Maaz Salman âś…
Maaz Salman

Researcher

AIoT Laboratory

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.

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Interests
  • AI assisted IoT
  • UWOC
  • Passive RF Components
  • UWSN
  • Visible Light Communication
Education

  • 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

đź“š My Research Interests

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.

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Featured Publications
Machine learning-Assisted Object Monitoring supported by UWOC for IoUT
Machine Learning IoT

Machine learning-Assisted Object Monitoring supported by UWOC for IoUT

The research introduces an advanced fish movement tracking system that addresses critical challenges in underwater monitoring. By developing a lightweight sensor node integrated with an Inertial Measurement Unit (IMU) and underwater optical wireless communication (UWOC) modem, the system enables real-time data transmission and movement analysis. The technological innovation centers on sophisticated relay performance enhancement through software-based combining techniques like Majority Logic Combining (MLC), Equal Gain Combining (EGC), and Selection Combining (SC). These methods are intelligently integrated into the microcontroller to optimize communication reliability and signal processing in challenging underwater environments. A key contribution is the adaptive communication algorithm (ACA), which strategically exploits combining techniques to improve underwater wireless optical communication performance. The system's prototypes underwent rigorous testing in a 4-meter water tank, validating its feasibility and practical applicability. The research further advances underwater monitoring by developing machine learning models—including LSTM, Spatial Attention, RNN, Transformer, and GRU—trained on comprehensive IMU sensor data. These models analyze complex fish movement parameters, predicting acceleration states with remarkable precision and offering unprecedented insights into aquatic behavior.

Image-based VLC Signal Demodulation Using Machine Learning
Machine Learning Based Demodulation

Image-based VLC Signal Demodulation Using Machine Learning

This letter presents an image-based demodulation technique for OOK-modulated VLC signals from air quality sensors. We optimized system performance by transforming received signals into images using segmentation algorithms, bicubic interpolation, and image thresholding, enhancing demodulation accuracy through data augmentation. Experimental results show that our ML-driven demodulator achieves 97.58% accuracy, an extended communication range of up to 10 m, and improved noise tolerance. These advancements indicate that our proposed system is more efficient than conventional demodulation schemes. In future studies, we aim to improve VLC range, data rate, and noise tolerance by replacing the photodiode with an image sensor for various modulation schemes. We will also explore the effects of varying light conditions and signal interference in real-world VLC environments.

A Resource Efficient Encoding Algorithm for Underwater Wireless Optical Communication Link
Aqua-Sense: Relay-Based Underwater Optical Wireless Communication for IoUT Monitoring
UWOC

Aqua-Sense: Relay-Based Underwater Optical Wireless Communication for IoUT Monitoring

Water turbulence, range, and misalignment reduce underwater wireless optical communication (UWOC) performance. These obstacles may hinder large-scale deployment. Compared to acoustic and RF communication, UWOC can connect IoUT devices. “Aqua-sense,” a relay-based UWOC system, was designed and tested to improve communication connection performance and optical receiver reception area. EGC, MLC, and SC are used in the optical relay to increase diversity gain. A channel-aware algorithm powers the optical relay, or “opto-relay,” to increase communication connection performance. At 0.2 Mbps and 7.5 meters, the aqua-sense system had a 68% packet success rate in fairly murky water at 25 NTU. The sensor node, “opto-sense,” transmitted 0.5 Mbps in clear water with 0.01 NTU turbidity. Moderate water waves and air bubbles with a displacement and airflow rate of 5 liters/min within a 2-meter communication link range did not affect our results.

Multiuser Data Transmission Aided by Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface in Underwater Wireless Optical Communications
Optical Beams

Multiuser Data Transmission Aided by Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface in Underwater Wireless Optical Communications

The reconfigurable intelligent surface (RIS) can improve energy and spectrum efficiency by directing radio waves. Recent wireless communications research has focused on it. We utilized the simultaneous transmit and reflect (STAR)-RIS to an underwater UWOC system in this work. Despite underwater turbulence, beam attenuation, blockage, and pointing mistakes, a STAR-RIS obtains a UWOC channel to allow multiple users to transfer data in all directions. Exponential and generalized Gamma distributions were used to calculate underwater turbulence-induced fading, and numerical analysis was used to evaluate the outage probability, bit error rate (BER), and channel capacity of direct, conventional, and STAR-RIS assisted UWOC channels. BER performance was also assessed based on reflective element count, modulation method, pointing errors, blockage levels, and transmit-reflection coefficient. Monte-Carlo simulations verified the analytical BER and outage probability conclusions for the average signal-to-noise ratio. Experiments showed the receiving luminous intensity performance compared to the incident beam luminous intensity of the proposed STAR-RIS aided UWOC channel.

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Recent Publications
Maaz Salman, Javad Bolboli, Kaleem Ullah, Wan-Young Chung (2024). Machine learning-Assisted Object Monitoring supported by UWOC for IoUT. IEEE IoT Journal.
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Kaleem Ullah, Maaz Salman, Javad Bolboli, Wan-Young Chung (2024). Image-based VLC Signal Demodulation Using Machine Learning. IEEE Comm Ltr.
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Maaz Salman, Wan-Young Chung (2024). A Resource Efficient Encoding Algorithm for Underwater Wireless Optical Communication Link. In IHCI 2023.
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Maaz Salman, Javad Bolboli, RP Naik, Wan-Young Chung (2024). Aqua-Sense: Relay-Based Underwater Optical Wireless Communication for IoUT Monitoring. IEEE OJCOMS.
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RP Naik, Maaz Salman, Javad Bolboli, Wan-Young Chung (2024). Multiuser Data Transmission Aided by Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface in Underwater Wireless Optical Communications. IEEE TVT.
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