Atique B. Zakir’s work focuses on the intersection of the Internet of Things (IoT), artificial intelligence, and applied deep learning. He is the first author of a study on an IoT-enabled acoustic trap that leverages real-time AI for the surveillance of Aedes mosquitoes.
Additionally, his research contributions include utilizing deep learning for the smart monitoring of seed germination and growth in sustainable agriculture. He has also contributed to developing deep-learning enabled methods for the rapid and low-cost detection of microplastics in consumer products.
Undergraduate Research Assistant – Microsystems & Nanoengineering Lab, University of Dhaka
Mar 2023 – Aug 2025
Conducted primary research on IoT-enabled AI surveillance and automated agricultural monitoring, resulting in a first-author manuscript submission and a peer-reviewed publication.
Developed and deployed deep learning models (YOLOv5/v11) on Raspberry Pi and cloud servers for real-time object detection, achieving up to 98% accuracy and 0.92 mAP.
Contributed to multiple successful research projects, leading to one peer-reviewed publication, one first-author manuscript submission, and acknowledgements in another published paper.
Manuscripts in Review
Zakir, A. B., Dipto, A. R., Hawladar, N., et al. (2025). MAST-CloudNet: IoT-Enabled Acoustic Trap with Real-Time AI Surveillance of Aedes. IEEE Internet of Things Journal
Peer-Reviewed Publications
Yeasmin, S., Dipto, A. R., Zakir, A. B., et al. (2024). Nanopriming and AI for Sustainable Agriculture: Boosting Seed Germination and Seedling Growth with Engineered Nanomaterials, and Smart Monitoring through Deep Learning. ACS Applied Nano Materials. DOI: 10.1021/acsanm.4c00109
Research Acknowledgements
Arju, M. Z. B. Z., Hridi, N. A., Dewan, L., et al. (2025). Deep-learning enabled rapid and low-cost detection of microplastics in consumer products following on-site extraction and image processing. RSC Advances. DOI: 10.1039/d4ra07991d
MAST-CloudNet: IoT-Enabled Acoustic Trap with Real-Time AI Surveillance of Aedes (4th Year Undergraduate Project) | Sep 2024 – May 2025
Developed edge-to-cloud pipeline for live Aedes surveillance using Raspberry Pi and cloud servers for YOLOv11 inference.
Implemented YOLOv11 model, achieving 0.92 mAP on local mosquito dataset.
Seedsight: Automated Seed Monitoring System | Mar 2023 – Oct 2023
Prepared dataset of 1000 germinated/non-germinated seed images.
Trained YOLOv5 model to achieve 98% accuracy in identifying germination percentages.
Built picamera-mounted two-axis CNC machine for high-quality image acquisition using Raspberry Pi, Arduino, and GRBL.
Project EUCLID: Antenna Radiation Pattern Measurement Tool | Jan 2024 – Mar 2024
Designed automated antenna rotation device using ESP32 for DU EEE Microwave Lab.
Enabled automated measurement of antenna radiation patterns, eliminating manual intervention.
Project ARCHER: Linux-Based Cost-Effective Computer System | Aug 2023 – Feb 2024
Secured $2500 funding for IEEE SIGHT DU.
Led software development, tested Linux distributions on low-cost boards (Orange Pi, Banana Pi) achieving Raspberry Pi-like performance.
Project BeSuddho: Water Purification System | Feb 2021 – Mar 2023
Developed C firmware for AVR (Atmega328p) for sensor data acquisition and actuator control.
Contributed to hardware implementation and debugging of circuits.
Programming Languages: Python, C, Assembly (AVR, x86)
Microcontrollers: STM32, ESP32, AVR
Tools & Platforms: Ultralytics (YOLO), PyTorch, MATLAB, Linux, Git, Shell Scripting
Hardware & CAD: IoT, Sensor Integration, PCB Design (Eagle), Cadence (VLSI)
IEEE SIGHT Student Branch Chapter DU – Student Activity Secretary, Executive Committee | Jan 2024 – Feb 2024
Playing guitar
Reading classic books
Shell scripting
Building fun electronics