Li, R., Shreya, S., Ricci, S., Bridarolli, D., Ielmini, D.
, Farkhani, H. & Moradi, F. (2023).
Thermal-Induced Multi-State Memristors for Neuromorphic Engineering. In
IEEE International Symposium on Circuits and Systems (ISCAS): Proceedings IEEE.
https://doi.org/10.1109/ISCAS46773.2023.10182122
Li, C.
, Kies, A., Zhou, K., Schlott, M., Sayed, O. E., Bilousova, M. & Stöcker, H. (2024).
Optimal Power Flow in a highly renewable power system based on attention neural networks.
Applied Energy,
359, Article 122779.
https://doi.org/10.1016/j.apenergy.2024.122779
Li, R., Rezaeiyan, Y., Böhnert, T., Schulman, A., Ferreira, R.
, Farkhani, H. & Moradi, F. (2024).
Temperature effect on a weighted vortex spin-torque nano-oscillator for neuromorphic computing.
Scientific Reports,
14(1), Article 10043.
https://doi.org/10.1038/s41598-024-60929-3
Leporowski, B. T., Tola, D., Hansen, C.
& Iosifidis, A. (2022).
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. In A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (Eds.),
Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (pp. 224-232). Springer.
https://doi.org/10.1007/978-3-030-90700-6_25
Leitenstorfer, A., Moskalenko, A. S., Kampfrath, T., Kono, J., Castro-Camus, E., Peng, K., Qureshi, N., Turchinovich, D., Tanaka, K., Markelz, A. G., Havenith, M., Hough, C., Joyce, H. J., Padilla, W. J., Zhou, B., Kim, K. Y., Zhang, X. C., Jepsen, P. U., Dhillon, S. ... Cunningham, J. (2023).
The 2023 terahertz science and technology roadmap.
Journal of Physics D: Applied Physics,
56(22), Article 223001.
https://doi.org/10.1088/1361-6463/acbe4c
Legaard, C. M., Tola, D., Schranz, T.
, Macedo, H. D. & Larsen, P. G. (2021).
A Universal Mechanism for Implementing Functional Mock-up Units. In G. Wagner, F. Werner, T. I. Ören & F. D. Rango (Eds.),
Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2021, Online Streaming, July 7-9, 2021 (pp. 121-129). SCITEPRESS Digital Library.
https://doi.org/10.5220/0010577601210129
Legaard, C. M., Schranz, T., Schweiger, G., Drgona, J., Falay, B.
, Gomes, C., Iosifidis, A., Abkar, M. & Larsen, P. G. (2023).
Constructing Neural Network Based Models for Simulating Dynamical Systems.
ACM Computing Surveys,
55(11), 1-34. Article 236.
https://doi.org/10.1145/3567591
Lee, L.-H., Hosio, S., Braud, T.
& Zhou, P. (2024).
A Roadmap Toward Metaversity: Recent Developments and Perspectives in Education. In D. Liu, R. Huang, A. H. S. Metwally, A. Tlili & E. Fan Lin (Eds.),
Application of the Metaverse in Education (pp. 73-95). Springer Nature.
https://doi.org/10.1007/978-981-97-1298-4_5
Lechuga, M. M. L.
, Nørremark, M., Jorguseski, L., D'Acunto, L., Politis, C. & Palattella, M. R. (2023).
Multi-Connectivity for Livestock Transport in Rural Areas. Paper presented at 2023 Joint EuCNC & 6G Summit, Gothenburg, Sweden.
Laursen, K., Zamani, M., Rezaeiyan, Y., Hosseini, S., Mondal, T., Corbett, B., Ouagazzal, A. M., Amalric, M.
& Moradi, F. (2023).
An Ultrasonically-Powered System for 1.06mm3 Implantable Optogenetics and Drug Delivery Dust.
IEEE Transactions on Circuits and Systems II: Express Briefs,
70(10), 3937-3941.
https://doi.org/10.1109/TCSII.2023.3289028
Lati, R. N.
, Rasmussen, J., Andujar, D., Dorado, J., Berge, T. W., Wellhausen, C., Pflanz, M., Nordmeyer, H., Schirrmann, M., Eizenberg, H., Neve, P.
, Jørgensen, R. N. & Christensen, S. (2021).
Site-specific weed management—constraints and opportunities for the weed research community: Insights from a workshop.
Weed Research,
61(3), 147-153.
https://doi.org/10.1111/wre.12469
Larsen, P. G., Macedo, H. D., Fitzgerald, J., Pfeifer, H., Benedikt, M., Tonetta, S., Marguglio, A., Gusmeroli, S. & Jr, G. S. (2020).
