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In recent years, the research has focused firmly on Autonomous Aerial Vehicles (AAVs) owing to their vast array of potential applications, to aid those applications this study presents a technical approach for source localization and landing trajectory identification for Autonomous Aerial Vehicle (AAV) landing, leveraging computer vision and photogrammetry techniques. The proposed method aims to achieve accurate and robust localization of the landing target area and precise determination of the AAV’s landing trajectory. The source localization module utilizes a computer vision system equipped with onboard cameras and advanced image processing algorithms. The system captures images of the target area and performs feature extraction and matching to estimate the position of the landing target. Additionally, the A* algorithm serves as a pivotal tool in deriving an optimized trajectory by harnessing the relative positions of the Autonomous Aerial Vehicle (AAV) and the designated landing target.

The advancement of autonomous technology in Unmanned Aerial Vehicles (UAVs) has piloted a new era in aviation. While UAVs were initially utilized only for the military, rescue, and disaster response, they are now being utilized for domestic and civilian purposes as well. In order to deal with its expanded applications and to increase autonomy, the ability for UAVs to perform autonomous landing will be a crucial component. Autonomous landing capability is greatly dependent on computer vision, which offers several advantages such as low cost, self-sufficiency, strong anti-interference capability, and accurate localization when combined with an Inertial Navigation System (INS). Another significant benefit of this technology is its compatibility with LiDAR technology, Digital Elevation Models (DEM), and the ability to seamlessly integrate these components. The landing area for UAVs can vary, ranging from static to dynamic or complex, depending on their environment. By comprehending these characteristics and the behavior of UAVs, this paper serves as a valuable reference for autonomous landing guided by computer vision and provides promising preliminary results with static image photogrammetry.

Abstract The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

Pipeline corrosion is one of the leading causes of failures in the transmission of gas and hazardous liquids in the oil and gas industry. In-line inspection is a non-destructive inspection for detecting corrosion defects in pipelines. Defects are measured in terms of their width, length and depth. Consecutive in-line inspection data are used to determine the pipeline’s corrosion growth rate and its remnant life, which set the operational and maintenance activities of the pipeline. The traditional approach of manually processing in-line inspection data has various weaknesses, including being time consuming due to huge data volume and complexity, prone to error, subject to biased judgement by experts and challenging for matching of in-line inspection datasets. This paper aimed to contribute to the adoption of machine learning approaches in classifying pipeline defects as per Pipeline Operator Forum requirements and matching in-line inspection data for determining the corrosion growth rate and remnant life of pipelines. Machine learning techniques, namely, decision tree, random forest, support vector machines and logistic regression, were applied in the classification of pipeline defects using Phyton programming. The performance of each technique in terms of the accuracy of results was compared. The results showed that the decision tree classifier model was the most accurate (99.9%) compared with the other classifiers.

Numerous recent innovations have been achieved with the goal of enhancing electric vehicles and the parts that go into them, particularly in the areas of managing energy, battery design and optimization, and autonomous driving. This promotes a more effective and sustainable eco-system and helps to build the next generation of electric car technology. This study offers insights into the most recent research and advancements in electric vehicles (EVs), as well as new, innovative, and promising technologies based on scientific data and facts associated with e-mobility from a technological standpoint, which may be achievable by 2030. Appropriate modeling and design strategies, including digital twins with connected Internet of Things (IoT), are discussed in this study. Vehicles with autonomous features have the potential to increase safety on roads, increase driving economy, and provide drivers more time to focus on other duties thanks to the Internet of Things idea. The enabling technology that entails a car moving out of a parking spot, traveling along a long highway, and then parking at the destination is also covered in this article. The development of autonomous vehicles depends on the data obtained for deployment in actual road conditions. There are also research gaps and proposals for autonomous, intelligent vehicles. One of the many social concerns that are described is the cause of an accident with an autonomous car. A smart device that can spot strange driving behavior and prevent accidents is briefly discussed. In addition, all EV-related fields are covered, including the likely technical challenges and knowledge gaps in each one, from in-depth battery material sciences through power electronics and powertrain engineering to market assessments and environmental assessments.

It has been observed that unmanned aerial vehicles (UAVs), also known as drones, have been used in a very different way over time. The advancements in key UAV areas include detection (including radio frequency and radar), classification (including micro, mini, close range, short range, medium range, medium-range endurance, low-altitude deep penetration, low-altitude long endurance, and medium-altitude long endurance), tracking (including lateral tracking, vertical tracking, moving aerial pan with moving target, and moving aerial tilt with moving target), and so forth. Even with all of these improvements and advantages, security and privacy can still be ensured by researching a number of key aspects of an unmanned aerial vehicle, such as through the jamming of the control signals of a UAV and redirecting them for any high-assault activity. This review article will examine the privacy issues related to drone standards and regulations. The manuscript will also provide a comprehensive answer to these limitations. In addition to updated information on current legislation and the many classes that can be used to establish communication between a ground control room and an unmanned aerial vehicle, this article provides a basic overview of unmanned aerial vehicles. After reading this review, readers will understand the shortcomings, the most recent advancements, and the strategies for addressing security issues, assaults, and limitations. The open research areas described in this manuscript can be utilized to create novel methods for strengthening the security and privacy of an unmanned aerial vehicle.

