Parallel computing using GPU for efficient traffic simulation

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2013.Cataloged from PDF version of thesis report.Includes bibliographical references (page 24).Parallel Computing can be made possible using the multiple cores of the Graphics Processing Unit (GPU) thanks to the modern programmable GPU models. This allows the use of parallel computing techniques to improve upon the computation time of large scale traffic simulations. This paper proposes the use of a multi-processor algorithm for creating efficient traffic simulation software. The method in consideration achieves this by separating the road network into regions which are individually computed as a threaded block inside the GPU and merged together using the Central Processing Unit to provide the final data of the simulation. A significant improvement in the computation time is observed when the proposed parallelization techniques are applied to the simulator.Sadat Sakif AhmedB. Computer Science and Engineerin

Quick Handover in 5G for High Speed Railways and Highways Using Forward Handover and PN Sequence Detection

The cellular users, on high speed railways andhighways, travel at a very high speed and follow a nearly straightpath, in general. Thus, they typically undergo a maximumfrequency of handovers in the cellular environment. This requiresa very fast triggering of the handover. In the existing method ofhandover in 5G cellular communication, for high speed users,neither the decision-making of handover nor the triggering ofhandover is sufficiently fast. This can lead to poor signal qualityand packet losses and in the worst case, radio link failure (RLF)during a handover. This paper proposes a forward handover basedmethod, combined with PN sequence detections, to facilitate aquicker handover for high speed users on railways and highways.The proposed method adds some complexity but can offer asignificant improvement in the overall handover delay. A simplisticsimulation is used to demonstrate the improvement of the proposedmethod

Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models

With the rise of social media and online news sources, fake news has become a significant issue globally. However, the detection of fake news in low resource languages like Bengali has received limited attention in research. In this paper, we propose a methodology consisting of four distinct approaches to classify fake news articles in Bengali using summarization and augmentation techniques with five pre-trained language models. Our approach includes translating English news articles and using augmentation techniques to curb the deficit of fake news articles. Our research also focused on summarizing the news to tackle the token length limitation of BERT based models. Through extensive experimentation and rigorous evaluation, we show the effectiveness of summarization and augmentation in the case of Bengali fake news detection. We evaluated our models using three separate test datasets. The BanglaBERT Base model, when combined with augmentation techniques, achieved an impressive accuracy of 96% on the first test dataset. On the second test dataset, the BanglaBERT model, trained with summarized augmented news articles achieved 97% accuracy. Lastly, the mBERT Base model achieved an accuracy of 86% on the third test dataset which was reserved for generalization performance evaluation. The datasets and implementations are available at https://github.com/arman-sakif/Bengali-Fake-News-DetectionComment: Under Revie