7 citations · 18 across the 5 of their papers we have counts for
5 papers
Maximizing UAV Cellular Connectivity with Reinforcement Learning for BVLoS Path Planning
Mehran Behjati, Rosdiadee Nordin, Nor Fadzilah Abdullah
This paper presents a reinforcement learning (RL) based approach for path planning of cellular connected unmanned aerial vehicles (UAVs) operating beyond visual line of sight (BVLo…
Empirical 3D Channel Modeling for Cellular-Connected UAVs: A Triple-Layer Machine Learning Approach
Haider A. H. Alobaidy, Mehran Behjati, Rosdiadee Nordin +3
This work proposes an empirical air to ground (A2G) propagation model specifically designed for cellular connected unmanned aerial vehicles (UAVs). An in depth aerial drive test wa…
Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML
Kong Ka Hing, Mehran Behjati
Hornbills, an iconic species of Malaysia's biodiversity, face threats from habi-tat loss, poaching, and environmental changes, necessitating accurate and real-time population monit…
Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices
Huam Ming Ken, Mehran Behjati
The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system desig…
Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances
Sara Yarham, Mehran Behjati
Monitoring air quality and environmental conditions is crucial for public health and effective urban planning. Current environmental monitoring approaches often rely on centralized…