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: Computer science & Renewable energy. 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.

DFT prediction of the stability and physical properties of M2GaB (M = Sc, V, Nb, Ta)

Abstract: This study explored the physical properties of MAX phase borides, M2GaB (M = Sc, V, Nb, Ta), aided by the density functional theory (DFT) for the first time. The optimized lattice constants were obtained by minimizing the total energy. We checked the thermodynamic stability by computing the formation energies. We calculated the electronic band structure, density of states (DOS), and charge density mapping to reveal the electronic ground state and bonding nature. The computed stiffness constants (Cij) confirmed the mechanical stability of the investigated compounds. Young's modulus, shear modulus, bulk modulus, and Poison's ratio have been computed using Cij. We calculated the Pugh's ratio, Poisson's ratio, and Cauchy pressure to judge the ductility/brittleness. The Vickers hardness values were calculated. The anisotropic properties of M2GaB compounds were studied by the elastic moduli's directional projections and the anisotropy indices' calculations. We have discussed the acoustic behavior of the M2GaB phases. We have also investigated the Grüneisen parameter, the Debye temperature ϴD, the thermal expansion coefficient, the specific heat, the Helmholtz free energy, the entropy, the internal energy, and the lattice thermal conductivity (Kph) of these borides. To determine the suitability as thermal barrier coating (TBC) materials or whether the compounds are appropriate for high-temperature applications, the minimum thermal conductivity (Kmin) and melting temperature (Tm) have also been calculated. The compounds' dynamical stability was examined using the phonon dispersion curves. The optical parameters have been studied, and it has been found that these compounds are compatible as coating materials to reduce solar heating.

An Opportunistic Two Transceiver Based MAC Protocol for Cognitive Radio Networks

Abstract: Enormous growth of wireless applications makes the spectrum scarcity problem worse day by day. Moreover, traditional fixed spectrum assignment policy causes serious spectrum underutilization problem. However, Cognitive Radio that enables opportunity based spectrum access for unlicensed users has recently been recognized as a heartening solution for spectrum scarcity and underutilization problem. Medium access control protocol is the key enabler or coordinator for this opportunity based channel access. However, recent studies show that in distributed cognitive radio networks MAC protocol suffers from severe performance degradation due to common control channel selection (CCC) problem, the multichannel hidden terminal problem (MHTP). In this paper, we propose a MAC protocol that eliminates CCC selection problem. Here, unlicensed users use dedicated band owned by them as a common control channel. Every unlicensed user equipped with two transceivers. One is control radio that helps SU’s to get updated control information and other is data radio. This clearly cuts down the number of collisions with other secondary and primary users in data transmission phase. To obtain the accurate sensing result, we propose cooperative sensing for idle channel detection. In this paper, we develop an analytical model to analyze important performance metrics. Simulation results show the effectiveness of our system.

Mechanical, optical and high-temperature magnetic properties of Sn-substituted Mg–Zn ferrites

Abstract: Sn-substituted Mg–Zn ferrites (0 ≤ x ≤ 0.10) have been synthesized using conventional ceramic technique and the mechanical, optical and high-temperature magnetic properties were studied. The formal...

PIR with P-Cache: A New Private Information Retrieval Protocol With Improved Performance

Abstract: The increasing model of e-commerce infrastructure opens the door for secure transaction of information over the Net, keeping some records private as users’ choice within a few seconds. A client, while frequently retrieving his records, seldom wishes to hide the identity of the records to the database server. Private Information Retrieval (PIR) protocols allow users to retrieve information from a database while keeping their query private. Existing protocols have pros and cons in terms of communication and computational complexity. In some PIRs the computational complexity is reduced to O(1), and optimal, but still holds the high communication complexity O(Ns) as there is a huge amount of communication needed to satisfy the query. In our research a new PIR, namely PIR with P-cache, is introduced, based on the concept of database caching technology. In an average case, whenever a client requests a query in the system, there is a high probability to satisfy it from the P-cache instead of accessing the main database server that contains a huge amount of records. Our protocol outperforms the existing protocols in terms of both communication and computational complexity for queries supporting from P-cache and hence an improved performance.

Emergency Vehicle Detection Using Deep Convolutional Neural Network

Abstract: In densely populated cities, emergency vehicles getting caught in traffic is a regular occurrence. As a result, emergency vehicles arrive late, resulting in asset and human life losses. It is critical to treat emergency vehicles differently to avoid losses. The purpose underlying this research is to preserve human lives and reduce losses. For this, an automated method for detecting emergency vehicles is implemented. Ambulance and fire trucks are considered an emergency, and other vehicles are considered non-emergency vehicles in the proposed method. Initially, it identifies several vehicles from an image. The YOLOv4 object detector accomplished this part of the method. The identified vehicles are the region of interest for the rest of the research. Finally, the method classifies the vehicles into emergencies or non-emergencies. This study contributes by developing a model based on rigorous testing and analysis and includes a viral algorithm in deep learning: convolutional neural network (CNN). Furthermore, the transfer learning technique with VGG16’s fine-tuned model is employed for emergency vehicle detection. On the Emergency Vehicle Identification v1 dataset, this model had an average accuracy of 82.03%.