✅About Me
Maaz Salman has received his Ph.D in Artificial Intelligence Convergence from Pukyong National University, Korea. He has conferred M.S. in Electrical Communication System Engineering from Soonchunhyang University, Asan, South Korea. His research areas include Artificial Intelligence (Machine Learning) assisted IoT, UIoT, Underwater Wireless Sensor Network (UWSN), Underwater Optical Wireless Communication (UWOC), Visible Light Communication (VLC), design of passive RF components, and simulation of RF front end components at microwave and radio spectrum.
- AI assisted IoT
- 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, analyze their performance, and publish academic papers that undergo peer review process. I utilize a diverse range of sensor data acquired by undersea and atmospheric sensor nodes to train machine learning models in order to predicts different patterens or parameters.
Reach out to collaborate.
1: Tooth aging monitoring using ultrasound and CNN
This study proposes a new, non-invasive method for dental age assessment using ultrasound signals and machine learning. The research analyzes ultrasound signals from teeth, both as spectrogram images (analyzed by MobileNetV2) and time-series data (analyzed by a 1D CNN), to classify teeth based on pulp-to-dentin ratio, a key indicator of age.
2: 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.
3: ML augmented Analysis of Biomaterial Concentration in Bioprinting via Ultrasound Signal
This study introduces a novel, non-invasive method for real-time monitoring of alginate and gelatin concentrations during bioprinting. This research utilizes ultrasound signals, collected non-invasively with a single-element transducer, to capture concentration-related information without any preprocessing. These signals are then analyzed using CNN based models: Hybrid CNN-LSTM, TCN, vision based ResNET18, and DeepconvLSTM to accurately and reliably determine the concentrations of both gelatin and alginate.
