Best Professional Papers
Anomaly Detection In ATM-Grade Software Defined Networks (#146)
Philipp Lellek, Frequentis AG
MALE RPAS Integration into European Airspace: Part 1 (#112)
Emmanuel Sunil, Royal Netherlands Aerospace Centre (NLR)
Evaluation of the Four-Dimensional Trajectory Live Flight Demonstration (4DT LFD) Project (#119)
Diana Liang, FAA
Feasibility of the Frequency Planning for LDACS Air-to-Air Communications in the L-band (#110)
Miguel A. Bellido-Manganell, German Aerospace Center (DLR)
Geospatial Object Detection using Machine Learning-Aviation Case Study(#169)
Durga Prasad Dhulipudi, IIIT-H, Honeywell
Urban Air Mobility Demand Estimation For Airport Access: A Los Angeles International Airport Case Study (#125)
Mihir Rimjha, Virginia Tech
Best Student Papers
Using Flight Shifting to Mitigate Delay in Multiple Airport Regions (#170)
Ang Li and Mark Hansen, Civil and Environmental Engineering Department, University of California, Berkeley, Berkeley, CA
This study aims to improve operational performance of a multiple airport region (MAR) by analyzing interdependent capacity scenarios of that MAR airports and redistributing airport traffic to make more efficient use of the available capacity
A Security Model for Controller-Pilot Data Communication Link (#111)
Suleman Khan and Andrei Gurtov Department of Computer and Information Science (IDA), Linköping University, Sweden; An Braeken, Industrial Engineering Department (INDI), Vrije Universiteit Brussel (VUB), Belgium; Pardeep Kumar, Swansea University, UK
A cryptographic mechanism to provide secure mobility for CPDLC that can enable data encryption and authentication. The protocol is formally verified with the Proverif tool.
A Decision-Tree based Continuous Learning framework for Real-Time Prediction of Runway Capacities (#154)
Lam Jun Guang Andy, Sameer Alam, Rajesh Piplani, Nimrod Lilith and Imen Dhief Saab-NTU Joint Research Lab, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
A machine learning algorithm for capacity prediction that utilizes innovative feature engineering methods to approximate a set of variables that better explain the dynamics of a runway system and can learn incrementally.