resources have propelled the evolution of smart agriculture and precision farming, underpinned by sensor-based crop monitoring and control systems, which are anticipated to revolutionize the agricultural sector. Notably, prevalent smart agriculture systems predominantly emphasize either IoT components for data monitoring and control or machine learning components for data analysis. Consequently, this project endeavours to develop a system that seamlessly integrates both IoT and machine learning components, culminating in an advanced system capable of real-time crop monitoring and growth prediction. Collaborating with the Urban Farming Farm under the auspices of the Kulliyyah of Economics and Management Science, an IoT system comprising soil moisture, temperature, and humidity sensors, alongside an actuator, is devised to facilitate data acquisition and required intervention specifically for Okra Fruit during the pre-harvesting stage. Subsequently, four distinct algorithms are trained with the collected dataset to ascertain the most optimal algorithm for predicting crop growth and harvesting time, resulting in the selection of the Random Forest Regression model, which attains the highest model score of 86%. Upon its integration into the comprehensive system for monitoring new data and predicting fruit growth, the model achieves an impressive 98% accuracy score. Future endeavours for this project aim to enhance its applicability and predictive capabilities through the incorporation of diverse datasets from various plant species, the expansion of crop predictions to encompass the entire growth cycle, the integration of additional sensors, and the enhancement of the system's scalability to cover larger areas.
Design and Implementation of a Human Machine Interface Control System for Power Plants
The Industrial Revolution (IR) 4.0 gave birth to various kinds of technology that can be used in applications in various industrial sectors, electricity, energy, and so on. One of them is the world of automation which can make it easier to handle a complex system. Virtual operation and monitoring technology has become a mainstay today, one of which is the Human Machine Interface (HMI). In this research, we will discuss the design and implementation of HMI in electrical power installation system applications which include a three-phase induction motor starting system. The aim of this research is to change the conventional installation system to an integrated digital system where later the system can be operated and monitored with the same devices. The research method used was the initial stage of designing hardware and software in the power system wiring section along with the HMI and PLC programs, and then the final stage was through testing at both stages. The results show that the system can operate the induction motor starting system and provide a trouble alarm with four notifications displayed on the HMI layer when a system failure occurs.
Design of Automatic Tekong Launcher System Using Progressive Methods
The Automatic Tekong Launcher System represents a cutting-edge advancement in sepak takraw equipment, seamlessly blending precision engineering with advanced automation to transform the role of the Tekong. At its foundation, the system is built from durable aluminum profiles, ensuring a robust and reliable structure. The launcher mechanism is crafted using CNC milling technology, reflecting a commitment to precision and optimal performance. Central to the system’s operation is a power supply, featuring three 30A AC to DC converters that provide consistent electricity to the motors. These motors include four high-speed 24V DC units and two stepper motors, capable of reaching speeds up to 2800 rpm. This setup allows for meticulous control over the ball's speed and trajectory, essential for precise launches. The brain of the system is a Programmable Logic Controller (PLC), which manages the intricate movements of both motors and sensors, enabling the automation of ball launching with minimal human intervention. For added versatility, the system incorporates joystick control, allowing for semi-automatic operation that enhances user interaction and adaptability to various gameplay scenarios. By integrating advanced automation, precision engineering, and a modular design, the Automatic Tekong Launcher System offers unparalleled performance in both training and competitive environments. Its ability to deliver consistent, accurate launches elevates the standard of sepak takraw, making it a revolutionary tool for players and coaches aiming to refine their skills and strategies.
Numerical Model Validation of Cyclone Separator with Square Cross-sectional Vortex Finder for Oil Palm Loose Fruit Collection
Cyclone separators are commonly employed in the industry for material separation because of their practicality. For the exact reason, the technology was expanded to harvest oil palm loose fruits. However, there is a scarcity of data in the utilization of computational fluid dynamics (CFD) in the field. Hence, a numerical model validation is essential in determining the model accuracy in representing flow of cyclone separators Consequently, the current study aims to validate the numerical model with experimental data on factors such as pressure drop, tangential velocity and axial velocity. Results indicate that the model behaves at a satisfactory level with minor errors. Upon successfully validating the numerical model, Reynold’s Stress Model (RSM) and Discreet Phase Model (DPM) were used to simulate an oil palm loose fruit collecting system where density 995.7 and size 0.04 was set to simulate oil palm loose fruits in the system. Turbulence intensity at the gas and particles outlet are specified at 5%. It was found that Rankine vortex was present in the system, confirming the working principal of the cyclone separator. Further optimization works was conducted by modifying the cyclone’s separator configuration to a square vortex finder where a collection efficiency of 92.08 % was recorded.
Utilization of Mediapipe Posture Recognition for the Usage in Estimating ASD Children Engagement Interacting with QTrobot
Imitation skills are one of the most important learning skills that are naturally developed by typically developed (TD) children at a young age. Unfortunately, this skill is lacking in special children who are diagnosed with Autism Spectrum Disorder (ASD). To enhance the ASD children’s imitation skills for a better social life, this paper proposes to develop and embed a robust gesture recognition system onto a therapy robot called QTrobot. This paper will discuss the utilization of Mediapipe posture recognition as part of estimating the ASD children engagement. MediaPipe posture recognition has the average accuracy of 96% and 60% for both straight facing the camera and 60 degrees away from the camera, respectively. Further enhancements have been done to embed the selected gesture recognition algorithm into QTrobot for developing an efficient Human-Robotic interaction (HRI). Using twenty healthy adult participants, the enhanced algorithm has achieved an average of 94.33% accuracy with an average of 10.5 frame rates per second in recognizing five selected gestures to be imitated by the participants, which are T pose, Strong pose, Super pose, Victory pose, and V pose. Plus, the participants experienced a useful and enjoyable interaction with the robot based on a the 5-point Likert scale of the Technology Acceptance Model (TAM) questionnaire.
