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.

Performance Analysis of a Microstrip Patch Antenna for Satellite Communications at K Band

Abstract: Microstrip patch antennas which have the multiplicity of features are receiving attention for satellite applications nowadays. A microstrip patch antenna operating at K band (18 GHz to 27 GHz) for satellite applications is designed in this paper. The antenna has been designed with CST Microwave Studio Suite software. The miniaturized antenna shows impressive results which are suitable for satellite communications. To implement the antenna for satellite application, prerequisites such as low return loss, high bandwidth, gain, far-field radiation are accomplished here. All the performance parameters are utilized to operate the antenna in K-band. The simulation data show satisfactory results for the above-mentioned applications.

An IoT-Based Heart Disease Detection System Using RNN

Abstract: With the growing social pressure, most people are facing health problems and most of these are causing because of frequent heart attacks. It is very important to design an effective system that can diagnose the forthcoming happening of heart attacks or atrial fibrillations. As IoT (Internet of Things) is the most emerging and prominent technology in the modern era, a combination of the personal computer and IoT has become very efficient technology. The combined system provides various features to the user such as in emergencies to detect heart disease possibility through symptoms, sending messages to doctors according to the stage of the possibility of disease, and helps in fixing it. In case of an emergency, the system sends an emergency report to the desired doctor. In this paper, we propose an IoT based heart disease detection system using a Recurrent Neural Network (RNN). In our approach, we use Long Short Term Memory (LSTM) algorithm. The proposed system can perform the diagnosis of heart disease using RNN. The developed system helps the physician to prescribe patients without being present physically. The estimation of the result claims that the proposed system can detect heart disease efficiently.

Implementation and performance analysis of a multi megawatt variable speed Wind Turbine using Matlab® and Simulink®

Abstract: This paper describes the implementation and investigation the functionality of the GE 3.6 MW Wind Turbine model implemented in Matlab® and Simulink® when exposed to a wind-step. The robustness of the Wind Turbine is tested by varying the aerodynamic parameters C p, max and λ opt . It is found that, the implemented model operate as expected in different wind speed regions under different disturbances e.g. wind speed step, grid-fault. However, while simulating in the low-wind speed region, it is found that, the pitch controller does not work according to the expected behavior. The pitch-angle tends to change, although it should not be the case for low-wind speed operation.

Application of smoothed particle hydrodynamics (SPH) for simulating various geotechnical problems

Abstract: Geotechnical engineering is considered one of the oldest disciplines in civil engineering. To date, to extract hidden information, numerical simulations have been performed using the traditional grid-based numerical approach in the Eulerian framework. However, this may not capture some geotechnical engineering problems caused by the large deformations of geomaterials. Therefore, in this research, an attempt is made to develop a particle-based method using smoothed particle hydrodynamics (SPH), which has proven to be an effective option for modeling geomaterials, to solve these problems through running different sets of numerical simulations. The developed model is validated using some benchmark solutions and then two important geotechnical problems, namely the bearing capacity and seepage flow profiles, are simulated. The simulated results represent the actual scenario quite well. Also, the flow process in terms of the accumulated strain is evaluated at different times, with the model justifying the proposed approach for simulating geotechnical problems.

Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network

Abstract: Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.