Human brain functions and behaviors during the transition state between sleep and wakefulness are not similar to these at alert wakefulness state. The transition state, which is called sleep inertia, has many unpleasant and dangerous effects on many situations that require full attention and fast response, such as driving. Within 30 minutes after waking up from sleep, the driver's performance might be impaired due to the sleep inertia effects. Groups of drivers that may drive within a short period after waking up are: workers who travel early in the morning; secondary drivers of long distance bus who sleep in the bus before taking over the job from the primary drivers; night travelers; and long haul truck drivers who stop at the rest area to sleep for a while and continue driving. Previous research used subjective self-report measurement, eye tracker, and a driving simulator to analyze the driver's performance during sleep inertia state. The physiological measures of the drivers, such as their brain signals have also been studied. However, the brain signals which are recorded in Electroencephalography (EEG) are typically analyzed in perspective of the power spectrum. This study proposes a hybrid of EEG features, which are fractal dimension and power spectrum, supported by behavioral data which is the driver's reaction time. This study finds the features that significantly differentiate between normal and sleep inertia drivers based on the classification accuracy and p-value of the statistical ANOVA. This study compares the results with other features (power spectrum, variance, sample entropy), and between EEG channel. This study record the EEG from the Fz, T7, Cz, Pz, and O1 channels. This study uses subjective and behavioral measurements to support the results. The results show that the hybrid of fractal dimension estimated by Katz's algorithm at the O1 channel and delta power from the Fz channel, and alpha power from the O1 channel, have better classifications than the power spectrum alone. Furthermore, the reaction time recorded from the LED reaction time task shows a significant difference between drivers with sleep inertia and normal (alert) drivers.

Unmodelled dynamic factors are either the left-over or the estimated factors such as lower or upper bound values while modelling any mechatronic system. Hence, with the inclusion of under-actuation in a system such as fewer number of actuators as compared to degrees of freedom, this will lead the system to high instability. These factors are changing instantly during multiple flights of quadrotor that is, the values of these factors in path following may vary from the values in hovering mode. Hence, it is one of the strenuous tasks to tackle these unmodelled dynamic factors for the multiple flight modes of an underactuated quadrotor craft. One of the better ways for tracking control of a quadrotor aerial vehicle with unmodelled dynamics is to observe and estimate the instant change in parameters. Thus, this paper exhibits an extensive review of several hybrid observer design methods being fused with some novel control strategies. In addition to this, the survey paper also summarises the limitations of the current state of the art approaches. This paper demonstrates an unexplored field of study where researchers must need to evaluate the performance of hybrid observer design methods.

Abstract Selecting an optimal importance density and ensuring optimal particle weights are central challenges in particle-based filtering. In this paper, we provide a two-step procedure to learn importance densities for particle-based filtering. The first stage importance density is constructed based on ensemble Kalman filter kernels. This is followed by learning a second stage importance density via weighted likelihood criteria. The importance density is learned by fitting Gaussian mixture models to a set of particles and weights. The weighted likelihood learning criteria ensure that the second stage importance density is closer to the true filtered density, thereby improving the particle filtering procedure. Particle weights recalculated based on the latter density are shown to mitigate particle weight degeneracy as the filtering procedure propagates in time. We illustrate the proposed methodology on 2D and 3D nonlinear dynamical systems.

In recent years, power electronic distribution transformers (PEDTs) have become an attractive replacement for conventional transformers. This is due to their smaller size, improved dynamic performance, and better power quality. However, their operation with a conventional controller such as a proportional integral (PI) does not give satisfactory performance. Thus, improved dynamic performance and high power quality of the PEDT can be achieved with an intelligent control. In this paper, a novel design topology and an improved controller for a PEDT are proposed. The proposed controller is based on the adaptive PI-fuzzy logic controller (API-FLC). The FLC is applied to adapt the PI controller gains when there is a sudden change in the system parameters. The PEDT with the novel controller is designed to achieve constant dc link voltages, sinusoidal voltage, and current with unity power factor under different operating conditions. The model is simulated and validated using MATLAB/Simulink software. Different operating conditions, including voltage sag, voltage harmonics, sudden load change, and short circuit, have been investigated. The results obtained with the API-FLC controller give a better dynamic performance and improved power quality when compared to the conventional PI controller. Therefore, the PEDT based on API-FLC has the advantages of eliminating transient power quality problems and improving system efficiency.

