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.

Dissipation of waste using dynamic perception and alarming system: A smart city application

Abstract: The heart of a city depends on its purification of air, cleanliness of the roads and highways and overall it's surrounding environment. But if the condition disrupted, then the people live in the city have to pay for this. Different kinds of diseases spread out in an epidemic form and it is becoming tougher to lead a healthy life. However, people can ignore this condition by raising their hand to build up a healthy city. So we constructed a system for integrating the citizen and authority in a common platform and work in together to make the Dhaka city healthier. The system is an android based application where the user himself can contribute to clean his city, notify volunteer to come forward or can inform city corporation. The amenities of this application are - it ameliorates the user to detect nearby dustbins location with path, helps to see available volunteer on the map, notify them using google push service notification and assists them to submit a report to authorities if a problem arise.

First Principles Study of SnO Under High Pressure

Abstract: This article reports the study of SnO by using the first-principles pseudopotential plane-wave method within the generalized gradient approximation (GGA). We have calculated the structural, elastic, electronic and optical of SnO under high pressure. The elastic properties such as the elastic constants Cij bulk modulus, shear modulus, Young modulus, anisotropic factor, Pugh ratio, Poisson ratio are calculated and analyzed. Mechanical stability of SnO at all pressure are confirmed by using Born stability criteria in terms of elastic constants and are associated with ductile behaviour based on G/B ratios. It is also found that SnO exhibits very high anisotropy. The energy band structure and density of states are also calculated and analyzed. The results show the semiconducting and metallic properties at 0 (zero) and high pressure, respectively. Furthermore, the optical properties such as dielectric function, refractive index, photoconductivity, absorption coefficients, loss function and reflectivity are also calculated. All the results are compared with those of the SnO where available but most of the results at high pressure are not compared due to unavailability of the results.

Highly efficient ultra thin Cu(In, Ga)Se 2 solar cell with Tin Selenide BSF

Abstract: Cu(In, Ga)Se 2 (CIGS) is a chalcopyrite based semiconductor used as a very promising material for high performance thin film solar cell applications. In this research work, numerical analysis of ultra thin CIGS solar cell towards the possibility of ultra thin layer of CIGS absorber has been performed by using wxAMPS simulator to examine the performance of the proposed cell. The main focus of this work is to explore the performance of ultra thin CIGS solar cell. A conversion efficiency of 19.48% (FF=0.77, Voc=0.86V and Jsc=29.45mA/cm2) has been found for 700 nm CIGS absorber layer without any BSF. It has been found that increased back surface recombination with ultra thin absorber layer deteriorates the cell efficiency. This problem has been solved by inserting a thin layer of Tin Selenide (SnSe) as Back Surface Field (BSF). A better conversion efficiency of 24% (FF=0.80, V oc =0.89V and J sc =33.47mA/cm2) has been achieved for the proposed cell with 700 nm CIGS absorber and 100 nm SnSe layer as BSF. Higher thermal stability has been achieved for the proposed cell with SnSe BSF than without BSF.

A blind audio watermarking method based on lifting wavelet transform and QR decomposition

Abstract: This paper presents a blind audio watermarking method based on lifting wavelet transform (LWT) and QR decomposition (QRD) for audio copyright protection. In our proposed method, initially the original audio is segmented into non-overlapping frames. Watermark information is embedded into the largest element of the upper triangular matrix obtained from the low frequency LWT coefficients of each frame. A blind watermark detection technique is introduced to identify the embedded watermark under various attacks. Simulation results indicate that the proposed watermarking method is highly robust against different attacks. In addition, it has high data payload and provides good imperceptible watermarked sounds. Moreover, it shows superior performance than the state-of-theart watermarking methods reported recently.

Induction motor inter-turn fault detection using heuristic noninvasive approach by artificial neural network with Levenberg Marquardt algorithm

Abstract: Induction motors are used extensively for various industrial applications. These motors have to endure and cope with a wide variety of environments and conditions, so with time gradually developing (incipient) faults build up. If the fault is not trace out in embryonic state, it leads degradation which eventually causes potential failure of the motors and huge economic loss in industry. Around two fifth incipient faults happen due to stator faults caused by mainly failure of inter-turn insulation. So this paper proposed an innovative idea to detect induction motor stator’s inter-turn short circuit fault using noninvasive heuristic approach by Artificial Neural Network (ANN). In this fault detection research, a 0.5 hp, single phase 50Hz induction motor at no load condition is used as an experimental prototype. Generalized Feed forward neural network is used as NN with Levenberg Marquardt gradient descend algorithm for training. Three input parameters (motor current, supply voltage and rotor speed) develop the feature space of NN to detect whether the motor is faulty or not by analyzing performance function. By the experiment, the proposed system is found more efficient, reliable, economical and smart than the existing system.