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.