Mathematical & Physical Modelling of a Quadrotor UAV

Unmanned aerial vehicles (UAVs) are now becoming a major topic of interest due to their flying capabilities attracting researchers who are working within various application. Quadrotors in particular are one of main types of UAVs that are now currently studied, where some of the main focuses are positional and attitude tracking. Currently, verifying these systems in simulation is generally processed through MATLAB/Simulink where the dynamics are thoroughly analyzed. In this paper, the results attained from the mathematical dynamics implemented in Simulink will be justified using ADAMS environment. This software was purposely developed to accurately model the dynamics of mechanical systems in 3D without considering any equations of motion. SolidWorks is used to design the quadrotor frame that satisfies the properties of the proposed system in Simulink. Setting the control inputs as angular velocity of each motor will generate a relative thrust in order for the vehicle to achieve motion. Finally, the dynamic behavior on ADAMS and Simulink are compared as the control inputs are identically applied, which has revealed a marginal difference between the resultant motions

Enhanced Anomaly Detection in Wireless 5G Networks With Hybrid Learning Technique Using AWID3 Dataset

In recent years, the expansion of the Internet of Things and 5G networks has significantly increased wireless traffic, heightening the risk of cyberattacks. Intrusion detection systems have become essential for safeguarding wireless networks by providing real-time threat detection and response. This study presents a comprehensive review and implementation of machine learning-based techniques for detecting various types of wireless attacks, with a focus on improving detection accuracy through ensemble learning. The AWID3 dataset, based on the IEEE 802.11 standard, was used for experimentation. The study was conducted in multiple phases: (1) evaluating six machine learning algorithms (random forest, J48, naïve Bayes, logistic regression, decision tree, and deep neural networks) using three feature selection methods (information gain, gain ratio, and chi-squared); (2) developing a hybrid ensemble model by integrating the strengths of deep neural network, random forest, XGBoost, and LightGBM, with logistic regression as a meta-classifier; and (3) validating performance using key metrics: accuracy, precision, recall, and F1-score. The proposed hybrid model achieved a peak accuracy of 99.75%, outperforming benchmark models in the literature. These results demonstrate the superior performance and robustness of the proposed hybrid approach. By addressing multiple network layers and leveraging ensemble learning, this research highlights the critical role of hybrid models in achieving reliable and accurate intrusion detection for wireless environments

Sensitivity of Robot-Aided Remote Object Detection in Forests under Variation of Light Illumination

Forests degradation and deforestation are increasingly becoming a risk to the world’s ecosystem with major effects on climate change. Mitigating these dangers is tackled through reliable management of monitoring tree species, insect infestations and wildlife behaviour. Although forest rangers can use artificial intelligence and machine learning techniques to analyse forest health through visionary sensing, exploring the accuracy of object detection under low illuminations such as sunsets, clouds or below dense forest canopy is often ignored. In this paper, we have investigated the importance of illumination on detection through a high definition GoPro9 camera as compared to the low-cost RaspberryPi camera. An external sensing platform accommodated by a quadruped robot is developed to carry the hardware, one of the first implementations of autonomous system in forest health monitoring. The compound-scaled object detection, YOLOv5s model pretrained on COCO dataset containing 800,000 instances, used for person detection, is retrained on custom dataset to detect forest health indicators such as burrows and deadwood. The system is tested and evaluated under various lighting conditions to detect objects located at various distances from the vision sensors. This study concludes that YOLOv5s model can detect a person and forest health indicators up to a distance of 10m with accuracy of 67% and 51% respectively at dusk which shows that light exposure has a major effect on detection performance. Furthermore, the quadruped robot carrying the sensing platform managed to successfully shift between different positions while carrying out the detection

Digital Mapping Of Invasive Acacia Mangium Willd. Trees Along Telisai-Lumut Highway Along The Andulau Forest Reserve

Invasive alien Acacia trees have become a serious environmental problem in Brunei Darussalam, spreading into the vulnerable heath and mixed dipterocarp forest ecosystem where it has started replacing the native flora and contributing to forest fire. In this work, we study the spread of Acacia trees by analyzing images taken by drones along a newly developed highway within the vicinity of Andulau Forest Reserve in Brunei Darussalam. Based on the analysis, we aim to understand the Acacia spread and its habitat preference, which will be a critical factor in planning the future roadmap to maintain a sustainable and healthy forest ecosystem, and safety from potential forest fires. The Unmanned Aerial Vehicles (UAVs) were utilized to capture high-resolution images along the Telisai-Lumut highway and were subsequently analyzed images using ArcGIS software, to map and study the Acacia’s distribution and habitat preferences, which will aid in understanding of Acacia’s rapid dispersion. Our preliminary results show highest Acacia density and numbers closer to the highway. The barren loose sandy soil combined with the open terrain limits local forest tree growth but seems to provide good habitat for Acacia trees. Our results suggest that the highway provides an important dispersal opportunity for Acacia trees, bringing them in direct proximity of an undisturbed forest reserve. This may increase the risk of spread of this species into the forest, and importantly, given the fire proneness of Acacia, may lead to wildfires that threaten the neighbouring forest reserve. Keeping vegetation short and removing Acacia’s close to the highway may mitigate these risks. Efforts such as spreading awareness on Acacia’s invasiveness, identification and removal of Acacia trees, habitat restoration projects and meticulous evaluation for any introduced species should be done

