Mobility Compass

Discover mobility and transportation research. Find experts, partners, networks.

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The Mobility Compass is an open tool for improving networking and interdisciplinary exchange within mobility and transport research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

To Graph

8.284 Topics available

To Map

890 Locations available

479.575 PEOPLE
479.575 People People

479.575 People

Show results for 479.575 people that are selected by your search filters.

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PeopleLocationsStatistics
Ziakopoulos, ApostolosAthens
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  • 2023
Vigliani, AlessandroTurin
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  • 2023
Catani, JacopoRome
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Statheros, ThomasStevenage
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Utriainen, RoniTampere
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Guglieri, GiorgioTurin
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Martínez Sánchez, Joaquín
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Tobolar, Jakub
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Volodarets, M.
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Piwowar, Piotr
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Tennoy, AudOslo
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Matos, Ana Rita
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Cicevic, Svetlana
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Sommer, CarstenKassel
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Liu, Meiqi
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Pirdavani, AliHasselt
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Niklaß, Malte
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Lima, PedroBraga
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Turunen, Anu W.
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Antunes, Carlos Henggeler
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Krasnov, Oleg A.
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Lopes, Joao P.
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Turan, Osman
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Lučanin, VojkanBelgrade
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Tanaskovic, Jovan
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Kayacan, Erdal

  • Google
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Aarhus University

in Cooperation with on an Cooperation-Score of 37%

Topics

  • drone
  • data
  • infrastructure
  • video
  • neural network
  • surveillance
  • coding system
  • airport
  • architecture
  • data file
  • harbour
  • base line
  • reconstruction
  • layout
  • bird
  • warehouse
  • heuristic method
  • altitude
  • traffic monitoring
  • trajectory
  • learning
  • deep learning
  • assessment
  • modeling
  • vehicle component
  • helicopter
  • estimating
  • aerodynamic
  • engine
  • computer science
  • railway train
  • theory
  • control device
  • algorithm
  • simulation
  • design
  • experiment
  • law
  • motor
  • sliding mode control
  • motor vehicle
  • aeronautics
  • modernization
  • astronautics
  • gust
  • aircraft
  • fuzzy logic
  • trajectory control
  • fuzzy controller
  • time interval
  • feasibility analysis
  • additive
  • validation
  • test bed
  • yaw
  • rotor
  • show 26 more
  • show less

Publications

  • 2020UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance14citations
  • 2020AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance60citations
  • 2019Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge10citations
  • 2018Evaluation of the influence of build and print orientations of unmanned aerial vehicle parts fabricated using fused deposition modeling process26citations
  • 2018Receding horizon control of a 3 DOF helicopter using online estimation of aerodynamic parameters19citations
  • 2017Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles38citations
  • 2017Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks24citations
  • 2017Type-2 Fuzzy Logic Trajectory Tracking Control of Quadrotor VTOL Aircraft With Elliptic Membership Functions129citations
  • 2016Recurrent Interval Type-2 Fuzzy Control of 2-DOF Helicopter With Finite Time Training Algorithm10citations
  • 2016Piezoceramic materials for energy harvesting on 3D printed unmanned aerial vehicles: A feasibility study1citations
  • 2016Additive manufacturing of unmanned aerial vehicles: Current status, recent advances, and future perspectivescitations
  • 2016Type-2 Fuzzy Logic Trajectory Tracking Control of Quadrotor VTOL Aircraft With Elliptic Membership Functions129citations
  • 2016Design and experimental validation of single input type-2 fuzzy pid controllers as applied to 3 DOF helicopter testbed24citations
  • 2015Optimal sliding mode type-2 TSK fuzzy control of a 2-DOF helicopter11citations
  • 2015Controlling the pitch and yaw angles of a 2-DOF helicopter using interval type-2 fuzzy neural networks10citations
  • 2010Neuro-adaptive approach for controlling a quad-rotor helicopter using sliding mode learning algorithm3citations

Places of action

Chart of shared publication
Bozcan, Ilker
1 / 1 shared
Bozcan, I.
1 / 1 shared
Sarabakha, A.
1 / 1 shared
Govdeli, Y.
2 / 2 shared
Wong, Z. W.
2 / 2 shared
Ravindrababu, S.
1 / 1 shared
Mehndiratta, M.
2 / 4 shared
Hagras, Hani
1 / 3 shared
Khanesar, Mojtaba Ahmadieh
5 / 6 shared
Imanberdiyev, Nursultan
1 / 1 shared
Sarabakha, Andriy
1 / 1 shared
Ahmadieh Khanesar, Mojtaba
5 / 7 shared
Reyhanoglu, Mahmut
1 / 2 shared
Rubio-hervas, Jaime
1 / 1 shared
Maslim, R.
1 / 1 shared
Nicholas, N. J. H.
1 / 1 shared
Kocer, B. B.
1 / 1 shared
Yee, Y. W.
1 / 1 shared
Maslim, Reinaldo
1 / 1 shared
Kumbasar, T.
1 / 1 shared
Kaynak, O.
2 / 3 shared
Shakev, Nikola
1 / 4 shared
Topalov, Andon V.
1 / 4 shared
Chart of publication period
2020
2019
2018
2017
2016
2015
2010

Co-Authors (by relevance)

  • Bozcan, Ilker
  • Bozcan, I.
  • Sarabakha, A.
  • Govdeli, Y.
  • Wong, Z. W.
  • Ravindrababu, S.
  • Mehndiratta, M.
  • Hagras, Hani
  • Khanesar, Mojtaba Ahmadieh
  • Imanberdiyev, Nursultan
  • Sarabakha, Andriy
  • Ahmadieh Khanesar, Mojtaba
  • Reyhanoglu, Mahmut
  • Rubio-hervas, Jaime
  • Maslim, R.
  • Nicholas, N. J. H.
  • Kocer, B. B.
  • Yee, Y. W.
  • Maslim, Reinaldo
  • Kumbasar, T.
  • Kaynak, O.
  • Shakev, Nikola
  • Topalov, Andon V.