Publikationer fra instituttet

Under publikationsliste finder du en samlet liste over de publikationer, som er lavet af medarbejdere ved Institut for Elektro- og Computerteknologi.

Publikationsliste

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Laursen, K. (2021). Ultra-Low Power IC Design for Ultrasonically-Powered Implants. [Ph.d.-afhandling, Aarhus Universitet]. Aarhus Universitet.

Lauritsen, S. M. (2022). MACHINE LEARNING BASED PREDICTION OF CLINICAL DETERIORATION IN GENERAL WARDS. [Ph.d.-afhandling, Aarhus Universitet].

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. I F. De Rango, T. Ören & M. Obaidat (red.), Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications SIMULTECH - Volume 1 (s. 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. I A. Cerone & M. Roveri (red.), Software Engineering and Formal Methods - SEFM 2017 Collocated Workshops: DataMod, FAACS, MSE, CoSim-CPS, and FOCLASA, Revised Selected Papers (s. 377-390). Springer. https://doi.org/10.1007/978-3-319-74781-1_26

Larsen, J. J., Pedersen, S. S., Foged, N. & Auken, E. (2021). Suppression of very low frequency radio noise in transient electromagnetic data with semi-tapered gates. Geoscientific Instrumentation, Methods and Data Systems, 10(1), 81-90. Artikel 8. https://doi.org/10.5194/gi-10-81-2021

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. I Model Based Space Systems and Software Engineering (MBSE2020) https://indico.esa.int/event/329/attachments/3868/5508/Abstracts_combined.pdf

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. I M. S. Obaidat, T. Oren & F. D. Rango (red.), Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2020 (s. 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 præsenteret på Symposium on Artificial Intelligence for Learning Health Systems.

Larsen, J. J., Lévy, L. & Asif, M. R. (2022). Removal of powerline noise in geophysical data sets with a scientific machine-learning based approach. IEEE Transactions on Geoscience and Remote Sensing, 60, Artikel 5923410. https://doi.org/10.1109/TGRS.2022.3223737

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. I J. Fitzgerald, C. Gomes & P. G. Larsen (red.), The Engineering of Digital Twins (s. 363-386). Springer. https://doi.org/10.1007/978-3-031-66719-0_15

Larsen, J. J., Kass, M. A., Grombacher, D. & Griffiths, M. P. (2024). Feasibility of Surface Nuclear Magnetic Resonance With Absurdly Long Excitation Pulses. Geophysical Research Letters, 51(20), Artikel e2024GL110826. https://doi.org/10.1029/2024GL110826

Larsen, P. G., Esterle, L., Fitzgerald, J. & Frasheri, M. (2023). Fault Injection in Co-simulation and Digital Twins for Cyber-Physical Robotic Systems. I Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (s. 222-236). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-40132-9_14

Larsen, J. J., Kass, A., Grombacher, D. & Griffiths, M. P. (2024). Surface NMR with Very Long Transmitter Pulses. Abstract fra AGU Fall meeting 2024, Washington DC, USA.

Larsen, P. G., Talasila, P. & Fitzgerald, J. (2024). Towards the Composition of Digital Twins. I Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (s. 103-122). Springer. https://doi.org/10.1007/978-3-031-67114-2_5

Larsen, J. J., Griffiths, M., Vang, M., Liu, L. & Grombacher, D. (2021). Apsu - A compact surface NMR instrument for groundwater investigations. I SEG Technical Program Expanded Abstracts 2021 (s. 3135-3139). Society of Exploration Geophysicists. https://doi.org/10.1190/segam2021-3582046.1

Larsen, J. J., Asif, M. R. & Rafiei Foroushani, M. (2025). Benchmarking deep learning models for wetland mapping in Denmark using high-resolution earth observation data. Abstract fra EGU General Assembly 2025, Vienna, Østrig. https://doi.org/10.5194/egusphere-egu25-4365

Lakshminarayanan, S., Duecker, D., Sarabakha, A., Ganguly, A., Takayama, L. & Haddadin, S. (2024). Estimation of External Force acting on Underwater Robots. I 2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024 (s. 3125-3131). IEEE. https://doi.org/10.1109/CASE59546.2024.10711608

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. Artikel 9371681. https://doi.org/10.1109/ACCESS.2021.3064382

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Within-layer Diversity Reduces Generalization Gap. Afhandling præsenteret på International Conference on Machine Learning Workshop on Information Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning, 2021. https://arxiv.org/pdf/2106.06012.pdf

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). On Feature Diversity in Energy-based models. Afhandling præsenteret på International Conference on Learning Representations Workshop on Energy-Based Models, , Vienna. https://openreview.net/pdf?id=ks3Q08yy66r

Laakom, F., Chumachenko, K., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Learning to ignore: rethinking attention in CNNs. I 32rd British Machine Vision Conference https://arxiv.org/pdf/2111.05684.pdf

Laakom, F., Raitoharju, J., Nikkanen, J., Iosifidis, A. & Gabbouj, M. (2021). Robust channel-wise illumination estimation. I 32rd British Machine Vision Conference https://arxiv.org/pdf/2111.05681.pdf

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). WLD-Reg: A Data-Dependent Within-Layer Diversity Regularizer. I B. Williams, Y. Chen & J. Neville (red.), AAAI-23 Technical Tracks 7 (s. 8421-8429). Artikel 190493 AAAI Press. https://doi.org/10.1609/aaai.v37i7.26015

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2023). Learning Distinct Features Helps, Provably. I D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis & F. Bonchi (red.), Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II (s. 206-222). Springer. https://doi.org/10.1007/978-3-031-43415-0_13

