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.

Low Cost, Portable ECG Monitoring and Alarming System Based on Deep Learning

Abstract: Electrocardiogram (ECG) has been the golden standard for the detection of cardiovascular disease for many years. Any electrical impulse disruption that causes the heart to the contract may lead to arrhythmia. Arrhythmia patients have no indications of having an arrhythmia, but a doctor may recognize arrhythmias in a routine test. Therefore, continuous wearable personal monitoring system plays a big role, and it's become popular day by day. This research focuses on designing and developing a method for predicting arrhythmia (atrial fibrillation) along with monitoring the ECG signals. To create an arrhythmia prediction model and an Android-based real-time ECG surveillance system, Long Short-Term Memories neural network, Recurrent Neural Network, TensorFlow and Keras library are applied here. Those deep learning models and algorithms help to achieve overall 97.57% accuracy on arrhythmia prediction. The system is being designed with Raspberry pi 3, Arduino UNO, AD8232 single lead ECG sensor, HC-05 Bluetooth, biomedical sensor pad and battery. This system will make easier for doctors to monitor the ECG of their patients outside the hospital and also help for remote ECG monitoring. The total components cost of this research work is around USD 58.

Active Vision-Based Attention Monitoring System for Non-Distracted Driving

Abstract: Inattentive driving is a key reason of road mishaps causing more deaths than speeding or drunk driving Research efforts have been made to monitor drivers’ attentional states and provide support to drivers Both invasive and non-invasive methods have been applied to track driver’s attentional states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort In this paper, a vision-based scheme is proposed for monitoring the attentional states of the drivers The system comprises four major modules-cue extraction and parameter estimation, state of attention estimation, monitoring and decision making, and level of attention estimation The system estimates the attentional level and classifies the attentional states based on the percentage of eyelid closure over time (PERCLOS), the frequency of yawning and gaze direction Various experiments were conducted with human participants to assess the performance of the suggested scheme, which demonstrates the system’s effectiveness with 92% accuracy

Evaluation of natural hazard risk for coastal districts of Bangladesh using the INFORM approach

Abstract: Coastal districts of Bangladesh are increasingly suffering from climatic hazards (e.g., cyclone, flood, saltwater intrusion). Tropical cyclones and floods particularly affect millions of people almost every year. Massive property damages and life losses are a common scenario when these disasters hit the coastal belt. Moreover, due to climate change impact, saltwater intrusion is increasing significantly in the coastal districts, especially during dry seasons impacting crop production and drinking water sources. The amount of asset and population exposed to natural hazards have increased significantly in the coastal belt, which has exacerbated the risk of natural hazards. Previous studies on the disaster risk of coastal districts of Bangladesh did not explicitly account for the key vulnerability and lack of coping capacity parameters that are crucial for a comprehensive risk assessment. This study assesses the disaster risk of 19 coastal districts of Bangladesh using the Index for Risk Management (INFORM) model. The multi-layered INFORM framework particularly assesses the risk of coastal communities considering socioeconomic vulnerabilities and vulnerable groups as well as institutional and infrastructural coping capacity under multiple hazards. Forty-five core risk indicators are used to evaluate the final risk score of the communities. The outcome of the study shows that 11 districts are facing moderate to very high level of risk in the coastal belt. The island district, Bhola, has the highest level of risk among the coastal districts, whereas Cox's Bazar, Chandpur, and Feni districts fall into the lowest risk region. In addition to hazard susceptibility, poverty & development, vulnerable health condition and access to the health care system are key vulnerable factors that potentially increase the overall risk. The outcome of this study is expected to be useful for preparing an effective disaster risk mitigation plan by decision-makers.