Gokul Sidarth

Snakebite, one of the most common and catastrophic environmental sicknesses, occurs due to the ignorance of its importance toward public health. The rich protein and peptide toxin nature of snake venom makes snake bite envenomation clinically challenging and a scientifically attractive issue. In most cases, the severity of snake bite envenomation mainly depends on the quality of first aid or snake bite management measure given to the victim prior to hospital treatment. In countries with field management strategies (such as pressure immobilization technique (PIT)), including Australia, the number of fatalities due to snake bites is considerably less compared with those in other countries without such precautionary measures. PIT involves the wrapping of a bandage or a crepe over the bitten area with a standard pressure of 55--70 and 40--70 mm Hg for lower and upper extremities, respectively. This technique delays the absorption rate or venom spread inside the body. However, the PIT displays a noticeable failure rate due to its sensitivity toward the pressure range that must be maintained when gripping the bandage around the bitten area. Off-the-shelf bandages with visual markers aid in the process of training on PIT. Despite the visual markers on the bandage, human interpretation of these markers differs, which causes discrepancies in applying correct pressure. In this paper, a mixed reality-based virtual reality (VR) training tool for PIT training is proposed. The VR application assists in training individuals to self-validate the correctness of pressure range applied to the bandage. The application provides a passive haptic response and a visual feedback on an augmented live stream of the camera to indicate whether the pressure is within the range. Visual feedback is obtained using a feature extraction technique, which adds novelty to the proposed research. Feedback suggests that the VR-based training tool will assist individuals in obtaining real-time feedback on the correctness of the bandage pressure and further understand the process of PIT.

Increasing silicon solar cell efficiency plays a vital role in improving the dominant market share of photo-voltaic systems in the renewable energy sector. The performance of the solar cells can be evaluated by making a profound analysis on various effective parameters, such as the sheet resistance, doping concentration, thickness of the solar cell, arbitrary dopant profile, etc., using software simulation tools, such as PC1D. In this paper, we present the observations obtained from the evaluation carried out on the impact of sheet resistance on the solar cell’s parameters using PC1D software. After which, the EDNA2 simulation tool was used to analyse the emitter saturation current density for the chosen arbitrary dopant profile. Results indicated that the diffusion profile with low surface concentration and shallow junction depth can improve the blue response at the frontal side of the solar cell. The emitter saturation current density decreases from 66.52 to 36.82 fA/cm2 for the s...

Customization, enhanced quality of streamlined maintenance services and uplifted productivity are some of the key highlights from the rapidly evolving concept of Industry 4.0. IoT (Internet of things) based service infrastructure models designed for delivering enterprise services with capabilities of pro-actively sensing malfunctions and responding with preventive measures to streamline the automated service offered is one of the prime application of this concept. Continuous maintenance services increase the optimum through-life cost and in-service life cycle of the product providing the customer with the feel of full ownership. In-service feedbacks also help the manufactures to identify issues with respect to the designs and improve it in the future versions. In this paper, as a proof of concept a cloud-based IoT service infrastructure for providing real-time prognostic and supervised vehicle maintenance system is proposed. This proposed system aims at providing an enterprise service infrastructure to the registered vehicle service centers to keep track of the real-time vehicle diagnostic information of their client's vehicle over cloud and use prognostic algorithms to identify any malfunctions or abnormal behavior of the vehicles for automatically scheduling a service appointment and automating the maintenance cycle of the vehicle. In addition to this, the system provides features like remote supervision and diagnostics maintenance enabling technicians to fix issues remotely, ensuring streamlined and reliable service. Initially, before building the proposed prototype system, a few experimental trails where conducted for analyzing the use of different IoT models used in the development to identify the best-suited approach. The results indicated that the publisher-subscriber (NodeJS) based model outperforms the request-response (PHP) based model in terms of the hits per second and mean request time for an increased number of active users. The results of the initial tests justify the reason for the using the publisher-subscriber based IOT architecture. The conceptualized enterprise infrastructure illustrated in the manuscript aims at providing a streamlined maintenance service.

Minimizing the photon losses by depositing an anti-reflection layer can increase the conversion efficiency of the solar cells. In this paper, the impact of anti-reflection coating (ARC) for enhancing the efficiency of silicon solar cells is presented. Initially, the refractive indices and reflectance of various ARC materials were computed numerically using the OPAL2 calculator. After which, the reflectance of SiO2,TiO2,SiNx with different refractive indices (n) were used for analyzing the performance of a silicon solar cells coated with these materials using PC1D simulator. SiNx and TiO2 as single-layer anti-reflection coating (SLARC) yielded a short circuit current density (Jsc) of 38.4 mA/cm2 and 38.09mA/cm2 respectively. Highest efficiency of 20.7% was obtained for the SiNx ARC layer with n=2.15. With Double-layer anti-reflection coating (DLARC), the Jsc improved by ∼0.5 mA/cm2 for SiO2/SiNx layer and hence the efficiency by 0.3%. Blue loss reduces significantly for the DLARC com...