Modelling Odour Profiles and Temperature Intensity of Water: A Comparative Analysis using Case-Based Reasoning and K-Nearest Neighbours
Water, a vital resource for human life and global economic activities, prompts ongoing water quality studies in Malaysia due to excessive usage. This study investigates Malaysia's water resources condition, focusing on identifying water through its odor profile relative to temperature intensity. Clean and pure drinking water is crucial for global human health, necessitating knowledge of water source content to mitigate health risks from water quality degradation caused by inorganic contaminants, heavy metals, and microbial pollutants. Recent interest in water quality stems from the high demand for clean water and population growth. This research employs E-Anfun, mimicking the human nose, to establish a case library profile for tap and lake water samples based on odor attributes. Using Case-Based Reasoning (CBR) and K-Nearest Neighbour (KNN), the study classifies water odor profiles and evaluates performance. The E-nose simplifies the process with gas sensors of varying odor sensitivity. Samples collected based on temperature intensity are managed using Microsoft Excel and MATLAB for normalization. CBR, utilizing four cycles, intelligently classifies by solving new problems based on prior successful solutions. KNN enhances CBR by classifying data samples based on proximity to learning data. Evaluation using a recognized confusion matrix indicates 100% accuracy, sensitivity, and specificity for CBR. For KNN, the accuracy increases with the ratio, starting at 97.056% for k=3 with a 10:90 ratio, accompanied by 84.833% sensitivity and 98.369% specificity. Both CBR and KNN successfully classify tap and lake water odour profiles.
Analysis of GPS and GSM with GPRS-Based Vehicle Tracking and Monitoring System
Vehicle security has become a major concern in today's world. Due to the increasing number of vehicle thefts, GSM and GPS-based vehicle tracking systems have gained attention to help users keep track of their vehicles. The main objective of this study is to develop an accurate and reliable device to support vehicle owners in tracking the whereabouts of their vehicles. Such devices will allow vehicles to be tracked remotely from a distance by utilizing the already available mobile networks. Specifically, the system will use the GPS (Global Positioning System) Module to collect the vehicle coordinates (latitude and longitude) and send them to the user's mobile upon request, which also allows for the expansion of knowledge on GPS (Global Positioning System), GSM (Global Systems for Mobile) and GPRS (General Packet Radio Service) technology, as well as the SIM interface. It will take some analysis to make this endeavour succeed. A Google Spreadsheet will be used as a database to store the location data from GPS sensors, and information will be sent to it via a connection to the General Packet Radio Service (GPRS) network system. The information will be synced and shown on a visual map so that the user can simply access and track the whereabouts of the vehicle. There is a discussion concerning the accuracy of the GPS sensor being utilised over several places, including urban and rural areas, to determine the device's accuracy. The system's dependability and ability to perform well based on the data collected also have both been studied and analysed. As a result, this paper offers a well-presented study to develop a tracking system to determine the time and position of the monitoring vehicle.
Mechanomyography in Assessing Muscle Spasticity: A Systematic Literature Review
Mechanomyography (MMG) has gained significant prominence in the domain of scientific inquiry, exhibiting widespread applications in diverse areas including sensor advancement, signal processing methodologies, characterization of muscle spasticity, diagnosis of neurological disorders, and as a valuable tool in medical rehabilitation. However, despite the considerable body of existing MMG research, there remains a paucity of comprehensive investigations in these domains in the past. The primary objective of this systematic review is to conduct a comprehensive analysis of the available literature pertaining to the evaluation of muscle spasticity assessment using mechanomyography (MMG) in a systematic and categorical manner. By applying the pre-established search criteria to five prominent databases, a total of 63 pertinent studies that met the inclusion criteria for our review. Through a thorough scrutiny of the 10 meticulously selected records, we unveiled the extensive diversity in protocols and parameters employed in the assessment of muscle spasticity using mechanomyography (MMG). Accelerometers and piezoelectric sensor used for mechanomyography (MMG) are currently in the nascent phase of their development, as evidenced by the findings of this systematic review. Notably, this review also highlights the influence of sensor placement on muscles as a potential factor affecting the acquired signal. In consideration of these findings, it can be concluded that further research is warranted to advance MMG, particularly in the domains of sensor refinement, with specific attention to accelerometers, and the refinement of signal processing techniques. Additionally, future investigations should aim to expand the scope of MMG applications in clinical settings and rehabilitation practices.
Integrated Hand and Eye Communication Device for Intensive Care Unit (ICU) Patients
Numerous Intensive Care Unit (ICU) patients may be unable to speak or move their body due to being intubated or weak muscles. This makes the communication harder, as they are voiceless in expressing their thoughts and needs. This study focuses on the development of an integrated hand and eye communication device based on hand gesture recognition and eye movement tracking with an output display. The device comes with a dual mode, where the hand gesture and eye movement are detected using flex sensors and reflectance sensors respectively. Arduino Uno is used as the microcontroller and the output window is programmed using Java programming language. Four messages communicated in ICU patients are installed on the system. The device has been tested at the laboratory stage with healthy subjects. The results with healthy individuals in the laboratory validate that the device is successful in conveying the intended messages correctly for all trials. The resulting sensors measurement curves are consistent across all subjects and messages. The developed device will contribute towards a better communication between the patients and healthare providers, leading to a more convinient and efficient patient care.