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.

Turbo like multi-stage threshold decoding for self-orthogonal convolutional codes

Abstract: This paper presents a new dimension of soft decoding turbo-like multi-stage threshold decoding (TLMTD) for self-orthogonal convolutional codes (SOCCs) The TLMTD uses comparatively shorter constraint length conventional code of multi-stage threshold decoding (MTD) system The bit error performance is considered for several types of soft decoding algorithms on the additive white Gaussian noise (AWGN) channel When threshold value used as a priori threshold value for other decoding stage, TLMTD realizes better performance in waterfall and error floor regions Moreover, the TLMTD gives 020 dB more coding gain compared to MTD for equivalent SOCCs at the bit error rate less than 104

Automatic Bengali Document Categorization Based on Deep Convolution Nets

Abstract: Automatic document categorization has gained much attention by natural language processing (NLP) researches due to the enormous availability of text resources in digital form in recent years. It is the process of assigning a document into one or more categories that help the document manipulate and sort quickly. An efficient information processing system is required due to the rapid growth of Bengali text contents in digital form for searching, organizing, and retrieving tasks. In this paper, we proposed a framework for classifying Bengali text documents using deep convolution nets. The proposed framework consists of word embedding and document classifier models. Experiments with more than 1 million Bengali text documents reveals that the proposed system worthy of classifying documents with 94.96% accuracy.

Nonlinear propagation of ion-acoustic waves through the Burgers equation in weakly relativistic plasmas

Abstract: The Burgers equation is obtained to study the characteristics of nonlinear propagation of ionacoustic shock, singular kink, and periodic waves in weakly relativistic plasmas containing relativistic thermal ions, nonextensive distributed electrons, Boltzmann distributed positrons, and kinematic viscosity of ions using the well-known reductive perturbation technique. This equation is solved by employing the (G'/G)-expansion method taking unperturbed positron-to-electron concentration ratio, electron-to-positron temperature ratio, strength of electrons nonextensivity, ion kinematic viscosity, and weakly relativistic streaming factor. The influences of plasma parameters on nonlinear propagation of ion-acoustic shock, periodic, and singular kink waves are displayed graphically and the relevant physical explanations are described. It is found that these parameters extensively modify the shock structures excitation. The obtained results may be useful in understanding the features of small but finite amplitude localized relativistic ion-acoustic shock waves in an unmagnetized plasma system for some astrophysical compact objects and space plasmas.

Automatic Bengali Document Categorization Based on Word Embedding and Statistical Learning Approaches

Abstract: The automated categorization of text documents into predetermined categories has witnessed a growing in the last few years, due to the huge availability of documents in digital form and the ensuing need to organize them. Automatic document categorization is the process of assigning one or more categories or classes to a document, making it easier to manipulate and sort. This paper proposes a Bengali document categorization technique based on word2vec word embedding model and stochastic gradient descent (SGD) statistical learning algorithm with multi-class svm. The semantic features of a document are extracting by Word2Vec and SGD improve the classification complexity with multi-class SVM that classify the unlabeled data. The experimental result with 10000 training and 4651 testing documents shows the 93.33% accuracy.