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
Cognitive behavior-in-the-loop: Towards an attentive driving in intelligent transportation systems
(Institute of Electrical and Electronics Engineers Inc., 2024-01-01) Munir M.S.; Kim K.T.; Abedin S.F.; Alam, Md. Golam Rabiul; Saad W.; Hong C.S.
This article introduces a novel attentive driving framework in intelligent transportation systems (ITS) to investigate the influence of cognitive behavior on distracting driving activities that lead to inattention while driving. Therefore, this work proposes a holistic computational and communication framework that can monitor on-compartment real-time multimodal sensory observation such as physiological, camera, and environmental inputs while capable of distraction detection and emotion recognition for driver's mood stabilization. In particular, this work develops a capsule network for distraction detection, a 1-D convolutional neural network for emotion recognition, an a priori algorithm for sequential context fusion, and a Bayesian network for recommending auditory stimulus content for driver mood stabilization and audio-visual safety messages for road safety. Further, an asynchronous client control scheme has developed to overcome the challenges of multitime scale sensory observations and communicate among the multimodel sensory hubs. Finally, a prototype is developed and tested in a simulation environment. The quantitative analysis results show that the proposed framework can successfully detect around 89% and 87% of distractive activities and the affective state of a driver, respectively. Finally, based on experimental results, the proposed system demonstrates the capability to sustain a driver's attention for approximately 97% of the time, with a confidence level of 95%.
Energy-efficient synchronization in industrial internet of things: An intelligent neighbor-knowledge approach
(Institute of Electrical and Electronics Engineers Inc., 2024-06-01) Elsharief M.; Emran A.; Hassan H.; Sabuj, Saifur Rahman; Jo H.S.
In the Industrial Internet of Things (IIoT), energy efficiency is a paramount concern as it directly affects operational longevity. Traditional approaches, like flooding for time synchronization, often result in redundant message transmissions, thereby wasting energy. This article introduces an intelligent neighbor-knowledge synchronization (INKS) method to mitigate this problem. The INKS algorithm leverages each node's understanding of its neighboring nodes to optimize the synchronization process, thereby reducing the total number of synchronization messages and conserving energy. The INKS is implemented and evaluated using real wireless sensor networks with varying configurations. The experimental results demonstrate their superior performance to existing techniques, such as rapid flooding multiple one-way broadcast time synchronization (RMTS). Additionally, the performance of INKS is evaluated through simulations conducted on large-scale networks. For the network topology of the four-way grid, the findings reveal that INKS reduces the number of transmitted messages by approximately 72% compared to RMTS. Moreover, INKS matches the efficiency of scheduling-based low-energy synchronization for IIoT.
An industry-4.0-complaint sustainable bitcoin model through optimized transaction selection and sustainable block integration
(Institute of Electrical and Electronics Engineers Inc., 2022-12-01) Monem, Maruf; Alam, Md. Golam Rabiul; Abdullah-Al-Wadud M.; Huda S.; Hassan M.M.; Fortino G.
Cryptocurrencies are the new form of trade that has revolutionized how we look into our financial institutions. Bitcoin dominates the industry with the highest market share among the hundreds of other cryptocurrencies. However, high energy consumption leading to increasing carbon emission, prioritizing high-value transactions, and long waiting times are some of the flaws preventing it from reaching its full potential. Owing to the block rewards getting halved every four years, miners and researchers are fearful that this would be the breaking point of Bitcoin's success. This article proposes an Industry-4.0-compliant next-generation Bitcoin architecture by introducing a dynamic and sustainable block concept. Along with our modified knapsack algorithms, i.e., priority-based 0/1 knapsack and advanced-priority-based 0/1 knapsack, we can ensure a balanced transaction selection, quicker verification, higher transaction throughput, reduced carbon emission, and increased earnings for the miners. Moreover, with the addition of only one of our proposed sustainable blocks, we can cut down verification times by 50% and increase throughput by 39%. We can also reduce carbon emissions per transaction by 61.3%, which would help reduce Bitcoins' large carbon footprint, enabling us to approach greener digital transactions.
Multiple CUAV-enabled mMTC and URLLC services: Review of energy efficiency and latency performance
(Institute of Electrical and Electronics Engineers Inc., 2023-09-01) Sabuj, Saifur Rahman; Ahmed S.; Jo H.S.
Cognitive unmanned aerial vehicles (CUAVs) play a vital role in next-generation wireless networks as they assist in massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) services. This study focuses on multiple CUAV-enabled networks wherein CUAVs are paired with each other. We analyze the data rate, energy efficiency, and latency of such networks by applying the finite information block length theory, wherein mMTC and URLLC information use a non-orthogonal multiple access technique. Furthermore, we formulate an optimization problem to maximize the energy efficiency of paired CUAV devices by jointly optimizing the transmission power of the mMTC and URLLC information to satisfy the latency requirement. The numerical results indicate that our proposed multiple-CUAV-enabled scheme enhances the network performance of CUAV devices in terms of energy efficiency and latency better than the existing scheme.
Adaptive fuzzy attention inference to control a microgrid under extreme fault on grid bus
(Institute of Electrical and Electronics Engineers Inc., 2025-01-01) Mahim, Tanvir M.; Rahim, A.H.M.A.; Rahman, M. Mosaddequr
Power quality of a microgrid falls due to large penetration in renewable sources with rapid fluctuations. This article develops a double Q Learning (DQL) based adaptive fuzzy attention inference (FAI) to control a microgrid connected to the grid. Detailed dynamic modeling is presented with renewable sources such as photovoltaic (PV), wind, fuel, and microalternators with respective power electronics interfaces. The inference scheme controls the phase angle of the static compensator (STATCOM) coupled with capacitive energy storage device in the microgrid. STATCOM provides reactive power, while the capacitive storage corrects real power imbalances. Numerical results showed the developed control scheme restores stability rapidly in the event of severe symmetrical three-phase to ground fault on the grid bus. Rule base and corresponding membership function of the inference dynamically adapts based on the DQL mechanics. Prioritized experience and shallow neural net reward model are integrated where importance sampling of the temporal difference based rewards, contributes to overcome microgrid transients. The proposed control outperform conventional optimized proportional-integral-derivative (PID), model predictive, and sliding mode control schemes to restore normal operation of the converters across different levels of electromechanical transients.