Laakom, F., Sohrab, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2025). Convolutional autoencoder-based multimodal one-class classification. I 2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media Companion, CISM Companion 2025 IEEE. https://doi.org/10.1109/CISMCompanion65074.2025.11032368

Kusumanchi, P., Keller, S. S. & Davidsen, R. S. (2024). Developments in the design and microfabrication of photovoltaic retinal implants. Micro and Nano Engineering, 25, Artikel 100289. https://doi.org/10.1016/j.mne.2024.100289

Kusumanchi, P., Madsen, J. G., Bek, T., Keller, S. S. & Davidsen, R. S. (2025). Electrical stimulation of neuroretinas with 3D pyrolytic carbon electrodes. Biomedical Microdevices, 27(1), Artikel 7. https://doi.org/10.1007/s10544-024-00729-8

Kusumanchi, P., Madsen, J. G., Bek, T. & Davidsen, R. S. (2023). Electrical Stimulation of retinal tissue with pyrolytic carbon microelectrodes. Poster-session præsenteret på The EYE and the Chip, 13th World Research Congress on Artificial Vision, Southfield, Michigan, USA.

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, Artikel 132189. https://doi.org/10.1016/j.energy.2024.132189

Kunrath, K., Leka, S., Vestergaard, L. S., Presser, M. & Ramanujan, D. (2024). A Digital Tool for Scaffolding Innovation Learning in Engineering Education with Local Industry Needs. International Journal of Engineering Education, 40(4), 801-814.

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, V., Mukherjee, M., Lloret, J., Zhang, Q. & Kumari, M. (2022). Delay-optimal and Incentive-aware Computation Offloading for Reconfigurable Intelligent Surface-assisted Mobile Edge Computing. IEEE Networking Letters, 4(3), 127-131. https://doi.org/10.1109/LNET.2022.3187720

Kumar, R. R., Nielsen, L., Volet, N. & Heck, M. (2023). Intra-cavity coherent combining of DBR lasers on an InP generic foundry platform. Optics Letters, 48(7), 1614-1617. https://doi.org/10.1364/OL.483889

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

Kulik, T., Macedo, H. D., Talasila, P. & Larsen, P. G. (2021). Modelling the HUBCAP Sandbox Architecture In VDM: A Study In Security. I J. Fitzgerald, T. Oda & H. D. Macedo (red.), Proceedings of the 18th International Overture Workshop (s. 20-35) https://arxiv.org/pdf/2101.07261.pdf

Kulik, T., Boudjadar, J. & Aranha, D. F. (2021). Formally Verified Credentials Management for Industrial Control Systems. I Proceedings - 2021 IEEE/ACM 9th International Conference on Formal Methods in Software Engineering, FormaliSE 2021: 9th IEEE/ACM International Conference on Formal Methods in Software Engineering (s. 75-85). IEEE. https://doi.org/10.1109/FormaliSE52586.2021.00014

Kulik, T., Talasila, P., Greco, P., Veneziano, G., Marguglio, A., Sutton, L. F., Larsen, P. G. & Macedo, H. D. (2021). Extending the Formal Security Analysis of the HUBCAP sandbox. I H. D. Macedo, C. Thule & K. Pierce (red.), Proceedings of the 19th International Overture Workshop (s. 36-50) https://arxiv.org/abs/2110.09371

Kulik, T., Dongol, B., Larsen, P. G., Macedo, H. D., Schnieder, S., Tran-Jørgensen, P. W. V. & Woodcock, J. (2022). A Survey of Practical Formal Methods for Security. Formal Aspects of Computing, 34(1), Artikel 3522582. https://doi.org/10.1145/3522582

Kulik, T., Gomes, C., Macedo, H. D., Hallerstede, S. & Larsen, P. G. (2022). Towards Secure Digital Twins. I T. Margaria & B. Steffen (red.), Leveraging Applications of Formal Methods, Verification and Validation. Practice, ISoLA 2022 (s. 159-176). Springer. https://doi.org/10.1007/978-3-031-19762-8_11

Kulik, T., Kazemi, Z. & Larsen, P. G. (2024). Security and Privacy-related Issues in a Digital Twin Context. I J. Fitzgerald, C. Gomes & P. G. Larsen (red.), The Engineering of Digital Twins (s. 313-334). Springer. https://doi.org/10.1007/978-3-031-66719-0_13

Kuldeep, G. & Zhang, Q. (2020). Energy Concealment based Compressive Sensing Encryption for Perfect Secrecy for IoT. I 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings Artikel 9322181 IEEE. https://doi.org/10.1109/GLOBECOM42002.2020.9322181

Kuldeep, G. & Zhang, Q. (2020). Compressive Sensing based Multi-class Privacy-preserving Cloud Computing. I 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings (Bind 2020-January). Artikel 9348093 IEEE. https://doi.org/10.1109/GLOBECOM42002.2020.9348093

Kuldeep, G. & Zhang, Q. (2021). A Novel Efficient Secure and Error-Robust Scheme for Internet of Things Using Compressive Sensing. IEEE Access, 9, 40903-40914. Artikel 9373354. https://doi.org/10.1109/ACCESS.2021.3064700

Kuldeep, G. (2022). Lightweight Time-Series Signal Compression Period Extraction and Multiresolution using Difference Sequences. IEEE Internet of Things Journal, 9(9), 7043-7050. https://doi.org/10.1109/JIOT.2021.3113951

Kuldeep, G. & Zhang, Q. (2022). Multi-class privacy-preserving cloud computing based on compressive sensing for IoT. Journal of Information Security and Applications, 66, Artikel 103139. https://doi.org/10.1016/j.jisa.2022.103139

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