Chittagong University of Engineering & Technology

About: Chittagong University of Engineering & Technology is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Renewable energy & Dielectric. The organization has 1200 authors who have published 1444 publications receiving 10418 citations. The organization is also known as: Engineering College, Chittagong & Bangladesh Institute of Technology, Chittagong.

Voice Activated Portable Braille with Audio Feedback

Abstract: The goal of this paper is to create a Voice Activated Braille as a portable device that will help individuals who are blind or visually impaired without any partial help to recognize certain written characters. This is a system controlled by Arduino that will be able to direct blind people. The system acts mostly as a guide for the blind, a particular virtual environment for the visually disadvantaged, making them similarly optimistic to the rest of the world's regular citizens. Blind people will benefit from these innovative braille methods to tackle the challenge of information technology around the world. This system is compact and will keep blind people from partial assistance and can also help them read something without any help. Thus, the daily inconvenience of blind individuals can be significantly decreased and they can enjoy a certain level of freedom and activity.

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

Abstract: COVID-19 hits the world like a storm by arising pandemic situations for most of the countries around the world. The whole world is trying to overcome this pandemic situation. A better health care quality may help a country to tackle the pandemic. Making clusters of countries with similar types of health care quality provides an insight into the quality of health care in different countries. In the area of machine learning and data science, the K-means clustering algorithm is typically used to create clusters based on similarity. In this paper, we propose an efficient K-means clustering method that determines the initial centroids of the clusters efficiently. Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets. Experimental results show that our proposed method reduces the number of iterations and execution time to analyze COVID-19 while comparing with the traditional k-means clustering algorithm.

IoT Based Automatic Electricity Monitoring and Remote Load Control System Using PIC18F4550

Abstract: With the development in modern communication technology, every physical device is now connecting with the internet. IoT is getting emerging technology for connecting physical devices with the user. In this paper we combined existing energy meter with the IoT technology. By implementation of IoT in the case of meter reading for electricity can give customer relief in using electrical energy. In this work a digital energy meter is connected with cloud server via IoT device. It sends the amount of consumed energy of connected customer to webserver. There is a feature for disconnection in the case of unauthorized and unpaid consumption and also have option for renew the connection by paying bill online. We tried to build up a consumer and business friendly system.

Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data

Abstract: Accurate measurement of acoustic velocities of sedimentary rocks is essential for prediction of rock elastic constants and well failure analysis during drilling operations. Direct measurement by advanced logging tools such as dipole sonic imager is not always possible. For older wells, such data are not available in most cases. Therefore, it is an alternate way to develop a reliable correlation to estimate the shear wave velocity from existing log and/or core data. The objective of this research is to investigate the nature of dependency of different reservoir parameters on the shear wave velocity (Vs) of clastic sedimentary rocks, and to identify the parameter/variable which shows the highest level of dependency. In the study, data-driven connectionist models are developed using machine learning approach of least square support vector machine (LSSVM). The coupled simulated annealing (CSA) approach is utilized to optimize the tuning and kernel parameters in the model development. The performance of the simulation-based model is evaluated using statistical parameters. It is found that the most dependency predictor variable is the compressional wave velocity, followed by the rock porosity, bulk density and shale volume in turn. A new correlation is developed to estimate Vs, which captures the most influential parameters of sedimentary rocks. The new correlation is verified and compared with existing models using measured data of sandstone, and it exhibits a minimal error and high correlation coefficient (R2 = 0.96). The hybridized LSSVM-CSA connectionist model development strategy can be applied for further analysis to predict rock mechanical properties. Additionally, the improved correlation of Vs can be adopted to estimate rock elastic constants and conduct wellbore failure analysis for safe drilling and field development decisions, reducing the exploration costs.

Detection & Classification of Tumor Cells from Bone MR Imagery Using Connected Component Analysis & Neural Network

Abstract: Bone cancer is a class of diseases that are characterized by an unfettered growth of the cell and it is considered to be the main reasons of early death around the globe. Therefore, early detection and classification of the bone tumor are become needed to cure the patient. This study uses a connected component labeling algorithm for the detection of the bone tumor. In this work, the artificial neural network (ANN) is used for the classification of bone tumor. Total 220 bone MR images of previously verified patients are collected and the texture features of this images are used for the training and testing of the neural network. The obtained performance of the classification result exhibit that the neural network provides 92.50% success rate in bone tumor classification.