✅About Me
With a Ph.D. in AI Convergence and an M.S. in Electrical Communication Systems, I apply Machine Learning to IoT and complex signal/image analysis. My expertise includes: designing VLC/LoRa/UWOC modules, designing end-to-end ML pipelines for medical image processing; analyzing ultrasound signals with diverse ML models for classification and prediction; and performing geometric analysis and visualization. This complements my doctoral research in AI-assisted IoT/UIoT/UWSN/UWOC/VLC and foundational experience in RF/microwave component simulation, design, and fabrication.
- AI assisted IoT
- Applied AI for Biomedical Applications
- UWOC
- Passive RF Components
- UWSN
- Visible Light Communication
PhD Artificial Intelligence Convergence
Pukyong National University, Korea
M.S. Electrical and Electronic Engineering
Soonchunhyang University, Korea
B.S Telecommunication Engineering
University of Engineering and Technology, Pakistan
Welcome to my page. I have research expertise in the domains of Artificial Intelligence, Internet of Things, Underwater Wireless Sensor Network, Underwater Wireless Optical Communication, and RF Communication Component Design. I utilize various tools and simulation techniques to evaluate and develop communication modules (VLC and LoRA based), analyze their performance, and publish academic papers that undergo peer review process. I utilize a diverse range of sensor data (waveform/time-series/2D/3D images) to train machine learning models in order to predicts different patterens.
Reach out to collaborate.
- Non-invasive drug-release monitoring via AI-enhanced ultrasound in gelatin-magnesium gallate hydrogels. Y. S. Park, M. Salman, B. Sridharan, and H. G. Lim. Submitted, 2025.
- Development of ultrasound-based driver alcohol detection system for safety (UDADSS). J. H. Park, M. Salman, and H. G. Lim. Submitted, 2025.
1: DUI Detection via ML and Ultrasound
This study proposes a new, accurate, and low-cost method for detecting driver intoxication using ultrasonic sensors and machine learning, aiming to improve upon existing Driver Alcohol Detection System for Safety technologies. This research uses a simple, durable 40kHz ultrasonic transducer to detect varying concentrations of alcohol. The resulting signals are then analyzed using five different CNN based ML models.
2: ML aided Medical Image Analysis by leveraging Mask R-CNN/ SAM and Keypoint R-CNN
I design and implement an end-to-end analysis pipeline for red blood cell imaging using machine learning. I use CVAT to annotate images with bounding boxes, pixel-perfect segmentation masks, and keypoints for Keypoint R-CNN. I incorporate SwinIR to enhance image quality and fine-tune a Mask R-CNN (and Keypoint R-CNN when needed) on the custom dataset. I also leverage the SAM tool (Segment Anything Mask) for robust segmentation tasks. Additionally, I develop scripts to calculate geometric dimensions (area, bounding box, MinAreaRect, PCA axes) and to compute the percentage changes between “before” and “after” images, while generating visualizations that overlay these measurements and present bar charts of the changes.
