The field of Engineering is that which needs a high level of analytical thinking, intuitive knowledge, and technical know-how. The area of communication engineering deals with different components including, wireless mobile services, radio, broadband, web and satellites. There is a rapid decline in the quality of students produced by engineering faculties as a result of sufficient and quality methods and frameworks of student assessment. The production of high-potential engineers is limited by the utilization of old and traditional education methodology and frameworks. The student presentation estimation system in engineering institution is a motionless manual. Usually, the assessment of student’s performance using the traditional system is limited to the use of students’ performance scores, while failing to evaluate their performance based on activities or practical applications. In addition, such systems do not take cognizance of individual knowledge of students that connects to different activities within the learning environment. Recently, engineering institutions have started paying attention to evaluation solutions that are based on wireless networks and Internet of Things (IoT). Therefore, in this study, an automated system has been proposed for the assessment of engineering students. The proposed system is designed based on IoT and wireless communication networks with the aim of improving the process of virtual education. The data used in this study has been collected through the use of different IoT sensors within the premises of the college, and pre-processed using normalization. After the data was pre-processed, it was stored in cloud. In order to enable the classification of student’s activity, an Adaptive Layered Bayesian Belief Network (AL-BBN) classifier is proposed in this work. The student’s scores have been calculated using fuzzy logic, while Multi-Gradient Boosting Decision Tree (MGBDT) was proposed for decision making. The use of python simulation tool is employed in the implementation of the proposed system, and the evaluation of the performance benchmarks was done as well. Based on the findings of the study, the proposed conceptual model outperformed the existing ones in terms of improving the process of online learning.

Any secured system requires one or more logging policies to make that system safe. Static passwords alone cannot be furthermore enough for securing systems, even with strong passwords illegal intrusions occur or it suffers the risk of forgotten. Authentication using many levels (factors) might complicate the steps when intruders try to reach system resources. Any person to be authorized for logging-in a secured system must provide some predefined data or present some entities that identify his/her authority. Predefined information between the client and the system help to get more secure level of logging-in. In this paper, the user that aims to log-in to a secured system must provide a recognized RFID card with a mobile number, which is available in the secured systems database, then the secured system with a simple algorithm generates a One-time Password that is sent via GSM Arduino compatible shield to the user announcing him/her as an authorized person.

This article presents a model through which the reflection coefficient amplitude as well as phase of reflective intelligent surfaces can be estimated accurately. The reconfigurability of the surface was achieved by incorporating the varactor diodes into the surface of the cell unit. The manipulation of the phase of the reflection coefficient can be achieved by making adjustments to the biasing state of the varactors. The model, which makes use of a physics-based methodology and is based on a transmission-line circuit description of the Reconfigurable Intelligent surfaces (RIS) unit cells, considers every pertinent electrical and geometrical characteristics of the proposed surface. With the method proposed in this paper, fast and accurate RIS-based communication lines can be created. The recommended accuracy of the proposed method was confirmed through the use of a CST microwave studio full-wave simulations.

In this study, convolutional neural networks (CNN) and particle swarm optimization are used to offer a channel estimate technique for low power reconfigurable intelligent surface (RIS) assisted wireless communications (PSO). The suggested approach makes use of the RIS channels' sparsity to lower the CNN model's training complexity and uses PSO to optimize the CNN model's hyperparameters. The proposed system has been trained using 70% of dataset, 25% of data was used for testing and remaining 5% was used for cross-validation. In comparison to previous methods, simulation results demonstrate that the proposed method delivers correct channel estimate with much less computing cost. The suggested technique also exceeds current techniques in terms of bit error rate (BER) and mean squared error (MSE) performance. The research found 96.47% and 90.96% of accuracy for CNN and PSO algorithm respectively. Moverover, the network was trained using a dataset mentioned in methodology section for channel realizations, and achieved a mean squared error (MSE) value of 0.012 using CNN algorithm. Also, the study reported the proposed technique outperformed other state-of-the-art techniques. The proposed technique of PSO to optimize the channel estimation, and achieved a mean squared error (MSE) value of 0.0075.