Evaluating system usability of mobile augmented reality application for teaching Karnaugh-Maps

The education system evolves and transforms towards interactive and immersive learning tools in this digital age. Augmented reality has also evolved as a ubiquitous, robust, and effective technology for providing innovative educational tools. In engineering education, many abstract concepts require technological intervention for conceptual understanding and better instructional content. While learning through the immersive tools, system usability has great importance in terms of effectiveness, efficiency, and satisfaction. Effectiveness refers to users' accuracy and completeness in achieving defined goals; efficiency relates to expended resources about the precision and completeness with which users achieve their objectives; satisfaction deals with a positive attitude towards using the product. If the system fails to provide good usability, it may cause adverse effects such as increasing stress, lacking necessary features, increasing the users' cognitive load, and negatively impacting the student's motivation. In this study, two mobile augmented reality (MAR) applications were developed as an instructional tool to teach the students about Karnaugh maps in the digital electronics course. The first application is a Keypad-based MAR application that uses a keypad matrix for user interaction and the second application is a Marker-based MAR application that uses multiple markers to solve K-Map for producing an optimum solution of the given problem. An experimental study was conducted to determine the student's opinion of the developed MAR applications. The study was designed to determine the system usability of the two MAR applications using the System Usability Score (SUS) and Handheld Augmented Reality Usability Score (HARUS) models. 90 engineering students participated in the study, and they were randomly divided into two different groups: keypad-based group and Marker-based group. The keypad-based group included 47 students who had hands-on experience with a keypad-based MAR application, whereas the marker-based group included 43 students who had hands-on experience with multiple marker-based MAR applications. The experimental outcomes indicated that the keypad-based MAR application has better SUS and HARUS scores than the marker-based MAR application which suggests that the keypad-based MAR application has provided better user interaction.

Scope of Improvements in Aerospace Sector by Implementation of IoT

While considering the aerospace domain, the internet of things (IoT) provides the way for new development and this IoT technology allows many possibilities in the aerospace domain. This study aims to examine the theoretical aspect of IoT in the aerospace industry. And propose a system that enhances the flight journey experience from the flight booking of each customer. This will also improve the manufacturing end-to-end process in the aerospace industry with the help of IoT sensors. These can be achieved with help of the data collection (Previous sensor data), cloud computing, and machine learning. As per the proposing system, all IoT sensor data will be collected and saved the data in the cloud server. These data will be used for training the algorithm to achieve the optimum solution in the future.

Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson’s dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays

In the electrical power system, the coordination of directional overcurrent protection relays (DOPR) plays a preeminent role in protecting the electrical power system with the help of primary and back up protection to keep the system vigorous and to avoid unnecessary interruption. The coordination between these relays should be pursued at optimal value to minimize the total operating time of all main relays. The coordination of directional overcurrent relay is a highly constrained optimization problem. The DOPR problem has been solved by using a hybridized version of particle swarm optimization (HPSO). The hybridization is achieved by introducing simulated annealing (SA) in original PSO to avoid being trapped in local optima and successfully searching for a global optimum solution. The HPSO has been successfully applied to five case studies. Furthermore, the obtained results outperform the other traditional and state of the art techniques in terms of minimizing the total operating of DOPR and convergence characteristics, and require less computational time to achieve the global optimum solution.

Human Activity Recognition via Hybrid Deep Learning Based Model

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.

Automated Screening System for Covid Safety

COVID-19 is a life-threatening virus taking the lives of thousands of people every day throughout the world. Even though many organizations and companies worked hard and developed vaccines, production of vaccines at large scale to meet today’s demand is not an easy job as there is a shortage of raw materials and cases are rising steeply. Inoculation of every individual cannot be achieved in the foreseeable future. Even the government is vaccinating people in a phased manner prioritizing older people and people who are more vulnerable to the virus. The main objective of this work is to provide an optimum solution for COVID-19 indoor safety for industries, offices, and commercial places where footfall is high. This work focus on automation of temperature sensing and mask detection which is usually carried out by a person. Elimination of human intervention reduces the risk of contraction and spreading and avoids mistakes due to human negligence. Continuous monitoring of a person is not possible and there is no guarantee that a person who is entering a place wearing a mask puts it on until he leaves it. This research intends to implement mask detection along with surveillance which is cost effective as it does not require additional hardware setup.