Collateral vessels play an important role in the restoration of blood flow to the ischemic tissues of stroke patients, and the quality of collateral flow has major impact on reducing treatment delay and increasing the success rate of reperfusion. Due to high spatial resolution and rapid scan time, advance imaging using the cone-beam computed tomography (CBCT) is gaining more attention over the conventional angiography in acute stroke diagnosis. Detecting collateral vessels from CBCT images is a challenging task due to the presence of noises and artifacts, small-size and non-uniform structure of vessels. This paper presents a technique to objectively identify collateral vessels from non-collateral vessels. In our technique, several filters are used on the CBCT images of stroke patients to remove noises and artifacts, then multiscale top-hat transformation method is implemented on the pre-processed images to further enhance the vessels. Next, we applied three types of feature extraction methods which are gray level co-occurrence matrix (GLCM), moment invariant, and shape to explore which feature is best to classify the collateral vessels. These features are then used by the support vector machine (SVM), random forest, decision tree, and K-nearest neighbors (KNN) classifiers to classify vessels. Finally, the performance of these classifiers is evaluated in terms of accuracy, sensitivity, precision, recall, F-Measure, and area under the receiver operating characteristics curve. Our results show that all classifiers achieve promising classification accuracy above 90% and able to detect the collateral and non-collateral vessels from images.

The underactuated quadrotor unmanned aerial vehicle (UAV) is one of the nonlinear systems that have few actuators as compared to the degree of freedom (DOF); thus, it is a strenuous task to stabilize its attitude and positions. Moreover, an induction of unmodelled dynamic factors and uncertainties make it more difficult to control its maneuverability. In this paper, a model-free based single-dimension fuzzy sliding mode control (MFSDF-SMC) is proposed to control the attitude and positions of underactuated quadrotor UAV. The paper discusses the kinematic and dynamic models with unmodelled dynamic factors and unknown external disturbances. These unmodelled factors and disturbances may lead the quadrotor towards failure in tracking specific trajectory and may also generate some serious transient and steady-state issues. Furthermore, to avoid the problem of gimbal lock, the model is amalgamated with hyperbolic function to resolve the singularity issues dully developed due to Newton Euler’s dynamic modeling. The simulation results performed for MFSDF-SMC using MATLAB software R2020a are compared with conventional sliding mode control, fuzzy-based sliding control and single-dimension fuzzy-based sliding mode control without a model-free approach. The design and implementation of the model-free single dimension-based fuzzy sliding mode control (MFSDF-SMC) with an updated Lyapunov stability theorem is presented in this work. It is observed that MFSDF-SMC produces robust trajectory performance therefore, and the manuscript suggests the experimental setup to test the proposed algorithm in a noisy environment keeping the same conditions. The verification of the equipment used and its effective demonstration is also available for the reader within the manuscript.

In networked control system, such as WirelessHART that is characterized by stochastic delay, the use of proportional integral and differential (PID) controllers is inadequate. This is because PID performs poorly in handling time-delay processes. The main reason for this poor performance is the limitation in the range of stable gain of the controller. Time delay causes oscillatory response of the PID with large gain. Likewise, sluggish response is experienced with small gain of the PID. Also, dead time compensators like smith predictor and internal model controller are difficult to be implemented practically since they require exact model of the process to be controlled. Therefore, this paper proposes the application of setpoint weighting strategy to be used alongside PID controller in a WirelessHART network. This method extends significantly the range of the PID gain, while providing good set point tracking and load regulation. From the simulation and experimental results obtained, the capability of the approach to load regulation and tracking can be seen in its fast recovery from effect of disturbance with minimal overshoot. Thus, a two degree of freedom control is achieved. Results also showed that the method is robust to real-time random variable network delay and model mismatches.

Visualization of cerebral blood vessels is vital for stroke diagnosis and surgical planning. A suitable modality for the visualization of blood vessels is very important for the analysis of abnormalities of the cerebrovascular system, as it is the most complex blood circulation system in the human body and vulnerable to bleeding, infection, blood clot, stenosis, and many other forms of damage. Images produced by current imaging modalities are not promising because of noise, artifacts, and the complex structure of cerebral blood vessels. Therefore, there is a requirement for the accurate reconstruction of blood vessels to assist the clinician in making an accurate diagnosis and surgical planning. This paper presents an overall review of modeling techniques that can be classified into the three categories, i.e., image-based modeling, mathematical modeling, and hybrid modeling. Image-based modeling deals directly with medical images and which involves preprocessing, segmentation, feature extraction, and classification. Mathematical modeling exploits existing mathematical laws and equations, an example being an arterial bifurcation, which is assumed to follow a fractal and cube law, and a system of ordinary differential equations are solved to obtain pressure and velocity estimates in a branching network. Whereas, Hybrid modeling incorporates both image-based and mathematical modeling to attempt to produce a more detailed and realistic arterial structure. From the literature review and the analysis of the results, it can be summarized that hybrid models provide a faster and more robust technique, which can significantly help in diagnosis and surgical planning, such as for finding the shortest path for a stenting procedure.

A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure.

High reliability users of microelectronic devices have been derating junction temperature and other critical stress parameters to improve device reliability and extend operating life. The reliability of a semiconductor is determined by junction temperature. This paper gives a useful analysis on mathematical approach which can be implemented to predict temperature of a silicon die. The problem could be modeled as heat conduction equation. In this study, numerical approach based on implicit scheme and Arithmetic Mean (AM) iterative method will be applied to solve the governing heat conduction equation. Numerical results are also included in order to assert the effectiveness of the proposed technique.