A Cloud-Based Collaboration Platform for Model-Based Design of Cyber-Physical Systems. In F. De Rango, T. Ören & M. Obaidat (Eds.),
Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (Vol. Volume 1: SIMULTECH, pp. 263-270). SCITEPRESS Digital Library.
https://doi.org/10.5220/0009892802630270
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
Larsen, P. G., Soulioti, G.
, Macedo, H. D., Alifragkis, V., Fitzgerald, J., Livanos, N., Pfeifer, H., Pasquinelli, M., Benedict, M.
, Thule, C., Tonetta, S., Stritzelberger, B., Marguglio, A., Sutton, L. F., Obstbaum, M., Gusmeroli, S., Beutenmüller, F., Jr., G. S., Wijnands, Q.
& Talasila, P. (2020).
Enabling Combining Models and Tools in an Online MBSE Collaboration Platform. In
Model Based Space Systems and Software Engineering (MBSE2020) https://indico.esa.int/event/329/attachments/3868/5508/Abstracts_combined.pdf
Larsen, J. J., Griffiths, M., Vang, M., Liu, L. & Grombacher, D. (2021).
Apsu - A compact surface NMR instrument for groundwater investigations.
SEG Technical Program Expanded Abstracts,
2021-September, 3135-3139.
https://doi.org/10.1190/segam2021-3582046.1
Larsen, J. J., Langhof, R. B.
, Kjær, M. W., Vang, M., Liu, L., Griffiths, M. & Grombacher, D. (2022).
Efficient processing of surface NMR data with spectral analysis.
Geophysical Journal International,
229(1), 286-298.
https://doi.org/10.1093/gji/ggab472
Larsen, P. G., Macedo, H. D., Fitzgerald, J., Pfeifer, H., Benedikt, M., Tonetta, S., Marguglio, A., Veneziano, G., Sutton, L., Gusmeroli, S. & Suciu, G. (2022).
HUBCAP: A Novel Collaborative Approach to Model-Based Design of Cyber-Physical Systems. In M. S. Obaidat, T. Oren & F. D. Rango (Eds.),
Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2020 (pp. 90-110). Springer.
https://doi.org/10.1007/978-3-030-84811-8_5
Larsen, E. L., Espelund, U., Lauritsen, S. M., Thiesson, B., Lange, J., Friis Kjeldsen, G.
& Jørgensen, M. J. (2022).
+Priokritisk. Early identification of clinical detorioriation among hospitalized patients by explainable AI. Poster session presented at Symposium on Artificial Intelligence for Learning Health Systems.
Lading, T., Lindskow, T., Sharghbin, M., Benhassen, L. L., Bechsgaard, T. K., Røpcke, D. M., Nielsen, S. L. & Johansen, P. (2018).
Aortic valve repair techniques affect the annulus differently: an In Vitro characterization.
The Journal of Heart Valve Disease,
27(4), 251-58.
Laakom, F., Raitoharju, J., Nikkanen, J.
, Iosifidis, A. & Gabbouj, M. (2021).
INTEL-TAU: A Color Constancy Dataset.
IEEE Access,
9, 39560-39567. Article 9371681.
https://doi.org/10.1109/ACCESS.2021.3064382
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
Laakom, F., Raitoharju, J.
, Iosifidis, A. & Gabbouj, M. (2023).
Learning Distinct Features Helps, Provably. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis & F. Bonchi (Eds.),
Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II (pp. 206-222). Springer.
https://doi.org/10.1007/978-3-031-43415-0_13
Kuntuarova, S., Licklederer, T., Huynh, T., Zinsmeister, D., Hamacher, T.
& Perić, V. (2024).
Design and simulation of district heating networks: A review of modeling approaches and tools.
Energy,
305, Article 132189.
https://doi.org/10.1016/j.energy.2024.132189
Kumar, R. R., Hänsel, A., Far Brusatori, M., Nielsen, L., Augustin, L.
, Volet, N. & Heck, M. (2022).
A 10-kHz intrinsic linewidth coupled extended-cavity DBR laser monolithically integrated on an InP platform.
Optics Letters,
47(9), 2346-2349 .
https://doi.org/10.1364/OL.454478
Kumar, R. R., Hänsel, A., Far Brusatori, M., Nielsen, L., Arent, N. H., Volet, N. & Heck, M. (2022).
Sub-10 kHz Intrinsic Linewidth Extended Cavity DBR laser on InP Generic Foundry Platform.
Kumar, R. R., Hänsel, A., Castera, P.
, Volet, N. & Heck, M. J. R. (2024).
Low-kappa DBR grating filters on an InP generic photonic integration foundry platform.
Journal of the Optical Society of America B: Optical Physics,
41(4), 1054-1059.
https://doi.org/10.1364/JOSAB.518800