Mechatronic Model of a Compliant 3PRS Parallel Manipulator

Compliant mechanisms are widely used for instrumentation and measuring devices for their precision and high bandwidth. In this paper, the mechatronic model of a compliant 3PRS parallel manipulator is developed, integrating the inverse and direct kinematics, the inverse dynamic problem of the manipulator and the dynamics of the actuators and the control. The kinematic problem is solved, assuming a pseudo-rigid model for the deflection in the compliant revolute and spherical joints. The inverse dynamic problem is solved, using the Principle of Energy Equivalence. The mechatronic model allows the prediction of the bandwidth of the manipulator motion in the 3 degrees of freedom for a given control and set of actuators, helping in the design of the optimum solution. A prototype is built and validated, comparing experimental signals with the ones from the model.

Optimal tuning of a PID controller using a wound healing algorithm based on the clonal selection principle

Control strategies for uncertainties or nonlinear effects need to be developed for control systems. Optimization algorithms developed with the rapid development of computer technology are frequently used to improve the steady-state response with the help of the ambiguous mathematical characteristics or nonlinearity of control systems. In this study, an optimum proportional–integral–derivative (PID) controller was designed using the wound healing algorithm based on the clonal selection principle. The proposed algorithm is applied in self-tuning a PID controller in the ball and hoop system which represents a system of complex industrial processes. In order to adjust the PID parameters with the aid of the developed algorithm, an integral absolute error (IAE) has been chosen as the objective function. Thus, the system reached the optimum solution quickly and time was saved. The advantages of the proposed algorithm have been proved by comparing the obtained results with other algorithms. To succeed this, a programme was written in the MATLAB GUI environment.

A Review on Forwarding Strategies in NDN based Vehicular Networks

Named Data Networking (NDN) is a model that has been proposed by many researchers to alter the long-established IP based networking model. It derives the content centric approach rather than host-based approach. This is gaining even more traction in the wireless network and is able to replace the conventional IP-based networking. Up to now, NDN has proven to be fruitful when used with certain limitations in vehicular networks. Vehicular networks deal with exchanging information across fast moving complex vehicle network topology. The sending and receiving of information in such a scenario acts as a challenge and thus requires an effective forwarding strategy to address this problem. Different research work has provided with multiple forwarding strategy that solves the current problem up to some limit but further research work is still longed for to get an optimum solution. This paper provides a brief survey on current existing forwarding strategies related to vehicular networks using NDN as well as providing information on various resources and technologies used in it.

A new global optimum solution model based on multi-value dynamic programming for the cut-off grade optimization in a surface mining complex

In techno-economic concern, cut-off grade (COG) optimization is the key for efficient mineral liquidation from thehuge metalliferous surface mining sector. In this paper, a sequentially advancing algorithm based on discretemulti-value dynamic programming (MDP) has been developed to calculate the global optimum COG of alarge-scale open-pit metalliferous deposit. The proposed COG optimization algorithm aims to overcome thelimitations of straightforward classical techniques in determining the optimum COG. This discrete COG-MDPmodel is the first of its kind and has the novelty of dealing with the simulation of eight dynamic possibilities toachieve the maximal global Net Present Value (NPV). A high-level programming language (Python) has been usedto develop the computer model to deal with the complexity of handling a minimum of 500 series of dynamicvariables. This model can generate results in polynomial-time from the complex of mining, milling, and smeltingand refining system corresponding to various limiting conditions. The prime objective considered in the model isto optimize the COG of a metalliferous deposit. A working open-pit copper mining complex from India has beenused to validate the model. In this study, the optimum COG for the Malanjkhand copper deposit has been found tobe (0.33%, 0.23%, 0.52%, 0.26%, 0.27%, 0.22%, 0.24%) with a maximum NPV of ₹ (12204, 14653, 16948, 14609,21454, 26717, 38821) million corresponding to various scenarios. The findings also show that the present valuegradually hits zero after the project’s life cycle, confirming the typical pattern of other mining firms.