BRAC University Institutional Repository
Preserving Knowledge, Advancing Research, Sharing Scholarship
A digital platform for collecting, preserving, and sharing BRAC University’s scholarly, academic, and institutional outputs.
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Faculty members and students are invited to submit their research publications, theses, dissertations, and scholarly works to increase visibility, access, and long-term preservation.
Recent Submissions
RouteFinder: Real-time optimum vehicle routing using mobile phone network
(Institute of Electrical and Electronics Engineers Inc., 2016-01-05) Ahmadullah, Najiba; Islam, Shahpar; Ahmed, Tarem; Department of Electrical and Electronic Engineering
Road traffic congestion is a major issue in most mega cities. The traffic jams are often exacerbated by drivers habitually following the same routes. A real-time, optimum vehicle routing system that takes traffic density into account can be a possible solution to this problem. This paper presents RouteFinder, a system of providing real-time traffic density mapping on the driver's smartphone using an in-vehicle module built using inexpensive components that communicates with the existing mobile telephone network, to enable the driver to choose the least congested route to a desired destination. The hardware module contains the essential elements of a cellular handset such as a SIM card. The location of the vehicle is determined through a standard triangulation algorithm performed using signals from the three nearest cellular base stations, and the location information is constantly updated at relevant Home Location Registers (HLRs) / Visitor Location Register (VLRs) at the telecom service provider using an intermediate MySQL database. An Android application developed for the driver's smartphone shows the present locations of all vehicles in all routes from the origin to the selected destination, with colour codes distinguishing between moving and stationary vehicles. We have implemented our device in 10 vehicles in Dhaka city. Our sample calculations have shown significant savings not only in terms of time, but also in fuel consumption.
A technique of collecting online data for forecasting the output capacity of a remotely located WTG
(Institute of Electrical and Electronics Engineers Inc., 2010-01-01) Rocky T.H.; Ami S.M.; Ahsan, Q.
Unlike thermal generators, the forecasting of power output of a wind turbine generator (WTG), connected to the grid, is essentially required for the optimal operation of the power system as the output of a WTG randomly varies with the wind velocity and the density of air. The forecasting of the capacity output of a WTG requires the forecasted values of wind velocity and density of air. This paper develops a technique of collecting online data from the remote location of WTGs and presents an appropriate hardware model. The paper also presents test results of a prototype model.
Stepper motor performance under real-time multitasking environment
(Institute of Electrical and Electronics Engineers Inc., 2009-12-01) Azad, A.M.; Amin, A.A.; Faruk, A.A.; Alam, M.A.; Department of Electrical and Electronic Engineering
To get the maximum performance of stepper motor, different types of improvement and work has been done [1,6,2,5,8,9,10]. In this work, open loop control of stepper motor is considered under general purpose (Windows), Soft (Linux) and hard (RT-Linux) real-time operating systems. We have used the same controller program to operate the stepper motor in different operating system under multitasking environment. The Data AcQuisition (DAQ, PCL-812PG) card is used as a hardware interface and a GUI interface controller for IPC (Inter Process Communication) is also developed using RT Linux gcc compiler to make it user friendly. The main objective of this work is to show that the real-time error (jitter) is minimum incase of RT-Linux (Hard RTOS) over other soft real-time operating systems and thus observe the stability of the stepper motor.
Detection and analysis of fake news users' communities in social media
(Institute of Electrical and Electronics Engineers Inc., 2024-01-01) Amira A.; Derhab A.; Hadjar S.; Merazka M.; Alam, Md. Golam Rabiul; Hassan M.M.; Department of Computer Science and Engineering
The widespread use of social media platforms has led to an increase in the dissemination of fake news with the intention of manipulating public opinion and causing chaos and panic among the population. To address this issue, we focus on detecting the organized groups that participate together in fake news campaigns without prior knowledge of the news content or the profiles of social accounts. To this end, we propose a spatial-temporal similarity graph, a novel graph structure that connects social accounts that participate in the early stage of similar fake news campaigns. A community detection algorithm is applied on the similarity graph to cluster the users into communities. We propose a community labeling algorithm to label the communities as benign or malicious based on the output of a fake news classifier. Evaluation results show that the community labeling algorithm can correctly label the communities with an accuracy of 99.61%. In addition, we perform a statistical comparison analysis to identify the structural community features that are statistically significant between benign and malicious communities.
Human-behavior-based personalized meal recommendation and menu planning social system
(Institute of Electrical and Electronics Engineers Inc., 2023-08-01) Islam, Tanvir; Joyita, Anika Rahman; Alam, Md. Golam Rabiul; Mehedi Hassan M.; Hassan M.R.; Gravina R.; Department of Computer Science and Engineering
The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. It takes a lot of effort to learn people's food preferences and make recommendations based on their affects and nutrition. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social affect has some significant benefits over the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person's affect is detected using electroencephalography (EEG) signals. EEG allows to capture the brain signals and analyze them to anticipate affective state toward a food. In this study, we have used a 14-channel wireless Emotiv Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person's energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters.