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Found 12 papers, 0 papers with code
Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections
Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem.
Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks
A multi-period planning framework is proposed that exploits multi-step ahead traffic predictions to address service overprovisioning and improve adaptability to traffic changes, while ensuring the necessary quality-of-service (QoS) levels.
A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction
The rise of unmanned aerial vehicle (UAV) operations, as well as the vulnerability of the UAVs' sensors, has led to the need for proper monitoring systems for detecting any abnormal behavior of the UAV.
Machine Learning for Real-Time Anomaly Detection in Optical Networks
Specifically, for anomaly detection, a statistical hypothesis testing scheme is used, alleviating the limitations of supervised (SL) and unsupervised learning (UL) schemes, usually applied for this purpose.
Downing a Rogue Drone with a Team of Aerial Radio Signal Jammers
This work proposes a novel distributed control framework in which a team of pursuer agents equipped with a radio jamming device cooperate in order to track and radio-jam a rogue target in 3D space, with the ultimate purpose of disrupting its communication and navigation circuitry.
Modeling Soft-Failure Evolution for Triggering Timely Repair with Low QoT Margins
In this work, the capabilities of an encoder-decoder learning framework are leveraged to predict soft-failure evolution over a long future horizon.
An Autonomous Drone System with Jamming and Relative Positioning Capabilities
As the number of unauthorized operations of Unmanned Aerial Vehicles (UAVs) is rising, the implementation of a versatile counter-drone system is becoming a necessity.
Adaptive Frequency Band Selection for Accurate and Fast Positioning utilizing SOPs
Signals of opportunity (SOPs) are a promising technique that can be used for relative positioning in areas where global navigation satellite system (GNSS) information is unreliable or unavailable.
Scheduling Aerial Vehicles in an Urban Air Mobility Scheme
Highly populated cities face several challenges, one of them being the intense traffic congestion.
Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions
This is an extension of the well-known multiple traveling salesman problem.
Relative Positioning of Autonomous Systems using Signals of Opportunity
For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities.
Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical Networks
We examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements.
