AIUB Journal of Science and Engineering

Linearization of High frequency Class E Feedback Amplifier using Negative Impedance Method

2021-10-18T22:01:59+00:00

This work represents a novel method of linearization of Switch Mode Power Amplifier (SMPA). For this purpose, class E SMPA was designed and analyzed both before and after applying a linearization technique known as Negative Impedance method. One of the major characteristics of a standard SMPA is that they poses high efficiency than their linear counterpart but on the other hand they show highly nonlinear characteristics. The motivation behind this work is to harness this highly efficient amplifier and make it linear which can very useful for various applications in RF communication fields. All the schematics and simulations that are presented in this paper was performed using Cadence Virtuoso environment using “Spectre” simulator tool. For active components, 280nm process technology was used under “gpdk90” pdk which based on BSIM3v3 model. Circuit was designed to run at 2GHz with 2.5 V supply voltage. A mathematical model is also presented using data found from the analysis, with the help of MATLAB. Linearity was measured using Input referred Intercept Point of 3^rd order frequency (IIP3), which was improved from 3.7dBm to 17.86dBm with 57% percent efficiency providing an output power of 15.78dBm.

Impact of COVID-19 on Academic and Psychological aspects of Undergraduate Students in Bangladesh: A Case Study

2021-10-18T22:02:42+00:00

Undergraduate students are considered susceptible in terms of anxiety, drug abuse, depression, and bad dietary habits in comparison to the general people. Their academic and psychological facets have severely been altered due to COVID-19 pandemic. The objective of this study is to identify the effect of COVID-19 on the academic and psychological aspects of undergraduate students in Bangladesh accompanied by other pertinent factors. Data were accumulated from the undergraduate students of the Fall semester 2020-21 of American International University-Bangladesh (AIUB) by questionnaire provided through Microsoft Forms. The associations among the variables were assessed through the chi-square test. All the statistical analyses required to meet the goals of the study were done through Statistical Package for Social Sciences (SPSS). Nearly one-fourth of the students suffered from anxiety and depression at an extreme level while close to one-third of them suffered quite a bit signaling a tormenting psychological state of the students. Chi-square tests found that depression, anxiety, study hour, assessment of online learning, and income issue due to COVID-19 of the student had a highly significant association with effects on their study and psychological aspects. Failure to address the aforesaid issues during an epidemic might have negative consequences on the academic and psychological aspects in the long run.

Student performance classification and prediction in fully online environment using Decision tree

2021-10-18T22:01:37+00:00

Knowledge Discovery and Data Mining (KDD) is a multidisciplinary field of study that focuses on methodologies for extracting useful knowledge from data. During the latest Covid-19 pandemic, there was a significant uptick in online-based learning (e-learning) operations as every educational institution moved its operations to digital channels. To increase the quality of education in this new normal, it is necessary to determine the key factors in students’ performance. The main objective of this study is to exploit the regulating factors of education via digital platforms during the covid-19 pandemic by extracting knowledge and a set of rules by using the Decision Tree (j48) classifier. In this study, we developed a conceptual framework using four datasets, each with a different set of attributes and instances, collected from “X-University” and Microsoft teams. ‘Final term’ and ‘Mid-term’ examinations acted as the root node for all four datasets. The findings of this study would benefit higher education institutions by helping instructors and students to recognize the shortcomings and influences controlling students' performance in the online platforms during the covid-19 pandemic, as well as serve as an early warning framework for predicting students' deficiencies and low school performance.

A survey into COVID-19 Induced Pneumonia Detection and Feasibility of using UWB Medical Imaging

2021-10-18T22:02:21+00:00

This paper presents a survey into the currently thriving research on using machine learning for COVID-19 induced pneumonia detection through the use of radiographic scans, presents a brief review of the methodologies and assesses the classification results, and finally presents an alternative in the form of ultrawideband (UWB) imaging. Few works on UWB imaging is investigated and used as a source of inspiration for developing an UWB imaging system for detection of accumulation of fluid in lungs. The goal is to extract information about dielectric property variation from backscattered UWB signals to detect pneumonia caused by COVID-19. An edge fed Vivaldi antenna along with a multilayer planar model for lung is simulated in CST microwave studio and subjected to UWB excitation. The backscattered signals in the form of S-parameters are analyzed with various Delay-and-Sum (DAS) algorithms and images are constructed for lung tissues of different permittivity and conductivity, where higher values are supported to allude to the infected lungs.

Users Acceptance of Mobile Finance Service in Bangladesh and the impact of COVID-19: Extended UTAUT2

2021-10-18T22:01:15+00:00

Population of Bangladesh is 164 million, but there are 165 million mobile phone subscribers. Mobile phone usage is one of the fastest growing phenomena of the country. Globally, among many mobile based services, Mobile Financing is one of the most rapidly expanding sector. Bangladesh is yet to see a significant growth in this arena. There have been numerous studies conducted on the types of Mobile Financing Services (MFS) and their reach in Bangladesh. But not too many studies were conducted on the factors that influences users to adopt MFS and their behavioral intension. Moreover, the recent crisis of COVID-19 pandemic seemed to have an impact on the usage of MFS which is also another unexplored research domain. This study analyzes the factors influencing MFS users of Bangladesh and also explores the impact of COVID-19 on the user’s behavioral intension. The Unified Theory of Acceptance and Use of Technology (UTAUT) model in combination with Entrepreneurial Potential Model has been modified and adopted in this study. This study explores the correlation of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (V) on Behavioral Intention (BI) on MFS users. It also studies the moderation effect of COVID-19 on the relationship between MFS users’ Behavioral Intension and Use Behavior. The correlating factors effect positively on the MFS users’ behavioral intention. But the COVID-19 impact was found ineffective in moderating their use behavior. The framework of this research is a novel one and can be adopted for similar studies.

COVID-19: Risk Analysis in South Asia with Respect to Europe and North America

2021-10-18T22:00:54+00:00

Coronavirus Disease 2019 (COVID-19) was identified in late 2019 and world health Organization (WHO) declared as a pandemic on March 11, 2019. World top researchers, physician and pharmacists are trying to find out remedy but it is still in research phase. COVID-19 spread through the air by coughing or sneezing also depends on environment. In this paper, our main goal is to COVID-19 threat analysis in South Asian people based on their habits, culture, consciousness etc. compare to Europe and North American culture. The research work is formulated in three steps. First, we formulate a dynamic infection transmission model by considering the fertility rate, mortality rate, transmission rate, and cure rate of the COVD-19 caused death rate as variables. Second, we define the variables of the model based on the census of south Asia. Finally, we analyze the threat that COVID-19 can cause to the population of crowded country likes Bangladesh, India etc. in south Asia.

Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning

2021-10-18T22:00:31+00:00

In the Medical field, Brain Tumor Detection has become a critical and demanding task because of its several shapes, locations, and intensity of image. That’s why an automated system is important to aid physicians and radiologists in detecting and classifying brain tumors. In this research, we have discussed different machine learning as well as deep learning algorithm which are mostly used for image classification. We have also compared different models that are being used for tumor classification based on machine learning and deep learning. MRI images of Glioma tumor, Pituitary tumor, Meningioma tumor are the base of this research, and we have compared different techniques along with the accuracy of different classification models using those MRI images. We have used different deep learning pre-trained models for training the brain tumor images. Those pre-trained models have provided outstanding performance along with less power consumption and computational time. EfficientNet-B3 has provided the best accuracy of 98.16% among other models as well as traditional machine learning algorithms. The experimental result of this model is proven the best and most efficient for tumor detection and classification in comparison with other recent studies.