Expeditious urbanization and rapid industrialization have significantly influenced the rise of energy demand globally in the past two decades. Solar energy is considered a vital energy source that addresses this demand in a cost-effective and environmentally friendly manner. Improving solar cell efficiency is considered a prerequisite to reinforcing silicon solar cells’ growth in the energy market. In this study, the influence of various parameters like the thickness of the absorber or wafer, doping concentration, bulk resistivity, lifetime, and doping levels of the emitter and back surface field, along with the surface recombination velocity (front and back) on solar cell efficiency was investigated using PC1D simulation software. Inferences from the results indicated that the bulk resistivity of 1 Ω·cm; bulk lifetime of 2 ms; emitter (n+) doping concentration of 1×1020 cm−3 and shallow back surface field doping concentration of 1×1018 cm−3; surface recombination velocity maintaine...

In this paper, a novel deep neural network-based energy prediction algorithm for accurately forecasting the day-ahead hourly energy consumption profile of a residential building considering occupancy rate is proposed. Accurate estimation of residential load profiles helps energy providers and utility companies develop an optimal generation schedule to address the demand. Initially, a comprehensive multi-criteria analysis of different machine learning approaches used in energy consumption predictions was carried out. Later, a predictive micro-grid model was formulated to synthetically generate the stochastic load profiles considering occupancy rate as the critical input. Finally, the synthetically generated data were used to train the proposed eight-layer deep neural network-based model and evaluated using root mean square error and coefficient of determination as metrics. Observations from the results indicated that the proposed energy prediction algorithm yielded a coefficient of d...

This paper provides an overview on the past pieces of literature on emotion prediction systems and the different machine learning algorithms used to classify emotions. We propose a system which incorporates the emotion prediction system with a custom Smart Human Machine Interface (SHMI) for vehicle drivers to improve drive safety. This is achieved based on EEG signals and basic vehicle information's obtained from an OBD (On-Board Diagnostics) data. EEG signals are classified into four emotional states: happy, sad, relaxed and angry. In this paper, we present an initial development of the Smart Human Machine Interface (SHMI) for emotion detection for vehicle applications. To evaluate the classification of the EEG signals we use Russell's circumflex model, Higuchi Fractal Dimension (HFD), PSD (Power Spectral Density) for feature extraction and Support Vector Machines (SVM) for classification.

Marine robotics is a rapidly growing field, with applications of both Autonomous Underwater Vehicles (AUV) and Autonomous Surface Vehicles (ASV) becoming extensive and within reach for many people. Presented is a low-cost design for an ASV, focusing on the ability for the average person with only little mechanical and electrical skills to assemble. The ASV also incorporates a winch into its design, allowing the ASV to perform many tasks that make it stand out from other ASV systems available in the market, A novel system containing both an ASV and an AUV is introduced, where the designed ASV would be able to work with AUV systems to find and collect underwater objects.

The solar photo-voltaic systems control architecture has a substantial influence over the cost, efficiency, and accuracy of maximum power point tracking under partial shading conditions. In this paper, a novel distributed architecture of a building integrated photo-voltaic system equipped with a single maximum power point tracking controller is presented in order to address the drawbacks associated with respect to cost, complexity and efficiency of the existing photo-voltaic system architectures. In addition, a radial movement optimization based maximum power point tracking control algorithm is designed, developed, and validated using the proposed system architecture under five different partial shading conditions. The inferences obtained from the validation results of the proposed distributed system architecture indicated that cost was reduced by 75% when compared to the commonly used decentralised systems. The proposed distributed building integrated photo-voltaic system architect...

The time taken for the scheduling task in a control system to reduce the traffic within the system is one of significant field of research in modern era. There are different control systems that require time scheduling such as elevator control system, traffic control system and train control system. Currently, there are unique control logic strategies adopting scheduling algorithm that are implemented in real time systems like earliest deadline first and ant colony optimization. At the same time, the disadvantages possessed by them are the exponential dip in the performance ratio due to over loading. Despite of all the available resources there are many issues faced such as congestion in traffic networks due to non-adaptive scheduling algorithms, etc., which led to several misfortunes and danger for human life. Hence an improved algorithm that increases the efficiency of the system is required to validate the processing time and the deadlines. Our research is focused on validating a...

Virtual reality (VR) systems can generate environments that do not exist or are difficult to access. State-of-the-art VR is rapidly evolving and has resulted in enhanced user experience, which leads to a completely immersive experience. Advances in high-resolution displays and highly powerful computer graphics hardware drive the most substantial advancement in VR, which is the introduction of low-cost consumer-grade head-mounted displays (HMD). Despite being inexpensive and providing a high-quality VR experience, commonly used HMDs have a limited field of view, and giving multiple people access to the same virtual environment is inherently challenging. CAVE™ Automated Virtual Environments (CAVE) have benefitted from the same advances in computer graphics hardware and from improvements to binocular (stereo) projection technology, which has reduced the cost and complexity of such systems and increased the visual display quality (resolution, colour, frame rate, etc.). Unlike in HMDs, in a CAVE tracking system, interaction technologies and audio are distinct sub-systems that need to be designed to achieve the desired purpose. A designer needs to consider applications to be used in CAVE and optimise performance. In this paper, we present the design specifications of a reconfigurable CAVE-like VR system that incorporates 6-degree-of-freedom haptic interaction and 3D ambisonic audio. The system was designed for the Centre for Advanced Design in Engineering Training, VR Lab, at Deakin University. Future directions and different use cases along with a comparison matrix are presented to highlight the advantages of the presented system over other existing VR technologies.