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ICNS > 2021 > Best Paper Awards for ICNS 2021

Best Paper Awards for ICNS 2021

Posted: May 24, 2021 | Last Modified: April 18, 2022

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.

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Posted Under: 2021

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