A survey on blockchain technology in the maritime industry: Challenges and future perspectives

Blockchain technology has emerged as a potential solution to address the imperative need for enhancing security, transparency, and efficiency in the maritime industry, where increasing reliance on digital systems and data prevails. However, the integration of blockchain in the maritime sector is still an underexplored territory, necessitating a comprehensive investigation into its impact, challenges, and implementation strategies to harness its transformative potential effectively. This survey paper investigates the impact of Maritime Blockchain on Supply Chain Management, shedding light on its ability to enhance transparency, traceability, and overall efficiency in the complex realm of maritime logistics. Furthermore, the paper offers a practical roadmap for the integration of blockchain technology into the Maritime Industry, presenting a comprehensive framework that maritime stakeholders can adopt to unlock the advantages of blockchain in their operations. In addition to these aspects, the study conducts a thorough examination of the current network infrastructure in Ports and Vessels. This assessment provides a holistic view of the technological landscape within the maritime sector, which is crucial for understanding the challenges and opportunities for the successful implementation of blockchain technology. Moreover, the research identifies and analyzes specific Blockchain cybersecurity challenges that are pertinent to the Maritime Industry

Dynamic Modelling and Analysis of a Quadrotor Based on Selected Physical Parameters

Over the past decade, control techniques have been widely implemented on quadrotors to achieve the desired positions within the coordinate system. However, ensuring that the dynamics are correct and that similar results to a physical model can be obtained has been a question of interest. In this paper, the quadrotor dynamics are thoroughly analysed in simulation without using any controllers. Specifically, suitable actuators and propellers have been selected to generate ideal thrusts that will enforce the unmanned aerial vehicle (UAV) to lift. By using kinematics approach, one can analyse the expected motion of the UAV after a certain thrust is applied on all motors. Hence, the dynamics of the proposed quadrotor are recognised and verified through numerical simulations, leading to presenting the motions of the physical model. The results attained have illustrated promising results in which a comparative study between experimental and theoretical methods have presented little to no errors

Modelling, simulation and control of a novel structure varying quadrotor

In this work, a novel structure of a Quadrotor Unmanned Aerial Vehicle (UAV) is proposed to change the dynamics during flight. The proposed mechanism is presented which consists of extendable plates that move along the horizontal axes from the body frame respectively. Essentially, the main goal behind this novel architecture is to enhance performance and improve flight duration in reaching the desired position. The Euler dynamic model is derived to represent the multirotor equation of motion. Basic PID controllers were implemented to demonstrate the concept and to analyse the vehicle behaviour as the structure is altered during flight. A physical modelling software is also used to study the multi-body interactions of rigid bodies as well as the dynamic response. By comparing the performance between the proposed system and the traditional version, the paper reveals improved flight performance for attitude and position tracking. The mathematical representation of the dynamic system was also verified using Msc ADAMS as identical control inputs where simultaneously applied.</p

Smart Industrial Safety using Computer Vision

More than 2.3 million people worldwide suffer from work-related injuries or illnesses each year, resulting in more than 6,000 deaths per day. Providing an unclear work environment and failing to wear appropriate personal protective equipment have been identified as significant contributors to workplace accidents, making it imperative that employers prioritize workplace safety as a priority. Providing proper personal protective equipment (PPE) and maintaining a well-organized, clearly marked (unsafe) work environment can help prevent inconvenient workplace incidents. Furthermore, it promotes a safe working environment, reduces the likelihood of life-threatening events, and enhances overall business and economic conditions. Therefore, this paper proposes safe, smart manufacturing by implementing computer vision technology to detect appropriate PPE worn by workers and ensure a safe workspace to reduce the risk of human injuries. By utilising computer vision technology, we can identify PPE, such as gloves, helmets, and working forklifts, used by workers in the manufacturing environment. A precision of 80.6% and 86% have been reached using YOLOv8 for all classes in both datasets. In general, an extensive review of both datasets, including five performance metrics, is considered