This article presents a finite volume-based simulation study on the effect of fin length variation on the thermal performance of a bus duct conductor. The study was conducted by developing a numerical model using ANSYS FLUENT. IEC 60439-1 and IEC 60439-2 standards were employed as a guideline to conduct the experimental setup. It was found that increasing the fin length increases the total heat transfer rate. This is due to an increase in the surface area exposed to the fluid and a decrease in the thermal boundary layer thickness. However, it was observed that longer fin lengths contributed to greater frictional forces on the fluid that resulted in a drop in convective heat transfer coefficient. The current numerical study is expected to provide a better understanding of the effect of fin length on the thermal performance of a bus duct conductor.

Flying ad hoc networks (FANETs) are easy to deploy and cost-efficient, however they are limited by the static protocols used in 802.11 and CSMA-based networks to support high bandwidth multi-UAV applications. This work proposes an Anticipatory Dynamic Flow Priority Allocation (ADFPA) scheme to optimize the priority levels of outgoing traffic flows for a transmitting node to maximize the total network throughput. Unlike other deep reinforcement learning (DRL)-based schemes in centralized networks, ADFPA is designed to be distributed, multi-agent, and proactive. It uses current and forecasted multi-context information to optimize the priority levels of traffic flows in a decentralized and dynamic FANET. Furthermore, a traffic flow sampling and padding algorithm is proposed so that a trained agent can be redeployed in different environments without retraining to address the practicality issue. Our evaluations show that ADFPA outperforms other state-of-the-art schemes by a maximum of 37% and 59.4% in terms of the network throughput in the single and multi-transmitting nodes environment, respectively, while achieving the best fairness amongst all schemes. These improvements translate to better data transmission capabilities in a conventional FANET, and the proposed scheme can enable the use of a FANET architecture in more demanding applications without switching to centralized solutions.

Prediction of malignant ventricular arrhythmia is imperative to enable early diagnosis and prevent sudden cardiac death. There are a wide variety of methods employed in previous works with box counting of phase space reconstruction diagrams achieving the highest performance. However, there is no follow-up work investigating or improving this method. This work is performed to objectively assess the feasibility of box counting technique for prediction of malignant ventricular arrhythmia and to validate the prediction technique across larger data sets, including the widely acceptable MIT-BIH database. Box counting using different windowing methods, data representation methods of phase portrait and testing database are investigated for performance and versatility. By using windows of RR segments and linking phase portraits, this modified box counting method has higher resistance to signal noises, which are common in the electrocardiogram. It is verified using four Physionet databases (CUDB, SDDB, PTBDB and NSRDB) which contain either arrhythmic or control records. High prediction accuracy of 94.12%, sensitivity of 88.46% and specificity of 100% are achieved by the coefficient of variation derived from this method. This technique is proven useful in predicting malignant ventricular arrhythmia and its implementation is envisaged to enable early detection and diagnosis.

Sudden cardiac death (SCD) remains one of the top causes of high mortality rate. Early prediction of ventricular fibrillation (VF), and hence SCD, can improve the survival chance of a patient by enabling earlier treatment. Heart rate variability analysis (HRV) has been widely adopted by the researchers in VF prediction. Different combinations of features from multiple domains were explored but the spectral analysis was performed without the required preprocessing or on a shorter segment as opposed to the standards of The European and North American Task force on HRV. Thus, our study aimed to develop a robust prediction algorithm by including only time domain and nonlinear features while maintaining the prediction resolution of one minute. Nine time domain features and seven nonlinear features were extracted and classified using support vector machine (SVM) of different kernels. High accuracy of 94.7% and sensitivity of 100% were achieved using extraction of only two HRV features and Gaussian kernel SVM without complicated preprocessing of HRV signals. This algorithm with high accuracy and low computational burden is beneficial for embedded system and real-time application which could help alert the individuals sooner and hence improving patient survival chance.

Prediction of malignant ventricular arrhythmia (mVA) is essential to prevent sudden cardiac death. There were mainly three research clusters on mVA prediction using electrocardiogram (ECG): prediction using CUDB, SDDB and private databases. Comparability and generalization issue arose due to the different usage of arrhythmic datasets for analysis. Very few studies attempted short-term prediction of mVA using multiple databases, and those studies achieved low prediction performance. Our study aims to improve the prediction performance involving multiple databases and to promote the algorithm comparability by performing more comprehensive comparability study while including a more complete set of data available from the public databases. In our study, eight statistical box count features derived from phase space reconstruction on ECG signal were classified using maximum thresholding method. This was followed by performance benchmarking against the first two clusters of existing research and a performance evaluation using the combined set of databases. Our algorithm using box count coefficient of mean absolute deviation achieved over 90% of accuracy and over 4-minutes prediction time for all the three set of performance evaluations. This algorithm outperforms the existing work by introducing lower computational efforts.

ECG Holter is a device used to acquire and monitor the user heart rhythm. However, it is available only in a major healthcare facility as it is very costly. The objective of this work is to develop a portable ECG monitoring device with wireless transmission for early arrhythmia detection and personal monitoring. The heart of the device is based on Atmel ATmega328p processor, which acquires user ECG data through Analog Devices AD8232 ECG analog front-end chip. Data captured is stored offline in memory card before it is transmitted wirelessly to a cloud server for analysis purpose. Experiments indicate that the device able to sample the ECG data up to 1000 samples per second and Wi-Fi based transmission serves the best for data transfer to the cloud server. User and physician can easily access the data for viewing and analysis, eliminating the needs for users to travel to the hospital for ECG acquisition.

The automotive industry shows a gradual transition from a simple transportation model to a car that relies on electronics for safety control. A modern car will offer many features in which the car drive or park automatically; this shows the effort of the automotive industry in increasing the consumer's safety level on the road. The increasing awareness of safety results in reliance of today cars on the electronic controlling components such as engine, steering, transmission, braking system and airbags. This project proposes hardware and software co-design that provides flexibility, timing precision, performance, manageable software design, complexity and meets safety requirement. The solution is aligned with Application-Specific Integrated Circuit (ASIC), which features complex control algorithm, implementation in hardware and controllable through firmware.

Automotive industry is migrating to electronic based control which has promoted the evaluation and enhancement of engine control performance using electronic components in the research field. Modern engines are controlled by Engine Control Unit (ECU) where not only functions and control systems are in placed, but also the fail-safe mechanisms must be integrated in the ECU to ensure user safety when an error occurred. This work focuses on developing a reliable crankshaft function in an ECU using field-programmable gate array (FPGA). ABa test is carried out and the number of occurrences at unintended location are being monitored in the system to detect the random missing tooth. The reliability of the function is tested by evaluating response of the crankshaft function in an ECU when an unexpected missing tooth occurs during its operation. The developed system is able to detect the random missing tooth on a crank trigger wheel at an accuracy of 100% when the wheel is on a run at a constant wheel rotational speed. It also flags error message for further processing in other functional units of the ECU as a safe-fail mechanism for the system. Implementation of the random missing tooth detection in the crankshaft function is shown to work in the system which is developed using Verilog code