Laakom, F., Raitoharju, J., Passalis, N.
, Iosifidis, A. & Gabbouj, M. (2022).
Graph Embedding with Data Uncertainty.
IEEE Access,
10, 24232-24239.
https://doi.org/10.1109/ACCESS.2022.3155233
Gautam, C., Tiwari, A., Mishra, P. K., Suresh, S.
, Iosifidis, A. & Tanveer, M. (2021).
Graph-Embedded Multi-Layer Kernel Ridge Regression for One-Class Classification.
Cognitive Computation,
13(2), 552-569.
https://doi.org/10.1007/s12559-020-09804-7
Shreya, S., Jenkins, A.
, Rezaeiyan, Y., Li, R., Bohnert, T., Benetti, L., Ferreira, R.
, Moradi, F. & Farkhani, H. (2023).
Granular vortex spin-torque nano oscillator for reservoir computing.
Scientific Reports,
13(1), Article 16722.
https://doi.org/10.1038/s41598-023-43923-z
Pham, X. H., Bozcan, I., Sarabakha, A., Haddadin, S.
& Kayacan, E. (2021).
GateNet: An Efficient Deep Neural Network Architecture for Gate Perception Using Fish-Eye Camera in Autonomous Drone Racing. 4176-4183. Paper presented at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Prag, Czech Republic.
https://sarabakha.info/files/papers/conference/IROS_2021.pdf
Larsen, P. G., Fitzgerald, J.
, Gomes, C., Woodcock, J., Basagiannis, S., Ulisse, A.
, Esterle, L., Lucani Rötter, D. E., Hansen, S. T. & Oakes, B. J. (2024).
Future Directions and Challenges. In J. Fitzgerald, C. Gomes & P. G. Larsen (Eds.),
The Engineering of Digital Twins (pp. 363-386). Springer.
https://doi.org/10.1007/978-3-031-66719-0_15
Saleem, B., Badar, R., Manzoor, A., Judge, M. A.
, Boudjadar, J. & Islam, S. U. (2022).
Fully Adaptive Recurrent Neuro-Fuzzy Control for Power System Stability Enhancement in Multi Machine System.
IEEE Access,
10, 36464-36476.
https://doi.org/10.1109/ACCESS.2022.3164455
Grombacher, D., Griffiths, M. P., Liu, L., Vang, M. & Larsen, J. J. (2022).
Frequency Shifting Steady-State Surface NMR Signals to Avoid Problematic Narrowband-Noise Sources.
Geophysical Research Letters,
49(7), Article e2021GL097402.
https://doi.org/10.1029/2021GL097402
Shreya, S., Jenkins, A., Bohnert, T., Ferreira, R.
, Moradi, F. & Farkhani, H. (2023).
Frequency Sensing and Detection using Granular Vortex MTJ Nano Oscillator. Poster session presented at IEEE INTERMAG Conference, Japan.
https://doi.org/10.1109/INTERMAGShortPapers58606.2023.10228828
Thomsen, S. T., Brusatori, M. F., Arent, N. H., Kumar, R. R. & Volet, N. (2023).
Frequency noise measurements using coherent self-heterodyne detection.
Optics Letters,
48(24), 6372-6375.
https://doi.org/10.1364/OL.505960
Griffiths, M. P., Grombacher, D., Liu, L., Vang, M. Ø. & Larsen, J. J. (2022).
Forward Modeling Steady-State Free Precession in Surface NMR.
IEEE Geoscience and Remote Sensing Letters,
60, Article 4513510.
https://doi.org/10.1109/TGRS.2022.3221624
Baxter, J., van Acker, B.
, Kristensen, M., Wright, T., Cavalcanti, A.
& Gomes, C. (2025).
Formal Architectural Patterns for Adaptive Robotic Software. In A. Boronat & G. Fraser (Eds.),
Fundamental Approaches to Software Engineering. FASE 2025 (pp. 145-165). Springer.
https://doi.org/10.1007/978-3-031-90900-9_8
Passalis, N., Kanniainen, J., Gabbouj, M.
, Iosifidis, A. & Tefas, A. (2021).
Forecasting Financial Time Series Using Robust Deep Adaptive Input Normalization.
Journal of Signal Processing Systems,
93(10), 1235-1251.
https://doi.org/10.1007/s11265-020-01624-0
Mohseni, S.-R., Jahanshahi Zeitouni, M., Parvaresh, A., Abrazeh, S.
, Gheisarnejad Chirani, M. & Khooban, M. H. (2023).
FMI real-time co-simulation-based machine deep learning control of HVAC systems in smart buildings: Digital-twins technology.
Transactions of the Institute of Measurement and Control,
45(4), 661-673.
https://doi.org/10.1177/01423312221119635
Sahana, G., Gebreyesus, G., Cheruiyot Bett , R., Kinyua, J., Roos, N., MBI Tanga, C., Mwikirize, C., Akol, R., Khamis, F. M.
, Karstoft, H., Bjerge, K., Nkirote Kunyanga , C.
, Hansen, L. S., Nielsen, H. M., Lund, M. S., Geoffrey, S., Walusimbi, S. & Nakimbugwe, D. (2024).
FLYgene: Advancing Sustainable Breeding Programs and Genomic Tools for Black Soldier Fly (Hermetia illucens) in Kenya and Uganda. In
Book of Abstracts of the 75th Annual Meeting of the European Federation of Animal Science (pp. 633-633)
https://docs.eaap.org/boa/2024_Florence_EAAP_Book_Abstracts.pdf
Domini, D., Aguzzi, G.
, Esterle, L. & Viroli, M. (2024).
Field-Based Coordination for Federated Learning. In I. Castellani & F. Tiezzi (Eds.),
Coordination Models and Languages - 26th IFIP WG 6.1 International Conference, COORDINATION 2024, Held as Part of the 19th International Federated Conference on Distributed Computing Techniques, DisCoTec 2024, Proceedings (pp. 56-74). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-031-62697-5_4
Ding, Y., Zhang, S., Fan, B., Sun, W., Liao, Y.
& Zhou, P. Y. (2024).
FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated Recommendations. In
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 690-700). Association for Computing Machinery, Inc..
https://doi.org/10.1145/3640457.3688112
Larsen, P. G., Fitzgerald, J.
, Woodcock, J., Gamble, C., Payne, R. & Pierce, K. (2018).
Features of Integrated Model-Based Co-modelling and Co-simulation Technology. In A. Cerone & M. Roveri (Eds.),
Software Engineering and Formal Methods - SEFM 2017 Collocated Workshops: DataMod, FAACS, MSE, CoSim-CPS, and FOCLASA, Revised Selected Papers (pp. 377-390). Springer.
https://doi.org/10.1007/978-3-319-74781-1_26
Frasheri, M., Thule, C., Macedo, H. D., Lausdahl, K., Larsen, P. G. & Esterle, L. (2021).
Fault Injecting Co-simulations for Safety. 6-13. Paper presented at 5th International Conference on System Reliability and Safety, Palermo, Italy.
https://doi.org/10.1109/ICSRS53853.2021.9660728