Research

Description:
To address the frequent undervoltage issues caused by weak infrastructure in the Northern Grid of Bangladesh, this study proposes a data-driven framework that integrates deep learning with hardware solutions. By simulating the grid in DIgSILENT PowerFactory using real-world data, I employed a Long Short-Term Memory (LSTM) network to forecast bus voltages and rank them by stability. This predictive analysis identified the most vulnerable points in the system, leading to the strategic placement of Static VAR Compensators (SVCs) at the Rangpur, Panchagarh, and Joypurhat 132 kV buses. The results, validated against multiple datasets, demonstrate that this intelligent placement effectively eliminates undervoltage problems, providing a scalable model for grid reinforcement as demand continues to grow.  

First Author
Submitted in: 2026 5th International Conference on Power Systems and Electrical Technology (Currently Under Review)

Supervisor: Dr. Md. Ziaur Rahman Khan, Professor, Dept. of EEE, BUET

Written during Postgraduate period

Description:
This study presents the design and implementation of the BUET Low-Cost Accelerometer (BLCA), a compact earthquake monitoring module developed specifically for dense seismic networks in developing countries. Utilizing the high-performance ADXL355 MEMS sensor, the device employs a computationally efficient Peak Ground Acceleration (PGA) thresholding algorithm that enables rapid, on-site earthquake detection and improves warning lead times. Shake table experiments validated the module's accuracy, showing a 95% to 99% correlation between input and measured responses, while simulations of the Chi-Chi earthquake demonstrated a potential early warning lead time of approximately 7.4 seconds. Additionally, the system's low power consumption allows it to operate autonomously for several hours during power outages, providing a robust and affordable solution for continuous seismic monitoring and alert transmission.

Co-author
Submitted in: IEEE Transactions on Instrumentation and Measurement (Currently Under Review)

Supervisor: Dr. Zunaid Baten, Professor, Dept. of EEE, BUET and Dr. Tahmeed Malik Al-Hussaini, Dept. of CE, BUET

Funded by: RISE, BUET

Collaboration of Dept. of Civil Engineering and Dept. of EEE of BUET

Description:
This project introduces a deep learning framework designed for month-ahead electricity price forecasting, a critical but often overlooked area in deregulated power systems like ISO New England (ISO-NE). By combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, the proposed model effectively captures complex trends, residuals, and seasonal patterns in historical market data to predict the final locational marginal price (LMP). The simulation results indicate high accuracy, with forecasted prices closely matching real-world datasets, offering market participants a reliable tool for generation scheduling, bidding strategies, and risk management.

Supervised by: Dr. Sheikh Anowarul Fattah, Professor, Dept. of EEE, BUET

Part of Machine Learning and Pattern Recognition Course of M.Sc. 

Description:
This project details the development of an autonomous firefighting and rescue robot designed to serve as a first line of defense in high-rise buildings and industrial settings in Bangladesh. Utilizing a combination of IR and MQ2 gas sensors, the robot detects fire and smoke, moves toward the source, and mitigates the hazard using an onboard water tank and pump. A distinctive feature of this prototype is its Bangla voice recognition capability, which allows it to identify a victim's call for help and send a notification with location details to emergency services via a SIM 900a GSM module. Built with an Arduino UNO and powered by rechargeable lithium-ion batteries—designed for solar charging to align with Sustainable Development Goals (SDG)—the project offers a scalable, budget-friendly solution for improving fire response times and victim rescue efforts 

Supervised by: Dr. Celia Shehnaz, Professor, Dept. of EEE, BUET

Based on Control Systems Laboratory Project in Undergraduate 

Description:
This project details the development of a Bangla voice-based attendance system designed to replace manual, proxy-prone methods at BUET with a seamless biometric solution. Utilizing MATLAB, the system employs a subspace K-Nearest Neighbors (KNN) classifier and Mahalanobis distance to identify students based on unique vocal characteristics extracted via Mel Frequency Cepstral Coefficients (MFCC) and pitch. The researchers compiled a robust database of 4,740 voice samples from 237 individuals, implementing a 5th-order low-pass filter to mitigate classroom noise. Experimental results demonstrated high effectiveness, achieving a final individual detection accuracy of 95.62% for IDs and 95.26% for names, significantly outperforming previous models. Future implementation plans involve a central database and a dedicated mobile application, with potential upgrades to deep learning frameworks for enhanced precision. 

Supervised by: Dr. Celia Shehnaz, Professor, Dept. of EEE, BUET

Based on Digital Signal Processing Laboratory Project in Undergraduate